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--- title: GPR120 promotes neutrophil control of intestinal bacterial infection authors: - Zheng Zhou - Wenjing Yang - Tianming Yu - Yu Yu - Xiaojing Zhao - Yanbo Yu - Chuncai Gu - Anthony J Bilotta - Suxia Yao - Qihong Zhao - George Golovko - Mingsong Li - Yingzi Cong journal: Gut Microbes year: 2023 pmcid: PMC10026904 doi: 10.1080/19490976.2023.2190311 license: CC BY 4.0 --- # GPR120 promotes neutrophil control of intestinal bacterial infection ## ABSTRACT G-protein coupled receptor 120 (GPR 120) has been implicated in anti-inflammatory functions. However, how GPR120 regulates the neutrophil function remains unknown. This study investigated the role of GPR120 in the regulation of neutrophil function against enteric bacteria. 16S rRNA sequencing was used for measuring the gut microbiota of wild-type (WT) mice and Gpr120−/− mice. Citrobacter rodentium infection and dextran sulfate sodium (DSS)-induced colitis models were performed in WT and Gpr120−/− mice. Mouse peritoneal-derived primary neutrophils were used to determine the neutrophil functions. Gpr120−/− mice showed altered microbiota composition. Gpr120−/− mice exhibited less capacity to clear intestinal Citrobacter rodentium and more severe intestinal inflammation upon infection or DSS insults. Depletion of neutrophils decreased the intestinal clearance of Citrobacter rodentium. GPR120 agonist, CpdA, enhanced WT neutrophil production of reactive oxygen species (ROS) and extracellular traps (NETs), and GPR120-deficient neutrophils demonstrated a lower level of ROS and NETs. CpdA-treated neutrophils showed an enhanced capacity to inhibit the growth of Citrobacter rodentium, which was abrogated by the inhibition of either NETs or ROS. CpdA promoted neutrophil inhibition of the growth of commensal bacteria Escherichia coli O9:H4 and pathobiont Escherichia coli O83:H1 isolated from a Crohn’s disease patient. Mechanically, mTOR activation and glycolysis mediated GPR120 induction of ROS and NETs in neutrophils. Additionally, CpdA promoted the neutrophil production of IL-17 and IL-22, and treatment with a conditioned medium of GPR120-activated neutrophils increased intestinal epithelial cell barrier functions. Our study demonstrated the critical role of GPR120 in neutrophils in protection against enteric bacterial invasion. ## Introduction The intestinal mucosa consists of a single layer of epithelial cells covered by a mucus layer and a number of immune cells underneath. Under normal steady conditions, the intestine is separated from a large quantity of the microbiome, dietary substance, and ingested toxins by the mucus layer and epithelial cells. Meanwhile, there are a lot of immune cells that accumulate into injured mucosa, where commensal microbiota and pathogens invade when intestinal inflammation occurs1. It has been shown that innate immune cells, especially neutrophils, are crucial to protecting against bacterial invasion in the intestine2,3. Neutrophils have long been viewed as the effector cells in acute and chronic inflammation4. Large numbers of neutrophils accumulate in the intestinal mucosa and phagocytose pathogenic microbes upon intestinal inflammation, which damages the intestinal barrier5. However, the protective role of neutrophils has also been recognized in regulating intestinal inflammation3. It has been reported that neutrophils can eliminate bacteria through the production of reactive oxygen species (ROS), formation of extracellular traps (NETs), and secretion of several cytokines6–8. However, how the neutrophil function is regulated is still not completely understood. Dietary ω-3 polyunsaturated fatty acids (PUFA) have been implicated in regulating intestinal diseases9. As a receptor for ω-3 PUFA, G-protein coupled receptor 120 (GPR120) plays a critical role in various physiologic homeostasis mechanisms10. It has also been shown that GPR120 agonist inhibits proinflammatory cytokine production by macrophages and promotes IL-10 production in CD4+ T cells11–13. Although its role in adipocytes, obesity, and diabetes is well established11,14,15, the effect of GPR120 in regulation of intestinal microbiota homeostasis is still unknown. In this report, we demonstrated that GPR120 enhances neutrophil function in controlling gut bacteria, which contributes to inhibiting intestinal inflammation and infection. GPR120 agonist promotes neutrophils to inhibit bacterial growth through upregulation of ROS production and NETs formation, which is mediated by mTOR and glycolysis. In addition, GPR120 increases the neutrophil production of IL-17A and IL-22 and intestinal epithelial cell barrier function. ## GPR 120 regulates the gut microbiota. To determine the role of GPR 120 in regulating gut microbiota, we assessed the total gut microbiota between WT and Gpr 120−/− mice by determining 16S DNA counts in feces. We found that there were more bacteria in Gpr 120−/− mice compared with WT mice (Figure 1a). We then determined the intestinal microbiota composition using 16S rRNA sequencing analysis. The principal coordinates based on a Bray-Curtis comparison clearly separated the samples from the WT and Gpr 120−/− mice (Figure 1b). Taxonomically, although there were no significant differences in relative bacteria abundance at the phylum level (Figure 1c), Gpr120−/− mice exhibited a decreased tendency to harbor Bacteroidetes, which has been shown to be decreased in patients with inflammatory bowel disease (IBD)16. Furthermore, we found that Bacteroidales S24–7 group, which belongs to Bacteroidetes phylum, was significantly decreased, and Clostridiales vadinBB60 group, which belongs to Firmicutes phylum, was increased (Figure 1d). Taken together, these results indicate that GPR120 regulates the growth of certain gut microbiota. Figure 1.Gpr120−/− mice demonstrate altered gut microbiota composition. Notes: (a) 16S counts were measured in fecal pellets collected from WT and Gpr120−/− mice using qRT-PCR. ( b-d) Fecal pellets were collected from WT and Gpr120−/− mice, and 16s rRNA sequencing was performed. PCA analyses were determined using Bray-Curtis comparison (b). The differences in microbiota abundance by phylum were determined between WT and Gpr120−/− mice (c). The differences in the Bacteroidales S24-7 group and Clostridiales vadinBB60 group were determined between WT and Gpr120−/− mice (d). Data were expressed as mean ± SEM. Statistical significance was tested by a two-tailed unpaired Student t-test (a, c, and d) or Bray-Curtis comparison (b). * $p \leq 0.05$, *$p \leq 0.01.$ ## Deficiency of GPR120 promotes intestinal inflammation with decreasing clearance of intestinal pathogen To investigate whether GPR120 regulates pathogen clearance and intestinal inflammation, we first performed Citrobacter rodentium, which is similar to human enteropathogenic Escherichia coli* associated with IBD [16], infection in WT and Gpr120−/− mice. After 10 days, we found that Gpr120−/− mice demonstrated more severe colitis compared to WT mice, as demonstrated by higher pathological scores and elevated intestinal TNF-α expression (Figures 2a-c). In addition, Gpr120−/− mice exhibited increased intestinal Citrobacter rodentium counts in feces and spleens (Figures 2d-e), indicating that deficiency of GPR120 decreases intestinal pathogen clearance and promotes bacteria translocation to the spleen. To confirm the role of GPR120 in regulating intestinal inflammation, we performed DSS-induced colitis in WT and Gpr120−/− mice. Consistently, deficiency of GPR120 exacerbated intestinal inflammation induced by DSS (Figures 2f-h). These data suggested the importance of GPR120 in the clearance of intestinal pathogens and control of intestinal inflammation. Figure 2.Gpr120−/− mice are impaired in the clearance of intestinal Citrobacter rodentium and are susceptible to intestinal inflammation. Notes: (a-e) WT and Gpr120−/− mice ($$n = 5$$) were infected with Citrobacter rodentium(5 × 108 colony‐forming units (CFU)/mouse) by oral gavage. Mice were sacrificed on day 10 post-infection. ( a) Representative histopathology images of the colons were shown. ( b) Histological scores were assessed. ( c) TNF-α secretion in colons was determined. ( d-e) Fecal pallets and spleens were collected on day 10, and CFU in feces (d) and spleens (e) were quantified. ( f-h) WT and Gpr120−/− mice ($$n = 5$$) were treated with $2\%$ DSS in drinking water for 7 days and normal drinking water for another 3 days. Mice were sacrificed on day 10. ( f) Representative histopathology images of the colons were shown. ( g) Histological scores were assessed. ( h) TNF-α secretion in colons was determined. One representative of three independent experiments was shown. Data were expressed as mean ± SEM. Statistical significance was tested by the non-parametric two-tailed Mann-Whitney U test (b and g) or the two-tailed unpaired Student's t-test (c-e, and h). * $p \leq 0.05$, *$p \leq 0.01.$ ## Neutrophils protect the intestine against enteric infection of Citrobacter rodentium As the first line of defense against gut infection, massive neutrophils accumulate at the inflammation site. We next investigated the role of neutrophils in protecting the host against enteric infection. The WT mice were orally infected with Citrobacter rodentium, and then treated with the control IgG antibody or anti-Ly-6 G neutralizing antibody to deplete neutrophils. Mouse weights were monitored daily, and mice were sacrificed on day 10. The efficiency of the depletion was shown in Supplementary Figure S1A-C. Neutrophil-depleted mice demonstrated more severe intestinal inflammation (Figures 3a-b) than the IgG-treated mice. Additionally, intestinal TNFα secretion was increased in the neutrophil-depleted mice (Figure 3c). Furthermore, the depletion of neutrophils increased Citrobacter rodentium* counts in feces (Figure 3d) and spleens (Figure 3e), indicating that neutrophils enhance the clearance of Citrobacter rodentium in the intestine and decrease bacteria translocation to other organs. Figure 3.Depletion of neutrophils decreases the intestinal clearance of Citrobacter rodentium. Notes: WT mice ($$n = 5$$/group) were infected with Citrobacter rodentium(5 × 108 colony‐forming units (CFU)/mouse) by oral gavage. One group of mice was administered with anti-Ly6G antibody, and another was given anti-IgG antibody as a control daily. Mice were sacrificed on day 10 post-infection. ( a) Representative histopathology images of the colons were shown. ( b) Histological scores were assessed. ( c) TNF-α secretion in colons was determined. ( d-e) Fecal pallets and spleens were collected on day 10, and CFU in feces (d) and spleens (e) were quantified. One representative of three independent experiments was shown. Data were expressed as mean ± SEM. Statistical significance was tested by the non-parametric two-tailed Mann-Whitney U test (b) or the two-tailed unpaired Student t-test (c-e). * $p \leq 0.05$, *$p \leq 0.01.$ ## GPR120 enhances neutrophil functions related to bacterial killing Given that GPR120 regulates intestinal inflammation and intestinal bacteria clearance (Figure 2), and neutrophils are crucial for protection from enteric pathogen infection (Figure 3), we next asked whether GPR120 modulates neutrophil functions to clear bacteria. Neutrophils are involved in killing bacteria and pathogens, which is mainly dependent on the production of ROS and the formation of extracellular traps (NETs)17–20. Because of the short life of freshly prepared intestinal neutrophils and the easy preparation of peritoneal neutrophils in large quantities21, we used peritoneal neutrophils for in vitro experiments in this study. The purity of prepared peritoneal neutrophils was regularly >$95\%$ (Supplementary Figure S2A). We treated WT and GPR120-deficient neutrophils with or without CpdA, a GPR120-selective agonist22. We first determined the toxicity of CpdA on neutrophils and found that CpdA did not affect neutrophils’ viability with doses less than 10 µM (Supplementary Figures S2B-D). Next, we determined whether GPR120 affects ROS production in neutrophils. CpdA-treated WT, but not GPR120-deficient, neutrophils produced a higher level of ROS than control neutrophils (Figure 4a), indicating that CpdA specifically affects on GPR120. In addition, the ROS level was decreased in GPR120-deficient neutrophils compared with WT neutrophils. Then, we investigated the role of GPR120 in regulating the formation of NETs. Neutrophils were treated with or without CpdA in the presence of Hoechst 33,342, a dye that stains the primary component of NETs and nucleic acid. We found that CpdA promoted NETs formation in WT neutrophils, and the level of NETs was decreased in GPR120-deficient neutrophils compared with WT neutrophils (Figure 4b). Consistently, treatment with DHA, a ω-3 PUFA, a natural GPR 120 ligand, promoted ROS production and NETs formation (Supplementary Figures S3A-B). Figure 4.GPR120 agonist promotes neutrophil inhibition of enteric bacterial growth through the upregulation of ROS and NETs. Notes: (a-b) WT or GPR120-deficient neutrophils ($$n = 4$$/group) were treated with or without CpdA (3 µM) for 1 h. ROS production was measured using the Amplex Red Hydrogen Peroxide Assay Kit (a). WT or GPR120-deficient neutrophils were then stained with Hoechst 33342 (blue), and representative NETs were shown (b). ( c) WT or GPR120-deficient neutrophils ($$n = 5$$/group) were pre-treated with or without CpdA (3 µM) for 1 h, and then co-cultured with Citrobacter rodentium* in the plates for 12 h. The bacterial suspensions were then transferred to solid MacConkey’s agar culture plates overnight, and CFU was counted. ( d) WT neutrophils ($$n = 6$$/group) were pre-treated with or without CpdA (3 µM) for 1 h and then co-cultured with Citrobacter rodentium (or Citrobacter rodentium were cultured alone) in the presence of DPI or/and GSK484 in the plates for 12 h. The bacterial suspensions were then transferred to solid MacConkey’s agar culture plates overnight, and CFU was counted in the plates for 12 h. The bacterial suspensions were then transferred to solid MacConkey’s agar culture plates overnight, and CFU was counted. ( e-f) Neutrophils were pre-treated with or without CpdA (3 µM) for 1 h, and then co-cultured with Escherichia coli O9:H4 (e) and Escherichia coli O83:H1 (f) for 12 h. The bacterial suspensions were then transferred to Luria Broth’s agar culture plates overnight, and CFU was counted. One representative of three independent experiments was shown. Data were expressed as mean ± SEM. Statistical significance was tested by the two-tailed unpaired Student’s t-test (a, c, and e-f) or one-way ANOVA (d). $p \leq 0.01$, $p \leq 0.001$, *$p \leq 0.0001.$ ## CpdA promotes the neutrophil killing of bacteria To investigate whether CpdA affects neutrophils to kill bacteria, we conducted an anti-bacterial experiment by culturing Citrobacter rodentium* with CpdA-treated or control WT and GPR120-deficient neutrophils. As shown in Figure 4c, CpdA-treated WT, but not GPR120-deficient, neutrophils significantly reduced Citrobacter rodentium counts compared with control neutrophils, while the counts were higher when cultured with GPR120-deficient neutrophils compared with WT neutrophils. In addition, DHA-pre-treated neutrophils showed higher capacity to inhibit the growth of Citrobacter rodentium (Supplementary Figure S3C). To determine whether GPR120 promotes neutrophils to kill bacteria through the induction of ROS or/and NETs formation, we cultured Citrobacter rodentium with CpdA-treated or control neutrophils in the presence of the ROS inhibitor, Diphenyleneiodonium (DPI)23, or the NETs inhibitor, GSK48419. Both control and CpdA-treated neutrophils suppressed the growth of Citrobacter rodentium (Figure 4d). Inhibition of ROS or NETs abrogated the CpdA-neutrophil inhibition of bacterial growth (Figure 4d), which was enhanced by the combination of these two inhibitors. In addition, DPI and GSK484 themselves had no effect on growth of Citrobacter rodentium (Supplementary Figure S4). These data indicated that CpdA enhances neutrophil killing of bacteria at least partially through induction of ROS and NETs. Next, we investigated whether GPR120 enhancement of neutrophil killing is bacterial strain-specific, we cultured Escherichia coli O9:H4, the intestinal commensal bacteria, with CpdA-treated or control neutrophils. CpdA promoted the neutrophil killing of the commensal bacteria (Figure 4e). To determine whether GPR120 also enhances neutrophil killing of pathogenic bacteria in IBD, we cultured Escherichia coli O83:H1, a pathobiont isolated from a patient with Crohn’s disease24, with CpdA-treated or control neutrophils. We found that CpdA-treated neutrophils inhibited the growth of Escherichia coli O83:H1 (Figure 4f). Taken all together, these data demonstrated that GPR120 enhances neutrophil killing of both gut commensal bacteria and pathobionts. ## GPR120 promotes the neutrophil killing of bacteria through the activation of the mTOR pathway To investigate the mechanisms underlying GPR120 regulation of neutrophil functions, we determined whether GPR120 enhances mTOR activation, which has been reported to mediate several functions regulated by GPR120 in other cell types25. We found that CpdA promoted mTOR activation in neutrophils (Figure 5A). To investigate whether GPR120 regulates neutrophil function through enhanced activation of mTOR, we added rapamycin, an mTOR inhibitor, to the neutrophil cultures with CpdA. Blockade of mTOR suppressed ROS production and NETs formation induced by CpdA (Figures 5B-c). Figure 5.mTOR mediates GPR120 induction of neutrophil production of ROS and formation of NETs. Notes: (a) Neutrophils were treated with or without CpdA (3 µM) for 5 min. The phosphorylated mTOR levels were determined by FACS. ( b-c) Neutrophils were treated with or without CpdA (3 µM) in the presence or absence of rapamycin (2 μM) for 1 h. ROS production (b) and NETs formation (c) were assessed. ( d) Neutrophils were pre-treated with or without CpdA (3 µM) in the presence or absence of rapamycin (2 μM) for 1 h, and then co-cultured with Citrobacter rodentium in the plates for 12 h. The bacterial suspensions were then transferred to solid MacConkey’s agar culture plates overnight, and CFU was counted. One representative of three independent experiments was shown. Data were expressed as mean ± SEM. Statistical significance was tested using the two-tailed one-way ANOVA (b and d). * $p \leq 0.001$, $p \leq 0.0001.$ We then investigated whether GPR120 promotes the neutrophil killing of bacteria through the activation of mTOR. We pre-treated neutrophils with CpdA in the presence or absence of rapamycin and then collected the neutrophils to co-culture with Citrobacter rodentium. As shown in Figure 5D, CpdA-treated neutrophils suppressed Citrobacter rodentium* growth compared with control neutrophils. However, this effect was abrogated in neutrophils treated with CpdA and mTOR inhibitor. Taken together, these data suggest that GPR120 promotes the neutrophil killing of bacteria by neutrophils through the activation of the mTOR pathway. ## Glycolysis mediates GPR120 induction of bacteria-killing by neutrophils It has been shown that metabolism is crucial in regulating various functions in different types of cells26,27. Next, we investigated whether GPR120 affects glycolysis and mitochondrial oxidation, the two major metabolic events in neutrophils. We treated neutrophils with or without CpdA for 1 h and then measured their energy phenotype using Seahorse XF Cell Energy Phenotype Test Kit. This assay measures the two major energy-producing pathways, mitochondrial respiration and glycolysis, under baseline and stressed conditions, which are stimulated by the ATP synthase inhibitor oligomycin and the mitochondrial uncoupling agent FCCP. The energy phenotype was shown in Figure 6a. Specifically, there was no difference in Oxygen Consumption Rate (OCR), which represents the mitochondrial respiration level, both in baseline and stressed conditions (Figure 6b). However, CpdA-treated neutrophils showed an increased level of Extracellular Acidification Rate (ECAR), which represents the glycolysis levels, compared with control neutrophils under baseline and stressed conditions (Figure 6c). Taken together, GPR120 promotes glycolysis but not mitochondrial respiration under both baseline and stressed conditions. Figure 6.GPR120 regulates NETs formation in neutrophils through the upregulation of glycolysis. Notes: (a-c) Neutrophils ($$n = 4$$/group) were treated with or without CpdA (3 µM) for 1 h, and then OCR (a and b) and ECAR (a and c) levels were detected by Seahorse XF Cell Energy Phenotype Test using a Seahorse XF96 Analyzer. ( d-e) Neutrophils were pre-treated with or without CpdA (3 µM) in the presence or absence of 2DG (250 µM) for 1 h. ROS production (d) and NETs formation (e) was determined. ( f) Neutrophils were pre-treated with or without CpdA (3 µM) in the presence or absence of 2DG (250 µM) for 1 h, and then co-cultured with Citrobacter rodentium in the plates for 12 h. The bacterial suspensions were then transferred to solid MacConkey’s agar culture plates overnight, and CFU was counted. One representative of two independent experiments was shown. Data were expressed as mean ± SEM. Statistical significance was tested by the two-tailed unpaired Student's t-test (b and c) or the two-tailed one-way ANOVA (d and f). * $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001$, *$p \leq 0.0001.$ Next, we determined whether glycolysis is involved in the GPR120 regulation of neutrophil functions. We treated neutrophils with or without CpdA in the presence or absence of 2-Deoxy-D-glucose (2DG), an inhibitor of glycolysis. Treatment with 2DG did not affect ROS production induced by CpdA (Figure 6d) but suppressed GPR120 induction of NETs formation (Figure 6e). Furthermore, the capacity to inhibit Citrobacter rodentium* growth was decreased in neutrophils pre-treated with CpdA and 2DG compared with neutrophils treated with CpdA alone (Figure 6f). These results indicated that the GPR120 promotes neutrophils killing of bacteria at least partially through upregulation of glycolysis. ## GPR120-activated neutrophils produce higher levels of IL-17A and IL-22 and promote intestinal epithelial cell barrier function It has been shown that neutrophils produce IL-17A and IL-22, which promote intestinal production of antimicrobial peptides to inhibit bacterial growth and suppress intestinal inflammation28,29, as well as TGF-β, which induces intestinal epithelial cell production of amphiregulin to promote intestinal epithelial barrier function30. We next investigated whether GPR120 also regulates neutrophil production of IL-17A, IL-22 and TGF-β. We treated neutrophils with or without CpdA in the absence or presence of IL-23, which stimulates IL-17A and IL-22 in neutrophils31. As shown in Figures 7a-b, IL-23 promoted IL-17 and IL-22 production, which was further enhanced by CpdA treatment. In addition, GPR120-deficient neutrophils produced significantly lower levels of IL-17A and IL-22 in the presence of IL-23 (Figure 7c). However, CpdA did not affect TGF-β production (Supplementary Figure S5), indicating that GPR120 specifically regulates IL-22 and IL-17 production in neutrophils. In addition, DHA did not affect IL-17A and IL-22 production (Supplementary Figure S3D). Figure 7.GPR120 regulates IL-17A and IL-22 production and IEC barrier function in neutrophils. Notes: (a-b) WT neutrophils ($$n = 4$$/group) were treated with or without CpdA (3 µM) in the presence or absence of IL-23 (20 ng/mL) for 24 h, and IL-17A (a) and IL-22 (b) production in culture supernatants were measured by ELISA. ( c) WT and GPR120-deficient neutrophils were treated with IL-23 (20 ng/mL) for 24 h, and IL-17A and IL-22 levels were determined. ( d-g) Neutrophils ($$n = 4$$/group) were treated with IL-23 in the presence or absence of CpdA (3 µM)/rapamycin (2 μM, d-e) or 2DG (250 µM, f-g) for 24 h, and IL-17 (d and f) and IL-22 (e and g) production in culture supernatants were measured by ELISA. One representative of three independent experiments was shown. ( d-i) Mode-K cells ($$n = 3$$/group) were cultured in the medium of control or CpdA-treated neutrophils, and the genes of Reg3g and Tjp1 were determined after 24 h. (J) Mode-K cells ($$n = 3$$/group) in the upper inserts were cultured in the medium of control or CpdA-treated neutrophils and treated with proinflammatory cytokines (10 ng/mL of LPS, 40 ng/mL of TNF-α, and 20 ng/mL of IL-1β). TEER levels were determined by Epithelial Volt-Ohm at different time points, and the percentage of original TEER was calculated. One representative of three independent experiments was shown. Data were expressed as mean ± SEM. Statistical significance was tested by the two-tailed unpaired Student's t-test (a-B) or the two-tailed one-way ANOVA (c-F). $p \leq 0.01$, $p \leq 0.001$, *$p \leq 0.0001.$ To determine whether mTOR and glycolysis also mediate GPR120 induction of neutrophil production of IL-17A and IL-22, we treated neutrophils with CpdA in the presence or absence of mTOR inhibitor rapamycin or glycolysis inhibitor 2-DG, and measured IL-17A and IL-22 24 h later. As shown in Figures 7d-e, the addition of rapamycin inhibited GPR120 induction of IL-17A and IL-22. However, inhibition of glycolysis decreased IL-17A production but increased IL-22 production induced by CpdA (Figures 7e-f), indicating that glycolysis differentially regulated GPR120 induction of IL-17A and IL-22 in neutrophils. Considering that IL-17A and IL-22 do not affect bacteria directly, we did not investigate whether CpdA induction of IL-17A and IL-22 contributes to inhibiting bacterial growth in this study. IL-17A and IL-22 have been reported to participate in the anti-bacterial activity by promoting intestinal barrier functions32,33. We collected the culture medium from control and CpdA-treated neutrophils and treated the murine IEC cell line, Mode-K, with the conditioned medium. We found that the conditioned medium of CpdA-treated neutrophils induced higher levels of Reg3γ, an antimicrobial peptide, and tight junction protein– (TJP) expression (Figures 7h-i). In addition, the conditioned medium from GPR120-treated neutrophils promoted intestinal integrity upon proinflammatory cytokine insults, as demonstrated by a higher level of transepithelial/transendothelial electrical resistance (TEER) (Figure 7j). ## Discussion Accumulating evidence demonstrates that neutrophils are critical in regulating homeostasis in several tissues and systems, including the intestine. Besides, dietary components also participate in modulating intestinal homeostasis. ω-3 PUFA is commonly consumed from daily diet and has been implicated in the regulation of intestinal microbiota and intestinal disorders. In the current study, we demonstrated that GPR120, a recently recognized receptor for ω-3 PUFA, promotes neutrophil production of ROS and formation of NETs as well as expression of IL-17A and IL-22 via activation of mTOR and upregulation of glycolysis, which contributes to host against bacteria invade (Figure 8). Therefore, our study provides novel insights into how GPR120 contributes to the maintenance of intestinal microbiota homeostasis through the regulation of neutrophil functions. Figure 8.The schematic diagram of GPR120 regulation of neutrophil anti-bacterial function. Notes: GPR120 activation in neutrophils induces NETs formation and ROS production, which directly enhance anti-bacterial activity. In addition, GPR120 promotes IL-17A and IL-22 production in neutrophils, which contributes to intestinal epithelial barrier integrity. These processes were differentially regulated by enhanced glycolysis and mTOR activation. The gut microbiota has been well recognized in the regulation of intestinal health and diseases, including IBD. Various mechanisms that control the homeostasis of gut microbiota, and different gut bacteria differentially regulate host responses to gut microbiota and pathogens to promote or inhibit intestinal inflammation. It has been reported that Bacteroidetes is* decreased in IBD patients16. Bacteroidales S24–7 group, which belongs to Bacteroidetes phylum, has been demonstrated to be greatly decreased after the onset of colitis34, indicating its role in modulating intestinal inflammation. In this study, we found that the intestinal bacterial load was increased in Gpr120−/− mice, and Gpr120−/− mice showed different intestinal microbiota profiles, in which the Bacteroidales S24–7 group was significantly decreased, suggesting that GPR120 regulates the amounts and composition of gut microbiota, which may favor the maintenance of a healthy gut microbiome. Neutrophils are recruited to the infection site and then kill bacteria and pathogens mainly by producing ROS and forming of NETs directly17–20. Although the effects of GPR120 on other innate cells, including macrophages and dendritic cells, have been investigated35, the role of GPR120 in regulating functions in neutrophils is still unknown. In this study, we found that the GPR120 agonist promoted both ROS production and NETs formation in neutrophils. Interestingly, GPR120 agonist enhanced neutrophils to inhibit the growth of enteric bacteria, not only the enteric pathogen Citrobacter rodentium, but also gut commensal bacteria Escherichia coli O9:H4 and pathobiont Escherichia coli O83:H1, which might explain the higher intestinal bacterial load in Gpr120−/− mice, indicating that GPR120 contributes to the maintenance of gut microbiota homeostasis probably through regulation of neutrophil controlling of gut bacteria. Furthermore, inhibition of ROS or NETs suppressed neutrophils to kill bacteria induced by GPR120 agonist, indicating that GPR120 promotes neutrophils to inhibit bacteria through upregulation of ROS and formation of NETs. Under healthy conditions, IECs and mucus layer separate commensal bacteria and immune cells, including neutrophils, underneath IECs. Upon injury or colitis or pathogen invasion, commensal bacteria and pathogens could reach IEC and lamina propria. In this case, activation of GPR120 in neutrophils promotes the killing of both commensal bacteria and pathogens when they invade intestinal lamina propria. Although GPR120 promotes neutrophils to kill bacteria, we cannot exclude the possibility of other cells that express GPR120 in changing bacterial composition in Gpr120−/− mice. GPR120 affects neutrophil anti-bacterial function, which might contribute, at least partially, to the altered composition of gut microbiota in Gpr120−/− mice. Therefore, neutrophil-specific GPR120KO mice is important in exploring the role of neutrophil-expressed GPR120 in vivo. Glycolysis is considered the major metabolism pathway in neutrophils; however, other metabolic pathways, including the pentose phosphate pathway (PPP), the citric acid cycle, and oxidation pathways, have been described recently36. In this study, we demonstrated that neutrophils were more glycolytic after the treatment with the GPR120 agonist, whereas the oxidation levels were not altered. Consistent with previous studies showing that glycolysis is involved in NETs formation37,38, inhibition of glycolysis suppressed NETs formation induced by GPR120 agonist, which further inhibited neutrophils from killing bacteria. However, the blockade of glycolysis did not affect ROS production, which mainly relies on PPP in neutrophils12. Whether PPP is also involved in GPR120 regulation of neutrophils’ anti-bacterial functions was not investigated in this study but should be further studied in the future. In summary, our study demonstrates that GPR120 promotes neutrophil control of gut bacterial growth. Our study thus provides evidence for GPR120 as a new potential therapeutic target for suppressing intestinal infection and inflammation. ## Mice Wild-type (WT) C57BL/6 mice were obtained from the Jackson Laboratory, and C57BL/6 Gpr120−/− mice were obtained from Bristol-Myers Squibb. All the mice were bred and maintained in the animal facilities at the University of Texas Medical Branch. Both male and female mice were used. All experiments were reviewed and approved by the Institutional Animal Care and Use Committees of the University of Texas Medical Branch. ## Reagents Neutralizing antibody against Ly-6 G (1A8) was purchased from Bio X Cell. Flow cytometry antibodies, FITC- CD11b, PE/Cy7-Ly6G, and Percp/cy5.5-pmTOR, were purchased from Biolegend. Elisa kits were purchased from BioLegend. Mouse recombinant IL-23 was purchased from BioLegend. Culture medium RPMI 1640, DMEM, and HBSS buffer were purchased from Corning. Thioglycolate broth, CpdA, rapamycin, and 2-Deoxy-D-glucose (2DG) were purchased from Sigma-Aldrich. Diphenyleneiodonium (DPI) and hydrochloride (GSK484) were obtained from Cayman. Amplex® red hydrogen peroxide/peroxidase assay kit and Live/Dead Fixable Dead Cell Stain Kit were purchased from Thermo Fisher Scientific. Citrobacter rodentium strain DBS100 (ATCC) were obtained from ATCC. Escherichia coli O9:H439 and Escherichia coli O83:H124 were kindly provided by Dr. Alfredo Torres of UTMB. ## Citrobacter rodentium infection mouse model WT and Gpr120-/- mice were orally administrated with Citrobacter rodentium (5 × 108/mouse) on day 0. Mice were sacrificed on day 10 post-infection. For antibody treatment, WT mice were intraperitoneally injected with anti-IgG (4 mg/kg) or anti-Ly6G (4 mg/kg) every day from day 0. ## DSS-induced colitis mouse model WT and Gpr120-/- mice were orally treated with $2\%$ DSS (w/v) in drinking water for 7 days, and the water was changed to normal drinking water for another 3 days. Mice were sacrificed on day 10. ## Fecal and splenic Citrobacter rodentium culture Fresh feces were collected and suspended in cold PBS. After a series of 10-fold dilution, the fecal suspension was seeded onto MacConkey’s agar culture plates. Bacteria CFU counts were normalized to fecal weights. Spleens were immediately collected when mice were sacrificed, homogenized in cold PBS, and seeded onto MacConkey’s agar culture plates. Bacteria counts were normalized to fecal weights. The total bacteria CFU counts in every spleen were determined. ## Ex vivo organ culture The colons were removed and longitudinally opened. After washing the cold RPMI medium three times, two 3-mm circular full-thickness pieces of the colons were obtained using a 3-mm dermal punch and placed in 1 ml complete RPMI media for 24 h at 37 °C with $5\%$ CO2. Culture supernatants from the culture were collected for analysis of cytokine content. ## ELISA The cytokine production was measured using ELISA kits (IL-17A, IL-22, TGF-β, and TNF-α) according to the manufacturer’s instructions. Ninety-six-well plates were coated with the indicated cytokine capture antibody overnight at 4°C. After blocking using $1\%$ BSA, samples were added to wells and incubated at room temperature for 2 h, followed by incubation with a detection antibody for 1 h. HRP-labeled streptavidin was then incubated for 30 min. After adding tetramethylbenzidine substrate, the absorbance of each well was measured at 450 nm. ## H&E staining and pathological scoring Colonic tissues were Swiss-rolled and fixed in $10\%$ buffered formalin for 24 h. After dehydration, tissues were embedded, and 5-µm sections were cut. H&E staining was performed after a series of hydration40. Images were captured by a Leica microscope. Pathological scores were determined by different key parameters based on different mouse models41. ## Quantitative PCR Bacterial 16S rDNA: Fecal pellets were collected, and fecal DNA was extracted using phenol chloroform. The same amount of DNA was used for measuring 16S counts by quantitative PCR. Primers are the following: 16S forward: 5’-TCCTACGGGAGGCAGCAGT-3’; 16S reverse: 5’-GGACTAC- CAGGGTATCTAATCCTGTT-3’42. The data were normalized to eukaryotic β-actin. Gene expression in Mode-K cells: After treatment, Mode-K cells were collected, and total RNA was extracted by TRizol. 200 ng of RNA was used for reverse transcription, and Reg3g and Tjp1 levels were determined by real-time PCR. Primers are following: Reg3g forward: 5’-TCCCAGGCTTATGGCTCCTA-3’; Reg3g reverse: 5’-GCAGGCCAGTTCTGCATCA-3’; Tjp1 forward: 5’- GTTGGTACGGTGCCCTGAAAGA-3’; Tjp1 reverse: 5’- GCTGACAGGTAGGACAGACGAT-3’. The relative expression was normalized to Gapdh. ## 16S rRNA Sequencing Fecal bacterial DNA was isolated using a QIAAMP PowerFecal DNA kit (Cat# 12830–50, Lot# 160044775, Qiagen) according to the manufacturer’s instructions. The microbiome samples were analyzed using barcoded high-throughput amplicon sequencing of the bacterial 16S rRNA gene. Quality control and taxonomical assignment of the resulted reads were performed using CLC Genomics Workbench 21.0. Microbial Genomics Module (http://www.clcbio.com). Low-quality reads containing nucleotides with a quality threshold below 0.05 (using the modified Richard Mott algorithm), as well as reads with two or more unknown nucleotides, were removed from the analysis. Reference-based OTU picking was performed using the SILVA SSU v132 $97\%$ database43. Sequences present in more than one copy but not clustered to the database were then placed into de novo OTUs ($97\%$ similarity) and aligned against the reference database with $80\%$ similarity threshold to assign the “closest” taxonomical name where possible. Chimeras were removed from the dataset if the absolute crossover cost was 3 using a k-mer size of 6. The beta diversity was estimated using the Bray-Curtis method based on PCoA axes representing the top three highest variances. ## Neutrophil isolation Mice were injected with 1 ml of $3\%$ thioglycolate broth into the peritoneal cavity. After 4 h, mice were sacrificed, and 10 ml cold PBS buffer containing $5\%$ FBS was injected subsequently intraperitoneally subsequently. After a gentle massage of the abdomen, peritoneal fluid was transferred into centrifuge tubes. Neutrophils were purified using $50\%$ Percoll at 1200 rpm for 20 min. ## Neutrophil culture Neutrophils were cultured in the RPMI 1640 medium containing penicillin-streptomycin and fetal bovine serum at 37°C and $5\%$ CO2 in the presence or absence of CpdA (3 µM) or DHA (5 µM), as well as other inhibitors indicated in the Figure legends. For collecting medium for determining IL-17A and IL-22 production, neutrophils were treated with or without IL-23 (20 ng/mL). ## Viability assay Resazurin (44 µM) was added to the neutrophil culture medium. Cell viability was calculated by subtracting the absorbance at 595 nm from the absorbance at 570 nm at the time points indicated. ## Reactive oxygen substrate assay Amplex® red hydrogen peroxide/peroxidase assay kit was used for measuring ROS production secreted by neutrophils. The reaction mixture, which contains 50 µM Amplex® Red reagent and 0.1 U/mL HRP in HBSS, was added into the 96-well plate and pre-warmed at 37°C for 10 min. WT or GPR120-deficient neutrophils were treated with or without CpdA (3 µM), DHA (5 µM), rapamycin (2 µM), or 2DG (250 µM), and then added to the plate. The ROS was determined by the fluorescence for excitation at 560 nm and emission detection at ~590 nm. ## NETs staining Peritoneal neutrophils were treated with or without CpdA (3 µM), DHA (5 µM), rapamycin (2 µM), or 2DG (250 µM), and then seeded on the poly-lysin-coated coverslips. After 1 h, neutrophils were fixed with $4\%$ paraformaldehyde and stained with Hoechst 33,342 (1 µg/ml) at room temperature for 5 min. The NETs were visualized on a Cytation 5 microscope. ## In vitro bacterial killing by neutrophils Peritoneal neutrophils were cultured with or without CpdA (3 µM) in the presence of rapamycin (2 µM) or 2DG (250 µM) for 1 h. Neutrophils were collected and co-cultured with appropriate aliquots of Citrobacter rodentium strain DBS100 (ATCC), Escherichia coli O9:H4, or Escherichia coli O83:H1, with an initial OD600 value of 0.1–0.2, in the presence or absence of DPI (10 µM) or GSK484 (25uM). The bacterial suspensions were incubated at 37°C for 12 h under aerobic conditions and then transferred to solid MacConkey’s agar culture plates (Citrobacter rodentium) or Luria Broth’s agar culture plates (Escherichia coli O9:H4 and Escherichia coli O83:H1) overnight. Finally, the colony-forming units (CFU) were counted. ## Cell metabolism measurement Neutrophils were pre-treated with or without CpdA (3 µM). After 30 min of treatment, cells (5 × 105 cells per well) were suspended in Seahorse XF media and seeded into a 96-well Seahorse plate, which was pre-coated with poly-lysin, and subjected to the Seahorse XF Cell Energy Phenotype Assay to determine oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) under baseline and stressed conditions. ## Mode-K cell culture and treatment Neutrophils were cultured with or without CpdA (3 µM) in the RPMI 1640 medium containing penicillin-streptomycin and fetal bovine serum at 37°C and $5\%$ CO2, and the medium was collected after 24 h. Mode-K cells were cultured in $80\%$ DMEM medium containing penicillin-streptomycin, fetal bovine serum, and non-essential amino acid and $20\%$ culture medium of neutrophils at 37°C and $5\%$ CO2. Mode-K cells were collected after 24 h for gene expression. ## Transepithelial electrical resistance (TEER) assay Mode-K cells (300 K) were suspended in 200 µL of DMEM culture medium and then seeded in the insets (0.4 µm polyester membrane). The insets were carefully inserted into the lower chamber (600 µL of DMEM culture medium) of the 24-well Transwell plates. After 24 h, cells were attached to the insert, and the medium in the inserts was changed to a conditioned medium of neutrophils in the presence of proinflammatory cytokines (10 ng/mL of LPS, 40 ng/mL of TNF-α, and 20 ng/mL of IL-1β). TEER levels were determined by Epithelial Volt-Ohm Meter (Millicell ESR-2) at time points indicated. ## Statistical analysis All the data were analyzed using Prism 9.0 (GraphPad Software, San Diego, CA) and presented as mean ± SEM. Analyses were based on whether the data were normally distributed and the number of tested groups for comparison. * $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001$, *$p \leq 0.0001.$ ## Grant support This work was supported by NIH grants DK112436, DK125011, AI150210, DK124132. ## Abbreviations CFUcolony-forming unitsDPIDiphenyleneiodoniumECARExtracellular Acidification RateGPR120G-protein coupled receptor 120NETsneutrophil extracellular trapsOCROxygen Consumption RatePPPpentose phosphate pathwayPUFApolyunsaturated fatty acidsROSreactive oxygen speciesWTwild-type2DG2-Deoxy-D-glucose. ## Disclosure statement No potential conflict of interest was reported by the authors. ## Supplementary material Supplemental data for this article can be accessed online at https://doi.org/$\frac{10.1080}{19490976.2023.2190311.}$ ## Data availability statement 16S rRNA sequencing data have been deposited in SRA under the BioProject number PRJNA716350 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA716350). ## Author contributions Conceptualization: W.Y., and Y.C.; Methodology: Z.Z., W.Y., G.G., and Y.C.; Investigation: Z.Z., W.Y., T.Y., Y.Y. 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--- title: Functional effects of human milk oligosaccharides (HMOs) authors: - Meltem Dinleyici - Jana Barbieur - Ener Cagri Dinleyici - Yvan Vandenplas journal: Gut Microbes year: 2023 pmcid: PMC10026937 doi: 10.1080/19490976.2023.2186115 license: CC BY 4.0 --- # Functional effects of human milk oligosaccharides (HMOs) ## ABSTRACT Human milk oligosaccharides (HMOs) are the third most important solid component in human milk and act in tandem with other bioactive components. Individual HMO levels and distribution vary greatly between mothers by multiple variables, such as secretor status, race, geographic region, environmental conditions, season, maternal diet, and weight, gestational age and mode of delivery. HMOs improve the gastrointestinal barrier and also promote a bifidobacterium-rich gut microbiome, which protects against infection, strengthens the epithelial barrier, and creates immunomodulatory metabolites. HMOs fulfil a variety of physiologic functions including potential support to the immune system, brain development, and cognitive function. Supplementing infant formula with HMOs is safe and promotes a healthy development of the infant revealing benefits for microbiota composition and infection prevention. Because of limited data comparing the effect of non-human oligosaccharides to HMOs, it is not known if HMOs offer an additional clinical benefit over non-human oligosaccharides. Better knowledge of the factors influencing HMO composition and their functions will help to understand their short- and long-term benefits. ## GRAPHICAL ABSTRACT ## Introduction The World Health Organization (WHO) and pediatric societies recommend breastfeeding within the first hour of life and to breastfeed exclusively for the first six months, continuing for up to two years1–3. Human milk is the only recommended source of nutrition for newborns because of its unique composition and the fact that it is naturally occurring and ideally suited to support crucial developmental processes in infancy. In addition, to providing essential nutrients, human milk also contains a plethora of bioactive components that promote healthy growth and development and help to preserve a healthy microbiota and the infant’s immune system.4–6. There are numerous health benefits associated with breastfeeding and human milk, both for mothers (lower risks of breast and ovarian cancer, hypertension, and type 2 diabetes) and their newborns (short- and long term). Short-term benefits include fewer cases of diarrhea, pneumonia, otitis media, atopic dermatitis, and sudden infant death syndrome; long-term benefits include fewer cases of type 2 diabetes, leukemia, autistic spectrum disorders, and obesity; and beneficial effects on IQ and social behavior5,7–13. The difference between non-breastfed and breastfed infants in morbidity and mortality was hypothesized to be related to the composition of human milk. The relationship between breastfeeding and infant’s health is based on its nutritional and bioactive components including human milk oligosaccharides (HMOs)4,14,15. In the early 1900s, Moro and Tissier independently found a predominance of bifidobacteria in the stools of breastfed compared to non-breastfed infants16. It was discovered that the oligosaccharides present in human milk did stimulate the growth of bifidobacteria, and in the 1950s the first clear description of the structure of the most abundant HMOs were unraveled17–19. HMOs provide a variety of physiologic functions, including the establishment of a balanced infant’s gut microbiota, the strengthening of the gastrointestinal barrier, prevention of infections, and potential support to the immune system, brain, and cognitive development4–6,14,15. This review aims to summarize up-to-date information about the functional effects of HMOs, such as supporting the development of a healthy gastro-intestinal microbiome, inhibiting the adhesion of pathogens, promoting the development of a balanced the immune system, and their contribution to brain development and cognitive function. ## Method We searched for relevant studies published in the English language in PubMed, EmBase, Scopus between 2000 and August 2022. We used search terms: “human milk oligosaccharide” AND “breast feeding”, OR “breastfed”, OR “human milk”, OR “formula”, OR “infant formula” and OR “nutrition”. We researched the relevant literature and summarized the most up-to-date information about the functional effects of HMOs, as well as, evaluated preclinical, observational, and randomized controlled clinical trials with HMO-containing infant formulas. ## Human Milk Oligosaccharides (HMOs): composition and related factors Human milk contains numerous structurally different oligosaccharides, indigestible carbohydrates for humans. Human milk contains much more oligosaccharides than the milk of any animal. Human milk oligosaccharides (HMOs) are the third most important solid component in human milk after lactose and lipids, while having a minimal nutritional value for the infant4–6,20,21. Over 200 structurally different HMOs have currently been identified20,22. HMOs withstand both heat and cold, and remain therefore unaffected by pasteurization and freeze-drying23. HMOs are resistant to pancreatic and brush border enzymes, as well as to the low stomach pH. The majority of HMOs are either metabolized by the infant’s gut microbiota or excreted intact. Approximately, 1 to $2\%$ of the ingested HMOs are absorbed, get into the systemic circulation, and are eliminated via urine14. HMOs are multifunctional, unconjugated, and non-digestible glycans. HMOs are build out of five monosaccharide components: galactose, glucose, fucose, N-acetylglucosamine, and the sialic acid derivative N-acetyl-neuraminic acid14,15,24. Abbreviations of common HMOs were shown in Table 1. Table 1.Abbreviation of HMOs.2′-FL2′fucosyllactose 23′-SL3′sialyllactose6′-SL6′sialyllactoseDFL2,3-di-O-fucosyllactoseDFLacdifucosyllactoseDFLNHdifucosyllacto-N-hexaoseDFLNTdifucosyllacto-N-tetroseDSLNHdisialyllacto-N-hexaoseDSLNTDisialyllacto-N-tetraoseFDSLNHfucodisialyllacto-N-hexaoseFLNHfucosyllacto-N-hexaoseLNDFH-Ilacto-N-difucohexaose:LNFP Ilacto-N-fucopentaose ILNFP IIlacto-N-fucopentaose IILNFP-IIIlacto-N-fucopentaose IIILNHlacto-N-hexaoseLNnTLacto-N-neotetraoseLNTLacto-N-tetraoseLSTbsialyl-lacto-N-tetraose bLSTcsialyl-lacto-N-tetraose c Three major HMO categories are present in human milk of secretor mothers6,14,15,25,26 Neutral fucosylated HMOs (35–$50\%$; e.g., 2′-FL and DFL)Acidic sialylated HMOs (12–$14\%$) e.g., 3′-SL and 6′-SLNeutral non-fucosylated HMOs (42–$55\%$, e.g., LNnT, LNT). The levels and distribution of HMOs vary widely from woman to woman but also for a single woman according to the duration of lactation and many other variables (such as regional, seasonal etc.)5,27,28. Conze et al27 performed a weighted analysis of 2′-FL, 3′- FL, LNT, 3′-SL, and 6′-SL concentrations in human milk from previously published reports and reported the following median (± standard deviation) levels: for 2’-FL: 2.56 ± 0.054 (IQR 1.14–3.89 g/L), for 3’-FL a median of 0.32 ± 0.045 (IQR 0.057–1.1 g/L), for LNT 0.82 ± 0.0057 (IQR 0.35–1.5 g/L), for 3’-SL 0.23 ± 0.0018 (IQR 0.10–0.42 g/L) and for 6’-SL 0.33 ± 0.003 (IQR 0.09–0.54 g/L)27. HMOs range in concentration from 20 to 25 g/L (average 9–22 g/L) in colostrum to 10–15 g/L (average 8–19 g/L) in mature milk, and 4–6 g/L after 6 months15,22,29–34. About 10 grams of HMOs are consumed daily by a term infant ingesting 800 milliliters of human milk26. Individual HMO concentrations vary by secretor status and Lewis blood-type status, race, geographic region, ethnicity, environmental conditions, season, maternal diet, physiological status, parity, gestational age, and mode of delivery4,5,14,15,28,32,33,35–41. In secretor women (account for 70–$80\%$ of all women), 2′-FL is the most prevalent HMO, and persists at around 1 g/L after one year35,36. Most HMO concentrations decrease over the course of lactation. However, some HMOs, including 3’-SL, 3’-FL, and DSLNT increase in concentration throughout the first months of breastfeeding and even beyond one year of lactation4,30,33,37. Recent research by Plows and colleagues33 examined HMO levels over two-years and confirmed that the majority of HMO concentrations decrease significantly over the course of lactation among Hispanic mothers in the United States, with the exception of 2’-FL, LSTb, and DSLNT, which showed no change, as well as a 10-fold increase of 3’-FL, and a 2-fold increase of 3’-SL from the first month to the 24th month of lactation. Although it is not known if these variations in HMO-levels have a clinical impact, the stability or growth of certain HMOs during lactation suggests that they may have crucial biological activities33. Maternal secretor and Lewis blood-type status affect HMO fucosylation. Le gene encodes Lewis blood group antigens (FUT3 gene) and generates fucosylated HMOs in mammary glands. Se is another HMOs-related gene5,42. Se and Le genes encode mammary gland enzymes FUT2 and FUT3 involved in fucosylated HMO production. Se and Le genes encode FUT2 and FUT3, which classify lactating mothers into four types14. Lactating mothers who express active FUT2 are called “secretors,” and their milk is rich in 2′-FL and LNFP I. Non-secretors are lactating mothers who do not express active FUT2. Their milk contains few or no 1–2 fucosylated HMOs, including 2′-FL14. Variations in FUT2 negative genotypes contribute to geographic variances in HMO profiles43. Secretor mothers have greater mean total HMO concentrations than non-secretor mothers, and most HMOs differ by secretor status, but not DSLNT37. Lactating mothers that express FUT3 are Lewis-positive, and their milk contains 3’-FL and LNFP II. Lewis-negative mothers don’t produce FUT3. Non-secretor mothers’ milk has more neutral, non-fucosylated HMOs due to a lack of FUT240. Cheema et al.28 found that human milk samples were dominated by five HMOs: 2′-FL, 3’-FL, LNT, DFLNT, and LNFP II. The secretor mothers exhibited larger amounts of 2′-FL, DFLac, LNnT, LNFP I, DFLNT, and LSTc, whereas non-secretors had higher concentrations of 3FL, LNFPII, LNT, LSTb, DFLNH, and FDSLNH28. Se and Le gene mutations alter FUT2 and FUT3 enzyme production, modifying the HMO structure14. The most important variations within HMO distribution are the amount of fucosylated HMOs, which are prominent in secretor individuals44. Although the genetic profile of the mother was found to have a significant effect on the HMO composition in the mother’s breast milk, particularly fucosylated HMOs, the stage of lactation is a major determinant of the HMO quantity, and epigenetics may also have a significant effect on the HMOs’ expression4,20,30. HMO concentrations and profiles vary geographically. Among healthy breastfeeding women of 11 different nationalities, McGuire et al.43 found that the concentration of 3’-FL was at least four times higher in milk collected in Sweden than in milk collected in rural Gambia, while the concentration of DSLNT was about four times lower in Sweden than in rural Gambia. Furthermore, in Gambia, lactating mothers produce considerably less HMOs (LNnT) during the wet than during the dry season45. Additional maternal and environmental variables contribute to HMO variability, although their impact may be modest16. It was reported that after a cesarean section, human milk had lower levels of 3′-SL, 2′-FL, and 6′-GL than after vaginal delivery32. Parity affects as well the concentration of HMOs28. While parity was found to be negatively associated with LNFP III in non-secretor mothers, it was found to be positively associated with LNFP II and FDSLNH in both secretor and non-secretor mothers28. It is likely that parity affects HMO content due to the correlation between maternal body mass index (BMI) and human milk fatty acid composition as well as fat and protein concentration, which increases with each additional delivery28. Regarding the effects of prematurity on HMOs, higher levels of 3′-SL, 6′-SL, LNT, and LNDFH-I were detected in maternal milk after preterm than after term delivery. At the same time, the proportions of 3′-SL and 6′-SL also differed considerably according to the milk maturation stage46–48. FUT2-dependent HMOs like 2’-FL and LNFP I are slightly lower in early milk of mothers who delivered preterm28. But again, as stated before, it is not known if these variations in HMO concentration do have a major clinical impact. Maternal adiposity has been reported to be positively, negatively or not related to the amount of individual and/or total HMO concentrations. Maternal body composition was shown to be related to human milk microbiota, HMO composition, and newborn body composition28. Maternal obesity was associated with lower concentrations of several fucosylated and sialylated HMOs. Infants born to obese mothers had reduced intakes of numerous fucosylated and sialylated HMOs, and obesity in mothers was associated with lower concentrations of these HMOs49. Milk from mothers who were overweight before pregnancy had higher concentrations of LNT and LNnT than milk from mothers who had a normal weight50. Only among secretor mothers has pre-pregnancy BMI been found to have a positive correlation with both 2′-FL and DFLac51. Depending on maternal secretor status, correlations between maternal weight, BMI, and body composition measurements and 2′-FL and LNH concentrations varied28. Adiposity measurements were positively associated with 2′-FL and FLNH concentrations in secretor and non-secretor mothers, and with 3′-SL concentrations in non-secretors28. McGuire et al.43 also showed a positive correlation between maternal weight and 2′-FL and BMI, but not LNH, FLNH, and FLNH. They also discovered a positive correlation between weight and LNFP III and DFLNT, and a negative correlation between weight and BMI and LNnT and DSLNT. Selma-Royo et al.52 found no connection between maternal BMI and either individual HMO profiles or clusters of HMOs. Secretor mothers have a greater dietary effect on HMO profiles than non-secretor mothers. Dietary fibers, polyphenols, and several insoluble polysaccharides, pectin, and MUFA are associated with the secretor HMO profiles. However, Plows et al.33 found that increases in HMOs over the course of 24 months of lactation were unaffected by maternal age, BMI or socioeconomic level. In Norwegian mothers, no difference in HMO composition was reported between vegan, vegetarian, and non-vegetarian mothers53. In summary:HMO composition is influenced by many variables, including genetic background, environment, dietary intake, and many other factors. However, except for secretor versus non-secretor mothers, there is little evidence that these changes are of clinical impact. ## HMOs and anthropometry There is limited information about how HMOs affect infant body composition. Total HMO intake is not related with growth and adiposity, although some specific HMOs are related with infant growth in the first six months. The difference in weight between breastfed newborns of secretor and non-secretor women may be explained in part by the fact that several HMOs are both positively and adversely linked with baby food responsiveness4,28,48,54. A narrative review reported that several observational studies have investigated if a link could be found between HMOs and infant growth in term-born breastfed infants4. Only few relationships were consistently reported across studies4. FLNH, LNnT, and LNFP III were negatively associated with infant anthropometric measurements and body composition, while DFLNH was positively associated4. Cheema et al28 demonstrated that anthropometrics, fat-free mass, and adiposity are all strongly linked with HMO intake, with correlations modulated by secretor status. Certain HMOs, such DFLNH and LNnT, appear to serve a protective role by controlling fat formation, perhaps protecting newborns from later-life obesity28. Regardless of maternal secretor status, child body composition was positively associated with 2′-FL, 3-FL, DFLac, DFLNH, DFLNT, and LSTb intakes28. In infants of non-secretor mothers, DFLNT concentrations were positively- and FLNH, 6′-SL, and FDSLNH were negatively associated with infant anthropometric measurements and body composition28. In infants born from secretor mothers, 3′-SL intake was linked to weight, length, fat-free mass, and weight for age28. 3‘SL was the only HMO linked with greater weight for length increases in the first four months of lactation in a recent European multicenter study of 370 mother-infant dyads55. Still in secretor mothers, HMO composition at three months after birth was linked to weight and height during the first five years of life51,56. An inverse relationship between HMO diversity and LNnT concentration and a direct relationship between 2′-FL concentration and z-scores was reported for children’s height and weight z-scores51. However, other studies reported different: a negative correlation between LNnT and food responsiveness in the first month of life, but DFLNT and DSLNT showed this correlation solely among secretors54. Positive associations were seen between DSLNH, FLNH, LNH, LSTc, and food responsiveness at 6 months in both the overall population and in secretors exclusively54. In a Gambian study, researchers found that different HMOs, and 3’-SL in particular, affected infants’ weight-for-age z scores, whereas relative sialylation of HMOs did not45. Infants receiving higher total HMO concentrations had higher percentages of fat-free mass and a lower fat-to-fat-free mass ratio and fat-free mass-to-fat-mass ratios57. Alderete et al.58 showed that lower infant weight at one and six months, as well as reduced lean and fat mass at six months, were associated with higher levels of LNFP1 and a positive correlation was observed with greater fat mass and LNFP-II and DSLNT. In 2016, two cohorts of mothers in Malawi, one in healthy 6-month-old, and another in severely stunted infants, HMO compositions were studied59. Breast milk of undernourished infants has lower levels of HMOs than the milk that healthy babies received59. Among secreting mothers, there was no difference in HMO concentrations between those infants who were healthy or stunted. But milk from non-secretor mothers of stunted infants had lower levels of fucosylated and sialylated HMOs than infants with normal growth60. These results suggest that milk of non-secretor mothers would be less conducive to child growth due to an inefficient compensation for a lack of fucosylated HMOs48,60. According to data from Bangladesh, there is an increased likelihood of severe acute malnutrition for every unit increase in the relative abundance of sialylated HMOs61. Fifty-four percent of the infants with severe acute malnutrition and $58\%$ of the infants who were not malnourished were born to women who were secretors. Fucosylated or undecorated HMOs were not shown to be significantly linked to severe acute malnutrition. This suggests that human milk with a higher relative abundance of sialylated HMOs might have a detrimental effect on the nutritional health of children under the age of61. Two hypotheses may be related to the plausibility of HMOs on anthropometric measurements: i) certain HMO-microbiota pairs may affect infant anthropometry, and ii) HMOs affect food-responsiveness and appetite via a microbiome-driven process that affects the entero-endocrine system or the central nervous system4. The developing gut microbiome is regarded as a crucial determinant determining infant growth, along with the environment, genes, epigenetics, and metabolism62. Sprenger et al.4 hypothesize that differences in maternal nutritional status and in the composition of the mother’s gut microbiota (including epigenetic and genetic changes) may be significant confounding variables. Randomized controlled trials (RCTs) and mechanistic studies are needed to show if the inclusion of specific HMOs could aid to promote growth in specific circumstances of faltering growth or in preterm-born infants. ## HMOs and microbiota Gut microbiota composition is established in early life and is influenced by many variables, such as delivery mode, gestational age, maternal, and infant/toddler nutrition, antibiotic use, presence of siblings, local environment, geographic location, and host genetics has short- and long-lasting effects on health63. The content of HMOs in mother’s milk is one of the variables determining the composition of the gut microbiota in the infant63. Infant microbiota is characterized primarily by low diversity and high variability, even more than in adults64. Breastfed infants have a significantly different microbiota and metabolome compared to formula-fed ones65. Bifidobacteria are among the first colonizers of the infant gut and sustaining this abundance of Bifidobacteria is crucial to preserving the gut microbiota composition. Several studies have shown that HMOs influence the gut microbiota composition via bifidogenic and anti-pathogenic effects and by potentially interacting with the gut epithelium to alter the physical interactions between microbes and their hosts66. Breastfeeding, due to the supply of HMOs into the gut, promotes the growth of specific HMO-utilizing Bifidobacterium species which are nearly accounting for 50–$90\%$ of the total bacterial population found in the feces of breastfed newborns67. In the first 1000 days of life, the gut microbiota of healthy breastfed infants is typically dominated by ‘infant-type’ bifidobacteria, including Bifidobacterium longum subsp. Infantis, B. bifidum, B. breve, and B. longum subsp. longum68. Some members of the Bifidobacterium genus can metabolize HMOs, but not all of them can, and not all HMOs cause the same changes in the composition and/or activity of the gut microbiota and have the same effects on host well-being and health. B. longum subsp. Infantis is the most effective consumer of HMOs, and B. bifidum and B. breve can also partially consume HMOs20. Bifidobacterium bifidum and B. longum subsp. infantis, two avid HMO consumers, dominate through inhibitory effects in which the early arriving species apparently depletes resources for later arriving species69. Bifidobacterium longum would be a moderate competitor, as it cannot consume LNnT, but can consume LNT and specific fucosylated sugars such as 2′-FL, 3-FL, LDFT, and LNFP I. Bifidobacterium breve, a species with limited HMO-utilization ability, limited to LNT and LNnT, can benefit from facilitative priority effects and dominates by utilizing fucose, an HMO degradant not utilized by the other bifidobacterial species like B. bifidum and B. infantis69. Several Bacteroides species are known to utilize HMOs as well. Bacteroides have been reported to dominate in the absence of bifidobacteria, and mutual exclusion may be occurring through the depletion of HMOs68. Bacteroides thetaiotamicron, found in a healthy mature gut, provides metabolic and immune support and is an effective HMO degrader70. The diversity of bifidobacteria in is closely correlated with whether or not the mother is a secretor for the enzyme FUT271. Observational studies showed that secretor milk status (due to its high levels of 2′-FL and other Fucosyl-HMOs) are associated with bifidobacteria dominated early gut microbiota in breastfed infants43,72,73. Stool from infants with a microbiome harboring this 2‘FL utilizing capacity has been shown to have a lower pH and provides better protection against specific diarrheal diseases73. Bifidobacteria isolated from the stool of secretor breast milk-fed infants were able to utilize 2′-FL as the sole carbon source, indicating a more pronounced bifidobacterial metabolic activity targeting fucosylated HMOs4,74. Conversely, the gut microbiota of infants born to non-secretor mothers is depleted of bifidobacteria because to the absence of 2′-FL in human milk, which may result in a diminished level of biological defense against infections34. In contrast, bifidobacteria colonization is slowed by non-secretor human milk, while Clostridium and Enterobacteriaceae are encouraged73. When HMOs are fermented by bacteria, SCFAs are produced, creating a low-pH environment in the colon that encourages the growth of beneficial bacteria and inhibits pathogens20,75. These SCFAs have multiple beneficial physiological effects, such as acting as anti-inflammatory agents, serving as energy substrates for intestinal epithelial cells, and promoting gastrointestinal motility6,76. Cross-feeding (when one kind of bacterium’s metabolic byproducts are used as a food source by another type of bacterium in the environment) is encouraged by the presence of HMOs77,78. The bifidobacterial population in the infant’s gut is composed of a co-group of multiple Bifidobacterium strains, rather than one strain dominating, and competing to the exclusion of all others. On the one hand, the cross-feeding effect among bifidobacterial species/strains is associated with the ability to thrive in HMOs of multiple Bifidobacterium members in the infant’s gut. Fermentation products of HMO-degrading infant-type Bifidobacterium species may suppress other gut microbes and opportunistic pathogens that do not use HMOs. This competitive advantage in the HMO use of the developing gastrointestinal tract greatly affects the survival and persistence of beneficial Bifidobacterium species and lessens the burden of potentially harmful or pathogenic bacteria6,68. On the other hand, certain bifidobacterial taxa cooperate with non-bifidobacterial taxa (including HMO consumers and non-HMO consumers) to maximize the nutrient consumption of HMOs, thus contributing to increased bifidobacterial diversity and dominance-gaining68. Schwab et al.79 showed that Eubacterium hallii consumes the fermentation products of HMO by bifidobacteria and generates butyrate and propionate. The cooperation of the bacterial community in the neonatal intestine to maximize the utilization of HMOs, so as to maintain the intestinal immune balance of newborns. Overall, infant-type Bifidobacterium species are well adapted to the infant gut and efficiently consume HMOs, and their presence influences both immediate and long-term health outcomes68,80. Since HMO composition differs between mothers, it’s reasonable to assume that each mother’s milk has a unique effect on her infant’s gut microbiota. In addition to the widespread indirect effects resulting from microbial fermentation of HMOs, recent research has described the direct benefits of HMOs on gut health6. 3´-FL stimulated production of mucin and antimicrobial peptides in goblet cells, and 2′-FL may have a similar effect on goblet cell function when inflammatory stressors are also present81. Natividad et al.82 used in vitro models that replicate the microbial ecology and the intestinal epithelium to evaluate the impact of lactose, 2’-FL, 2’-FL + LNnT, and a mixture of six HMOs (2’-FL, LNnT, DFLac, LNT, 3’-SL, and 6’−SL) on newborn gut microbiota and intestinal barrier integrity. Although the SCFA levels were higher and bifidogenic potential was present in all the products examined, only the fermented medium from the HMOs provided protection against inflammatory gut barrier disruption. The most butyrate-producing bacteria were enriched by the six HMOs formulation, whereas 2’-FL/LNnT and six HMOs promoted the greatest diversity within the Bifidobactericeae family82. Since the intestinal epithelial glycocalyx is crucial for microbial colonization, Kong et al.83 conducted the first study to examine the development of this barrier in relation to HMOs. They found that 2′-FL and 3-FL stimulate glycocalyx formation and have a direct effect on the growth of epithelial cell lines. HMOs have been proven to directly modulate goblet cells, causing them to produce more mucus, another important component of the intestinal barrier system81. There is a limited information on the complicated relationships between the human milk microbiome and different types of HMOs5,28. Although the potential biological influence on the newborn is still unclear, there is an association between maternal secretor status and HMOs with human milk microbiota71. Maternal factors including body composition are related to human milk microbiota and HMO composition. Individual HMO concentrations may influence human milk bacterial profiles during the exclusive breastfeeding period. Total HMOs and 2′-FL were positively associated with the relative amount of Staphylococcus, whereas 3′-SL was negatively correlated with the proportions of Ralstonia and Novosphingobium in 16 human milk samples84. Staphylococcus epidermidis, Streptococcus salivarius, Cutibacterium acnes, Gemella haemolysans, and Veillonella nakazawae all had correlations (positive and negative) with HMO concentrations28. In colostrum, a higher total HMO concentration is associated with higher counts of Bifidobacteria. Sialylated HMOs were positively correlated with B. breve, and non-fucosylated/non-sialylated HMOs were positively correlated with B. longum. There were also favorable associations found between fucosylated HMOs and Akkermansia muciniphila and between fucosylated/sialylated HMOs and Staphylococcus aureus85. Only in non-secretor mothers, several HMOs were correlated negatively with Streptococcus parasanguis, Gemella haemolysans, and Cutibacterium acnes. Among the secretor mothers, 3′-SL was negatively associated with Staphylococcus epidermidis. Moossavi et al.86 found that 3′-SL, 6′-SL, LSTb, LSTc, DSLNT, and DSLNH all have positive relationships with Staphylococcus spp. HMOs are the third most important component of human milk and are crucial for the development of a healthy early life gut microbiome. As a result, it is evident that HMOs encourage the growth of a bifidobacteria-rich gut microbiome. ## HMOs and necrotizing enterocolitis (NEC) In preterm newborns, breastfeeding has been linked to a lower incidence of NEC compared to formula feeding4,87,88. In a murine model of NEC, HMOs raise mucin levels and lower bacterial attachment89. FUT-2 non-secretor and low secretor status in premature newborns is associated with a higher risk for NEC, gram-negative sepsis, and death90. HMO diversity and specifically DSLNT were shown in observational studies to be associated with NEC4,87,91–93. Although explanations for the association between DSLNT and NEC remain elusive, an age-appropriate microbiome progression was suggested91. DSLNT was shown to increase survival rate and reduce pathology scores in a rat model of NEC94. More studies are needed to understand the link between DSLNT and NEC risk. Protective effects against (severity of) NEC were observed for 6’−SL and 2’-FL in experimental models87,94–96. Both 2’−FL and 6’-SL suppress toll like receptor-4 activation, which is linked to the onset of NEC, and hence decrease inflammation in mouse and piglet models of NEC95. However, clinical observations could not confirm a relation between 2′-FL or 6’−SL with NEC risk. ## HMO and infections In, the amount of HMOs is associated with a decreased prevalence of diarrhea, overall infections, and morbidity97–100. FUT2 alleles are associated with a higher risk of infant gastrointestinal and respiratory illnesses101. At the age of 2 years, diarrhea due to stable toxin-Escherichia coli infection and of unknown etiology were both reduced in breastfed infants with high levels of alpha 1,2-linked fucosylated-HMOs102. Higher levels of LNFP-II in colostrum were associated with reduced respiratory and gastrointestinal infections by 6 and 12 weeks98. Torres-Roldan et al.110 investigated the HMOs’ composition and infection rates in very-low-birth-weight infants, FDSLNH was found to protect for late-onset neonatal sepsis103. In breastfed newborns in Mexico, the incidence of Campylobacter diarrhea was decreased in infants whose mothers’ milk had a high percentage of 2′-FL97. The protection offered by HMOs was limited to the duration of breastfeeding105. Furthermore, high levels of LNDFH-I, another 2-linked fucosyloligaosaccharide, protect against calicivirus diarrhea including norovirus97. Population studies show significantly higher levels of LNnT, 2’−FL and 6’-SL in milk of mothers of rotavirus-positive neonates with gastrointestinal symptoms104. However, it is unknown whether high levels of these HMOs are a natural reaction to the rotavirus infection or whether they provide poorer protection against a rotavirus infection than lower levels104. Secretor-positive human milk inhibits norovirus particles, while secretor-negative milk does not, suggesting that alpha 1,2 linked fucosylated-HMOs may be implicated105. Both 3-FL and 2’-FL have been found to bind norovirus33. Higher concentrations of LNF-II in human milk at two weeks postpartum were associated with fewer respiratory problems in infants by 6 and 12 weeks of age106. Mother’s milk of sick infants contains more of certain HMOs (LNT) than healthy infants, while other HMOs (LNFP1) are less frequent in sick infants45. However, the levels of HMOs could not be related to physician reported data on infections (otitis media, upper and lower respiratory tract infections)107. HIV-infected women have larger relative abundances of 3’-SL in their milk than HIV-negative mothers108. HIV-infected women with total HMOs above the median (1.87 g/L) are less likely to transmit HIV via breastfeeding, although there was no difference related to secretor or Lewis status109. A higher LNnT concentration correlated with reduced transmission. Independent of other known risk factors, higher concentrations of non-3’-SL HMOs were associated with decreased likelihood of postnatal HIV transmission. In Zambian children, breastfeeding was protective against mortality only in uninfected children with high concentrations of fucosylated HMOs110. Higher amounts of 2’-FL and LNFP I, as well as 3-FL and LNFP II/III, were substantially associated with a decreased mortality in children who were not HIV-infected110. Breastfeeding was found to reduce mortality risk for HIV-infected children, but no consistent relationships were found between HMOs and mortality110. Some potential modes of action for HMOs include weakening, preventing, and deviating pathogens from adhering to their cognate cell surface ligands6. Several viruses and bacteria have been found to bind to HMOs4. Many infectious agents, including viruses (including influenza virus, respiratory syncytial virus, coronaviruses, rotavirus, HIV, and norovirus), bacteria (including Streptococcus pneumoniae, Haemophilus influenza, Group B streptococci (GBS)), and protozoan parasites, require adhesion to the surface of epithelial cells in order to replicate and, in some cases, infiltrate and cause disease80,111. HMOs act as soluble decoy receptors that block the attachment of specific viral, bacterial, or protozoan parasite pathogens to the epithelial cell surface117. Pathogens that are not bound to the cell surface are washed away harmlessly. Animal models have indicated that increasing acetate, in combination with other metabolites, increases protection from gastrointestinal and respiratory infections112,113. Regarding to anti-infective properties of HMOs, studies showed6,80,114,115 2’-FL: C. jejuni, Enteropathogenic E. coli, Salmonella enterica, rotavirus, norovirus, respiratory syncytial virus3’-FL: Enteropathogenic E. coli, Salmonella enterica, norovirus,LNT: Vibrio cholerae toxin, Group B streptococcus, Entamoeba histolytica3’-SL: Enteropathogenic E. coli, Vibrio cholerae toxin, Helicobacter pylori, Pseudomonas aeruginosa, rotavirus, influenza6’-SL: Enteropathogenic E. coli, Helicobacter pylori, Pseudomonas aeruginosa, influenza A H1N1, rotavirusLNnT: pneumococci, influenza Some HMOs are bacteriostatic against GBS, causing neonatal sepsis, pneumonia, and meningitis87. Non-sialylated HMOs, LNT and LNDFH-I (1–2 mg/L daily), delay the growth of GBS with 96–$98\%$116. HMOs also showed antibacterial action against, Acinetobacter baumannii, and Staphylococcus aureus41. In neonates, HMOs alter the growth and morphogenesis of C. albicans, which then makes it more difficult for the pathogen to attach, invade, and cause disease126. According to basic and animal research, HMOs appear to have a role in the treatment and prevention of bacterial, viral, protozoal, and fungal diseases. It is important to note that the majority of the evidence presented in support of the anti-adhesive effects of HMOs originates from experimental studies. It will need well-designed and powered mother-infant dyad observation studies and, more crucially, intervention studies to demonstrate that a single HMO or a mixture of several HMOs reduces the incidence and/or severity of a diversity of infectious diseases. ## HMO and immune development The immune system develops over the course of gestation and continues to be postnatal in relation to exposure of microorganisms. HMOs can modify host epithelial and immune cell responses and contribute to the development of the gastrointestinal immune system4–6,20,48,117. It has been hypothesized that HMOs influence the responses of epithelial cells and immune cells by modifying cell proliferation, differentiation, and apoptosis, as well as cell signaling pathways and cell surface glycosylation, so modulating immunological functions. Intestinal epithelial barrier cells can be directly affected by HMOs of varying structures. Direct interactions between HMOs and infant intestinal epithelial cells affect their gene expression, cell cycle, and cell surface glycosylation and regulate their growth, differentiation and apoptosis20. The establishment of the infant gut microbiota and its metabolic activity is thought to be an important mechanism through which HMOs affect immune system development4. In addition, when HMOs reach the colon and are then absorbed intact into the circulation, they may play a systemic immunomodulatory role by mediating cell-cell interactions in the immune system. Intestinal health and intestinal barrier function constitute the first defense line in innate immunity4–6,20,48,118,119. As shown in vitro, HMOs inhibit cell proliferation, promote cell differentiation, death, and maturation, and strengthen the barrier function7,31,94,120. Modulations in gene expression caused by HMOs have an immediate effect on intestinal epithelial cells, altering their surface glycans and eliciting different cellular responses. The generation of cytokines by lymphocytes is altered by HMOs, which may result in a more balanced TH1/TH2 response. Growing evidence from in vitro research suggests that HMOs directly control immunological responses by altering immune cell populations and cytokine release in infants, in addition to their indirect effects on the immune system via changes in gut microbiota94. HMOs may also affect immune system receptors. Galectins, glycan-binding proteins, regulate intracellular signaling, cell – cell communication, proliferation, and survival121. Galectins may be HMO receptors for the immune system development5. HMOs can act locally or systemically on mucosa-associated lymphoid cells15. HMOs contain tolerogenic factors influencing human monocyte-derived dendritic cells and elevated Interleukin (IL)-10, IL-27, and IL-6 levels but not IL-12p70 and tumor necrosis factor-alpha122. 2′-FL increases Th1-type interferon-gamma and regulates IL-10 production, suggesting a Th1 response123. CD11(+) mesenteric lymph node dendritic cells exposed to 3’-SL can produce cytokines that boost Th1 and Th17 immune cells124. Three weeks of 2’-FL administration to Caco-2Bbe cells, reduced the permeability and upregulated tight junction proteins125. 2’-FL can boost innate and adaptive immunity in influenza-specific mouse models and reduce respiratory viral infections126. In a mouse influenza vaccination model, dietary 2′-FL improved humoral and cellular immune responses, boosting vaccine-specific delayed-type hypersensitivity and immunoglobulin proliferation.127. ## HMO and allergy The prebiotic effects and the immunological programming provided by HMOs also affect individual susceptibility to allergies. A balanced microbiota and microbiome provide immunological benefits by lowering the risk of allergic disorders through the synthesis of SCFAs, such as butyrate and propionate, which have anti-inflammatory and anti-allergic qualities. It is known since more than 20 years that the gastrointestinal microbiota differs in allergic and non-allergic infants before symptoms of allergy develop120,128,129. A significant reduction in the probability of acquiring immunoglobulin E (IgE) mediated eczema at the age of two years was observed in C-section-born, allergy-prone breastfed infants whose mothers expressed FUT2, resulting in 2′-FL synthesis in human milk39. C-section infants who were administered human milk containing FUT2-dependent oligosaccharides were shown to have a lower incidence for IgE-associated eczema at the age of 2 years39. It was only in infants born via C-section that these associations between IgE-associated eczema and consumption of FUT2-dependent milk oligosaccharides were observed38. The authors did not find an association with HMOs and allergic disorders at 5 years of age39. When compared to milk with high LNFP III concentrations, infants who received human milk with low LNFP III concentrations were more likely to develop cow’s milk protein allergy (CMPA)38. The mothers’ FUT2 status was associated with a delayed onset of CMPA, and CMPA infants born to non-secretor moms (FUT2 negative) were more likely to develop IgE-mediated CMPA. Lower levels of DSLNT and 6′-SL were associated with atopic dermatitis38. Concentrations of nine neutral HMOs were not associated with the chance of having an allergic disease up to the age of 18 months, according to a case-control study in 20 mother-infant pairs from a larger birth cohort97. Regarding to relationship between food sensitization, a large clinical observation study (421 mother – infant dyads) demonstrates that HMO composition is associated with the development of food sensitization130. The HMO profiles associated with lower risk of food sensitization were characterized by higher concentrations of FDSLNH, LNFP II, LNnT, LNFP I, LSTc and FLNH, and lower concentrations of LNH, LNT, 2′-FL, and DSLNH130. In an ovalbumin sensitized mouse model, 2’-FL and 6-FL stabilize mast cells by inducing expression of T regulatory cells and activate the IL-10(+) regulatory cells to reduced symptoms of food allergy131. By influencing the colonization of the gut microbiota and producing butyrate, microbiota composition of human milk helps the prevention of development of food allergies50. The development of a microbiome dominated by bifidobacteria was significantly delayed in infants fed secretor-negative human milk compared to those fed secretor-positive breastmilk at three months of age132. In particular, B. breve has been linked to a decreased incidence of eczema133. Among infants with a family history of atopy, reduced Bifidobacteriaceae abundance in infancy is related with a higher risk of eczema133. However, another study found no significant association between the intake of particular HMOs (measured at 6 weeks and 6 months) and the risk of atopic dermatitis134. Breastfeeding has been shown to reduce the likelihood of developing food allergy, eczema, and asthma, at least during early life, although there is a lack of consistency in reporting of breastfeeding duration, diagnostic criteria for atopic dermatitis, and assessment age135. ## HMO and brain/cognitive development Sialic acid is considered a key conditioned nutrient during early development. Although the mechanisms are not completely understood, the high levels of sialic acid in human milk, especially in the form of sialylated milk oligosaccharides, are considered an important bioactive component linked to infant brain and cognitive development4,6,136. Both 3’−SL and 6’-SL have been shown to enhance learning and memory and play a role in the gut microbiota-brain axis137–139. Cho et al.149 showed that the association between human milk 3’-SL concentration and cognition, particularly language functions, in typically children who received human milk containing alpha tetrasaccharide (an HMO, which only be detected in the mothers with blood type A. High levels of 6′-SL have been linked to better cognitive and motor development at 18 months of age, as well as better language development at 12 months of age140,141. In the brain, fucosylated proteins are found along the neuronal synapses, particularly in the hippocampus, where they play a crucial role in the development of memory and learning142. There is experimental evidence that 2’-FL interferes with cognitive processes, including enhanced cognitive ability, learning, and memory143. Early exposure to 2′-FL and 6`-SL represents a critical time window for the positive influence on the cognitive development at 2 years48,140,141. Although human data are scant, one study found that breastfed infants with greater 2’-FL intake at one month of birth had better cognitive development at 24 months of age and improved motor skills144,145. A higher concentration of fucosylated HMOs was linked to better linguistic development between the ages of 12 and 18 months140. In summary, studies suggest a role of HMOs in brain and cognitive development, but more data are needed. The mechanisms of action need to be further unraveled. ## HMO and diabetes 2’-FL, 3’-SL, 6-SL, and LNnT may have protective effects on the development of type-1 diabetes. In an animal model, early life intake of HMOs delayed and suppressed type-1 diabetes development in non-obese diabetic mice and reduced the development of severe pancreatic insulitis in later life126. ## Effects of HMOs containing infant formula on anthropometry Although the WHO recommends exclusive breastfeeding since birth to 6 months of age, some infants will not receive human milk. The energy and nutrition need of a growing infant can be met by infant formula, which typically is cow’s milk based. However, cows and human milk differ substantially in the composition of macro- and micro-nutrients, and in the content of bioactive components26. In fact, HMOs are virtually absent in cow’s milk (or any animal milk), and their variety is much lower than in human milk146. Observational studies revealed that many disorders such as NEC, irritable bowel syndrome, obesity, allergies, and eczema, are more common in formula-fed compared to breastfed infants20. The early microbiota development and effect on immune system development in cow’s milk formula fed infants might be affected by the lack of HMOs147. Nowadays, it is possible to supplement infant formula with mixtures of HMOs. The effects of HMOs in infant formula have been evaluated in several randomized clinical trials (Table 2). Table 2.HMOs in formula in research. ReferenceHMOStudy siteInclusion criteriaInterventionControl group(s)Study durationPrimary outcomeSecondary outcomes: Test versus controlPuccio et al.11820172‘FL + LNnTBelgium, Italy0 to 14 days0–6 months:Starter formula+ 1.0 g/L 2‘FL+ 0.5 g/LNnT6–12 monthsFollow-up formulal0–6 monthsStarter formula6–12 monthsFollow-up formulaStarter formula: Intact protein, cow’s milk – based, whey-predominant infant formula with long-chain polyunsaturated fatty acid.12 monthsWeight gain: similarMean difference [$95\%$ CI] test vs control: −0.30 [−1.94, 1.34] g/dayThe formula with 2′-FL and LNnT was well-tolerated. The formula with 2′-FL and LNnT supported normal, age-appropriate infant growth during the 4-month exclusive feeding period, after the introduction of complementary foods from 4 to 6 months, and following the switch to a standard follow-up formula without HMOs at 6 months up to 12 months of age. Infants receiving formula with 2′-FL and LNnT had significantly softer stools and fewer episodes of nighttime wake-ups at age two months, and infants born by cesarean section also had a lower incidence of colic at four months of age. Infants receiving HMO containing formula had significantly fewer parental reports of parent-reported morbidities related to lower respiratory tract infections as well as antipyretic and antibiotic. Alliet et al.15920222’FL +L. Reuteri DSM 17,938Belgium, Italy2017–2019Healthy, term (37–42 weeks gestation) infants aged≤14 days at enrollment with a birth weight between 2500 and 4500 gStandard bovine milk-based whey predominant formula with an energy density of 670 kcal/L containing 75 g/L lactose, 34 g/L fat, 14 g/L protein (60:40 whey: casein ratio), and L. reuteri DSM 17,938+ 1 g/L 2’FL which iStandard bovine milk-based whey predominant formula with an energy density of 670 kcal/L containing 75 g/L lactose, 34 g/L fat, 14 g/L protein (60:40 whey:casein ratio), and L. reuteri DSM 17,938Breastfed infants180 daysWeight gain in HMO formula was non-inferior to a non-HMO supplemented formula. Anthropometric Z-scores, parent-reported stooling characteristics, gastrointestinal symptoms and associated behaviors, and AEs were comparable between formula groups. The microbiota composition in formula containing 2’-FL and L. reuteri DSM 17,938 approaching BF.At age 1 month, Clostridioides difficile counts were significantly lower in study group than control. Bifidobacterium relative abundance in study fromula tracked toward that in breast-fed. Marriage et al.14920152‘FL + GOSUnited States2013–2014Healthy, singleton infants (birth weight≥2490 g), who were enrolled by day of life 5Test formula 1: formula with 2.4 g/l GOSTest formula 2: formula with 2.4 g/l GOS+0.2 g/l 2’FLTest formula 3: formula with 2.4 g/l GOS+1 g/l 2’FLBreastfed infants4 monthsNo significant differences among feeding groups for weight, length or head circumference gain during the 4-month study period ($$p \leq 0.016$$ from day 14–28 and $$p \leq 0.022$$ from day 84–119).2’FL was found in the plasma and urine of infants fed a 2’FL formula, no significant differences were seen in 2’FL uptake relative to concentration fed (no p value mentioned).Goehring et al.16520162‘FL + GOSUnited StatesHealthy singleton infants (birth weight≥2490 g) who were enrolled by 5 daysTest formula 1: formula with 2.4 g/l GOSTest formula 2: formula with 2.4 g/l GOS+0.2 g/l 2’FLTest formula 3: formula with 2.4 g/l GOS+1 g/l 2’FLBreastfed infants4 monthsBreastfed infants and infants fed experimental formulas with added 2’FL were not different. They had $29\%$-$83\%$ lower concentrations of plasma inflammatory cytokines than infants fed the formula with only GOS added. Peripheral blood mononuclear cells were not different in breastfed infants compared to the infants fed a 2’FL supplemented formula. However, they had significantly lower concentrations of TNF-α (p ≤ 0.05), interferon γ (p ≤ 0.05) than infants fed the GOS-only formula. Parshat et al.15020212’FL, 3-FL, LNT, 3’-SL and 6’-SL.Germany, Italy, and Spain2018 × 2020.Healthy f infants≤14 days of age, born at full term (≥37 and≤42 weeks of gestational age), singleton birth, with a birth weight of 2500–4500 g and an APGAR score of 9 or 10 as assessed within the first 15 min after birth.($$n = 341$$)5.75 g/L of a mix of 2’FL, 3-FL, LNT, 3’-SL and 6’-SL.Infant formula without HMOsBreastfed infants4 monthsNo significant difference in weight gain between both formula groups ($p \leq 0.001$).No difference in length or head circumference gain between both formula groups. The test formula was well tolerated. Test group: softer stools at higher stool frequency than control formula group. Adverse events: similar in all groups. Lasekan et al.15220222’FL, 3-FL, LNT, 3’-SL and 6’-SL.United StatesSeptember 2019 through December 2020, mostly during the COVID-19 pandemicSingleton, healthy term infants (gestational age 37–42 weeks) between 0 and 14 days of age at enrollment, with a birth weight≥2490 g.($$n = 222$$)5.75 g/L of a mix of 2’-FL, 3-FL, LNT, 2’-SL and 6’-SL.Infant formula without HMOsBreastfed infants4 monthsThe primary outcome for this study was weight gain per day, from D14 to D119: No significant differences in weight gain (p ≥ 0.337).Secondary variables were weight, interval weight gain per day, length, interval length gain per day, head circumference and interval HC gain per day. No significant differences among the three groups regarding gains in weight and length (p ≥ 0.05).More soft, frequent and yellow stools in the test group in comparison to the standard milk-based formula group. This makes the stools more similar to breastfed infants. Parental responses indicated that the test group had a higher average loose stool dimension score compared with control at D119 (all p ≤ 0.033).The standard milk-based formula group saw significantly more frequently ($$p \leq 0.044$$) healthcare professionals for illness in comparison to the test group. Estorninos et al.1642022MilkOligoSacch-arides (MOS)Phillipines2016–2018Healthy term, singleton birth (37–42 weeks of gestation), postnatal age of 21–26 d at enrollment, 3) weight-for-length and head circumference-for-age z-scores between −3 and+3 andn = 230Control formula with MOS ingredient total of 7.2 g oligosaccharides per liter. Breastfed infantsControl formula: $65\%$ intact whey protein (enriched in α-lactalbumin) and $35\%$ casein protein ratio, carbohydrates consisting of $100\%$ lactose, and a vegetable oil blend high in sn-2 palmitate.4 monthsOverall microbiota composition in test group was different from standard formula group ($p \leq 0.05$).Bifidobacterial abundance was higher ($p \leq 0.05$) in test group compared to standard formula group, approaching breastfed infants. Significantly lower number of Clostridioides difficile ($p \leq 0.001$) and Clostridium perfringens ($p \leq 0.01$) in test group compared to standard formula group. Fecal secretory IgA in test group was significantly higher ($p \leq 0.001$) compared to standard formula group and closer to breastfed infants. Test group and breastfed infants had significant lower fecal calcium excretion ($p \leq 0.005$) and fecal pH ($p \leq 0.001$) and higher lactate ($p \leq 0.001$) compared to standard formula group. Bosheva et al.2520222’-FL, DFL, LNT, 3’-SL, 6’-SLBulgaria, Hungary, and Poland2018 and November 2021Healthy and full-term, with birth weight between 2,500 and 4,500 g, and aged≥7– ≤21 days at enrollment. $$n = 50$$Test formula 1: 1.5 g/L of a mix of 2’-FL, DFL, LNT, 3’-SL, 6’-SLTest formula 2: 2.5 g/L of a mix of 2’-FL, DFL, LNT, 3’-SL, 6’-SLStandard cow’s milk-based formulaBreastfed infants15 monthsMicrobiota in the two test groups were significantly ($p \leq 0.01$) different in comparison to the standard cow’s milk formula group (SFG).Significantly higher abundance of Bifidobacterium longum subsp. Infantis ($p \leq 0.05$) in test group in comparison to SFG.Significantly ($p \leq 0.05$) lower number of Clostridioides difficile in test group in comparison to SFG and comparable to breastfed infants. Higher secretory immunoglobulin A and lower alpha-1-antitrypsin ($p \leq 0.05$) in the test group in comparison to SFG.Vandenplas et al.15120202’-FL, 3’-GL, scGOS/lcFOSBelgium, Hungary, Poland, Spain Ukraine215 fully formula fed infants≤14 days oldInfant formula contained $26\%$ fermented formula with postbiotics derived from the Lactofidus fermentation process (including 3′-GL), 0.8 g/100 mL scGOS/lcFOS (9:1), and 0.1 g/100 mL 2′-FLStandard infant formula containing the oligosaccharides scGOS/lcFOS (0.8 g/100 mL; 9:1), but no 2′-FL, postbiotics, or milk fat. Breastfed infantsUntil 17 weeks of ageEquivalence in weight gain between the test formula and control infant formula up to 17 weeks of ageSupported an adequate growth, was well-tolerated, and no safety concerns were revealed given the absence of clinically relevant differences in the number adverse event.sNowak-Wegrzyn et al. 16820192‘FL + LNnTUnited States2017–201864 children with CMPAAged 2 months to 4 yearsExtensively hydrolyzed formula with 1 g/L 2’-FL and 0.5 g/L LNnTExtensively hydrolyzed formula without HMOs7–9 daysThe study formula met the clinical hypoallergenicity criteria. Vandenplas et al. 16920222‘FL + LNnTPoland, Italy, United Kingdom, Spain, Hungary, Belgium, SingaporeFebruary 2017 and August 2018Full-term infants aged 0–6 months with physician-diagnosed CMPAN = $194100\%$ whey-based EHF supplemented with 2′-FL at a concentration of 1.0 g/L and LNnT at 0.5 g/L$100\%$ whey-based extensively hydrolyzed formula without HMOs4 monthsDaily weight gain with the test formula was noninferior to the formula without HMOs ($p \leq 0.005$).No significant group differences in anthropometric parameters. The formula was tolerated well, and the safety profiles of the test and control formulas were similarHighly significant reduction in CMPA symptoms, as evidenced by a fall in CoMiSS to levels reported in healthy infants test formula. Showed significant reduction in frequency of upper respiratory tract infections ($$p \leq 0.003$$) and otitis media ($$p \leq 0.045$$) compared to the control formula. Berger et al. 16120202‘FL + LNnT Follow-up study Puccio et al.0–6 months:Starter formula+ 1.0 g/L 2‘FL+ 0.5 g/LNnT6–12 monthsFollow-up formula0–6 monthsStarter formula6–12 monthsFollow-up formulaStarter formula: Intact protein, cow’s milk – based, whey-predominant infant formula with long-chain polyunsaturated fatty acid. The microbiota of formula-fed 3-month-old infants was different if they received HMOs and closer to the microbiota of BF infants. This was observed for the microbial diversity, the global composition at the genus level, and the abundance of several major genera typical of that age period. An increase of bifidobacteria, a decrease of Escherichia and unclassified Peptostreptococcaceae, a family to which Clostridium difficile belongsGold et al. 15420222‘FL + LNnTAustralia2018–2021Term infants aged 1–8 months with physician-diagnosed moderate-to-severe CMPAamino acid-based formula (AAF)with2′-FL and LNnT, at concentrations of 1.0 g/L and 0.5 g/LAmino acid-based formula (AAF)12 monthsInfants with moderate-to-severe CMPA fed the study formula with two HMO achieved adequate growth, with some catch-up growth. The formula was safe and well- tolerated. The gut microbiome characterization demonstrated a significant early enrichment in HMO-utilizing, infant-type bifidobacteria, and later enrichment in Bacteroides and butyrate producing taxa in the second half of the first year. Conversely, there was a significant reduction in Proteobacteria. Microbiome changes were associated with a significant rise in fecal SCFA concentrations from enrollment to 12 months of age. Ramirez-Farias et al. 15320212’-FLUnited StatesInfants (0–60 days of age) with suspected food protein allergy, persistent feeding intolerance, or presenting conditions where an extensively hydrolyzed formula (eHF)Hypoallergenic casein-based powdered eHF formula with 0.2 g/L of 2′-FL.Hypoallergenic casein-based powdered eHF2 monthsThe primary outcome was maintenance of weight for age z-score during the study. HF formula with added 2′-FL was well accepted enabling adequate volume to demonstrate a statistically significant improvement of weight for age z-scores. The formula was safe and well tolerated and consumption of the formula over 60 days showed improvement and resolution of persistent symptoms. Tolerance measures such as mean rank stool consistency, stool color, average volume of formula intake and percent of feedings associated with spit-up/vomit after 1 hr of feeding per day were comparable to other eHF feeding studies that did not contain 2′-FL.Storm et al. 17920192‘FLUnited StatesHealthy, full-term (≥37 weeks gestation; ≥2500 and≤4500 g birth weight), singleton infants, ages 14 ± 5 days, who had been exclusively formula-fed for at least 3 days prior to enrollment were recruited for this trial$.100\%$ whey protein, partially hydrolyzed, contained B lactis (0.67 kcal/mL and 2.2 g protein/L).+0.25 g/L 2′FL$100\%$ whey protein, partially hydrolyzed, contained B lactis (0.67 kcal/mL and 2.2 g protein/L).6 weeksThe primary outcome was comparison of Infant Gastrointestinal Symptom Questionnaire, and the scores for the Test and Control group were similar at baseline and during the follow-up. Anthrpomoteric measurements were similar. Based on data recorded by caregivers in the 2-day diaries, stool frequency and consistency did not differ significantly between groups. Crying and fussing duration and vomiting frequency were similar between groupsAverage formula consumption volumes did not differ between formula groups. Adverse events were similar, and both formulas are well-tolerated. Reported infections/infestations were lower in 2’-FL group ($8\%$ vs. $23\%$; $$p \leq .05$$).Roman et al. 15620202‘FL + LNnTL. Reuteri DSM 17,938Spain2018–2019Healthy, term (37–42 weeks of gestation) infantsenrolled at age 7 days to 2 months. L. Reuteri DSM 17,938Exclusively formula-fed group who received a milk-based formula with 2’ FL and LNnT,L. Reuteri DSM 17,938Exclusively breastfed infants,A group mixed fed with both formula and human milk8 weeksFormula-fed infants, either exclusively or mixed fed,receiving the HMO-supplemented formula had age-appropriate growth in line with the WHO standards,The formula was well tolerated, and GI tolerance in the formula- fed infants was comparable to that in breastfed infants. Kajzer et al. 15720162’-FL and scFOS Full term, singleton infants (birth weight≥2490 g) enrolled between 0 and 8 days of age. Experimental Formula 2 contained 2 g/L scFOS and 0.2 g/L 2′FL ($$n = 46$$).Experimental Formula 1 did not contain oligosaccharidesBreast-fed group.35 daysThe primary outcome was average mean rank stool consistency from Study Day 1 to Visit 3 and there were no differences between the groups. There were also no differences among groups for predominant stool consistency from Study Day 1 to Visit 3. The average number of stools per day for the HM group was significantly greater than EF1 ($p \leq 0.0001$) and EF2 ($p \leq 0.0001$) from Study Day 1 to Visit 3 formula groups. At Visit 3, there were no differences between groups for average volume of study formula intake, number of study formula feedings per day, anthropometric data or percent feedings with spit-up/vomit. Leung et al. 16620202’-FLChinaChildren aged 1–2.5 years($$n = 461$$)2’-FLYoung child formula or one of three new YCFs containing bioactive proteins and/or the HMO 2’-fucosyllactose (2’−FL) and/or milk fat for six months. There were no significant between-group differences in incidence of upper respiratory tract infection and duration of gastrointestinal tract infections. L. reuteri: Lactobacillus reuteri, HMO: Human milk oligosaccharide, eHF: extensively hydrolyzed formula, GI: gastrointestinal, BF: breast-fed. HMO production technologies involve novel processes, which are approved by the regulatory authorities, such as the European Food Safety Agency (EFSA) or the Federal Drug Administration (FDA) in the United States. Both the EFSA in 2015 and the FDA in 2016 approved 2’-FL and LNnT to be added to infant formula, and the first formulas containing HMOs were commercialized in Spain and the USA in 2016. The EFSA indicated that the addition of 2′-FL and LNnT at a ratio of 2:1 to infant formula is safe below 1 -year-old, with a maximum dosage for 2′-FL of 1.2 g/L and for LNnT of 0.6 g/L20. In 2019, the FDA stipulated that the maximum dosage of 2′-FL in infant formula is 2.4 g/L, and for LNnT 0.6 g/L20. HMOs have obtained the Generally Recognized as Safe (GRAS) status. The number of HMOs that can be synthesized on an industrial scale has steadily increased, and nowadays formulas containing seven HMOs (2’-FL, 3´-FL, LDFT, LNnT, LNT, 3’-SL, 6’−SL) are studied. Some oligosaccharides, identical to those in human milk, can be produced by fermentation or other techniques. To be clear, the oligosaccharides added to infant formula do not originate from human milk, even if they have an identical structure. Therefore, HMOs that do not originate from human milk should preferably be called “human identical milk oligosaccharides” (HiMOs)4. Already in 2005, LNnT was shown to be safe in 228 infants aged 6–24 months during a 16-week follow-up period, with a slight non-significant trend for higher weight and height148. Marriage et al149 conducted a prospective, randomized, controlled growth and tolerance study, with a formula containing 2′-FL and GOS in healthy full-term infants and showed similar weight, length, and head circumference to breastfed babies from enrollment (0–5 days) to four months149. This formula was well-tolerated and comparable for average stool consistency, number of stools per day, and percent of feedings associated with spitting up or vomit with the control group fed GOS supplemented formula. The formula supplemented with 2′-FL resulted in a growth similar to that of breast-fed infants149. In a multicenter, RCT in Italy and Belgium, Puccio et al.118 reported the first clinical trial with infant formula supplemented with 2′-FL (1.0 g/L) and LNnT (0.5 g/L) up to the age of 6 months118. The 2′-FL and LNnT supplemented formula was well-tolerated and supported age-appropriate growth; infant had softer stools and fewer nighttime wake-ups at two months, while cesarean-born babies had a lower incidence of colic at four months118. Infants receiving HMO-containing formula had significantly fewer parent-reported lower respiratory tract infections, antipyretic, and antibiotic use up to the age of 12 months (although the supplementation was limited to the age of 6 months)118. Parschat et al160 conducted a multicenter, randomized, controlled, parallel-group clinical study in Germany, Italy, and Spain to evaluate the safety and tolerability of a five HMO blend (5.75 g/L total, comprising $52\%$ 2′-FL, $13\%$ 3’-FL, $26\%$ LNT, $4\%$ 3′-SL, and $5\%$ 6′-SL) and its effect on growth when applied over a 16-week period150. The primary outcome was the mean daily body weight increment over a 4-month period. The observed mean values for daily weight increase of~28.7 g/day were similar to those reported in studies comparing infant formula with 2′-FL plus GOS, 2′-FL plus LNnT, or 2′-FL plus 3′-GL and GOS/FOS118,149–151. Lasekan et al152 performed a randomized, double-blind, controlled parallel feeding trial with five HMOs (2′-FL, 3-FL, LNT, 3′-SL, and 6′-SL) containing formula in the United States, mostly during the COVID-19 pandemic, while stay-at-home orders were in place152. The test formula was again found to be safe and well tolerated and weight gain and length did not differ between the groups. Compared to the control group, infants given test formula had more frequent and softer stools152. Vandenplas et al.151 studied growth, safety, and tolerance in healthy infants consuming a partly fermented infant formula with postbiotics and the HMOs 3′–GL) and 2′-FL, and a specific prebiotic mixture of short-chain GOS (scGOS) and long-chain fructo-oligosaccharides (lcFOS). Equivalence in weight gain (primary endpoint), length, and head circumference gain of up to 17 weeks was also confirmed with the test formula. There were no statistically significant differences between the formula groups for regurgitation, vomiting, watery, or hard stools at any timepoint151. Ramirez-Farias and colleagues153 examined extensively hydrolyzed formula (eHF) with 2′-FL (0.2 g/L) for growth, tolerance, and compliance in a non-randomized, single-group, multicenter study. Infants (0–60 days old) with suspected food protein allergy, persistent feeding intolerance, or presenting conditions where an eHF was deemed appropriate were enrolled in a 2-month feeding with an experimental formula. This study shows that eHF formula with 2′-FL was well-tolerated and provided a significant improvement of weight for age z-scores153. An eHF with two HMOs (2′-FL at 1.0 g/L and LNnT at 0.5 g/L) confirmed a non-inferiority of the test formula for weight gain per day at the 4-month visit, and there were no statistically significant differences between the groups on any of the anthropometric parameters measured during the course of the trial. Gold et al.154 showed in an open-label, non-randomized, multicenter study of an amino acid-based formula supplemented with two HMOs (2′-FL and LNnT) for 4 months, with the option to continue feeding it for additional 8 months, and showed that the weight-for-age Z score improved from −0.31 at the start of the trial to+0.28 at the end of the study. Additionally, linear and head growth followed the WHO child growth reference and showed a similar, slight upward trend. ## HMOs in infant formula and gastro-intestinal tolerance Infant formula supplemented with 2′FL alone, 2′FL combined with LNnT, and a blend of five HMOs (2′-FL, 3-FL, LNT, 3′-SL, 6′-SL) in formula with intact and hydrolyzed protein have all been shown to be well tolerated in clinical trials118,149,150,153,155–157. Stool consistency, flatulence, and the frequency of spitting up/vomiting were similar in infants given formula containing with or without HMOs149,157,158. In an RCT testing, a mix of 5 HMOs in infant formula, the stools in the HMO supplemented formula group were more soft, frequent, and yellow. They were more similar to the breastfed infants’ stools than the stools of the non-supplemented formula group150,172. A formula including 2′-FL and LNnT showed softer stool consistency in another investigation118. Stool consistency in infants fed 2′-FL and FOS-containing formula was found to be comparable to that of breast-fed infants157,158. ## The effect of HMOs in infant formula on microbiota composition There is a significant difference in intestinal microbiota composition between breast-fed infants and formula, without supplementation of biotics, fed infants136. Supplementation with HMOs may therefore potentially increase bifidobacteria and bring microbiota composition closer to that of breastfed infants. In RCT, the microbiota composition in a 2’-FL formula (1 g/L) group was only just significantly different at 2 months and just not at 3 months of age, bringing the microbiota composition somehow closer to that of breastfed infants159. The development of the microbiota composition was tested via stool cultures during incubation with 2’-FL of three breast and three formula fed infants160. The composition of the microbiome at baseline was dependent on the mode of feeding and on the ability to degrade 2’-FL. When looking at the degradation of 2’-FL, the fecal cultures could be divided into slow and fast degraders regardless of mode of feeding. However, since there were only six infants no conclusions can be drawn160. Another multicenter study examined fecal cultures of infants receiving either a formula with a mix of five HMOs at a concentration of 1.5 g/L, a formula with a mix of five HMOs at a concentration of 2.5 g/L, a non-supplemented formula, or breast milk25. The microbiota composition of infants receiving formulas supplemented with HMOs was significantly different to those in the non-supplemented group and were closer to the composition of the breastfed infants. The concentration of B. infantis was statistically higher in the HMO supplemented than in the non-supplemented group, approaching the composition of breastfed infants. Significantly less Clostridium difficile was seen in the HMO supplemented group in comparison to the non-supplemented group suggesting a lesser chance of diarrheal illness. No significant differences were seen between the lower and higher dose HMO supplemented formulas25. In a randomized, double-blind, multicenter clinical experiment, after a three-month intervention, infant formula containing 2′-FL and LNnT enhanced the abundance of Bifidobacterium and Streptococcus and changed the microbiome of cesarean section infants’ group to that observed in vaginal delivery infants161. This study suggests that the association between formula with 2′-FL and LNnT and lower parent-reported morbidity and medication use may be linked to gut microbiota community types161. A bifidogenic effect in infants receiving formula with two HMOs (2′-FL and LNnT) which was more pronounced in the cesarean-born infants, however, found no effect on B. infantis161,162. An amino acid-based formula supplemented with 1 g/L 2’FL and 0.5 g/L LNnT confirmed an enrichment in Bifidobacteria and reduction of Proteobacteria154. Bifidobacteria abundance and metabolic activity could be associated to decreased respiratory tract infections66,72,163. Increased gamma-glutamylation and N-acetylation of amino acids, and decreased inflammatory signaling lipids, are the three most notable molecular pathways66. Bosheva et al25 studied gut maturation effects (microbiota, metabolites, and selected maturation indicators) of an infant formula containing five HMOs (2′-FL, 3-FL, LNT, 3′-SL, 6′-SL). In the first 6 months of life, the HMO supplemented formula shifted the gut microbiome closer to that of breastfed infants with higher bifidobacteria, particularly B. infantis, and lower C. difficile 25. Formula with these 5 HMOs suggest that the HMOs may boost infant intestinal immune development and gut barrier function. HMO-supplemented formula helps restore dysbiosis in cesarean-born infants25. Estorninos and colleagues164 evaluated the effects of bovine milk-derived oligosaccharides (primarily composed of GOS with inherent concentrations of sialylated oligosaccharides structurally identical to some in human milk) and reported similar effects on gut microbiota and intestinal immunity in healthy term formula-fed infants164. ## Effects of HMOs containing infant formula on infectious disease prevention Breastfed children are less likely to suffer from respiratory and gastrointestinal infections than formula fed infants26,98,118,149. Research with formulas supplemented with HMOs found (as a secondary outcome) a decreased rate of respiratory tract infections and bronchitis, as well as a decreased need for antibiotics and antipyretics118,161,165. These effects did persist beyond the six-month intervention period118,161. Further analyses of the same data have linked a microbiome community structure highly dominated by Bifidobacterium species at 3 months of age with a decreased need for antibiotics, lending credence to the observation that 2’-FL and LNnT supplementation reduces the risk of respiratory infections and the need for antibiotics161. Acetate, one of the compounds produced by the HMO-stimulated metabolic activity of Bifidobacterium, may aid in lowering the risk of respiratory tract infections. Another study found that infants who were fed a formula containing 2′-FL (0.2 g/L) and GOS (2.2 g/L) had a lower incidence of illnesses and infestations as reported by the investigators149. Supporting the hypothesis that the HMO-containing formula provides immune system benefits, in the study by Lasekan et al.152 fewer infants needed to visit a healthcare professional. However, Parschat et al.150 found no evidence that infant formula containing five HMOs reduced the risk of infection in infants. Leung et al.166 enrolled 461 infants aged 1–2.5 years in China in an RCT testing three young child formulas containing bioactive proteins and/or 2’-FL and/or milk fat for six months and found no difference in the incidence of upper respiratory or gastrointestinal tract infections between all groups. In summary:There is theoretical evidence that HMO supplementation in formula fed infants may have beneficial impacts on microbiota composition, immunological function, and other parameters, hence reducing the prevalence of infections. However, clinical data are not unequivocal and no study was powered to evaluate the effect on infections as a primary outcome. ## Effects of HMOs containing infant formula on the immune system The effects of HMO 2′-FL enriched feeding formulae on immune function biomarkers in term infants were studied165. At the age of three months, the groups receiving an HMO-supplemented formula had a higher secretory immunoglobulin A and lower alpha-1-antitrypsin in comparison to the non-supplemented group possibly offering immunological benefits25. A randomized, double-blind, controlled growth and tolerance study was conducted with healthy singleton infants who were enrolled by 5 days of age and fed either formula or human milk exclusively from the time of enrollment to the age of 4 months165. GOS was given to the control group, whereas GOS plus either 0.2 or 1.0 g/L 2′-FL was given to the study group and compared to the breastfeeding reference group. Concentrations of plasma inflammatory cytokines were 29–$83\%$ lower in infants fed formulas with 2’-FL and GOS than did infants fed the control formula including GOS only. Infants whose formula contained 2′-FL showed innate cytokine profiles more similar to those of breastfed infants. Biomarkers of immune functions such as plasma cytokine concentrations, cytokines released by ex vivo stimulation of peripheral blood mononuclear cells (PBMCs), and percentages of major lymphocyte subsets within the PBMCs population were used in this study to demonstrate the impact of 2′-FL-fortified formulas on the developing immune system. 2′-FL reduced the gap in total T lymphocyte proportions between breastfed infants, which is an indicator of improved adaptive immunity. The discrepancies in apoptotic cell percentages between breastfeeding and control groups were also reduced by 2′-FL, especially in CD8+ T cells and CD8+ T cell subset. These results suggest that compared to GOS alone, supplementing infant formula with 2′-FL promotes immunological development and modulation in a way that is comparable to that of breastfed infants165. ## Effects of HMOs containing infant formula on allergy Infants diagnosed with CMPA who are not breastfed are treated with a cow’s milk elimination diet, eHF or amino acid formula167. Preclinical studies have indicated that 2′-FL can reduce allergic responses in a food allergy model120,131. Laboratory analysis of 2′-FL and LNnT batches showed no evidence of residual milk allergens, despite the fact that HMOs are produced via biofermentation from lactose, which in theory might bring a risk of residual milk allergen contamination168. An eHF with two HMOs (2′-FL at 1.0 g/L and LNnT at 0.5 g/L) showed a similar reduction in the supplemented and non-supplemented eHF, with the Cow’s Milk-Related Symptom Score (CoMiSSTM) dropping to the levels seen in presumed healthy infants. Otitis media and upper respiratory tract infections were significantly reduced in the HMO group by 12 months, and lower respiratory tract and gastrointestinal infections were reduced by 30–$40\%$, however without statistical significance169. In an open-label study testing an AAF with two HMOs (2´-FL and LNnT) a significant reduction in symptoms was noted between enrollment and Visit 1, as reported by parents, and between Visit 1 and subsequent visits, as assessed by physicians154. Control of skin symptoms was generally excellent. ## Non-human Oligosaccharides Non-human oligosaccharides were also shown to enhance the development of a bifidobacteria dominated gastrointestinal microbiome26. RCTs evaluating GOS/FOS as well as only-GOS enriched formulas have demonstrated a stimulating effect on the growth of Bifidobacteria and/or Lactobacilli 26. GOS, FOS, and GOS/FOS mixtures (the most studied being a 9:1 mixture of scGOS and lcFOS) are the most researched prebiotics components26,170,171. Clinical studies have shown that supplementation of infant formula with a mixture of scGOS and lcFOS (9:1) leads to a more favorable gut microbiota composition and activity, closer to that observed in breastfed infants. There was no statistically significant difference between infants fed GOS/FOS enriched formulas and those receiving regular formulas in terms of weight, height, or head circumference172. Moreover, scGOS and lcFOS in infant formula has also been associated with a lower number of infections, fever episodes, and antibiotic prescriptions170,171. Beneficial effects on Bifidobacteria and Lactobacilli growth in infants given a scGOS/lcFOS supplemented formula were observed to be sustained even after the formula was discontinued, at least for a few months172. Infant formulae with added prebiotics have been linked to a lower fecal pH and a SCFAs pattern closer to that of breastfed infants, without increased frequency of stool26. Non-human oligosaccharides also promote the growth of a bifidobacteria-dominated gut microbiome, selectively stimulate the growth of Bifidobacteria and/or Lactobacilli26. Clinical investigations have demonstrated that adding a mixture of scGOS and lcFOS (9:1) to infant formula results in a more favorable gut microbiota composition and activity, closer to breastfed infants. Beneficial effects on Bifidobacteria and Lactobacilli growth in infants fed with a scGOS/lcFOS supplemented formula were observed to be sustained even after months of discontinuing the formula178. It has been shown that some bifidobacteria only grow in the presence of human milk oligosaccharides45. However, it is not known if this has any clinical impact for the infant. There are almost no data comparing the effects of HMO and non-human oligosaccharides in infants. Only in the study by Marriage et al.149 there was only-GOS group compared to two GOS group with different levels of 2´-FL. As a consequence, there is no evidence to state that HMOs added to infant formula are more effective than non-human oligosaccharides. ## Limitations Today, there is still a dearth of information on the addition of HMOs to infant formula. No definitive conclusions can be drawn on whether supplemented or non-supplemented formula yields better clinical outcomes because to the limited data from the current research. Due to the differences in design and primary outcomes of the clinical trials, there is inconsistency in the findings. The optimal dosing of HMOs also necessitates fine-tuning. There are substantial variations in the studies in terms of study design, location, lactation sampling, the number of time periods at which development parameters are assessed, the specific HMOs that were analyzed, and the statistical methodologies utilized to predict the correlations4. The majority of the included studies have a relatively small sample size to quantify disease outcomes, which reduces their precision and statistical ability to find meaningful relationships. Because of these differences, it is not possible to do a meta-analysis. The benefits of sialylated-HMOs are not well recognized, despite the fact that neutral oligosaccharides like 2′-FL and 3′-FL have been the subject of substantial research into their involvement in infant nutrition, growth, and development in both pre-clinical and clinical settings. There is an immediate need for more investigations on the health advantages of HMOs in human milk with varying structural compositions136. There are almost 200 different oligosaccharides in human milk, but today only five are added to infant formula, while studies with seven are going on. An increase in the number of HMOs used could enhance the outcomes. However, the ideal dosage of HMOs in infant formula is still up for debate, as HMO levels fluctuate in breast milk. Therefore, if the formula is administered at a consistent HMO concentration and ratio, formula-fed infants may consume less of specific HMOs in the early stages of the trial, but more HMOs afterward than breastfed infants. The fact that statistical associations do not imply a causal relation further emphasizes the need for randomized, placebo-controlled interventional trials and supplementary mechanistic studies. In conclusion, HMOs are a major ingredient of human milk, which is the best source of nutrition for infants. HMOs act in tandem with other bioactive components and also act through many pathways that converge to specific activities, as is predicted from many biological processes. HMOs are known to support a healthy gut microbiome, build the gastrointestinal barrier and promote brain growth and cognitive function, among other important physiological roles. A growing body of research also suggests that particular HMOs contribute to the development of immunological competence, both locally and systemically, in part through influencing the metabolism of particular bacteria, such as particular Bifidobacterium species. The study of milk microbiota and HMOs relies heavily on the strain-specific characterization of beneficial human microbiota organisms and their consumption of specified HMOs. Human milk research is a promising field since more benefits and correlations between components will be uncovered as time goes on. Regarding formula feeding, more clinical trials in children are needed comparing the multiple effects of non-human to human oligosaccharides supplementation. ## Disclosure statement No potential conflict of interest was reported by the authors. ## Author contribution All authors contributed to evaluation of available article, summarizing the data, drafting, and critical review of the manuscript. ## References 1. 1.WHO. 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--- title: Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning authors: - Martin Hultman - Marcus Larsson - Tomas Strömberg - Ingemar Fredriksson journal: Journal of Biomedical Optics year: 2023 pmcid: PMC10027009 doi: 10.1117/1.JBO.28.3.036007 license: CC BY 4.0 --- # Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning ## Abstract. ### Significance Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static and dynamic scatterers, multiple Doppler shifts, and the speed-distribution of blood. It has been demonstrated how to account for these effects in laser Doppler flowmetry (LDF) using inverse Monte Carlo (MC) algorithms. This allows for a speed-resolved perfusion measure in absolute units %RBC × mm/s, improving the physiological interpretation of the data. Until now, this has been limited to a single-point LDF technique but recent advances in multi-exposure LSCI (MELSCI) enable the analysis in an imaging modality. ### Aim To present a method for speed-resolved perfusion imaging in absolute units %RBC × mm/s, computed from multi-exposure speckle contrast images. ### Approach An artificial neural network (ANN) was trained on a large simulated dataset of multi-exposure contrast values and corresponding speed-resolved perfusion. The dataset was generated using MC simulations of photon transport in randomized skin models covering a wide range of physiologically relevant geometrical and optical tissue properties. The ANN was evaluated on in vivo data sets captured during an occlusion provocation. ### Results Speed-resolved perfusion was estimated in the three speed intervals 0 to 1 mm/s, 1 to 10 mm/s, and >10 mm/s, with relative errors $9.8\%$, $12\%$, and $19\%$, respectively. The perfusion had a linear response to changes in both blood tissue fraction and blood flow speed and was less affected by tissue properties compared with single-exposure LSCI. The image quality was subjectively higher compared with LSCI, revealing previously unseen macro- and microvascular structures. ### Conclusions The ANN, trained on modeled data, calculates speed-resolved perfusion in absolute units from multi-exposure speckle contrast. This method facilitates the physiological interpretation of measurements using MELSCI and may increase the clinical impact of the technique. ## Introduction Laser speckle contrast imaging (LSCI) is a noninvasive measurement technique for assessing spatial perfusion maps of the microcirculation.1 When coherent laser light scatters from moving red blood cells (RBCs) in tissue, it changes frequency due to the Doppler effect. Backscattered light from the tissue will therefore create a fluctuating speckle interference pattern on the camera sensor. These fluctuations cause motion blur in the recorded image, which can be quantified as the local contrast in a region of pixels. Several mathematical models have been proposed to establish a relation between the speckle contrast and blood flow speed or perfusion (speed × concentration). However, the amount of Doppler shifted light and the distribution of Doppler frequencies can affect a single-exposure contrast value in similar ways. This contributes to limit LSCI to relative estimates of perfusion in arbitrary units. It has been demonstrated that perfusion by LSCI follows a nonlinear and unknown relation to perfusion measured by laser Doppler flowmetry (LDF)2–5 and to actual tissue perfusion.6 Multi-exposure laser speckle contrast imaging (MELSCI) quantifies how the speckle contrast decreases with increasing exposure time, and allows for more realistic models of light–tissue interaction and speckle decorrelation when estimating perfusion.7 However, the mathematical models proposed for estimating perfusion from MELSCI are based on several assumptions,8 specifically that photons undergo at most one dynamic scattering event, and that the electric field autocorrelation function can be approximated as either a Gaussian or Lorentzian distribution.9 These assumptions are not valid for most tissues, as discussed in Refs. 10 and 11. Furthermore, using these models for analyzing MELSCI data yields relative perfusion estimates expressed in arbitrary units. This makes physiological interpretation difficult. An alternative technique is to use a computational model based on Monte Carlo (MC) simulations in multilayered tissue models, where the limitations in the abovementioned assumptions are not necessary, and more complex light-tissue interactions can be considered. Specifically, in LDF it has been demonstrated how this, together with an inverse optimization algorithm, can be used to compute speed-resolved perfusion in absolute units %RBC × mm/s.12–14 By decomposing the perfusion into distinct speed components, each associated with a specific range of physiologically relevant speeds, more detailed and physiologically relevant information about the microcirculation is obtained. For example, this has been used to detect a reduced nutritive capillary flow in patients with type 2 diabetes.15 The drawback of inverse methods is the slow speed of the optimization when fitting the modeled data to measurements. Although this is viable for single-point LDF, it is too slow for an imaging modality such as MELSCI. To address this problem, machine learning has recently been proposed to train artificial neural networks (ANNs) that can estimate perfusion from multi-exposure contrast.2 Similar techniques have been used to estimate blood oxygen saturation from spectral data.16,17 This offloads the heavy computations to the training stage of the network and enables real-time estimations of microcirculation properties in imaging modalities.18 The aim of this study is to develop a method for speed-resolved perfusion imaging, computed from MELSCI data in the physiologically relevant unit %RBC × mm/s. The method utilizes ANN based on MC simulations of realistic complex tissue models accounting for expected variations in relevant skin properties. The result of the method is demonstrated in an arterial occlusion-release provocation. ## Tissue Model To generate realistic training data for the ANN, MC simulations of the light-tissue interaction in a three-layer skin tissue model were performed. This process has been described in detail elsewhere6,16 and will only be summarized here in the context of MELSCI. Optical properties used in the model are relevant for the laser wavelength 780 nm. The first layer in the tissue model is a bloodless epidermis layer with variable thickness (tepi) and melanin concentration. The absorption coefficient of the epidermis layer (μa,epi) is dependent on melanin and is generally low at the used laser wavelength. The second and third layers are dermis layers with variable blood tissue fractions, blood speed distributions, scattering coefficients, and vessel diameters. The upper dermis layer has a fixed thickness of 0.2 mm, and the lower dermis layer is infinite. The reduced scattering coefficient (μs′) is the same in all layers. The absorption coefficient of the dermis layers (μa,dermis) is given by their blood tissue fraction (cblood), oxygen saturations (Soxy), and vessel diameters (dvessels), where the latter affects the absorption through the vessel packaging effect.19,20 The parameters cblood, Soxy, and dvessels are modeled to be different in the two dermis layers, where the actual values are calculated from a randomized average value and a randomized difference parameter, to ensure the parameters do not differ too much between the layers. The parameters of interest in the 100,000 randomized training models are summarized in Fig. 1. The average values for cblood, Soxy, and dvessels are given in the histograms. The last histogram shows the average speed ⟨vRBC⟩ of the RBC speed distributions, described in more detail below. The choice of the distributions was based on three principles; [1] the range of the distributions should cover essentially all expected values in skin tissue for all skin sites and all skin types; [2] allow for less probability of parameter values that are rarely expected, such as μs′<0.5 mm−121; [3] have higher probability for ranges where a small change has a large effect on the contrast data, thus exponential decay in the histograms of many of the parameters as Beer–Lambert’s law predicts lower absolute change for high μa or large path-lengths (related for example to high tepi). Fig. 1: Normalized histograms of skin parameter distributions in the tissue models. The x-axes are scaled to remove the 1% upper tail. When modeling speckle contrast, it is common to assume either a Gaussian or Lorentzian distribution for the electric field autocorrelation function,9,22 but the validity of both assumptions has been questioned.11 Here, we instead model the speed distribution of RBCs, cRBC(v), directly as a weighted sum of 10 uniform distributions from 0 mm/s to exponentially increasing top speeds, representing vessels with parabolic flow profiles with 10 different average speeds. The weights are randomized for each tissue model to create variations in the shape of the distribution. Examples of speed distributions are presented in Fig. 2. Additional details can be found in Ref. 6. Fig. 2: Examples of RBC speed distributions cRBC(v), for three different mean speeds as presented in the legend. The distributions were normalized to have a total probability of 1. ## Doppler Histogram Calculation Tissue model simulations were performed for calculating Doppler histograms (the optical Doppler spectrum) as previously described in detail.13 The simulations and calculations are summarized in this section. MC simulations were performed with varying epidermis thickness and tissue scattering covering the ranges found in Fig. 1 for these parameters. In each MC simulation, the total path length of each photon in each of the three layers was stored and used to calculate path-length distributions in each layer. For a given set of the model parameters tepi and μs′, the path-length distribution in each layer was interpolated from the simulated path-length distributions. Using the path-length distribution in each layer, Beer–Lambert’s law was applied to add the absorption effect of μa,epi and μa,dermis, modifying the path-length distributions. These modified path-length distributions were the first input to the calculation of the Doppler histogram of the model. A single-shifted Doppler histogram was calculated for light Doppler shifted once by a red blood cell with a certain speed. This was done analytically as described in the appendix of Ref. 13, assuming a random angle between the light and the direction of the red blood cell and a scattering angle based on the Gegenbauer kernel phase function.23 The phase function parameters were set to gGk=0.948 and αGk=1.0, resulting in an anisotropy factor of 0.991.24 The Doppler histogram for light shifted n times was calculated as the cross-correlation of the single-shifted Doppler histogram with itself n−1 times. Probability distributions of the number of Doppler shifts were given by Poisson distributions based on the pathlengths and fraction of red blood cells. The fact that blood is confined in distinct blood vessels rather than being evenly distributed in the layer, i.e. the vessel packaging effect, was also accounted for when calculating the distribution of the number of Doppler shifts. Finally, the speed distribution, the path-length distributions, the distributions of the amount of Doppler shifts, and the Doppler histograms for various numbers of Doppler shifts and speeds, were used to calculate a final Doppler histogram H(f). Although the entire process is complex, the calculation scheme is efficient, and it takes less than a millisecond to calculate a Doppler histogram for a specific set of model parameters. ## Speckle Contrast Calculation The Doppler histogram H(f) obtained as described in the previous section was converted into multi-exposure contrast K(T) using a mathematical framework that is thoroughly described in Refs. 6 and 25. In short, the electric field autocorrelation function g[1](τ) was obtained from the Doppler histogram using the Wiener–Khinchine theorem g[1](τ)=\|F−1{H(f)}\|,[1]where F−1 is the inverse Fourier transform. The contrast was then obtained as26 K2(T)=2βT∫0T\|g[1](τ)\|2(1−τT)dτ,[2]where the coherence factor β was set to one, assuming a calibrated system with $K = 1$ for a static target. The contrast computed by this method is noise-free, making it inappropriate for training models that should be applied to real data containing noise. Therefore, a noise component Knoise(T) was added to the simulated contrast data before training the neural network. The noise component model has been described and validated in Ref. 2. In summary, speckle simulations showed that the noise for each exposure time T could be modeled as a sequence of random contrast offsets depending on the average contrast over all exposure times ⟨K(T)⟩T and the noise value at the previous exposure time, i.e.,Knoise(T)=⟨K(T)⟩T×{ξTη1,$T = 1$Knoise(T2)+ξTηdiff(T),T=[2,4,8,16,32,64],[3]where ξT are random numbers drawn from a normal distribution with zero mean and unit variance, and the constants η1 and ηdiff(T) were determined from the speckle simulations and dependent on the number of speckles per detector pixel. ## Biological Zero When using the laser Doppler technique in measurements at low-flow or zero-flow conditions in skin tissue, for example during brachial occlusion, we have observed a residual high frequency energy in the Doppler power spectrum (ongoing work), corresponding to the well-known biological zero (BZ) effect.27,28 During occlusion, BZ partially originates from Brownian motion of RBCs and from the redistribution of blood between vessels, possibly including reverse flow in the capillaries.29 In addition, movements of macromolecules in the interstitial space contribute to the BZ.30 Due to the small size of these macromolecules, the scattering angles are large compared to scattering from RBCs, resulting in large Doppler shifts. This addition to the power spectrum cannot be explained by Doppler shifts from RBCs, regardless of which speed distributions of the RBCs are used. Furthermore, the residual energy is not present when measuring on flow of phantoms perfused with whole blood,14 making us confident in assuming that it does not originate from the blood itself, but from some kind of other tissue movements, well in line with the movement of macromolecules. We observe the same effect when using the MELSCI technique. Similar to the residual we observe in the Doppler spectrum, the shape of the contrast curve in measured data during occlusion cannot be explained by any realistic speed distribution of the RBCs. However, since we observe the same phenomenon using both LDF and MELSCI, we are confident that it does not originate from faulty calibrations or instrument noise. We must account for this macromolecule effect on BZ when generating training data from the skin models, otherwise, the training data will not be valid for low-flow conditions. This can be done by adding a BZ component HBZ to the Doppler histogram before computing the contrast. This was empirically modeled as HBZ(f)={0for $f = 010$ξoffset+fξslopefor f>0,[4]where ξoffset∈N(−5.3,0.125) and ξslope∈N(−5.2×10−5,6×10−6) represent random numbers from normal distributions N(μ,σ). These distributions are based on analysis of laser Doppler spectra recorded during occlusion provocations in a substudy of the Swedish cardiopulmonary bioimaging study (SCAPIS).31 Note that HBZ has a negligible additive effect on the Doppler histogram under normal flow conditions. ## Perfusion Speed-resolved perfusion can be computed from the modeled speed distribution as P[vmin,vmax]=∫vminvmaxvcRBC(v)dv,[5]where the perfusion corresponds to the speed-range [vmin,vmax], and cRBC(v) is the speed distribution describing the concentration of RBCs moving with speed v. This distribution sums to the total tissue RBC fraction and has the unit %RBC/(mm/s). The unit of speed-resolved perfusion is thus %RBC × mm/s, i.e. concentration × speed. In this study, we present speed-resolved perfusion in the ranges 0 to 1 mm/s, 1 to 10 mm/s, and >10 mm/s. Additionally, the total perfusion, i.e. the sum of all three speed components, is presented. When calculating the modeled perfusion Ptrue, the contribution from each skin layer is considered so that Ptrue reflects the sampling volume. For example, if the epidermis thickness is increased but blood tissue fractions and speed distributions are kept constant in the dermis layers, the total perfusion will slightly decrease since the epidermis layer (with zero perfusion) will constitute a larger part of the sampling volume. See Ref. 13 for further details. For comparison, we also compute a perfusion estimate from single-exposure contrast as PSE(T)=1K(T)−1.[6] There are other single exposure models where P is proportional to K−2, e.g. Ref. 32. We have done the analysis using this model as well, but the performance was quantitatively worse than for Eq. [ 6], and the conclusions were unchanged by this choice. Furthermore, Eq. [ 6] is relatively common in clinical settings as it is used in the PeriCam PSI (Perimed AB, Järfälla, Stockholm, Sweden), and thus a good comparison for the potential benefits of our new model. ## Artificial Neural Network Perfusion Model A neural network was trained on the data from the tissue model to estimate speed-resolved perfusion in a single pixel of the multi-exposure contrast images. Thus, the network had seven inputs representing squared multi-exposure contrast K2(T) calculated at T=[1,2,4,8,16,32,64] ms exposure time and three outputs representing speed-resolved perfusion in the ranges 0 to 1, 1 to 10, and >10 mm/s. The neural network was a fully connected network with a single hidden layer with 25 nodes and hyperbolic tangent (tanh) activation. Linear activation was used for the output layer. To obtain a perfusion image, the model was applied to each individual pixel of the contrast image. The architecture was determined by the process described in Sec. 2.6.1. The dataset consisted of 100,000 tissue models, divided into $\frac{70}{15}$/$15\%$ for training, validation, and test, respectively. The validation set was used for early stopping and in-training hyperparameter tuning, and the test set was used to get an unbiased performance metric for comparison of multiple networks. 25 networks were trained from random initializations, and the best performing on the $15\%$ test-split was selected as the final model. This model was then evaluated on a separate dataset of 100,000 tissue models, to get the final unbiased performance metrics. The training was done using the Deep Learning toolbox in MATLAB 2021b (MathWorks Inc., MA). The model was trained using a weighted mean square error (wMSE), where the individual errors of each speed-component were weighted by their contribution to the total perfusion. Specifically, wMSE(j)=1N∑$i = 1$N(PANN,j,i−Ptrue,j,i)2Ptrue,tot,i,j={1,2,3},[7]where Ptrue,tot,i=∑$j = 13$Ptrue,j,i,[8]and j indicates the speed component (0 to 1, 1 to 10, >10 mm/s, respectively, for $j = 1$, 2, and 3), PANN is the network output (predicted speed-resolved perfusion), Ptrue is the target output (true speed-resolved perfusion calculated directly from the tissue model), and N is the number of samples, i.e. number of models in the training, validation, or test set. For evaluation, the weighted mean absolute percentage error (wMAPE) was used, due to the easier interpretability wMAPE(j)=100N∑$i = 1$N\|PANN,j,i−Ptrue,j,i\|Ptrue,tot,i,j={1,2,3}.[9] For each speed component j, this metric informs the prediction error relative to the total perfusion. This accounts for the speed distribution and does not inflate small relative errors at low perfusion values, unless the total perfusion is also low. An example is warranted. Suppose we have the true speed-resolved perfusion Ptrue=[0.01,0.1,1], and the prediction PANN=Ptrue+0.01=[0.02,0.11,1.01]. The unweighted absolute percentage error would be MAPE=100\|(PANN,j−Ptrue,j)/Ptrue,j\|=[100,10,1]%, whereas the weighted absolute percentage error would be wMAPE=100\|(PANN,j−Ptrue,j)/Ptrue,tot\|≈[0.9,0.9,0.9]%, better reflecting the fact that the low speed-component is small in this case and thus should not be given as much weight in the total error. Empirical results confirmed that using wMSE for training resulted in better overall performance compared to unweighted MSE. Note that wMAPE is only an applicable metric for the speed components. For total perfusion and single-exposure perfusion, unweighted MAPE is used instead. We also present the coefficient of determination, R2, between estimated and true perfusion, which represents the proportion of the variance in perfusion explained by the model. ## Choice of ANN architecture To determine the optimal ANN architecture described above, considering both accuracy and computational complexity, a large number of network architectures were evaluated in a hyperparameter grid search over number of hidden layers and their sizes, activation functions, and regularization weights. The results showed that two hidden layers performed slightly better than the single hidden layer architecture but not enough to warrant the increase in computations. Similarly, performance increased with larger layers but did not warrant a size above 25 nodes. The small size also enables efficient real-time implementations. Using L2-regularization did not significantly affect the results and thus was not used in the final model. This is likely due to the small size of the model, making overfitting unlikely. Finally, the activation functions tanh [Eq. 10(a)], rectified linear unit [ReLU, Eq. 10(b)], saturated linear unit [SatLU, Eq. 10(c)], and softplus [Eq. 10(d)] were investigated. Tanh performed best and softplus only slightly worse, whereas ReLU and SatLU performed significantly worse, possibly due to the nonsmooth transitions at 0 for ReLU and at 0 and 1 for SatLU tanh(x)=(e2x−1)/(e2x+1),(10a)ReLU(x)=max(0,x),(10b)SatLU(x)=min(1,max(0,x)),(10c)Softplus(x)=log(1+ex).(10d) To enable the use of the model in clinical applications, real-time processing is a necessity. For this reason, the models were kept as small as possible without impacting accuracy. The average processing time for one 180×320×7 multi-exposure contrast image was 33 ms using a single-threaded MATLAB implementation running on a 3.5 GHz Intel Xeon CPU. Despite not being the focus of this study, this model already runs in real-time. More optimized implementations would likely reduce this time further. ## Restricted tissue model In addition to the main ANN model, we trained a separate model on a restricted training set, where all tissue parameters not directly related to perfusion were set to the median of their respective distributions in Fig. 1. In other words, tepi, μs′, μa,epi, dvessels, and Soxy were fixed at one value in all 100,000 tissue models, whereas cblood and the parameters of the speed distribution were unchanged from the original distributions. Note that μa,dermis still varied due to changes in cblood. Additionally, the noise component Knoise(T) was omitted from this data. This dataset represents a situation where the only influence on the speckle contrast is from the parameters directly determining the perfusion. This allows investigating the core method without confounding factors and sets a rough upper bound on the achievable performance. The network architecture of this model was the same as that of the main model. ## In-Vivo Measurements and Measurement System The MELSCI instrument used in this study has been thoroughly described in previous publications.18,33 In summary, the technique is based on the continuous acquisition of images with 1 ms exposure time from a 1000 frames-per-second high-speed CMOS sensor. The images are successively accumulated to create longer synthetic exposure times at 2, 4, 8, 16, 32, and 64 ms.33 The use of synthetic exposure times was first proposed and validated in Ref. 34. Further, the accumulation and speckle contrast algorithms are implemented in a field-programmable gate array (FPGA) closely integrated with the CMOS sensor. This enables continuous real-time acquisition and processing of multi-exposure contrast images, without any loss of information due to the negligible interframe time.18 Each set of 64 speckle images results in one multi-exposure contrast image, enabling a framerate for perfusion images of 15.6 frames-per-second. An arterial occlusion-release provocation of the forearm was performed in a healthy 24-year-old male volunteer. The subject did not consume any caffeine or nicotine for 6 h prior to the experiment and rested for 15 min before the measurement in a 23°C temperature-controlled room. The protocol followed 5+5+5 min of baseline, brachial occlusion at 200 mmHg, and reperfusion, and was approved by the Regional Ethical Review Board in Linköping, Sweden (D.no. $\frac{2018}{282}$-31). Written informed consent was given by the volunteer before the experiment. ## ANN Simulation Performance The performance of the main and restricted ANN models on the simulated evaluation sets is presented in Fig. 3. The three speed components were evaluated using the wMAPE metric [Eq. [ 9]] and total perfusion was evaluated using MAPE. The coefficient of determination R2 between predicted and target perfusion was also computed for all four perfusion estimates. The data was divided into 50 bins with 2000 points each, according to the true perfusion (x-axis). The mean predicted perfusion in each bin is shown by the black line, with the blue area representing the average deviation from the mean. The ideal prediction is indicated by the red dotted line. The top row shows the performance of the restricted model with only perfusion parameter variation and no noise, and the second row is the main model. The histograms in the last row show the distribution of true perfusion in the dataset. Fig. 3: The top row shows perfusion predicted by the ANN trained on the restricted tissue model compared to true perfusion [%RBC × mm/s]. The second row shows the same for the main model. The data were divided into 50 bins, each with 2000 data points, based on the true perfusion (x-axis). The black lines are the mean predicted perfusion in the bins, and the blue areas indicate the average deviation from the mean in each bin. The red dashed line is the ideal theoretical prediction. Summary metrics weighted mean absolute percentage error (wMAPE) for speed-resolved perfusion, mean absolute percentage error (MAPE) for total perfusion, as well as R2, is presented for both models. The histograms show the distribution of true perfusion in the dataset. The second row of Fig. 3 demonstrates the combined effect on perfusion from all skin parameters in many randomized models. The relatively low R2 for the lowest speed component for the main model (R2=0.41; 0 to 1 mm/s), increased with higher speed and for total perfusion. The same is true for the restricted model presented in the first row, where all metrics are significantly better than the main model case. To put these values in context, Fig. 4 shows similar performance plots as Fig. 3, using the main model for generating evaluation data, but for single-exposure perfusion estimated with Eq. [ 6]. To allow comparison between the arbitrary perfusion units from the single-exposure model and the absolute units of true perfusion, the estimated perfusion was normalized to have the same mean as true perfusion in the range 0 to $2.5\%$RBC × mm/s. This normalization was not done for the ANN model but would only have had a small effect on the current values. Regardless, the single-exposure model performs worse than the ANN model for both metrics (MAPE and R2) at all exposure times. Note that the plots in Fig. 4 compare with total perfusion and thus should only be compared to the fourth column in Fig. 3. Fig. 4: Predicted single-exposure perfusion PSE(T) [PU] evaluated against true total perfusion (%RBC × mm/s). To allow comparison between the arbitrary perfusion units and absolute perfusion units, the predicted values were normalized to have the same average as true perfusion in the displayed range. As in Fig. 3, the histograms show the distribution of true perfusion in the dataset. To further evaluate how specific skin parameters influence the perfusion, one skin parameter at a time was changed over a wide range of values while the rest of the parameters were unchanged, as presented in Figs. 5 and 6. The relative change in four perfusion estimates, PANN,tot, PSE(1 ms), PSE(8 ms), and PSE(64 ms) was compared to the relative change in true perfusion Ptrue,tot. Fig. 4(a) shows the median relative change in perfusion in 5000 tissue models due to changes in blood tissue fraction. Both PANN,tot and PSE(8 ms) had close to linear responses. Figure 5(b) shows similar changes when varying the mean blood speed. Here, PANN,tot had a significantly more linear response compared to all single-exposure perfusion estimates. Fig. 5: Relative change in perfusion estimates due to a change in (a) blood tissue fraction and (b) mean blood speed. Changes are relative to perfusion estimates at (a) 0.55% and (b) 1 mm/s, respectively. Each point is the median change in 5000 tissue models randomly selected from the test dataset. An ideal method should follow the dashed line. Note the log-log scales. Fig. 6: Relative change in perfusion estimates due to a change in (a) reduced scattering coefficient, (b) vessel diameter, and (c) epidermis thickness, indicating the largest sources of error in the absolute value of the estimated perfusion. Changes are relative to (a) 1.6 mm−1, (b) 0.055 mm, and (c) 0.205 mm, respectively. Each point is the median change in 5000 tissue models randomly selected from the test dataset. An ideal method should follow the dashed line. Figure 6 shows the median relative change in perfusion due to changes in reduced scattering coefficient, vessel diameter, and epidermis thickness, where almost no change is expected in the ideal case (some minor changes due to changes in sample volume are expected in the ideal case). PANN,tot was highly sensitive to changes in scattering coefficient [Fig. 6(a)], although less sensitive than the single-exposure methods. While PANN,tot was less sensitive to vessel diameter than the single-exposure methods, it still deviated by up to $40\%$ for very small vessel diameters [Fig. 6(b)]. PANN,tot was almost insensitive to changes in the epidermis thickness, whereas the single-exposure methods were increasingly sensitive with higher exposure time. ## In-Vivo Measurements Figure 7 shows an intensity image of the forearm in the occlusion-release experiment and the region-of-interests (ROI) used in the analysis. The large black ROI was used to automatically determine the color scale for the perfusion images in Figs. 8 and 9 and for studying spatial variations in the perfusion. The small ROIs were used to extract time-resolved perfusion for Figs. 10 and 11 and were placed on high-flow (red) and low-flow (blue) regions outside any visible large vessels, to highlight the spatiotemporal heterogeneity of the microcirculation. Fig. 7: Intensity image of the forearm presented in the occlusion-release experiment. The large black ROI was used to automatically determine the color scale for the perfusion images in Figs. 8 and 9, and for studying spatial variations in the perfusion. The red and blue ROIs were used to extract the time traces in Figs. 10 and 11 in a high-flow and low-flow region, respectively. Fig. 8: In-vivo measurement of a forearm during an occlusion-release provocation. Columns represent the three phases of the provocation; baseline, occlusion, and reperfusion. Rows represent the three speed components and total perfusion. The color scale in each image was selected for all images to be visually comparable, using three times the mean perfusion in the large ROI in Fig. 7. Perfusion scales in the unit %RBC × mm/s are displayed in the colorbar below each image. The high perfusion area in the upper left corner of the images is an artifact due to a low intensity. Similarly, the visually high perfusion at the edges of the arm in the occlusion images is due to the low perfusion scale in combination with the low intensity. Fig. 9: Perfusion estimates based on single-exposure LSCI using the inverse contrast model described in Eq. (6), for exposure times 1, 8, and 64 ms. The color scale in each image was selected for all images to have the same apparent mean, based on the perfusion in the large ROI in Fig. 7. Perfusion values in arbitrary units are presented in the colorbars below each image. Fig. 10: Speed-resolved perfusion in (a) a low-flow ROI and (b) high-flow ROI during the occlusion-release provocation (baseline 0 to 5 min, occlusion 5 to 10 min, reperfusion 10 to 15 min). Care was taken to place the ROIs (see Fig. 7) outside any large vessels visible in the perfusion and intensity images. Zoom plots show 12 s during each of the three phases of the provocation. Fig. 11: Single-exposure perfusion PSE(T) at 1, 8, and 64 ms exposure time, for the same ROIs as Fig. 9. The data were baseline-normalized to enable comparison between the different exposure times. Figure 8 presents speed-resolved perfusion images during an arterial occlusion-release provocation of the forearm of a healthy volunteer. The images are time averages over 2 s (32 images), selected at 2:00 min (baseline), 9:00 min (occlusion), and 10:06 min (early after release). The absolute value of perfusion is very different for the different speed components and the three phases of the provocation, making direct comparison difficult when using the same color scale for all images. To visually enhance the similarities and differences in the perfusion measures, the color scale of each image was scaled as three times the corresponding mean value computed in the region marked by the large ROI in Fig. 7 (the factor 3 was chosen empirically). The perfusion scales in the unit %RBC × mm/s are shown in the colorbars below each image. Furthermore, to quantify the heterogeneity of the microcirculation, Table 1 summarizes the coefficient of variation (CV) in the large ROI marked in Fig. 7, for each perfusion image in Fig. 8. The CV increases for the higher speed components, indicating a more spatially heterogeneous distribution of large vessels, assuming that higher speeds are more prominent in larger vessels. This is consistent with the morphology of the microvasculature.35 The visible vessels in Fig. 8 are easily observed in the occlusion images for the higher speed components. Table 1 \| Unnamed: 0 \| Baseline \| Occlusion \| Reperfusion \| \| --- \| --- \| --- \| --- \| \| 0–1 mm/s \| 13 \| 14 \| 19 \| \| 1–10 mm/s \| 22 \| 27 \| 28 \| \| >10 mm/s \| 32 \| 117 \| 36 \| \| Total \| 23 \| 26 \| 31 \| Figure 9 presents images at the same time-points as in Fig. 8, with perfusion estimated by single-exposure contrast at 1, 8, and 64 ms, according to Eq. [ 6]. Color scales were selected in the same way as described for Fig. 8. The vascular structures observed during occlusion in the ANN perfusion (see Fig. 8; 1 to 10 mm/s, >10 mm/s and total perfusion), are not seen in the corresponding single-exposure images (Fig. 9). The subjective heterogeneity in Fig. 9 is lower than that in Fig. 8, especially for the longer exposure times, but difficult to directly compare due to the increased noise. Figure 10 presents time- and speed-resolved perfusion of a low-flow region (top) and a high-flow region (bottom), according to the small ROIs shown in Fig. 7. A 0.5-s mean filter was applied to the graphs in the full timescale to suppress the heart-beat variations and increase visibility of long-term changes. The zoomed-in plots show unfiltered data in 12-second sections during baseline, occlusion, and reperfusion. Gridlines in the zoom plots indicate 1-s intervals. Note that for the low-flow ROI, all speed components are of approximately the same magnitude in baseline and at reperfusion, whereas for the high-flow ROI, the high-speed component is dominant at both baseline and at reperfusion. Furthermore, for both the low-flow and the high-flow ROIs, both the 1 to 10 mm/s and the >10 mm/s speed components show heart-synchronous pulsations at reperfusion. Figure 11 presents single-exposure perfusion [Eq. [ 6]] in the same two ROIs, at 1, 8, and 64 ms exposure time. The perfusion at each exposure time was normalized to baseline (first 5 min) to allow for comparison between the exposure times. ## Discussion The method presented in this paper addresses several of the long-standing issues with LSCI and more recently MELSCI; the arbitrary units, limitation to relative measurements, and the inability to distinguish between perfusion from high concentration with low speed and perfusion from low concentration with high speed. The new method allows for direct inter-individual comparisons and the establishment of normative microcirculatory perfusion levels, which potentially increases the clinical usefulness of speckle imaging. The ability to separate slow flow, roughly corresponding to flow in capillaries, from fast flow, roughly corresponding to arterioles and venules, is especially important. This can enable direct analysis of the nutritive capillary flow, which otherwise would likely be masked by the much higher perfusion in the larger vessels. When evaluating the ANN model, two aspects are especially important to consider. First, the absolute accuracy of the model, and second, the response to changes in the dynamic skin properties. The absolute accuracy for each speed component and the total perfusion is summarized in Fig. 3. We first observe that the restricted model performs very well, indicating that the multi-exposure contrast contains significantly more information about the speed-resolved perfusion than is apparent when only examining the main model. This performance could likely be increased further by extending the range of exposure times, especially to lower values. However, this should be weighed against the increased hardware and processing requirements. Compared to the restricted model, the main model performs significantly worse. We observed that the estimation error for total perfusion was ∼$34\%$. This shows that the confounding tissue parameters affect the contrast such that the true information about the perfusion is partially lost. This is especially true for the low-speed component (0 to 1 mm/s), which has a nonlinear response to true perfusion when influenced by the effect of tissue parameters and noise. It is possible that this could be addressed by estimating the most important tissue parameters using complementary methods and provide them as input to the model so it can learn to compensate for these effects. This will be an important topic of future research. It is also important to examine the error values (e.g., MAPE = $34\%$ for total perfusion) in the proper context. As we demonstrate in Fig. 4, the corresponding errors when using single-exposure LSCI is at best $67\%$, obtained at $T = 1$ ms. The error increases with higher exposure times and is over $100\%$ for $T = 64$ ms. True perfusion is very difficult to estimate from speckle contrast since the contrast is also affected by tissue properties that are independent from perfusion. Conventional LDF perfusion, on the other hand, is affected by the tissue properties in a similar way to speckle contrast. As we have demonstrated in a previous study, contrast from the same exposure times used in this study can be used to estimate LDF perfusion almost perfectly,2 but this is not the case for true perfusion. It is also important to realize that the $34\%$ error does not reflect the uncertainty for relative changes in perfusion for a single individual at a single skin site, but rather the accuracy across the whole dataset with very wide distributions of skin properties, as seen in Fig. 1. To better describe how the model behaves in a single individual, the analysis in Fig. 5 is more relevant, where we see that the model has a linear response to both blood concentration and mean blood speed. In other words, given a single individual, the absolute accuracy in perfusion is determined by the static skin properties, but the model responds linearly to changes in perfusion, for example during provocations. This also indicates that perfusion from tissues with similar properties can likely be compared with a much smaller variation than $34\%$. For example, measurements on the forearm in different individuals would likely fall in the same region in the space of tissue properties, thus resulting in comparable perfusion values between individuals. Conversely, comparing perfusion in the forearm and palm might give higher variations even in a single individual, due to the differences in tissue properties. When examining Fig. 5, we observe some deviations in the estimated PANN,tot from the ideal line. PANN,tot slightly overestimates perfusion at high average speeds, and begins to deviate from the true value Ptrue,tot at low blood tissue fractions (<$0.2\%$), where it has a less linear response than the single-exposure perfusion estimates PSE(1 ms) and PSE(8 ms). The combined mean percentage deviation for Figs. 5(a) and 5(b) is PANN,tot: $8\%$, PSE(1 ms): $14\%$, PSE(8 ms): $17\%$, and PSE(64 ms): $44\%$. Thus, when considering the combination of linearity to both parameters, PANN,tot is overall the stronger model. Figure 5 also demonstrates that the choice of exposure time for single-exposure LSCI has a large impact on the accuracy of the perfusion estimates relative to speed changes. Lower exposure time results in a more linear response to changes in the speed, whereas an exposure time of 8 ms is almost perfectly linear relative to changes in blood tissue fraction. It has been suggested that 5 ms is the optimal exposure time for LSCI,36 but this will likely depend on the application. Figure 6 summarizes the largest contributing factors to the error in estimated perfusion. For different values of scattering and vessel diameter, we observe an almost unnoticeable change in true perfusion, whereas all perfusion estimates are significantly affected. The small change in true perfusion is due to minor changes in the sampling volume. Changes in the epidermis thickness have a small effect on the true perfusion, and slightly higher effect on the perfusion estimates, especially for thin epidermis layers. The effect is smaller on PANN than on all single-exposure estimates for changes in scattering, vessel diameter, and epidermis thickness. We also performed this analysis for changes in oxygen saturation and epidermis absorption coefficient (representing melanin concentration) and found expectedly low changes in both PANN and PSE of <$0.5\%$ and $2\%$ across the whole tested ranges, Soxy∈[0,100]%, μa,epi∈[0.01,3.5] mm−1, respectively. However, the effect of those parameters is likely higher if a laser with a shorter wavelength than 780 nm is used, where the absorption is generally higher both for melanin and hemoglobin, and generally differs more between oxygenized and reduced hemoglobin. In summary, the analysis of Fig. 6 demonstrates that some skin properties, especially scattering and vessel diameter, affect the speckle contrast in a way that cannot be decoupled from changes in blood concentration or speed, thus affecting the estimated perfusion. The magnitude of the effects can be decreased by limiting the range of parameter values in the training data, but this might also have the opposite effect if the distributions are too narrow to accurately cover all tissues in real data. The influence of tissue properties on the absolute accuracy of the perfusion estimate will be an important topic of research for future studies. Since these properties cannot be directly measured using MELSCI, solutions will likely require integrating additional modalities into the imaging system. Figure 8 presents speed-resolved perfusion images from the arterial occlusion-release provocation. The first observation is that the subjective image quality of these perfusion estimates is higher than in the single-exposure perfusion estimates in Fig. 9, both in terms of less noise and in providing visible vessel structures. It is worth noting that the single-exposure estimates are based on exactly the same 64 ms of data as the ANN perfusion estimate. For example, the 8 ms single-exposure contrast is the average of 8 consecutive contrast images during the 64 ms. See Ref. 18 for the details of the contrast algorithm. While the absolute value of the perfusion on the large vessels should be interpreted carefully since the ANN model is trained to estimate microcirculatory perfusion, the fact that they are clearly visible is important, as this means that further analysis can be performed on skin regions that do not contain the large vessels. Since most of the large vessels are not visible in the single-exposure images, with these methods, there is a risk of analyzing tissue that is not representative of the microcirculation. We can observe how the perfusion becomes more concentrated to high-perfusion sites for higher speed components. This indicates an increased sensitivity to perfusion in larger vessels, which are expected to be more heterogeneously distributed and have higher flow speeds. However, the anatomical interpretation of these high-flow spots deserves further studies. To quantify the heterogeneity, we computed the coefficient of variation (CV) for each image in Fig. 8, which is summarized in Table 1. Here we observe that the CV increases toward higher speed components, corresponding to the more heterogeneous perfusion. This effect is especially noticeable in the images during the occlusion, where the perfusion in the 0 to 1 mm/s interval is more homogeneous, whereas the >10 mm/s interval has perfusion mostly concentrated in the large vessels, with low perfusion in between the large vessels. The perfusion in the large vessels during the occlusion is possibly due to Brownian motion in the blood, although these perfusion values should be interpreted with care since the training dataset did not include this morphology. In Fig. 10, we present the speed-resolved perfusion in two ROIs, manually selected on a low-flow and a high-flow perfusion site by inspecting the images in Fig. 8. This demonstrates the two extremes of the heterogeneous perfusion, with an ∼2 times higher reperfusion peak for total perfusion in the high-flow ROI compared to the low-flow ROI. The zoomed-in plots in Fig. 10 show the different speed components in more detail. Note that in both ROIs during the occlusion, the 0 to 1 mm/s perfusion is the highest of the three speed components, corresponding to a low-speed Brownian motion, and the >10 mm/s perfusion is the lowest. For the high-flow ROI, this is the opposite order compared to baseline. In the reperfusion phase, the 0 to 1 mm/s component is the lowest and the >10 mm/s component is the highest. This behavior is expected and demonstrates multiple large shifts in the speed distribution during the experiment. The heart-beat signal is also clearly visible during the reperfusion for the two highest speed components, and a strong flowmotion at ∼0.075 Hz (13 s period) can be seen especially in the high-flow ROI, indicating high myogenic activity.37,38 If we inspect the amplitudes of cardiac-related pulses in the reperfusion phase, we find that it is ∼4 times higher in the high-flow ROI compared with the low-flow ROI. This further indicates that the high-flow ROI contains larger vessels where cardiac pulses are more visible. For comparison, in Fig. 11 we present the same ROIs analyzed with the single-exposure perfusion model [Eq. [ 6]]. The single-exposure perfusion from different exposure times is naturally similar since the data were normalized to baseline to allow comparison, but we also observe that the differences between the low-flow and high-flow region are smaller than in the speed-resolved data in Fig. 10. It is also apparent that the noise level is higher compared to the ANN model. This is expected since the ANN is specifically trained to account for the measurement noise present in the speckle contrast, and the single-exposure model is not. There are a few limitations to this work, and some disadvantages compared to conventional single-exposure LSCI. First, our model is specifically trained on a dataset mimicking skin tissue. While it would be possible to change the model to target other tissues, this was not a part of this work. Second, the combination of the complex tissue model and machine learning makes it difficult to investigate the reason our method works or the cases when it fails. The benefits of our model should be weighed against the higher transparency inherent in a model such as Eq. [ 6], or other MELSCI models (e.g., Ref. 7). The complex model, in combination with the custom MELSCI system, also makes replication of this study quite difficult. Another limitation of the current study is that we did not perform validation measurements in a controlled flow phantom. Given the complex tissue morphology targeted by our model, such validation setups are likely difficult to achieve. However, the main components of the presented methodology have been independently validated in previous studies.2,14 Overall, the results presented in this paper illustrate possible analyses enabled by speed-resolved perfusion. A similar principle has been useful for analysis in a pointwise measuring system, where a reduced low-flow speed-component, interpreted as reduced nutritive capillary flow, could be observed in patients with type 2 diabetes compared to healthy controls.15,39 For speed-resolved perfusion imaging, it is reasonable to assume that the added spatial information can remove the uncertainty present in single-point measurements due to the spatial heterogeneity in the microcirculation. 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--- title: Mass cytometry identifies characteristic immune cell subsets in bronchoalveolar lavage fluid from interstitial lung diseases authors: - Kentaro Hata - Toyoshi Yanagihara - Keisuke Matsubara - Kazufumi Kunimura - Kunihiro Suzuki - Kazuya Tsubouchi - Daisuke Eto - Hiroyuki Ando - Maki Uehara - Satoshi Ikegame - Yoshihiro Baba - Yoshinori Fukui - Isamu Okamoto journal: Frontiers in Immunology year: 2023 pmcid: PMC10027011 doi: 10.3389/fimmu.2023.1145814 license: CC BY 4.0 --- # Mass cytometry identifies characteristic immune cell subsets in bronchoalveolar lavage fluid from interstitial lung diseases ## Abstract Immune cells have been implicated in interstitial lung diseases (ILDs), although their phenotypes and effector mechanisms remain poorly understood. To better understand these cells, we conducted an exploratory mass cytometry analysis of immune cell subsets in bronchoalveolar lavage fluid (BALF) from patients with idiopathic pulmonary fibrosis (IPF), connective-tissue disease (CTD)-related ILD, and sarcoidosis, using two panels including 64 markers. Among myeloid cells, we observed the expansion of CD14+ CD36hi CD84hiCCR2– monocyte populations in IPF. These CD14+ CD36hi CD84hi CCR2– subsets were also increased in ILDs with a progressive phenotype, particularly in a case of acute exacerbation (AEx) of IPF. Analysis of B cells revealed the presence of cells at various stages of differentiation in BALF, with a higher percentage of IgG memory B cells in CTD-ILDs and a trend toward more FCRL5+ B cells. These FCRL5+ B cells were also present in the patient with AEx-IPF and sarcoidosis with advanced lung lesions. Among T cells, we found increased levels of IL-2R+ TIGIT+ LAG3+ CD4+ T cells in IPF, increased levels of CXCR3+ CD226+ CD4+ T cells in sarcoidosis, and increased levels of PD1+ TIGIT+ CD57+ CD8+ T cells in CTD-ILDs. Together, these findings underscore the diverse immunopathogenesis of ILDs. ## Introduction Interstitial lung disease (ILD) is a broad term for a group of disorders characterized by varying degrees of inflammation and scarring or fibrosis of the lung [1]. In the majority of cases, ILD is a chronic disease with progressive scarring of lung tissue. Idiopathic pulmonary fibrosis (IPF) is the most common form of ILD and has a median survival rate of 3–5 years [2]. Connective tissue disease (CTD), defined as systemic disorders characterized by autoimmune-mediated organ damage and circulating autoantibodies, is one of the common systemic diseases associated with ILD [3]. Sarcoidosis is a granulomatous disorder of uncertain etiology, impacting various organ systems. Pulmonary involvement, including ILDs, represents the majority of morbidity and mortality associated with sarcoidosis [4]. It has been suggested that immune cells are involved in the pathogenesis of ILDs (4–7), with various subsets of immune cells potentially contributing to the development of ILDs, particularly macrophages and lymphoid cells. Macrophages, the most abundant immune cells in the lungs [8], have been shown to play a key role in the initiation and progression of fibrotic responses [9]. The ability of macrophages to alter their functional traits in response to external stimuli allows them to exhibit a range of biological impacts. Recent studies have shown the existence of a unique population of macrophages in IPF lung explants (10–12) and murine models of pulmonary fibrosis [13, 14]. T cells have also been implicated in the development of ILDs [6, 15]. In addition, increasing evidence suggests that pathogenic B cells may contribute to the development of autoimmune diseases (16–18), although their role in the development of ILDs remains poorly understood. The inability to fully characterize immune cells in clinical specimens is partially due to a deficiency in available assays. While next-generation sequencing analysis has facilitated the thorough analysis of cells, there is a constraint on the number that can be examined. It can be challenging to accurately classify the type of immune cells solely based on transcripts. Mass cytometry, on the other hand, offers an in-depth, high-dimensional description of the immune cell population through the simultaneous utilization of multiple markers [19], which will significantly alter our understanding of ILDs. The objective of this study is to utilize mass cytometry to phenotype macrophages, B cells, and T cells in bronchoalveolar lavage fluid (BALF) cells from patients with IPF, CTD-ILD, and sarcoidosis. By employing both unbiased and manually classified methods, we functionally characterize subpopulations of myeloid cells, B cells, and T cells that are overexpressed in each disease, thereby identifying cells that may potentially affect disease progression. ## Patients Patients who underwent BALF collection and were newly diagnosed with IPF, CTD-ILD, and sarcoidosis between Jan 2017 and April 2022 at Kyushu University Hospital were eligible for enrollment in the study. The study was authorized by the Ethics Committee of Kyushu University Hospital (reference number 22117-00). The diagnostic criteria for IPF, CTD-ILD, and sarcoidosis were as described elsewhere (20–24). Interstitial pneumonia with autoimmune features (IPAF) was included in CTD-ILD in this study. Disease progression was defined as follows: a relative decline of at least $10\%$ in the predicted value of forced vital capacity (FVC), a relative decline of 5-$10\%$ in the predicted value of FVC accompanied by worsening respiratory symptoms or increased lung involvement on high-resolution CT imaging, or worsening respiratory symptoms and increased lung involvement within 24 months, as modified by the inclusion criteria of the INBUILD trial [25]. The experimental and analytical workflow is shown in Figure 1. Figure 1: Graphical abstract of the study. Bronchoalveolar lavage fluid (BALF) samples were collected from patients with idiopathic pulmonary fibrosis (IPF), connective-tissue disease (CTD)-related ILD, and sarcoidosis. Following CD45 barcoding for individual sample identification, BALF cells were analyzed with a T cell panel (35 markers) and B cell/myeloid cell panel (32 markers) using mass cytometry. The expansion of CD14+ CD36hi CD84hi monocyte populations was found in IPF and ILDs with a progressive phenotype. FCRL5+ B cells were increased in CTD-ILDs, acute exacerbation (AEx) of IPF, and sarcoidosis with advanced lung lesions. ## Mass cytometry Antibodies were purchased either already metal-tagged (Standard Biotools) or in purified form (Supplementary Table 1). Purified antibodies were conjugated with metals using the Maxpar Antibody Labeling Kit (Standard Biotools) according to the manufacturer’s instructions and stored at 4°C. The cell labeling was performed as previously described [26]. Briefly, cryopreserved BALF cells in Cellbanker1 (Takara #210409) were thawed in PBS and stained with Cell-ID™ Cisplatin-198Pt (Standard Biotools #201198, 1:2000 dilution) in PBS. Cells were incubated with FcR blocking reagent (Miltenyi, #130-059-901) and barcoded with each metal-labeled CD45 antibodies (Supplementary Table 1). After washing, CD45-labelled cells were mixed (maximum 6 samples together) and stained with APC-conjugated FCRL5 antibodies (BioLegend #340305)(for panel #2), followed by staining antibody cocktail (Panels #1 and #2, see Supplementary Table 1). The volume of antibodies was determined by preliminary experiments with metal minus one. After staining, cells were washed, fixed with $1.6\%$ formaldehyde, and resuspended in Cell-ID Intercalator 103Rh (Standard Biotools #201103A) in Fix and Perm buffer (Standard Biotools) at 4°C overnight. For acquisition, cells were resuspended in MaxPar Cell Acquisition Solution (Standard Biotools #201240) containing one-fifth EQ Four Element Calibration Beads (Standard Biotools #201078) and were acquired at a rate of 200–300 events/second on a Helios mass cytometer (Standard Biotools). Files were converted to FCS, randomized, and normalized for EQ bead intensity using the Helios software. Concatenating fcs files in the same group into one file was conducted by FlowJo v10.8 (BD Biosciences). Manual gating, visualization of t-distributed stochastic neighbor embedding (tSNE), Uniform manifold approximation and projection (UMAP) analysis, and Citrus analysis [27] were performed using Cytobank Premium (Cytobank Inc.). ## Data analysis Live cells were selected by the exclusion of cisplatin-positive cells and doublets. CD45+ were selected and further analyzed. We first conducted principal component analysis (PCA) using median expression as a univariate summary for each marker on total cells, myeloid cells (CD11b+CD11c+), and T cells (CD2+CD3+) in the three diseases of interest (IPF, CTD-ILD, and sarcoidosis) using R package factoextra, ggplot2, ggplotly in RStudio (version 2022.12.0 + 353 with R version 4.2.2) to compare samples at a gross level (Supplementary Figure 1). For myeloid cells, CD11b+CD11c+ cells were gated, and the Citrus algorithm was conducted with clustering channels of CD11b, CD11c, CD64, CD14, CD16, CD32, CD36, CD38, CD84, CD86, CD163, CD206, CD209, CD223, HLA-DR, CCR2, CCR5, ST2 using following parameters: association models = nearest shrunken centroid (PAMR), cluster characterization = abundance, minimum cluster size = $5\%$, cross-validation folds = 5, false discovery rate = $1\%$. Two cases of CTD-ILDs (dermatomyositis-related, IgG4-related) were excluded due to low cell numbers to calculate clusters. A UMAP analysis was performed on myeloid cells, incorporating clustering channels of CD11b, CD11c, CD64, CD14, CD16, CD206, HLA-DR, and CCR2 with the following parameters: numbers of neighbors = 10, minimum distance = 0.01 (comparison between IPF, CTD-ILD, and sarcoidosis), or with clustering channels of CD11b, CD11c, CD64, CD14, CD16, CD32, CD36, CD38, CD84, CD86, CD163, CD206, CD209, CD223, TIM-1, HLA-DR, CCR2, CCR5, ST2 using following parameters: numbers of neighbors = 15, minimum distance = 0.01 (comparison among IPF cases). For B cells, CD3–CD64– and CD19+ or CD138+ cells were gated, and viSNE was conducted with clustering channels of CD19, CD38, CD11c, IgA, IgG, CD138, CD21, ST2, CXCR5, CD24, CD27, TIM-1, IgM, HLA-DR, IgD, FCRL5 on individual files and concatenated files using following parameters: iterations = 1000, perplexity = 30, theta = 0.5. Two cases of CTD-ILDs (dermatomyositis-related, IgG4-related) were excluded due to low cell numbers to calculate clusters. For T cells, the CD2+CD3+ cell population was selectively gated in concatenated files. A UMAP analysis was performed on T cells, incorporating clustering channels of CD4, CD8, CD45RA, CD45RO, CCR7, CD28, and Fas, with the following parameters: numbers of neighbors = 10, minimum distance = 0.01. The Citrus algorithm was conducted with clustering channels of CD4, CD5, CD7, CD8a, CD11a, CD16, CD27, CD28, CD44, CD45RA, CD45RO, CD49d, CD57, CD69, CD226, Fas, IL-2R, PD-L1, PD-L2, PD-1, OX40, TIGIT, TIM3, CTLA-4, CD223 (LAG-3), BTLA, ICOS, ST2, CCR7, CXCR3, HLA-DR using following parameters: association models = nearest shrunken centroid (PAMR), cluster characterization = abundance, minimum cluster size = $5\%$, cross-validation folds = 5, false discovery rate = $1\%$. viSNE for T cells was conducted with clustering channels of CD4, CD5, CD7, CD8a, CD11a, CD16, CD27, CD28, CD44, CD45RA, CD45RO, CD49d, CD57, CD69, CD226, Fas, IL-2R, PD-L1, PD-L2, PD-1, OX40, TIGIT, TIM3, CTLA-4, CD223 (LAG-3), BTLA, ICOS, ST2, CCR7, CXCR3, HLA-DR using following parameters: iterations = 1000, perplexity = 30, theta = 0.5. Choosing between UMAP and viSNE as the dimensionality reduction tool was based on their ability to preserve the connections between global structures and the distances between cell groups (UMAP was better than viSNE) and their ability to display a clear, non-overlapping representation of cell subgroups that makes it easier to see the differences between groups (viSNE was better than UMAP). Heatmaps were generated using the R package ComplexHeatmap in RStudio by calculating the median expression levels of each channel within the Citrus-generated clusters, normalizing the values to a maximum of 100 to denote maximum expression. The numerical values of total cells, T cells, myeloid cells, and B cells in each case are shown in Supplementary Table 2. ## Statistical analysis For the Citrus algorism experiment, we employed a PAMR association model, using a stringent threshold of $1\%$ FDR, as outlined in the Data Analysis section. The Student’s two-tailed unpaired t-test was utilized to perform a comparative analysis between the two groups. The statistical analysis was conducted through a one-way ANOVA, complemented by Tukey’s multiple comparison tests, to determine the significance among the three groups. A two-way ANOVA, accompanied by Tukey’s multiple comparison tests, was performed to evaluate the manually gated cell proportions using GraphPad Prism 9 software. A P-value less than 0.05 was considered to indicate statistical significance. ## Patient characteristics We analyzed 8 cases of IPF, 13 of CTD-ILD, and 10 of sarcoidosis (Table 1). Among the IPF cases, one patient experienced acute exacerbation (AEx) of IPF on admission. Thirteen cases of CTD-ILDs were observed, comprising Sjogren’s syndrome (SjS) ($$n = 3$$), dermatomyositis (DM) ($$n = 3$$), systemic sclerosis (SSc) ($$n = 2$$), mixed connective tissue disease (MCTD) ($$n = 1$$), systemic lupus erythematosus (SLE) complicated with adult-onset Still’s disease (AOSD) ($$n = 1$$), IgG4-related disease ($$n = 1$$), rheumatoid arthritis (RA)($$n = 1$$), and IPAF ($$n = 1$$). The presence of autoantibodies and radiological patterns in each of the CTD-ILD cases and the diagnosis of the IPAF case were described in Supplementary Table 3. Table 1 \| Unnamed: 0 \| IPF \| CTD-ILD \| Sarcoidosis \| \| --- \| --- \| --- \| --- \| \| Number \| 8 \| 13 \| 10 \| \| Age \| 72.0 ± 7.6 \| 66.2 ± 15.1 \| 48.7 ± 15.2 \| \| Male \| 100 (8) \| 38.5 (5) \| 60 (6) \| \| Pack-years \| 26.2 ± 28.8 \| 15.5 ± 19.5 \| 13.7 ± 24.3 \| \| BALF differential cell counts (%) \| BALF differential cell counts (%) \| BALF differential cell counts (%) \| BALF differential cell counts (%) \| \| Macrophage \| 82.2 ± 10.5 \| 65.1 ± 25.7 \| 55.6 ± 29.9 \| \| Neutrophil \| 6.2 ± 3.9 \| 7.5 ± 18.1 \| 1.9 ± 2.7 \| \| Lymphocyte \| 9.5 ± 9.4 \| 26.0 ± 23.2 \| 42.0 ± 31.0 \| \| Eosinophil \| 2.0 ± 1.5 \| 1.4 ± 1.5 \| 0.6 ± 0.5 \| Differential cell counts for BALF revealed lymphocytosis in 1 out of 8 cases ($12.5\%$) of IPF as well as in 7 out of 13 cases ($53.8\%$) of CTD-ILD and 7 out of 11 cases ($63.6\%$) of sarcoidosis when the cut-off for the percentage of lymphocytes was set to >$20\%$. ## Expansion of CD14+CD36hiCD84hi CCR2– monocytes in BALF from patients with IPF To investigate myeloid cell populations (identified as CD45+CD11b+CD11c+) that could provide insight into ILD conditions, we first conducted UMAP to see the major myeloid populations. The UMAP plot categorized 4 major subtypes, monocytes, CCR2+ macrophages, alveolar macrophages, dendritic cells, with no significant difference in IPF, CTD-ILD, and sarcoidosis (Figures 2A, B). We next utilized the Citrus algorithm to further investigate differently abundant myeloid cell subpopulations through the analysis of 18 parameters. Our analysis identified 33 clusters of myeloid cells, of which 23 were significantly differentiated between the groups (Figure 2C and Supplementary Figure 2). Clusters #6074, #6025, #6066, and #6054 were prevalent in sarcoidosis and characterized by CD64+ CD11blo CD14– CD223(LAG3)+ HLA-DR+ CD163hi expression (Figures 2D, E). Cluster #6059, which was abundant in CTD-ILD, was marked by CD64+ CD11c+ CD11b+ CD38hi expression (Figures 2D, E). Clusters #6075, #6064, and #6056, which were prevalent in IPF, were comprised of CD64+ CD11bhi CD11chi CD14+ CD36hi CD84hi CCR2– monocyte subpopulations (Figures 2D, E), different from CD14+CCR2+ monocyte subpopulations (clusters #6078, #6082) (Figures 2D, 3A). A recent report indicated that CD36hi CD84hi macrophages were increased in IPF compared to control and COPD lungs [28], which is consistent with our findings. Notably, the CD14+ CD36hi CD84hi CCR2– monocyte subpopulation was also increased in ILDs with a progressive phenotype (Figures 3B, C), suggesting that these cells may have pathogenic properties. Figure 2: Characterization of myeloid cell subsets in BALF from patients with IPF, CTD-ILD, and sarcoidosis. (A) UMAP plots of concatenated samples visualizing the distribution of CD11b+CD11c+ myeloid cell subpopulations in BALF from patients with IPF, CTD-ILD, and sarcoidosis. Monocytes are defined by CD64+CD14+, CCR2+ macrophages (Mp) by CCR2+ CD64+ CD14–, Alveolar Mp by CD64+CD206+, dendritic cells (DC) by CD64– CD206– CD11c+ HLA-DR+, unidentified cells by CD64– CD206– CD11clo HLA-DR–. (B) The proportions of myeloid cell subpopulations in IPF, CTD-ILD, and sarcoidosis. Graphical plots represent individual samples. Statistical differences were analyzed by two-way ANOVA followed by Tukey’s multiple comparison test. n.s. not significant. (C) Citrus network tree visualizing the hierarchical relationship and intensity of each marker between identified myeloid cell populations gated by CD45+CD11b+ CD11c+ from IPF (n = 8), CTD-ILD (n = 11), and sarcoidosis (n = 10). Clusters with significant differences were represented in red, and those without significant differences in blue. Circle size reflects the number of cells within a given cluster. (D) Heatmap illustrating the expression markers across different clusters of myeloid cells as determined by the Citrus analysis. (E) Citrus-generated violin plots for eight representative and differentially regulated populations. Each cluster number (C#) corresponds to the number shown in panel (C). All differences in abundance were significant at a false discovery rate < 0.01. Figure 3: The proportion of CD14+CD36hiCD84hi monocytes was correlated with disease progression. (A) UMAP plots showing CD14, CCR2, CD36, and CD84 expression in myeloid cells. Red triangles indicate CD14+ CCR2– cell subpopulations. (B) The proportion of CD14+ CD36hi CD84hi monocytes (cluster #6064 defined by the Citrus analysis in Figure 2C ) abundance in myeloid cell populations in individual samples and (C) the correlation with disease progression. ** p < 0.001. Information for disease with clinical progression is also shown. IPF, idiopathic pulmonary fibrosis; CTD-ILD, connective-tissue disease-related interstitial lung disease; AEx, acute exacerbation; SSc, systemic sclerosis; SjS, Sjogren syndrome; SLE, systemic lupus erythematosus; RA, rheumatoid arthritis; DM, dermatomyositis; MCTD, mixed connective tissue disease; na, not accessed.* ## B cell subpopulations in the lungs of ILDs Next, we sought to determine whether there were differential representations of B cell subsets in BALF cells of individuals with ILDs. Utilizing CD45+CD3–CD64– and CD19+ or CD138+ as gating parameters, we were able to identify both B cells and plasma cells. It was observed that B cells tended to be more prevalent in individuals with CTD-ILDs and sarcoidosis compared to those with IPF, although the percentages of B cells/plasma cells remained low across all groups (Figure 4A). A t-SNE analysis of 17 parameters among B cells/plasma cells revealed the presence of various B cell subpopulations, including IgD+ naïve B cells, IgM+ memory B cells, IgG+ memory B cells, IgA+ memory B cells, IgD–CD27– double negative (DN) B cells, plasmablasts, and plasma cells (Figure 4B and Supplementary Figure 4). This marked the first time that these B cell subpopulations were identified. The abundance of these subpopulations varied between groups, with particularly high levels of IgG memory B cells observed in individuals with CTD-ILDs compared to the other groups (Figure 4C). Figure 4: Characterization of B cell subsets in BALF from patients with IPF, CTD-ILD, and sarcoidosis. (A) Percentage of B cells and plasma cells in CD45+ BALF cells. (B) t-stochastic neighborhood embedding (t-SNE) plots of concatenated samples visualizing the distribution of B cell sub-populations in CD64–CD3– and CD19+ or CD138+ gated B cells in BALF from patients with IPF, CTD-ILD, sarcoidosis. Naive B cells are defined by CD19+IgD+, IgM memory B cells: CD19+ IgM+ CD27+, IgG memory B cells: CD19+ IgG+ CD27+, IgA memory B cells: CD19+ IgA+ CD27+, CD11c– double negative (DN) B cells: CD19+ CD11c– IgD– CD27–, CD11c+ DN B cells: CD19+ CD11c+ IgD–CD27–, plasmablasts: CD19+ CD27+ CD38+ CD138–, plasma cells: CD19– CD138+ and IgG+ or IgA+. (C) Percentage of B cell sub-populations in IPF, CTD-ILD, and sarcoidosis. Graphical plots represent individual samples. (D) Percentage of FCRL5 expressing B cells in total B cells. ns: not significant, p < 0.05, p < 0.01, * p < 0.001. (E) The proportion of each B cell subsets in FCRL5 expressing B cells. (F) Representative chest CT images of patients with sarcoidosis exhibiting a high percentage of FCRL5 B cells and a low percentage of FCRL5 B cells in BALF.* Recent evidence indicated that CD11c+ DN B cells and FCRL5+ B cells are involved in the development of autoimmune conditions (16–18, 29). In this study, we, therefore, sought to examine these B cell subsets. While we did not observe any differences in CD11c+ DN B cells between individuals with IPF, CTD-ILDs, and sarcoidosis (Figure 4C), there was a tendency for FCRL5+ B cells to increase in CTD-ILDs relative to the other groups (Figure 4D). FCRL5+ B cells were mostly IgG+ or IgA+ in each group (IPF: $75\%$, CTD-ILD: $80.3\%$, Sarcoidosis: $91.7\%$) (Figure 4E), suggesting that these FCRL5+ B cells were class-switched B cells. Interestingly, $0\%$ of FCRL5+ B cells were in all IPF cases except for one, which was AEx of IPF. The higher FCRL5+ B cell percentage in sarcoidosis was associated with progressive lung involvement (Figure 4F). Conversely, a patient with sarcoidosis exhibiting a low percentage of FCRL5+ B cells, bilateral hilar lymphadenopathy, and small nodules on CT (Figure 4F) demonstrated marked improvement on follow-up. These findings suggest that FCRL5+ B cells may possess pathogenic properties in CTD-ILDs as well as in the context of AEx of IPF and sarcoidosis. ## Characteristic T cell subpopulations in the lungs of ILDs In addition to myeloid cell and B cell subsets, we investigated whether subsets of T cells differentially existed in ILDs. The prevalence of T cells appeared to be elevated in sarcoidosis and CTD-ILDs compared to IPF, with the CD4/CD8 ratio tending towards an increase in sarcoidosis, although not reaching statistical significance (Figure 5A). To further visualize T cell differentiation within diseased lungs, we constructed UMAP plots (Figure 5B). BALF T cells primarily exhibited memory or effector phenotypes, with a scarcity of naive T cells (Figures 5B, C). Specifically, we observed a higher abundance of transient memory CD4 T cells in sarcoidosis and a higher abundance of effector memory CD4 T cells in IPF (Figure 5C). We next utilized the Citrus algorithm to distinguish differently abundant T cell subpopulations in IPF, CTD-ILD, and sarcoidosis in an unsupervised manner through analysis of 31 parameters (Figure 5D and Supplementary Figure 5). Our analysis identified 31 clusters of T cells, of which 9 were significantly differentiated between the groups. Cluster #5508, prevalent in sarcoidosis, was characterized by CD4+ CD226+ CXCR3+ (Figures 5E, F). Cluster #5520, prevalent in IPF, was comprised of CD4+ IL-2R+ TIGIT+ LAG3+, which are considered to be CD4+ regulatory T cells (Tregs) (Figures 5E, F) [6]. Cluster #5527, which was abundant in CTD-ILDs, was marked by CD8+ CD57+ PD-1+ TIGIT+ (Figures 5E, F). Figure 5: Characterization of T cell subsets in BALF from patients with IPF, CTD-ILD, and sarcoidosis. (A) The proportion of T cells (defined by CD2+CD3+) among CD45+ BALF cells and the CD4+ T cell/CD8+ T cell ratio from IPF (n = 8), CTD-ILD (n = 13), and sarcoidosis (n = 10). ns: not significant. (B) UMAP plots of concatenated samples visualizing the distribution of CD2+CD3+ T cell differentiation in BALF from patients with IPF, CTD-ILD, and sarcoidosis. Central memory (CM) T cells are defined by CCR7+ CD45RO+ CD28+ Fas+, Transitional memory (TM) by CCR7– CD45RO+ CD28+ Fas+, Effector memory (EM) by CCR7– CD45RO+ CD28– Fas+, Terminal effector (TE) by CCR7– CD45RO+/– Fas–, Effector memory RA (EMRA) by CCR7– CD45RO– CD45RA+ Fas+/–. Arrows indicate the trajectory of T-cell differentiation. DN: CD4– CD8– double negative, DP: CD4+ CD8+ double positive. (C) Percentage of T cell subpopulations in IPF, CTD-ILD, and sarcoidosis. Graphical plots represent individual samples. Statistical differences were analyzed by two-way ANOVA followed by Tukey’s multiple comparison test. ns. not significant, * p < 0.01, **** p < 0.0001. (D) The Citrus network tree displays the hierarchical relationship and intensity of each marker among the T-cell populations in BALF from IPF (n = 8), CTD-ILD (n = 13), and sarcoidosis (n = 10). (E) Heatmap illustrating the expression markers across different clusters of T cells as determined by the Citrus analysis. (F) Citrus-generated violin plots for three representative and differentially regulated populations. Each cluster number (C#) corresponds to the number shown in panel (D). All differences in abundance are significant at a false discovery rate < 0.01.* ## Immunological phenotypes in a patient with acute exacerbation of IPF One of the patients suffering from IPF experienced an acute exacerbation. The patient complained of worsening dyspnea upon exertion and a dry cough. Upon admission, chest CT images revealed the emergence of bilateral diffuse ground glass opacities superimposed on a honeycomb pattern, accompanied by peripheral traction bronchiectasis primarily in the basal lungs (Figure 6A). The patient’s blood procalcitonin, beta-D-glucan, and cytomegalovirus antigenemia were negative. The results of a PCR test for SARS-CoV-2 were also negative. BAL was performed for differential diagnosis, and a culture of the BAL fluid showed no presence of bacteria/mycobacteria. BALF cell differentiation showed a preponderance of macrophages (Figure 6B). The patient was then diagnosed with an acute exacerbation of IPF and treated with pulse methylprednisolone, tacrolimus, antibiotics, and recombinant thrombomodulin. Upon admission, he required 5 L/min of oxygenation at rest. He showed improvement, requiring only 0.5 L/min of oxygenation, and was discharged three weeks after admission. We compared mass cytometry analysis of cell subpopulations between AEx ($$n = 1$$) and other cases ($$n = 7$$) of IPF. The proportion of CD14+ CD36hi CD84hi CCR2– monocyte populations (clusters #6056, #6064, #6075) in myeloid cells was highest in AEx compared to stable conditions in IPF (Figure 6C). A UMAP of myeloid cells showed increased CD36 and CD84 expression in AEx of IPF (Figure 6D). On the contrary, the proportions of myeloid clusters #6025 and #6054, characterized by CD64+ CD11blo CD14– CD223 (LAG3)+ HLA-DR+ CD163hi expression, were lowest in AEx of IPF (Figure 6C). t-SNE analysis of T cells showed a decreased proportion of CD8+ T cells and an increased proportion of CD4+ CD57– CD7+ CD44+ PD1– subsets in AEx of IPF (Figure 6E). Figure 6: Immunological phenotypes in a patient with acute exacerbation of IPF. (A) Chest computed tomography images of the patient upon admission reveal the emergence of bilateral diffuse ground glass opacities superimposed on a honeycomb pattern, accompanied by peripheral traction bronchiectasis primarily in the basal lungs. (B) Comparison of BALF cell differentiation and CD4/CD8 ratio between patients with a patient experiencing an acute exacerbation (AEx) of the condition and the other cases of IPF. (C) Citrus-generated plots for myeloid sub-populations in IPF patients with stable condition and AEx. Each cluster number (C#) corresponds to the number shown in Figure 2C . (D) A uniform manifold approximation and projection (UMAP) of myeloid cells (CD45+CD11b+CD11c+ gated) showing cell distributions and each marker expression in BALF cells from concatenated samples with AEx and other cases of IPF. (E) t-SNE plots visualizing the distribution of T cell subpopulations in BALF T cells (gated as CD45+CD2+CD3+) from patients with AEx and other cases of IPF. Double negative (DN) T cells were defined as CD4–CD8– T cells. A red arrow indicates expansion of the CD57-CD7+CD44+PD-1-CD4 T cell subpopulation in AEx-IPF. ## Immunological phenotypes in BALF cells from patients with CTD-ILD CTD-ILD encompasses diverse diseases that may exhibit divergent immune cell abnormalities. Hence, we have compared each CTD-ILD, that is, SjS, DM, SSc, RA, SLE, MCTD, IgG4-related, and IPAF. Figures 7A, B show that the CD28–CD4/CD28+CD4 ratio was significantly divergent between the diseases, with the highest ratio from SLE-related ILDs. Previous research indicated that CD28– CD4+ T cells were prevalent in the blood of SLE patients with nephritis [30]. These CD28– CD4+ T cells were infiltrated in the renal tissue and may have contributed to renal injury [30]. Given this prior report, we speculated that these CD28– CD4+ T cells might also play a pathological role in SLE-related ILDs. Subsequently, we performed a CITRUS analysis on T cell subsets between SjS and DM (the CITRUS analysis can be conducted if the sample size is three or more.) ( Figures 7C–E). The CITRUS analysis revealed that TIM-3hi CD8+ T cells were more abundant in patients with DM compared to those with SjS, which is concordant with the findings from prior publications [31, 32]. Figure 7: Immunological phenotypes in BALF cells from patients with CTD-ILD. (A) t-SNE plots visualizing the distribution of T cell subpopulations in BALF T cells (gated as CD45+CD2+CD3+) from patients with CTD-ILD. DN: CD4–CD8– double negative T cells. (B)The ratio of CD28– CD4 T cells/CD28+ CD4 T cells defined in t-SNE plots in each disease. (C) The Citrus network tree displays the hierarchical relationship and intensity of each marker among the T-cell populations in BALF from Sjogren syndrome-related ILD (n = 3) and dermatomyositis associated-ILD (n = 3). (D) Heatmap illustrating the expression markers across different clusters of T cells as determined by the Citrus analysis. (E) Citrus-generated violin plots for five representative and differentially regulated populations. Each cluster number (C#) corresponds to the number shown in panel (C). All differences in abundance are significant at a false discovery rate < 0.01. (F) UMAP plots visualizing the distribution of CD11b+CD11c+ myeloid cell subpopulations in BALF from patients with CTD-ILD. Monocytes are defined by CD64+CD14+, CCR2+ macrophages (Mp) by CCR2+ CD64+ CD14–, Alveolar Mp by CD64+CD206+, dendritic cells (DC) by CD64– CD206– CD11c+ HLA-DR+, unidentified cells by CD64– CD206– CD11clo HLA-DR–. (G) The proportions of myeloid cell subpopulations in CTD-ILD. In the myeloid subset, a lower proportion of alveolar macrophages with higher monocytes and CCR2+ macrophages were observed in IgG4-related ILDs (Figures 7F, G). Nevertheless, due to the very limited number of cases in each group, it is challenging to conclude whether the disparities in cell populations are intrinsic differences or not. ## Discussion We have here demonstrated the characteristic immune cell subpopulations present in BALF from patients with IPF, CTD-ILDs, and sarcoidosis. Our analysis revealed an expansion of CD14+CD36hiCD84hi CCR2– monocytes in patients with IPF, an increase in FCRL5+ B cell in patients with CTD-ILDs and AEx of IPF, increased levels of IL-2R+ TIGIT+ LAG3+ CD4+ T cells in IPF, increased levels of CXCR3+ CD226+ CD4+ T cells in sarcoidosis, and increased levels of PD1+ TIGIT+ CD57+ CD8+ T cells in CTD-ILDs. CD36 is a scavenger receptor expressed on the surface of immune and non-immune cells that acts as a signaling receptor for damage-associated molecular patterns (DAMPs) and pathogen-associated molecular patterns (PAMPs) and also serves as a transporter for long-chain free fatty acids [33]. CD84 is an immunoreceptor expressed on the surface of various immune cells that regulates a range of immunological processes, including T cell cytokine secretion, natural killer cell cytotoxicity, monocyte activation, autophagy, T–B interactions, and B cell tolerance at the germinal center checkpoint [34]. Ayaub et al. recently reported that CD36hi CD84hi macrophages were expanded as a specific subpopulation of macrophages in the lungs of patients with IPF compared to healthy or chronic obstructive pulmonary disease lungs using single-cell RNA-sequencing, mass cytometry, and flow cytometry [28]. Ayaub et al. demonstrated that these CD36hi CD84hi macrophages expressed both alveolar and interstitial lung macrophage markers (HLA-DR+, CD11b+, CD206+), which is consistent with our results. Importantly, our study demonstrated these macrophage populations could be detected from BALF samples, which is less invasive compared to a lung biopsy and more suitable for clinical applications. We further determined that these CD36hi CD84hi macrophages were CD14 positive, indicating that these cells originated from monocytes. We discovered these CD36hi CD84hi monocyte subpopulations were highest in AEx of IPF among all IPF cases. Nakashima et al. demonstrated that pulmonary fibrosis led to significant alterations in the bone marrow, including the expansion and activation of monocytic cells, which enhanced fibrosis upon subsequent lung injury [35]. The pathobiology of IPF AEx is likely triggered by an acute event that leads to widespread acute lung injury, along with the acceleration of underlying chronic factors contributing to the fibrotic process [23]. From this evidence, we speculate that these CD36hi CD84hi monocyte subpopulations may be involved in accelerating the AEx of IPF. Interestingly, these CD36hiCD84hi CD14+CCR2– monocyte populations were ST2+ and CCR5+ but distinct from CCR2+ monocyte populations (Figures 2D, 3A). Liang et al. demonstrated that the expression of CCL2, a ligand for CCR2, suppressed bleomycin-induced pulmonary fibrosis in mice [36]. In addition, it has been shown that CCR2 deficiency in a mouse model of silica-induced pulmonary fibrosis resulted in an expansion of the fibrotic area [37], suggesting that CCR2hi monocyte-derived macrophages may have a suppressive role in fibrosis. Increased monocyte count in blood samples has been identified as a cellular biomarker for poor outcomes in fibrotic diseases, including IPF [38]. A more detailed analysis of subsets of monocytes in the blood may be more useful for predicting the prognosis of ILDs. FCRL5, encoded by the immunoglobulin superfamily receptor translocation-associated 2 (IRTA2) gene, is a member of the Fc receptor-like family, and its expression is mainly restricted to B cells. It has nine extracellular immunoglobulin domains, two immunoreceptor tyrosine-based inhibitory motifs, and one presumed immunoreceptor tyrosine-based activation motif in its cytoplasmic tail [39]. FCRL5 signaling, in conjunction with B cell receptor activation and TLR9 engagement, can lead to B-cell proliferation, activation, isotype switching, and the production of IgG- and IgA-positive B cells [29]. Higher FCRL5 expression predicted response to rituximab in rheumatoid arthritis [40] and granulomatosis with polyangiitis and microscopic polyangiitis [16]. There are reports that single nucleotide variants in the FCRL5 gene increase an individual’s predisposition to multiple sclerosis [41] or SLE [42]. This evidence indicates the pathogenic role of FCRL5 on autoimmunity. This study demonstrated increased FCRL5+ B cells in CTD-ILDs and a case of AEx of IPF. A recent study has demonstrated that rituximab was not inferior to cyclophosphamide in treating patients with CTD-ILDs with fewer adverse events [43]. Collectively, higher FCRL5+ B cells in BALF could be used as a biomarker as a rationale for using rituximab. We observed higher levels of IL-2R+ CD4+ T cells in patients with IPF compared to those with CTD-ILDs and sarcoidosis. These IL-2R+ CD4+ T cells likely represent Tregs, given that IL-2R+ expressing CD4+ T cells are a sole, distinct T cell subpopulation characterized by the highest FOXP3 expression between T cell subsets [6]. In the study by Serezani et al. [ 6], the proportions of CD4 Tregs were a higher tendency in IPF compared to controls. Increased activated Treg proportion was correlated with the severity of IPF [44]. However, the precise roles of Tregs in the development of pulmonary fibrosis have not been fully elucidated, and the existing literature on Tregs in pulmonary fibrosis is inconsistent [45]. On the one hand, Tregs may contribute to the progression of pulmonary fibrosis by secreting platelet-derived growth factor (PDGF), transforming growth factor-β (TGF-β), and other factors that promote epithelial-mesenchymal transition and alter the Th1/Th2 balance. On the other hand, Tregs may inhibit fibrosis by promoting the repair of epithelial cell damage, inhibiting the accumulation of fibroblasts, and strongly suppressing the production and function of pro-inflammatory factors and cells. The roles of Treg would, thus, be dependent on the time and microenvironment in regard to pulmonary fibrosis. We would like to note that these Tregs express PD-L1. We previously reported the PD-L1 expression in T cells from BALF and the emergence of PD-1 and PD-L1 co-expressing T cells in particular situations, such as a severe immune-checkpoint inhibitor-related ILD [46] and adult T cell leukemia-affected lungs [47]. These PD-L1 expressing T cells, including CD4 T cells, may have an immune-suppressing function on neighboring effector T cells or a cis-regulatory fashion on itself [48] via PD-1/PD-L1 interaction. We showed increased levels of CXCR3+ CD226+ CD4+ T cells in sarcoidosis. CXCR3 ligands, CXCL9 and CXCL11, are augmented in BALF from pulmonary sarcoidosis [49]. These CXCL9 and CXCL11 are interferon-inducible chemokines and localized to epithelioid histiocytes and multinucleated giant cells forming non-necrotizing granulomas [49]. Our observation matches the results of the previous study and suggests that CXCR3 ligands may recruit CXCR3-expressing CD4 T cells, which propagate a type 1 immune response and cause granuloma formation. CD226, also known as DNAM-1, is an adhesion molecule that plays a role in activating T cells through the interaction with CD115 and CD112 as ligands [50], competing for TIGIT as reciprocal functions. This is the first report to mention CD226 expression in T cells from sarcoidosis. We speculate that CD4 T cells migrating to the lungs through CXCR3 may be activated through CD226, potentially contributing to the pathogenesis of sarcoidosis. CD57 expression divided T cell subpopulations in viSNE. CD57 expression is typically associated with NK cells, but it has also been found in T cells that have a more advanced differentiation state, reduced replicative capacity, and increased production of cytokines such as IFNγ (51–53). There is also some evidence that CD57+ CD8+ T cells may have cytotoxic functions and be correlated with autoimmune activity in type 1 diabetes [54]. We found increased levels of CD57+CD8+PD-1+TIGIT+ subsets in CTD-ILDs, and these CD8+ subsets may be related to an autoimmune condition. Our study has several limitations, such as the lack of data from healthy controls, the relatively small sample size, and the retrospective design, which resulted in missing clinical data for some cases. Selection biases may be present, as only patients who underwent the BAL procedure could enroll in this study. In summary, our study demonstrates different immune cell phenotypes in IPF, CTD-ILDs, and sarcoidosis. We discovered that CD14+CD36+CD84+ monocytes and FCRL5+ B cells, important immune subsets, are differentially expressed and may be pathogenic. Further studies based on these findings may result in the development of new cell-targeted strategies to inhibit ILD progression. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethics Committee of Kyushu University Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions Conceptualization: TY; Methodology: KH, TY, KM, KK, and YB; Investigation: KH, TY, KM, KK, KS, KT, DE, HA, and MU; Visualization: KH, TY, KM, KK, KS, KT, DE, HA, MU, SI, and YB; Funding acquisition: TY and YF; Supervision: SI, YB, YF, and IO; Writing-original draft: KH and TY; Writing-review and editing: SI, YB, YF, and IO. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1145814/full#supplementary-material ## References 1. Wijsenbeek M, Suzuki A, Maher TM. Interstitial lung diseases. Lancet (2022) 400. DOI: 10.1016/S0140-6736(22)01052-2 2. 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--- title: 'Triglycerides and leptin soluble receptor: Which one is the target to protect β-cells in patients with type 2 diabetes?' authors: - Hana Alzamil - Laila Aldokhi journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10027012 doi: 10.3389/fendo.2023.1077678 license: CC BY 4.0 --- # Triglycerides and leptin soluble receptor: Which one is the target to protect β-cells in patients with type 2 diabetes? ## Abstract ### Objectives to study the relationships of leptin and leptin SR with adiposity indices, and glycemic indices in patients with type 2 diabetes mellitus (T2DM) compared to healthy subjects. ### Methods This cross-sectional study involved 65 patients with T2DM and 63 healthy controls. Fasting plasma levels of leptin, leptin SR, insulin and lipid profile were measured by enzyme linked immunosorbent essay, basal insulin resistance and beta-cell function were assessed using the homeostasis model assessment. ### Results leptin SR level was significantly higher in T2DM patients than in controls (5.8 ± 1.6 and 4.8 ± 1.3 respectively; $$p \leq 0.001$$). In patients with T2DM, leptin SR was negatively correlated with homeostasis model of β-cell function and body fat mass while it has a significant positive correlation with glycosylated hemoglobin (HbA1c). The independent predictors for leptin SR in patients with T2DM were triglycerides (TG) and HbA1c. ### Conclusions elevated serum leptin SR level in patients with T2DM was positively correlated with TG and abnormal glucose metabolism which indicate that it plays a role in pathophysiology of T2DM. The association of elevated leptin SR level with high TG and deterioration of β-cell function indicate that in some individuals, particularly non-obese, dyslipidemia might be a cause rather than a complication of diabetes. ## Introduction The hormone leptin is secreted mainly by adipose tissues and plays many important roles in the regulation of energy balance by suppressing the intake of food and stimulating thermogenesis, thus leading to loss of weight [1]. Serum leptin levels are positively correlated with the percentage of body fat and body mass index [2]. It has been suggested that obese people are resistant to the effects of endogenous leptin and even after administration of exogenous leptin there is no significant effect on weight loss [3]. When leptin binds to its receptor in the hypothalamus, it stimulates many anorexigenic peptides and inhibit several orexigenic neuropeptides [4]. In humans’ plasma, leptin is found in bound, inactive form and free active forms. There is an equilibrium between the circulating binding protein and the free leptin. In lean subjects, most of serum leptin is bound to circulating binding proteins while in obese individuals the majority of circulating leptin remains free [5]. Leptin acts by binding to leptin receptor (OB-R), which has a single transmembrane domain and belongs to class 1 cytokine receptor family. The shedding of OB-R extracellular domain produces the main binding protein for leptin in the blood, leptin SR. Animal studies and tissue culture experiments showed that an increased serum leptin SR was associated with inhibition of leptin signal transduction [6]. Additionally, a study conducted in young populations found a relationship between serum leptin and its soluble binding protein levels on one hand with measures of adiposity and metabolic syndrome score on the other hand [7]. Morioka and coworkers suggested that leptin SR is a factor that can affect pancreatic beta cells secretory functions in patients with T2DM [8]. Additionally, Kang et al. found that triglycerides/glucose index can predict insulin resistance [9]. Recently, a group of researchers reported that TG level can significantly predict the risk of developing prediabetes and diabetes [10]. The present study aimed to investigate the levels of leptin, leptin SR and their correlation with lipid profile, obesity and glycemic control in patients with T2DM. We hypothesize that both leptin SR and TG have a profound effect on beta cell function and a complex interrelationship that needs further investigation. ## Methodology This cross-sectional study enrolled 128 participants, 65 subjects were diabetic patients (34 males and 31 females) and 63 subjects were healthy controls (36 males and 27 females). The control subjects were healthy employees recruited by local advertisement and the diabetic patients were recruited from primary care clinics at King Khalid University Hospital, Riyadh, Saudi Arabia. This study was conducted at the Department of Physiology, College of medicine at King Saud University. The control group were evaluated using detailed history, clinical examination and investigations. Recruited patient were known to have T2DM for at least six months and the duration ranges between six months and 11 years. All patients with T2DM were receiving oral hypoglycemic drugs and only 18 ($24\%$) patients were on lipid lowering agents. We excluded any patient with type 1 diabetes, acute infection, cardiovascular complications, nephropathy, neuropathy, amputation or those who needed admission. Patients and controls with pregnancy, using oral contraceptive pills or glucocorticoids were excluded. The study protocol was approved by the ethical committee of institutional review board of college of medicine, King Saud University with approval number: $\frac{03}{1342}$/R. All participants signed an informed consent and confidentiality was assured. Body composition measurement obtained using the body composition analyzer (Biospace-InBody 3.0. SNBS 300504E $\frac{2003}{04.272}$-Iyongieong-vi, yipjang-myeon, chanan-si, chungcheongnam-do, South Korea). The measurements included: body mass index (BMI), % body fat, lean body mass and waist-hip ratio (WHR). Before those measurements were taken, the subjects palms and soles were cleaned with electrolytes tissue, and information about their height, sex and age were fed to the machine. The subject was asked to stand with barefoot on the platform of the machine Fasting venous blood samples were analyzed for blood glucose, HbA1c, basal insulin, lipid profile, leptin and leptin SR. Measurement of HbA1c was performed by Helena Glyco-Tek Affinity Column method, (Helena Biosciences, Europe, Colima Avenue, Sunderland Enterprise Park, Sunderland, Tyne & Wear, SR53 x B, UK). Lipid profile was estimated using Knonelab Itelligent Diagnostics Systems (Konelab Corporation, Ruukintie 18, FIN-02320 Espoo, Finland). Insulin, leptin and leptin SR immunoassays were performed by quantitative standard sandwich ELISA technique using monoclonal antibody specific for these parameters with kits supplied by R&D Systems, (Abingdon, United Kingdom). The indices of basal insulin resistance and beta-cell function were assessed using the homeostasis model assessment (HOMA/IR and HOMA/B) in which HOMA/IR (mmol/L x µIU/mL) = fasting glucose (mmol/L) x fasting insulin (µIU/mL)/22.5 and HOMA/B = fasting insulin (µIU/mL) x 20/[fasting glucose (mmol/L) – 3.5]. For further analysis of the effect of obesity on leptin and leptin SR we subdivided the study group into four groups, non-obese control ($$n = 30$$), non-obese diabetic ($$n = 30$$), obese control ($$n = 33$$) and obese diabetic ($$n = 35$$). Obesity was defined as BMI more than 30 kg/m2 (according to WHO criteria). To test for the impact of glycemic control on leptin and leptin SR we subdivided patients into two groups, one with good control (HbA1c ≤ 7.5) which included 35 patients and the other with uncontrolled diabetes (HbA1c >7.5) represented by 30 patients. ## Statistical analysis The data were analyzed by the computer software program Statistical Package for Social Sciences (SPSS version 20, Chicago). Descriptive characteristics and the lipid profile of the subjects were expressed as Mean± Standard Deviation (SD). Kolmogorov-Smirnova and Shapiro-Wilk tests were used to see that data is following normal distribution or not. Those parameters which were not following normal distribution were analyzed by non-parametric Mann Whitney test. For continuous data with normal distribution Student’s t-test was used. Correlations between leptin, leptin SR, HbA1c, BFM and markers of insulin resistance were determined by simple regression analysis. A stepwise linear regression model was constructed for leptin and leptin SR as dependent variables to find the independent predictors for these variables in patients with T2DM. Anova test was used to compare the level of leptin and leptin SL between four groups (control non-obese, diabetic non-obese, control obese and diabetic obese) then post-hoc test was performed. ## Results Demographic characteristics, clinical features, insulin resistance indices and body composition for controls and T2DM patients were presented in Table 1. Age for control subjects ranges between 25 and 62 years (Mean: 47.22 ± 7.73) and for patients was 30-66 years (49.45 ± 10.2). Table 1 \| Variables \| Controls(n= 63) \| Patients(n= 65) \| P value \| \| --- \| --- \| --- \| --- \| \| M/F \| 36/27 \| 34/31 \| \| \| Age (years) \| 47.22 ± 7.73 \| 49.45 ± 10.2 \| 0.790 \| \| BMI \| 28.9 ± 4.2 \| 31.4 ± 5.7 \| 0.005* \| \| WHR \| 0.97 ± 0.07 \| 1.03 ± 0.08 \| 0.001* \| \| SBP (mmHg) \| 117.3 ± 14.6 \| 128.1 ± 16.8 \| 0.001* \| \| DBP (mmHg) \| 78.8 ± 9.8 \| 80.6 ± 9.0 \| 0.256 \| \| TC (mmol/L) \| 5.1 ± 0.8 \| 5.0 ± 0.9 \| 0.390 \| \| HDL (mmol/L) \| 1.2 ± 0.3 \| 0.9 ± 0.2 \| 0.001* \| \| TG (mmol/L) \| 1.3 ± 0.7 \| 1.9 ± 0.9 \| 0.001* \| \| FBG (mmol/L) \| 5.0 ± 0.5 \| 7.9 ± 2.6 \| 0.001* \| \| LDL (mmol/L) \| 3.3 ± 0.7 \| 3.2 ± 0.8 \| 0.346 \| \| HbA1c (%) \| \| 7.3 ± 1.8 \| – \| \| Fat Mass (kg) \| 26.6 ± 8.5 \| 30.2 ± 10.6 \| 0.040* \| \| Body Fat % \| 34.9 ± 8.2 \| 37.6 ± 7.4 \| 0.032* \| \| Basal Insulin uIU/ml \| 6.5 ± 3.3 \| 10.5 ± 14.4 \| 0.028* \| \| Homa IR \| 1.5 ± 0.8 \| 2.9 ± 2.4 \| 0.010* \| \| Homa B(%) \| 95.5 ± 61.3 \| 48.6 ± 34.5 \| 0.001* \| \| Leptin ng/ml \| 30.6 ± 19.8 \| 32.2 ± 19.5 \| 0.331 \| \| Leptin SR ng/ml \| 4.8 ± 1.3 \| 5.8 ± 1.6 \| 0.001* \| Leptin SR level was significantly higher in T2DM patients than in controls (5.8 ± 1.6 and 4.8 ± 1.3 respectively, $$P \leq 0.001$$) while the difference was not significant for leptin (32.2 ± 19.5 and 30.6 ± 19.8 respectively, $$P \leq 0.331$$). Using simple regression analysis we determined the correlations between levels of leptin and leptin SR with BFM and HOMA-IR, HOMA-B and HbA1c in patients with T2DM. Leptin SR correlated negatively with HOMA-B (r= -0.416, $$p \leq 0.001$$), while the correlation of leptin with HOMA-B was non-significant ($r = 0.222$, $$p \leq 0.075$$) (Figures 1A, B). Leptin correlated significantly with HOMA-IR ($r = 0.248$, $$p \leq 0.048$$) while the correlation of leptin SR with HOMA-IR was not significant ($r = 0.037$, $$p \leq 0.771$$) (Figures 1C, D). Figure 1: Correlation of Leptin SR (A, C, E, G) and leptin (B, D, F, H) levels with HOMA B, HOMA-IR, HbA1c and Fat Mass in patients with T2DM. Leptin SR had a significant negative correlation (r= -0.297, $$p \leq 0.016$$) with fat mass which was positively correlated with leptin level ($r = 0.652$, $$p \leq 0.001$$) (Figures 1E, F). Significant positive correlation between HbA1c and leptin SR was observed ($r = 0.440$, p=<0.001), while the correlation was not significant with leptin levels ($r = 0.005$, $$p \leq 0.971$$) (Figures 1G, H). A multivariate regressions analysis model was constructed to find significant predictors of both serum total leptin and leptin SR among adiposity measures, insulin resistance indices and lipid markers for patients with T2DM. We adjusted for age, sex, BMI, systolic blood pressure, HbA1c, TG, HDL and LDL (Table 2). The independent predictors for leptin in diabetic patients were BFM ($B = 0.705$, $p \leq 0.001$) and HbA1c (B= -0.311, $$p \leq 0.003$$) and for leptin SR were TG ($B = 0.325$, $$p \leq 0.003$$) and HbA1c ($B = 0.262$, $$p \leq 0.015$$) Table 2. Table 2 \| Unnamed: 0 \| B \| S.E (E) \| P \| \| --- \| --- \| --- \| --- \| \| Leptin \| Leptin \| Leptin \| Leptin \| \| Body Fat Mass \| 0.705 \| 0.250 \| 0.000 \| \| HbA1c \| -0.311 \| 0.210 \| 0.003 \| \| Leptin SR \| Leptin SR \| Leptin SR \| Leptin SR \| \| TG \| 0.325 \| 0.207 \| 0.003 \| \| HbA1c \| 0.262 \| 0.026 \| 0.015 \| The serum level of leptin was significantly higher in obese control vs non-obese control ($$p \leq 0.001$$) and in obese control vs non-obese diabetic patients ($p \leq 0.0001$). Leptin was significantly higher in obese diabetic patients vs non-obese control ($p \leq 0.001$) and in Obese diabetic patients vs non-obese diabetic patients, ($p \leq 0.0001$) as shown in Figure 2. Leptin SR level was significantly higher in non-obese diabetic vs Non obese control group ($$p \leq 0.006$$) and in non-obese diabetic vs obese control group ($$p \leq 0.002$$) as shown in Figure 3. Figure 2: Effect of obesity on serum leptin level (mean ± SEM). Non-obese control (n=30), Non-obese diabetic (n=30), obese control (n=33), obese diabetic (n=35) Obese control vs non-obese control, p=0.001, Obese control vs non-obese diabetic patients, p<0.0001. § Obese diabetic patients vs non-obese control, p<0.001, Obese diabetic patients vs non-obese diabetic patients, p<0.0001.* Figure 3: Effect of obesity on leptin soluble receptor (Leptin SR) level (mean ± SD), DM: Diabetes mellitus. Non-obese control (n=30), Non-obese diabetic (n=30), obese control (n=33), obese diabetic (n=35). § Non obese DM vs Non obese contol group (p=0.006).Non-obese DM vs obese control group (p=0.002).* The level of leptin and leptin SR were associated with the glycemic control, we observed that diabetic patients with poor control of blood glucose level (HbA1c >7.5) tend to have a higher serum level of leptin SR and a lower leptin level compared to patients with good control (Figure 4). Figure 4: effect of glycemic control on the serum level of leptin and leptin SR. A1c: glycosylated hemoglobin A1c, L: leptin, LSR: leptin soluble receptor. Well controlled (n=35), poorly controlled (n=30). § Significant difference in L with poor glycemic control (p<0.03), Significant difference in LSR with poor glycemic control (p<0.001).* ## Discussion Our study demonstrated that regardless of obesity, serum leptin SR level was significantly higher in patients with T2DM compared to healthy subjects. The elevated level of leptin SR was linked to dyslipidemia specifically TG level. On the other hand, leptin level was elevated in obese subjects whether they have diabetes or not and it was correlated with BFM. A group of researchers observed that leptin level increased suddenly at BMI of 24.6 while the level of leptin SR decreased rapidly at BMI of 30. However, further increase in BMI was not associated with a further decrease in synthesis of leptin SR [11]. Interestingly leptin SR level showed a strong positive correlation with HbA1c and a significant negative association with beta cell function. Moreover, leptin SR level increases while leptin level decreases with poor glycemic control. These associations of leptin SR with glycemic indices in our patients indicate that elevated leptin SR might play a significant role in disease manifestations and severity. Data is scarce with regards to the association of leptin SR with body composition, glycemic indices and HOMA-IR in patients with T2DM (7, 12 –13). Sun and colleagues reported that independent of obesity and leptin levels, there was a strong inverse association between high levels of circulating leptin SR and the risk for development of T2DM in American women [12]. In contrast, our findings suggested that increased plasma level of leptin SR in a milieu of low leptin level might play a role in pathophysiology of T2DM by causing impairment of β-cell function. One reason for low leptin SR level in Sun’s et al. study among patients with diabetes might be the higher leptin level associated with obesity in these subjects. In a previous study we reported that increased leptin level was related to obesity regardless of associated diabetes while elevated level of tumor necrosis factor-α was linked to obesity that is associated with diabetes [14]. In the current study we observed that although leptin SR was not correlated with BFM it was significantly associated with high level of TG and uncontrolled diabetes. Ogawa and coworkers’ study showed a positive correlation between leptin SR level and high density lipoprotein level [13]. Another study reported that serum leptin SR contributed to carotid intima media thickness in patients with T2DM [15]. Recently, Horii et al. concluded that accumulation of lipid droplets in β-cells was associated with insulin resistance which can lead to high levels of free fatty acids (FFAs) derived from degradation of triglycerides, the accumulated FFAs can flow into β-cell. In patients with T2DM due to insulin resistance, hyperglycemia combined with excess FFAs are linked to accumulation of lipid droplets in β-cell [16]. Additionally, a longitudinal study which lasted for 15 years found that patients with familial combined hyperlipidemia were at high risk to develop T2DM [17]. Substantially all steps in the pathway of lipotoxicity, starting with high food intake to the point of synthesis of ceramide, is protectively influenced by leptin [18]. The transport of leptin was found to be inhibited by TG through direct binding with leptin or its transporter while pharmacological intervention that reduces the level of TG reversed this inhibition of leptin transport [19].Also, we observed that leptin SR level have a strong positive correlation with HbA1c value and a negative correlation with HOMA-β. Similarly, previous studies demonstrated that leptin SR had positive correlation with HbA1c in patients with type1 diabetes [20] and negative correlation with HOMA-β in patients withT2DM [8]. We postulate that the elevated leptin SR in patients with T2DM in our study played a role in leptin resistance, in addition to its role in the impairment of β-cell function. Leptin was reported to decrease β cell apoptosis and lower α cell insulin resistance which usually leads to inhibition of the pathways leading to T2DM. Lowering TG level should be an early important step in management of diabetes to protect β cells and endothelial cells from apoptosis and atherosclerosis. In conclusion, our study showed that leptin SR is higher in patients with T2DM and is associated with abnormal β-cell function and thus it might be considered as an important marker in the pathogenesis of diabetes. Furthermore, leptin SR level is linked to HbA1c value and TG level so it can be used in monitoring the response to treatment in patients with diabetes. More studies are needed to explore the complex pathophysiological mechanisms in diabetes mellitus, and specifically focusing on investigating whether lowering TG levels would decrease leptin SR level and protect β-cell from its deleterious effect. Finding new pathways to manage T2DM will help in implementing precision medicine. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author. ## Ethics statement The studies involving human participants were reviewed and approved by Institutional Review Board, College of Medicine, King Saud University, Riyadh, Saudi Arabia. The patients/participants provided their written informed consent to participate in this study. ## Author contributions HA, literature review, data analysis, and writing manuscript. LA, idea, data collection, and manuscript revision. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Klok MD, Jakobsdottir S, Drent ML. The role of leptin and ghrelin in the regulation of food intake and body weight in humans: a review. Obes Rev (2007) 8 21-34. DOI: 10.1111/j.1467-789X.2006.00270.x 2. Schwartz MW, Peskind E, Raskind M, Boyko EJ, Porte D. Cerebrospinal fluid leptin levels: relationship to plasma levels and to adiposity in humans. Nat Med (1996) 2. DOI: 10.1038/nm0596-589 3. Jéquier E. Leptin signaling, adiposity, and energy balance. Ann N Y Acad Sci (2002) 967. DOI: 10.1111/j.1749-6632.2002.tb04293.x 4. Van der Klaauw AA. Neuropeptides in obesity and metabolic disease. Clin Chem (2017) 64. DOI: 10.1373/clinchem.2017.281568 5. Magni P, Liuzzi A, Ruscica M, Dozio E, Ferrario S, Bussi I. Free and bound plasma leptin in normal weight and obese men and women: relationship with body composition, resting energy expenditure, insulin-sensitivity, lipid profile and macronutrient preference. Clin Endocrinol (Oxf). (2005) 62. DOI: 10.1111/j.1365-2265.2005.02195.x 6. Schaab M, Kratzsch J. The soluble leptin receptor. Best Pract Research: Clin Endocrinol Metab (2015) 29 7. Hamnvik OP, Liu X, Petrou M, Gong H, Chamberland JP, Kim EH. Soluble leptin receptor and leptin are associated with baseline adiposity and metabolic risk factors, and predict adiposity, metabolic syndrome, and glucose levels at 2-year follow-up: the Cyprus metabolism prospective cohort study. Metabolism. (2011) 60 8. Morioka T, Emoto M, Yamazaki Y, Kurajoh M, Motoyama K, Mori K. Plasma soluble leptin receptor levels are associated with pancreatic β-cell dysfunction in patients with type 2 diabetes. J Diabetes Invest (2017) 9 9. Kang B, Yang Y, Lee EY, Yang HK, Kim HS, Lim SY. Triglycerides/glucose index is a useful surrogate marker of insulin resistance among adolescents. Int J Obes (2017) 41 10. Jasim OH, Mahmood MM, Ad'hiah AH. Significance of lipid profile parameters in predicting pre-diabetes. Arch Razi Inst (2022) 77. DOI: 10.22092/ARI.2021.356465.1846 11. Owecki M, Nikisch E, Miczke A, Pupek-Musialik D, Sowiński J. Leptin, soluble leptin receptors, free leptin index, and their relationship with insulin resistance and BMI: high normal BMI is the threshold for serum leptin increase in humans. Hormone Metab Res (2010) 42. DOI: 10.1055/s-0030-1253422 12. Sun Q, van Dam RM, Meigs JB, Franco OH, Mantzoros CS, Hu FB. Leptin and soluble leptin receptor levels in plasma and risk of type 2 diabetes in U. S. women: prospective study. Diabetes (2010) 59. DOI: 10.2337/db09-1343 13. Ogawa T, Hirose H, Yamamoto Y, Nishikai K, Miyashita K, Nakamura H. Relationships between serum soluble leptin receptor level and serum leptin and adiponectin levels, insulin resistance index, lipid profile, and leptin receptor gene polymorphisms in the Japanese population. Metabolism (2004) 53. DOI: 10.1016/j.metabol.2004.02.009 14. Alzamil H. Elevated serum TNF-α is related to obesity in type 2 diabetes mellitus and is associated with glycemic control and insulin resistance. J Obes (2020) 2020 15. Yamazaki Y, Emoto M, Morioka T, Kawano N, Lee E, Urata H. Clinical impact of the leptin to soluble leptin receptor ratio on subclinical carotid atherosclerosis in patients with type 2 diabetes. J Atheroscl Thromb (2013) 20. DOI: 10.5551/jat.14662 16. Horii T, Kozawa J, Fujita Y, Kawata S, Ozawa H, Ishibashi C. Lipid droplet accumulation in β cells in patients with type 2 diabetes is associated with insulin resistance, hyperglycemia and β cell dysfunction involving decreased insulin granules. Front Endocrinol (2022) 13. DOI: 10.3389/fendo.2022.996716 17. Brouwers MC, de Graaf J, Simons N, Meex S, Ten Doeschate S, van Heertum S. Incidence of type 2 diabetes in familial combined hyperlipidemia. BMJ Open Diabetes Res Care (2020) 8(1). DOI: 10.1136/bmjdrc-2019-001107 18. Unger RH, Roth MG. A new biology of diabetes revealed by leptin. Cell Metab (2015) 21 15-20. DOI: 10.1016/j.cmet.2014.10.011 19. Banks WA, Coon AB, Robinson SM, Moinuddin A, Shultz JM, Nakaoke R. Triglycerides induce leptin resistance at the blood-brain barrier. Diabetes (2004) 53. DOI: 10.2337/diabetes.53.5.1253 20. Kratzsch J, Deimel A, Galler A, Kapellen T, Klinghammer A, Kiess W. Increased serum soluble leptin receptor levels in children and adolescents with type 1 diabetes mellitus. Eur J endocrinology. (2004) 151. DOI: 10.1530/eje.0.1510475
--- title: Silencing of STE20-type kinase TAOK1 confers protection against hepatocellular lipotoxicity through metabolic rewiring authors: - Ying Xia - Emma Andersson - Sumit K. Anand - Emmelie Cansby - Mara Caputo - Sima Kumari - Rando Porosk - Kalle Kilk - Syam Nair - Hanns-Ulrich Marschall - Matthias Blüher - Margit Mahlapuu journal: Hepatology Communications year: 2023 pmcid: PMC10027040 doi: 10.1097/HC9.0000000000000037 license: CC BY 4.0 --- # Silencing of STE20-type kinase TAOK1 confers protection against hepatocellular lipotoxicity through metabolic rewiring ## Background: NAFLD has become the leading cause of chronic liver disease worldwide afflicting about one quarter of the adult population. NASH is a severe subtype of NAFLD, which in addition to hepatic steatosis connotes liver inflammation and hepatocyte ballooning. In light of the exponentially increasing prevalence of NAFLD, it is imperative to gain a better understanding of its molecular pathogenesis. The aim of this study was to examine the potential role of STE20-type kinase TAOK1 —a hepatocellular lipid droplet-associated protein—in the regulation of liver lipotoxicity and NAFLD etiology. ### Methods: The correlation between TAOK1 mRNA expression in liver biopsies and the severity of NAFLD was evaluated in a cohort of 62 participants. Immunofluorescence microscopy was applied to describe the subcellular localization of TAOK1 in human and mouse hepatocytes. Metabolic reprogramming and oxidative/endoplasmic reticulum stress were investigated in immortalized human hepatocytes, where TAOK1 was overexpressed or silenced by small interfering RNA, using functional assays, immunofluorescence microscopy, and colorimetric analysis. Migration, invasion, and epithelial-mesenchymal transition were examined in TAOK1-deficient human hepatoma-derived cells. Alterations in hepatocellular metabolic and pro-oncogenic signaling pathways were assessed by immunoblotting. ### Results: We observed a positive correlation between the TAOK1 mRNA abundance in human liver biopsies and key hallmarks of NAFLD (i.e., hepatic steatosis, inflammation, and ballooning). Furthermore, we found that TAOK1 protein fully colocalized with intracellular lipid droplets in human and mouse hepatocytes. The silencing of TAOK1 alleviated lipotoxicity in cultured human hepatocytes by accelerating lipid catabolism (mitochondrial β-oxidation and triacylglycerol secretion), suppressing lipid anabolism (fatty acid influx and lipogenesis), and mitigating oxidative/endoplasmic reticulum stress, and the opposite changes were detected in TAOK1-overexpressing cells. We also found decreased proliferative, migratory, and invasive capacity, as well as lower epithelial-mesenchymal transition in TAOK1-deficient human hepatoma-derived cells. Mechanistic studies revealed that TAOK1 knockdown inhibited ERK and JNK activation and repressed acetyl-CoA carboxylase (ACC) protein abundance in human hepatocytes. ### Conclusions: Together, we provide the first experimental evidence supporting the role of hepatic lipid droplet-decorating kinase TAOK1 in NAFLD development through mediating fatty acid partitioning between anabolic and catabolic pathways, regulating oxidative/endoplasmic reticulum stress, and modulating metabolic and pro-oncogenic signaling. ## INTRODUCTION NAFLD is emerging as the leading cause of chronic liver disease, afflicting ~$25\%$ of the global population.1,2 As a hepatic manifestation of metabolic syndrome, NAFLD is frequently associated with obesity, dyslipidemia, and type 2 diabetes.1 Excessive fat accumulation within hepatocytes is considered a key event in the initiation of NAFLD.2 As the disease advances, a subset of NAFLD patients progress to NASH, which in addition to hepatic steatosis is characterized by local inflammation and cell damage, carrying an increased risk of developing liver fibrosis, cirrhosis, and HCC.3 Thus, deciphering the molecular mechanisms underlying the initiation and aggravation of NAFLD is of high clinical importance to develop strategies for its prevention and management. In NAFLD, hydrophobic neutral lipids [primarily triacylglycerols (TAGs) and cholesteryl esters] accumulate within intrahepatocellular lipid droplets (LDs), covered by a monolayer of phospholipids and associated proteins.4 Notably, the best-characterized genetic risk factors controlling the susceptibility of NAFLD—PNPLA3 and HSD17B13—both encode proteins anchored to the hepatic LDs.5,6 Moreover, our recent studies have provided several lines of evidence that various STE20-type kinases—STK25, MST3, MST4, and TAOK3—bind to LDs and critically regulate the dynamic balance of lipid storage versus lipid utilization within the liver, contributing to the pathogenesis of NAFLD.7–16 Consequently, hepatic LD-associated proteins have emerged as potential targets for combating NAFLD and related metabolic disorders. We recently identified thousand and one kinase 1 (TAOK1; also known as MAP3K16 or PSK2) as a hepatic LD-binding protein based on a global proteomic analysis of LD fraction isolated from livers of high-fat diet-fed mice.10,15 TAOK1 belongs to the STE20 kinase family and is implicated in a range of functions in different cell types. TAOK1 has been reported to regulate p38 mitogen-activated protein kinase (MAPK)-mediated DNA damage responses by interacting with MEK3 in human cervical carcinoma cell line,17,18 to induce apoptotic morphological alterations by stimulating the JUN N-terminal kinase (JNK) pathway in human non–small cell lung carcinoma cell line (H1299),19 and to restrict cell proliferation by phosphorylating Hippo core components in human embryonic kidney cell line (HEK293).20 In addition, oxidative stress was found to enhance TAOK1 protein levels in human hepatocytes, and microRNA miR-706 directly inhibiting TAOK1 expression was shown to alleviate liver fibrosis in mice.21 Overexpression of TAOK1 has also been detected in a wide range of cancers including breast, colorectal, and lung cancer, as well as HCC.22 On the basis of its subcellular localization around hepatic LDs, we here hypothesized that TAOK1 may contribute to the molecular pathogenesis of human NAFLD. By combining gene expression analysis in liver biopsies with in vitro investigations in cultured hepatocytes, we provide the first evidence suggesting a possible role of TAOK1 in the development and progression of NAFLD through mediating fatty acid channeling between anabolic and catabolic pathways, regulating oxidative/endoplasmic reticulum (ER) stress, and modulating metabolic and pro-oncogenic signaling pathways. ## Analysis of liver biopsies of human participants The TAOK1 mRNA expression was determined in liver biopsies from 62 White individuals (men, $$n = 35$$; women, $$n = 27$$) who were recruited from subjects undergoing laparoscopic abdominal surgery for Roux-en-Y bypass ($$n = 12$$), sleeve gastrectomy ($$n = 9$$), or elective cholecystectomy ($$n = 41$$). Total body fat was analyzed by dual x-ray absorptiometry and liver fat content was assessed by single-proton magnetic resonance spectroscopy (1H-MRS), as previously described.23 After the overnight withdrawal of food, liver biopsies were collected during the surgery (between 08:00 and 10:00 am), immediately snap frozen in liquid nitrogen, and stored at −80°C for further preparation. In human liver biopsies, histological features were blindly evaluated by 2 specialized hepatopathologists in hematoxylin and eosin—and Oil Red O-stained sections using the well-validated NAFLD activity score (NAS), as recommended by the NASH Clinical Research Network classification system.24 Quantitative real-time PCR (qRT-PCR) analysis on liver biopsies was performed, as described below using the probes for TAOK1 (Hs01020477_m1) and 18S rRNA (Hs99999901_s1; Thermo Fisher Scientific, Waltham, MA), which span exon-exon boundaries to improve the specificity. For participant characteristics and details on inclusion/exclusion criteria, see Cansby et al.10 All investigations were approved by the Ethics Committee of the University of Leipzig, Germany (approval numbers 363-10-13122010 and 159-12-21052012) and conducted in compliance with both the Declarations of Helsinki and Istanbul. All patients enrolled in this study voluntarily provided written consent to use their anonymized data. ## Cell culture and transient transfections Immortalized human hepatocytes (IHHs; a kind gift from B. Staels, the Pasteur Institute of Lille, University of Lille Nord de France, Lille, France), HepG2-NTCP cells (human hepatoma-derived cells; a kind gift from S. Urban, Department of Infectious Diseases, University Hospital Heidelberg, Heidelberg, Germany), and LX-2 cells (human stellate cells; Millipore, Burlington, MA) were cultured and maintained, as previously described.16,25,26 THP-1 cells (human monocytic cells; American Type Culture Collection, Manassas, VA) were cultured and differentiated into macrophages induced by the treatment with 100 nmol/L phorbol 12-myristate 13-acetate (PMA; Sigma-Aldrich, St. Louis, MO) for 48 hours. For RNA interference, cultured human hepatocytes and liver nonparenchymal cells were transfected with human TAOK1 small interfering (si)RNA (M-004846-03; Dharmacon, Lafayette, CO) or scrambled siRNA (D-001206-13; Dharmacon) using Lipofectamine RNAiMax (Thermo Fisher Scientific). For overexpression, IHHs were transfected with human MYC-tagged TAOK1 expression plasmid (EX-T7024-M43; GeneCopoeia, Nivelles, Belgium) or an empty control plasmid (EX-NEG-M43; GeneCopoeia) using Lipofectamine 2000 (Thermo Fisher Scientific). Twenty-four hours after transfections, the culture medium was replaced by fresh medium, with or without supplementation of 100 µmol/L oleic acid (Sigma-Aldrich), for subsequent 48-hour incubation (Supplemental Figure S1, http://links.lww.com/HC9/A120). ## Assessment of lipid metabolism and oxidative/ER stress To quantify neutral lipids, mitochondrial activity/content, and superoxide radicals, cells were stained with Bodipy $\frac{493}{503}$ (Invitrogen, Carlsbad, CA), MitoTracker Red or Green (Thermo Fisher Scientific), or dihydroethidium (DHE; Life Technologies, Grand Island, NY), respectively. In parallel, cells were processed for immunofluorescence with anti-TAOK1, anti-adipose differentiation-related protein (ADRP), anti-LC3, anti-8-oxoguanine (8-oxoG), anti-4-hydroxynonenal (4-HNE), anti-E06, anti-KDEL, anti-C/EBP-homologous protein (CHOP), anti-peroxisomal biogenesis factor 5 (PEX5), or anti-peroxisomal membrane protein 70 kDa (PMP70) antibodies (Supplemental Table S1, http://links.lww.com/HC9/A121 for antibody information). Immunofluorescence images were acquired using a Zeiss Axio Observer microscope with the ZEN Blue software (Zeiss, Oberkochen, Germany). The labeled area was quantified in 6 randomly selected microscopic fields (×20) per well of the cell culture chamber using the ImageJ software (1.47v; National Institutes of Health, Bethesda, MD). Intracellular hydrogen peroxide (H2O2) and oxidative damage to proteins were detected using the dichlorodihydrofluorescein diacetate (DCFDA)/H2DCFDA-Cellular ROS Assay Kit (Abcam, Cambridge, UK) and the Protein Carbonyl Content Assay Kit (Sigma-Aldrich), respectively, according to the manufacturer’s instructions. Liver sections from mice fed a high-fat diet (45 kcal% fat; D12451; Research Diets, New Brunswick, NJ) were processed for immunofluorescence with anti-TAOK1, anti-ADRP, anti-glial fibrillary acidic protein (GFAP), or anti-F$\frac{4}{80}$ antibodies (Supplemental Table S1, http://links.lww.com/HC9/A121, for antibody information). β-oxidation, TAG secretion, incorporation of media-derived [3H]glucose and [3H]oleic acid into TAGs, and TAG hydrolase activity were measured in human hepatocytes, as described in the Supporting Materials http://links.lww.com/HC9/A206. The formation of both autophagosome and autolysosome was detected using the Premo Autophagy Tandem Sensor RFP-GFP-LC3B Kit (Thermo Fischer Scientific), according to the manufacturer’s instructions. ## Measurement of glucose metabolism Glycogen levels, glucose uptake, the rate of glycogenolysis, gluconeogenesis, basal glycolysis, and compensatory glycolysis were assessed in IHHs, as described in the Supporting Materials http://links.lww.com/HC9/A206. ## Evaluation of proliferation, apoptosis, migration, invasion, and epithelial-mesenchymal transition The proliferation of HepG2-NTCP cells was analyzed using the Click-iT EdU Proliferation Assay for Microplates Kit (Thermo Fisher Scientific), according to the manufacturer’s instructions. The Apoptosis/Necrosis Detection Kit (Abcam) was applied to monitor the initial/intermediate stages of apoptosis by staining with Apopxin Green for phosphatidylserine. The activation of caspase 3 and caspase 7 was determined using the Caspase-Glo $\frac{3}{7}$ Assay Kit (Promega, Stockholm, Sweden) following the manufacturer’s protocol. The capacity of migration and invasion and epithelial-mesenchymal transition were also assessed in HepG2-NTCP cells as described in the Supporting Materials, http://links.lww.com/HC9/A206. ## qRT-PCR, coimmunoprecipitation, and western blot RNA was isolated from tissue samples and cultured human hepatocytes and the following cDNA synthesis was performed, as described in the Supporting Materials, http://links.lww.com/HC9/A206. Coimmunoprecipitation was carried out using anti-MYC (Anti-c-MYC Magnetic Beads; Thermo Fisher Scientific) or anti-FLAG (Anti-FLAG M2 Magnetic Beads; Sigma-Aldrich) antibodies, according to the manufacturer’s instructions. Western blot analysis was performed as described previously27 (Supplemental Table S1, http://links.lww.com/HC9/A121, for antibody information). ## Statistical analysis Statistical significance between the groups was evaluated using the unpaired 2-tailed Student t test with a value of $p \leq 0.05$ considered statistically significant. Correlation between TAOK1 expression in human liver biopsies and hepatic lipid content, as well as NAS was examined by Spearman rank correlation analysis after the Kolmogorov-Smirnov test assessing normality of data. All statistical analyses were performed using SPSS statistics (v27; IBM Corporation, Armonk, NY). ## Association between hepatic TAOK1 expression and the severity of NAFLD in humans The pathological grade of NAFLD is clinically determined in liver biopsies using the NAS, which is composed of the histological scores of the severity of liver steatosis, lobular inflammation, and hepatocyte ballooning.24 Thus, we first analyzed the hepatic TAOK1 mRNA expression in relation to the NAS in a cohort of 62 participants with a wide range in BMI (22.7–45.6 kg/m2), body fat ($19.5\%$–$57.9\%$), and liver fat ($1.1\%$–$50.0\%$). We found a positive correlation between TAOK1 levels and all 3 features of the NAS, as well as the composite NAS (Figure 1A–D). In addition, we observed that the patient subset presenting NAS ≥5 (indicates definitive NASH; $$n = 24$$) had a slight but significant increase in TAOK1 abundance compared with the patient subset presenting NAS ≤4 (indicates simple steatosis or borderline NASH; $$n = 35$$) (Figure 1E). Consistent with the analysis of histological steatosis score, we detected a positive correlation between the hepatic TAOK1 transcript and liver fat content measured by magnetic resonance spectroscopy (Figure 1F). We found no association between TAOK1 expression and sex, waist-to-hip ratio, BMI, or whole blood HbA1c values of the participants; however, hepatic TAOK1 levels correlated positively with body fat (Supplemental Figure S2, http://links.lww.com/HC9/A120). Figure 1: Hepatic TAOK1 expression is significantly and positively correlated with the severity of NAFLD. (A-D) Correlation between TAOK1 mRNA abundance determined in human liver biopsies by qRT-PCR and the severity of the individual components of NAS (liver steatosis, inflammation, hepatocellular ballooning; A-C) as well as composite NAS (D). (E) Hepatic TAOK1 transcript levels in subjects with low versus high NAS (NAS≤4 versus NAS≥5, respectively). (F) Correlation between hepatic TAOK1 mRNA expression and liver fat content measured by magnetic resonance spectroscopy (1H-MRS). p<0.01. Abbreviations: NAS, NAFLD activity score; RQ, relative quantification; TAOK1, thousand and one kinase 1. ## TAOK1 coats intrahepatocellular LDs and is abundant in liver nonparenchymal cells Earlier studies by Northern blot have suggested a ubiquitous expression of human TAOK1.28 Consistently, the analysis of data available in the Genotype-Tissue Expression Portal and the Cancer Genome Atlas demonstrated the presence of TAOK1 in a broad range of human normal tissues and carcinoma types including the liver and HCC samples (Figure 2A; HCC designated as liver HCC, LIHC). Our previous studies using global proteomics by liquid chromatography–mass spectrometry technique detected TAOK1 in the LD fraction isolated from livers of obese mice.10,15 However, the differences between bona-fide LD proteins and contaminating proteins are difficult to determine by proteomic studies, since LDs are closely associated with a wide range of membrane-bound cellular organelles.4 To this end, we here further investigated the subcellular localization of endogenous TAOK1 in cultured human hepatocytes and in liver sections from high-fat diet-fed mice using immunofluorescence microscopy. We confirmed that TAOK1 protein is concentrated on the surface of intrahepatocellular LDs, visualized by ADRP staining (Figure 2B). Importantly, ADRP is the main LD-coating protein, which enables highly specific labeling of LD as it is rapidly degraded in the absence of LD binding.29 We also found that TAOK1 is abundant in liver nonparenchymal cells identified by immunostaining for GFAP (marker of hepatic stellate cells) or F$\frac{4}{80}$ (macrophage marker) (Figure 2C). Similarly, single-cell sequencing data from the Human Liver Cell Atlas showed that within the liver, TAOK1 is expressed in Kupffer cells and HSCs but even in endothelial cells, cholangiocytes, natural killer NK cells, NKT cells, and T cells, in addition to hepatocytes (Figure 2D). Interestingly, both western blot and immunofluorescence microscopy analysis demonstrated that the TAOK1 protein level was notably increased in the livers from mice fed a high-fat diet compared with age-matched chow-fed controls (Figure 2E; Supplemental Figure S3, http://links.lww.com/HC9/A120). Figure 2: TAOK1 coats intracellular LDs in hepatocytes and is abundant in liver nonparenchymal cells. (A) Gene expression of TAOK1 in 30 normal and 33 carcinoma tissue types from the Genotype-Tissue Expression Portal and the Cancer Genome Atlas. Cancer tissues are shown in red and the corresponding normal tissues are shown in green. The box plots show the median (line in a box), first-to-third quartiles (boxes), 1.5× the interquartile range (whiskers), and outer (dots). (B) Representative images of oleate-treated IHHs and liver sections from high-fat diet-fed mice, double-stained with antibodies for TAOK1 (green) and ADRP (violet); merged image shows colocalization in white; nuclei stained with DAPI (blue). The scale bars at the top and bottom represent 7.5 and 15 µm, respectively. (C) Representative images of liver sections from high-fat diet-fed mice double-stained with anti-TAOK1 (green) and anti-GFAP or anti-F4/80 (violet) antibodies; merged image shows colocalization in white; nuclei stained with DAPI (blue). The scale bars represent 15 µm. (D) Distribution of TAOK1 expression in human liver determined by single-cell sequencing data from the Human Liver Cell Atlas. (E) Liver lysates from mice fed with a high-fat diet for 20 weeks, and age-matched chow-fed controls, were assessed by western blot. Protein levels were analyzed by densitometry; representative western blots are shown with actin used as a loading control. Data are mean±SEM from 8 mice per group. p<0.01, *p<0.001. Abbreviations: ADRP, adipose differentiation-related protein; CD, chow diet; expr, expression; GFAP, glial fibrillary acidic protein; HFD, high-fat diet; HSCs, hepatic stellate cells; IHHs, immortalized human hepatocytes; NK, natural killer; ns, not significant; NTC, nontargeting control; TAOK1, thousand and one kinase 1. ## TAOK1 controls hepatocellular lipid partitioning Hepatocellular lipotoxicity is widely recognized as an initiating pathology in NAFLD/NASH.4 Thus, we analyzed the effect of modifying the abundance of TAOK1 on intracellular lipid accumulation in cultured human hepatocytes. For TAOK1 knockdown, IHHs were transfected with TAOK1-specific siRNA or with a nontargeting control (NTC) siRNA; in parallel to the experiments carried out under basal culture conditions, cells were also treated with oleic acid to replicate the environment of high-risk individuals. As expected, TAOK1 mRNA and protein expression was significantly diminished in IHHs transfected with TAOK1 siRNA (Figure 3A, B). Immunostainings with anti-TAOK1 antibody also demonstrated a high transfection efficacy in hepatocytes (Supplemental Figure S4, http://links.lww.com/HC9/A120). Figure 3: The silencing of TAOK1 stimulates lipid catabolism and suppresses lipid anabolism in human hepatocytes. IHHs were transfected with TAOK1 siRNA or NTC siRNA and cultured with or without oleate supplementation as indicated. TAOK1 mRNA (A) and protein (B) abundance were assessed by qRT-PCR and western blot, respectively. In (B), protein levels were analyzed by densitometry; representative western blots are shown with glyceraldehyde-3-phosphate dehydrogenase (GAPDH) used as a loading control. Representative images of cells stained with Bodipy (green), MitoTracker Red (red), or MitoTracker Green (green); nuclei stained with DAPI (blue) (C). The scale bars represent 20 µm. (D) Quantification of the staining. (E) Oxidation of radiolabeled palmitate. (F) Secretion of [3H]TAG into the media. (G) Fatty acid uptake rate. TAG synthesis from [3H]-labeled glucose (H) and [3H]-labeled oleic acid (I). Data are mean±SEM from 4–8 (A–D and F–I) or 15 (E) wells per group. p<0.05, p<0.01, p<0.001. Abbreviations: GAPDH, glyceraldehyde-3-phosphate dehydrogenase; NTC, nontargeting control; OA, oleic acid; TAG, triacylglycerol; TAOK1, thousand and one kinase 1; Transf, transfection. First, we stained the transfected cells with the lipophilic dye Bodipy $\frac{493}{503}$ to measure the amount of neutral lipids. Quantification of Bodipy-positive area indicated that the loss of TAOK1 significantly lowered lipid deposition in IHHs (Figure 3C, D). We also detected an increase in β-oxidation and the secretion of de novo synthesized TAG into the media in TAOK1-deficient IHHs (Figure 3E, F). Consistently, we found higher mitochondrial activity and content as evidenced by the enhanced staining of MitoTracker Red and Green, respectively, in IHHs transfected with TAOK1 siRNA versus NTC siRNA (Figure 3C, D). In parallel, the silencing of TAOK1 in IHHs significantly suppressed fatty acid influx and incorporation of [3H]-glucose and [3H]-oleic acid into intracellular TAG (Figure 3G–I). Notably, the impact of TAOK1 knockdown on hepatocellular lipid metabolism was similar in IHHs cultured with or without oleate supplementation. To investigate the metabolic effect of TAOK1 overexpression in human hepatocytes, we transfected IHHs with human MYC-tagged TAOK1 expression plasmid (Supplemental Figure S5A, B, http://links.lww.com/HC9/A120). A robust increase (about 300-fold) in the TAOK1 transcript levels was accompanied by a relatively modest rise (about 2-fold) in protein levels, which is likely explained by nonsense-mediated mRNA decay, triggered by the termination codon of the ORF positioned upstream of the most 3′ splice site in the expression plasmid.30 In contrast to our observations in TAOK1-deficient hepatocytes, the Bodipy-positive area was about 1.5- to 2-fold higher in IHHs transfected with MYC-TAOK1 compared with cells transfected with empty vector (Supplemental Figure S5C, http://links.lww.com/HC9/A120 D), which was paralleled by lower mitochondrial biogenesis and reduced fatty acid oxidation (Supplemental Figure S5C, D, S6A, http://links.lww.com/HC9/A120). In contrary, de novo lipogenesis was activated in TAOK1-overexpressing IHHs (Supplemental Figure S6B, C, http://links.lww.com/HC9/A120). We did not detect any difference in fatty acid uptake rate in IHHs with increased TAOK1 abundance (Supplemental Figure S6D, http://links.lww.com/HC9/A120) and TAG secretion was slightly lower only in TAOK1-overexpressing cells cultured under basal conditions (Supplemental Figure S6E, http://links.lww.com/HC9/A120). ## TAOK1 regulates lipolysis and autophagic flux in human hepatocytes On the basis of the close association between TAOK1 and intrahepatocellular LDs, we next examined the hypothesis that TAOK1 impacts on lipid mobilization from the droplets locally by enhancing canonical lipolysis. In this enzymatic process, a series of lipases [adipose triglyceride lipase (ATGL), hormone-sensitive lipase (HSL), and monoglyceride lipase (MGL)] act sequentially on the surface of LDs to reduce TAG into free fatty acids, which are then either processed via β-oxidation in the mitochondria or used for the synthesis and secretion of VLDL-TAG in the ER and Golgi.4 Indeed, we found that lipolysis was significantly activated in IHHs transfected with TAOK1 siRNA versus NTC siRNA (Figure 4A). Reciprocally, lipolysis was suppressed in IHHs overexpressing TAOK1 (Figure 4B). Figure 4: TAOK1 regulates lipolysis and autophagic flux in human hepatocytes. IHHs were transfected with TAOK1 siRNA or NTC siRNA (A and C–E), or with MYC-tagged TAOK1 expression plasmid or an empty control plasmid (B), and cultured with or without oleate supplementation as indicated. (A-B) TAG hydrolase activity was measured using [3H]triolein as the substrate. (C) Cell lysates were analyzed by western blot using antibodies specific for LC3, p62, or TAOK1. Protein levels were analyzed by densitometry; representative western blots are shown with GAPDH used as a loading control. (D) Representative images of cells stained with LC3 (green); nuclei stained with DAPI (blue). The scale bars represent 50 µm. Quantification of the staining. (E) Representative images of cells transfected with Tendom Sensor RFP-GFP-LC3B; merged image shows colocalization of RFP-LC3 (red) and GFP-LC3 (green) in yellow (arrows indicate autolysosomes while arrowheads indicate autophagosomes). The scale bars represent 10 µm (3 µm in the enlarged view). Quantification of the staining. Data are mean±SEM from 6 to 8 (A, B and D, E) or 12 (C) wells per group. p<0.05, p<0.01, p<0.001. Abbreviations: Ctrl, control; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; NTC, nontargeting control; OA, oleic acid; RFP, red fluorescent protein; TAG, triacylglycerol; TAOK1, thousand and one kinase 1. Within the past decade, selective autophagy (also called lipophagy) has emerged as an alternative mechanism for hepatic LD consumption. Here, LDs are first engulfed by a membrane bilayer to form an autophagosome, that then fuses with a degradative lysosome to form autolysosome, in which TAGs are hydrolyzed by lysosomal acid lipase to free fatty acids to be released for mitochondrial β-oxidation.4 To evaluate the possible role of increased autophagy in enhanced lipid catabolism observed in TAOK1-deficient hepatocytes, we next compared the abundance of autophagic markers in IHHs transfected with TAOK1 siRNA versus NTC siRNA. We found that the silencing of TAOK1 significantly stimulated the conversion of LC3I to LC3II and increased the number of LC3II-positive puncta (Figure 4C, D), which are the well-established markers of enhanced autophagic flux. Furthermore, we used the GFP-RFP-LC3 sensor that contains an acid-labile GFP and acid-resistant red fluorescent protein (RFP) to distinguish between autophagosome (red and green overlap resulting in yellow) and autolysosome (acidic environment resulting in red labeling only) localization of LC3.31 We found that both the number of yellow and red LC3 puncta representing LC3-positive autophagosomes and autolysosomes, respectively, was significantly higher in oleate-loaded TAOK1-deficient IHHs (Figure 4E). We also detected a decreased abundance of p62 protein in IHHs transfected with TAOK1 siRNA versus NTC siRNA (Figure 4C), which is observed in the conditions of autophagic induction.32 Together, these results suggest that the silencing of TAOK1 stimulates autophagy in human hepatocytes. ## TAOK1 modulates hepatocellular carbohydrate metabolism In addition to lipids, glycogen serves as a major storage form of cellular energy. Interestingly, we detected a significantly higher glycogen content in oleate-treated IHHs transfected with TAOK1 siRNA versus NTC siRNA (Figure 5A). Conceptually, increased intracellular glycogen levels in TAOK1-deficient IHHs could be caused by enhanced glycogen production or reduced glycogenolysis, or any combination of these mechanisms. To this end, we found that even if the protein abundance of the key rate-limiting enzyme in glycogen biosynthesis—glycogen synthase 2 (GYS2; active form)—was unchanged, the amount of phospho-GYS2 (Ser641; inactive form) was lower, in IHHs where TAOK1 was knocked down (Figure 5B). Notably, the silencing of TAOK1 in IHHs had no effect on glycogenolysis (Figure 5C). Figure 5: The silencing of TAOK1 has an impact on carbohydrate metabolism in human hepatocytes. IHHs were transfected with TAOK1 siRNA or NTC siRNA and cultured only with oleate supplementation (A, C–G) or both with or without oleate supplementation as indicated (B). (A) Measurement of glycogen levels. (B) Cell lysates were analyzed by western blot using antibodies specific for GYS2, phospho-GYS2 (Ser641), or TAOK1. Protein levels were analyzed by densitometry; representative western blots are shown with GAPDH used as a loading control. The rate of glucose release via glycogenolysis (C) and glucose production from gluconeogenesis (D). Basal (E) and compensatory glycolysis (F) were determined under basal condition and after sequential injection of rotenone/antimycin A and 2-deoxy-d-glucose, respectively. (G) Glucose uptake was assessed in the presence of insulin. Data are mean±SEM from 8–14 wells per group. p<0.05, p<0.01, p<0.001. Abbreviations: GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GYS2, glycogen synthase 2; NTC, nontargeting control; OA, oleic acid; TAOK1, thousand and one kinase 1. We also observed that gluconeogenesis (quantified by measuring the glucose production rate from pyruvate and lactate) was augmented in TAOK1-deficient IHHs (Figure 5D), which may relate to the increase in β-oxidation (Figure 3E). Glycolytic rate measured by the Seahorse XF Analyzer under baseline conditions, and compensatory glycolysis assessed after the inhibition of mitochondrial oxidative phosphorylation with rotenone and antimycin A, were similar in IHHs transfected with TAOK1 siRNA versus NTC siRNA (Figure 5E, F), and glucose uptake was unaffected (Figure 5G). ## TAOK1 regulates oxidative/ER stress in human hepatocytes Excessive lipid accumulation in hepatocytes is known to contribute to oxidative and ER stress, initiating the process of inflammation, cell death, and fibrogenesis in NASH.33 We therefore assessed oxidative and ER stress markers in oleate-loaded hepatocytes where TAOK1 was knocked down or overexpressed. We found that lower intrahepatocellular lipid accumulation in TAOK1-deficient IHHs was accompanied by decreased oxidative stress as evidenced by suppressed superoxide radical (O•−) and H2O2 content quantified by immunostaining for DHE and cellular DCFDA assay, respectively; reduced deposition of lipid peroxidation products and oxidized phospholipids measured by immunostaining for 4-HNE and E06, respectively; and diminished DNA and protein oxidation detected by immunostaining for 8-oxoG and protein carbonylation assay, respectively (Figure 6A–C). In parallel, we found that the silencing of TAOK1 in IHHs protected against ER stress as shown by reduced abundance of KDEL (an ER retrieval motif) and CHOP (an ER stress-induced apoptosis indicator) (Figure 6A, Supplemental Figure S7A, http://links.lww.com/HC9/A120). Consistently, the mRNA expression of several oxidative/ER stress markers was significantly lower in IHHs transfected with TAOK1 siRNA versus NTC siRNA (Figure 6D). Figure 6: The inhibition of TAOK1 lowers oxidative and ER stress in human hepatocytes. IHHs were transfected with TAOK1 siRNA or NTC siRNA and cultured with oleate supplementation. (A) Representative images of cells stained with DHE (red) or processed for immunofluorescence with anti-8-oxoG (green), anti-4-HNE (green), anti-E06 (green), anti-KDEL (green), anti-CHOP (red), anti-PEX5 (green), or anti-PMP70 (green) antibodies; nuclei stained with DAPI (blue). The scale bars represent 10 µm. Quantification of the staining. (B) Quantification of H2O2 content. (C) Measurement of protein carbonylation levels. (D) Relative mRNA expression of selected genes controlling oxidative and endoplasmic reticulum stress was assessed by qRT-PCR. Data are mean±SEM from 6 (A) or 10–12 (B–D) wells per group. p<0.05, p<0.01, p<0.001. Abbreviations: 4-HNE, 4-hydroxynonenal; CHOP, C/EBP-homologous protein; DHE, dihydroethidium; NTC, nontargeting control; PEX5, peroxisomal biogenesis factor 5; PMP70, peroxisomal membrane protein 70 kDa; TAOK1, thousand and one kinase 1. In contrary, the overexpression of TAOK1 in IHHs resulted in exacerbated oxidative damage and ER stress as evidenced by a significant increase in the area stained with DHE, 8-oxoG, 4-HNE, E06, KDEL, and CHOP (Supplemental Figure S8, http://links.lww.com/HC9/A120). Interestingly, we observed lower or higher peroxisomal activity, as indicated by altered levels of PEX5 (a peroxisome biogenesis marker) and PMP70 (a peroxisomal membrane marker), in IHHs where TAOK1 was silenced or overexpressed, respectively (Figure 6A, Supplemental Figure S7A, Supplemental Figure S8, http://links.lww.com/HC9/A120). ## TAOK1 may influence the susceptibility to HCC NAFLD has recently emerged as the leading cause of HCC, which is one of the most harmful malignant tumors.3 By analysis of the microarray GEO data sets of the 2 large cohorts of HCC subjects ($$n = 91$$ for GSE102079 and $$n = 214$$ for GSE14520), we found that TAOK1 gene expression was significantly higher in HCC tumors than in adjacent nontumor liver tissue ($p \leq 0.0001$). To further study the cell-autonomous mode of action of TAOK1 in the pathogenesis of HCC, we examined the proliferation, apoptosis, migration, and invasion, as well as the expression of epithelial-mesenchymal transition markers in TAOK1-deficient oleate-loaded HepG2-NTCP cells. We found that proliferation measured by EdU labeling assay was notably diminished in HepG2-NTCP cells where TAOK1 was knocked down and apoptosis quantified by Apopxin Green-positive cells and by caspase $\frac{3}{7}$ activity was also slightly lowered (Supplemental Figure S9, http://links.lww.com/HC9/A120). Transwell assays revealed significant suppression of migratory and invasive capacity in HepG2-NTCP cells transfected with TAOK1 siRNA versus NTC siRNA (Figure 7A, B). Furthermore, the silencing of TAOK1 reduced the levels of the mesenchymal marker N-cadherin and, conversely, increased the abundance of the epithelial marker E-cadherin (Figure 7C, D, Supplemental Figure S7B, http://links.lww.com/HC9/A120). Consistently, the mRNA expression of Slug and Zeb1, 2 transcription factors critical for epithelial-mesenchymal transition of cancer cells,34 was decreased in HepG2-NTCP cells where TAOK1 was knocked down (Figure 7C). In line with the results obtained in IHHs, we observed lower intrahepatocellular lipid storage in TAOK1-deficient HepG2-NTCP cells, which was accompanied by accelerated β-oxidation and TAG secretion, suppressed fatty acid influx and TAG synthesis, as well as diminished oxidative/ER stress (Supplemental Figure S10, http://links.lww.com/HC9/A120). Figure 7: The silencing of TAOK1 inhibits mobility, invasiveness, and epithelial-mesenchymal transition in human hepatoma-derived cells. HepG2-NTCP cells were transfected with TAOK1 siRNA or NTC siRNA and cultured with oleate supplementation. (A) TAOK1 protein abundance. Protein levels were analyzed by densitometry; representative western blots are shown with GAPDH used as a loading control. (B) Representative images of cells stained with crystal violet. The scale bars represent 20 µm. Quantification of the staining. (C) Relative mRNA expression of TAOK1 and selected genes controlling epithelial-mesenchymal transition was assessed by qRT-PCR. (D) Representative images of cells processed for immunofluorescence with anti-N-cadherin (green) or anti-E-cadherin (red) antibodies; nuclei stained with DAPI (blue). The scale bars represent 10 µm. Quantification of the staining. Data are mean±SEM from 6 (A–B and D) or 12 (C) wells per group. p<0.05, p<0.01, p<0.001. Abbreviations: GAPDH, glyceraldehyde-3-phosphate dehydrogenase; NTC, nontargeting control; TAOK1, thousand and one kinase 1 ## TAOK1 disruption in human hepatocytes alters the metabolic and pro-oncogenic pathways To explore the mechanisms by which TAOK1 deficiency suppresses lipotoxicity and metastatic capacity in human hepatocytes, we first monitored the phosphorylation of MAPKs extracellular signal-regulated kinase (ERK) and JNK, which are activated in human HCC.35 We observed significantly reduced phosphorylation of ERK$\frac{1}{2}$ (Thr202/Tyr204) and JNK$\frac{1}{2}$ (Thr183/Tyr185) in IHHs transfected with TAOK1 siRNA versus NTC siRNA (Figure 8A, B). Interestingly, we also found that the silencing of TAOK1 lowered the protein abundance of acetyl-CoA carboxylase (ACC)—a key regulator of lipid metabolism that suppresses β-oxidation and stimulates TAG synthesis 36—without any change in the ratio of phospho-ACC (Ser79; inactive form)/ACC (active form) (Figure 8C). Furthermore, the abundance of ATGL, the first lipase in the TAG hydrolysis pathway, was significantly increased in TAOK1-deficient IHHs [Figure 8D; HSL/phospho-HSL (Ser660) remained below the level of quantification]. Recently, TAOK proteins were reported to activate yes-associated protein (YAP) signaling by means of LATS$\frac{1}{2}$ phosphorylation in HEK293 cells.20,37,38 Here, we found no significant difference in the activation of LATS1 or YAP in IHHs transfected with TAOK1 siRNA versus NTC siRNA (Supplemental Figure S11, http://links.lww.com/HC9/A120). Notably, the total protein level of AKT was reduced, whereas the amount of phospho-AKT (Ser473) was elevated, in TAOK1-deficient IHHs (Supplemental Figure S12, http://links.lww.com/HC9/A120). Remarkably, changes caused by TAOK1 knockdown were largely similar in hepatocytes cultured under basal conditions or treated with oleic acid. Figure 8: TAOK1 interacts with STK25 and affects metabolic and pro-oncogenic pathways. IHHs were transfected with TAOK1 siRNA or NTC siRNA and cultured with or without oleate supplementation as indicated. Cell lysates were analyzed by western blot using antibodies specific for ERK or phospho-ERK (Thr202/Tyr204) (A), JNK or phospho-JNK (Thr183/Tyr185) (B), ACC or phospho-ACC (Ser79) (C), ATGL (D), or TAOK1. Protein levels were analyzed by densitometry; representative western blots are shown with GAPDH used as a loading control. Data are mean ± SEM from 11 to 12 wells per group. (E-F) Co-immunoprecipitation of TAOK1 and STK25 was performed from protein extracts of IHHs transfected with MYC-TAOK1, FLAG-STK25, FLAG-Control plasmid (E), and/or MYC-Control plasmid (F). Starting material (input), as well as protein immunoprecipitated using anti-MYC antibodies (E) or anti-FLAG antibodies (F) were analyzed by western blot using antibodies specific for TAOK1 or STK25; representative western blots are shown. (G) A working model of the function of TAOK1 in regulating hepatocellular lipotoxicity. The silencing of TAOK1 in hepatocytes inhibits LD anabolism through suppressing fatty acid uptake and TAG synthesis, stimulates LD catabolism through facilitating β-oxidation and VLDL-TAG secretion, and alleviates oxidative and ER stress. Mechanistically, the rate of canonical lipolysis and lipophagy, which both enhance lipid mobilization from the LDs for fatty acid oxidation and TAG secretion, are stimulated by TAOK1 knockdown. Furthermore, the silencing of TAOK1 decreases ACC protein abundance, which is expected to both reduce lipogenesis and augment β-oxidation, and increases canonical ATGL lipase levels. p<0.05, p<0.01, p<0.001. Abbreviations: ACC, acetyl-CoA carboxylase; ATGL, adipose triglyceride lipase; Ctrl, control; ER, endoplasmic reticulum; FFA, free fatty acid; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; IP, immunoprecipitated material; JNK, JUN N-terminal kinase; LD, lipid droplet; NTC, nontargeting control; OA, oleic acid; TAG, triacylglycerol; TAOK1, thousand and one kinase 1. Our previous studies by screening a genome-wide yeast 2-hybrid (Y2H) cDNA library derived from primary human hepatocytes, identified STE20-type kinase TAOK3 as a binding partner for STK2516—an LD-coating protein controlling both liver lipid synthesis and utilization.7–9,11,14,15,39 Importantly, the prey fragment of TAOK3 that interacts with STK25 (amino acids 1 to 447; harbors the N-terminal kinase domain and serine-rich domain of unknown function16) is highly conserved compared with TAOK1 ($75\%$ similarity in amino acid sequence). To test the hypothesis that TAOK1 also binds to STK25, we transfected IHHs with plasmids encoding MYC-TAOK1 and FLAG-STK25. Using anti-MYC and anti-FLAG immunoprecipitations, we were able to confirm a direct interaction between TAOK1 and STK25 proteins (Figure 8E, F). Of note, the silencing of TAOK1 in IHHs did not result in any alteration of STK25 protein abundance (Supplemental Figure S13, http://links.lww.com/HC9/A120). ## TAOK1 has no impact on the lipotoxicity of liver nonparenchymal cells Our results demonstrating an abundant expression of TAOK1 in liver macrophages and HSCs prompted us to investigate a potential role of TAOK1 in the regulation of lipotoxicity in these cell types. We found no changes in lipid deposition or H2O2 content in THP-1-derived macrophages or LX-2 human HSC line transfected with TAOK1 siRNA versus NTC siRNA (Supplemental Figure S14A-B, S15A-B, http://links.lww.com/HC9/A120). Consistently, the protein abundance of NADPH oxidase 2, inducible nitric oxide synthase, and TNFα—the key mediators for macrophage-associated proinflammatory state in the liver40,41—remained unaltered in TAOK1-deficient macrophages (Supplemental Figure S14C, http://links.lww.com/HC9/A120). Furthermore, the silencing of TAOK1 had no impact on the protein abundance of α smooth muscle actin (a marker for activated HSCs), TNFα (a proinflammatory marker), or TGFβ (a profibrotic mediator) in LX-2 cells (Supplemental Figure S15C, http://links.lww.com/HC9/A120). ## DISCUSSION STE20-type kinase TAOK1 has been identified as a component of hepatocellular LD proteome,10,15 suggesting a potential role in regulating liver steatosis and NAFLD development. In this study, we sought to investigate the association between hepatic TAOK1 expression and NAFLD severity and to decipher its mechanism of action in human hepatocytes. We observed that TAOK1 mRNA expression in human liver biopsies was positively correlated with the key hallmarks of NAFLD (ie, hepatic steatosis, inflammation, and ballooning) and TAOK1 protein abundance was increased in livers from high-fat diet-fed mice compared with lean controls. We also found that the in vitro knockdown of TAOK1 protected human hepatocytes against excessive lipid storage, as well as oxidative and ER stress, and the opposite changes were detected in TAOK1-overexpressing hepatocytes. Importantly, we show that the silencing of TAOK1 reprogrammed cellular metabolism by stimulating lipid catabolism (mitochondrial β-oxidation and TAG efflux) and inhibiting lipid anabolism (fatty acid influx and lipogenesis), collectively lowering ectopic fat storage within intrahepatocellular LDs (Figure 8G). Consistently, both the rate of canonical lipolysis and lipophagy, facilitating lipid mobilization from the LDs for β-oxidation and secretion, were significantly increased in TAOK1-deficient hepatocytes (Figure 8G). Remarkably, the alterations in lipid metabolism caused by the silencing of TAOK1 were largely similar in hepatocytes cultured with or without oleate supplementation. In parallel with the reduced fat accumulation, we observed markedly lowered incidences of oxidative/ER stress in hepatocytes where TAOK1 was knocked down. This finding is interesting in light of recent evidence demonstrating that oxidative/ER stress are key factors, which trigger NAFLD progression from simple steatosis toward NASH, as well as further aggravation to HCC.1 However, we surmise that, at this juncture, we cannot delineate whether the alterations in oxidative and ER stress in TAOK1-deficient hepatocytes were secondary to the reduction in cellular lipid accumulation or were mediated by a different pathway controlled by TAOK1. Mechanistically, we found that the silencing of TAOK1 significantly suppressed the abundance of ACC protein in human hepatocytes. This provides a plausible mechanism underlying the protection against ectopic fat storage observed in TAOK1-deficient hepatocytes since the enzymatic product of ACC, malonyl-CoA, is an intermediate of lipogenesis and also represses β-oxidation by means of the inhibition of the main mitochondrial fatty acid importer carnitine palmitoyltransferase 1 (CPT1).36 Of note, 2 liver-directed small-molecule ACC antagonists (GS-0976 and PF-0522134) have recently demonstrated efficacy in clinical phase II trials in patients diagnosed with NAFLD or NASH,42–45 by decreasing hepatic steatosis and lowering serum markers of liver injury/fibrosis, which highlights the potential of ACC as a drug discovery target in metabolic liver disease. Furthermore, we detected elevated levels of ATGL protein in hepatocytes where TAOK1 was silenced, which is expected to impact on the increased lipid utilization by enhancing canonical lipolysis rate.46–48 In parallel, we found that the knockdown of TAOK1 in hepatocytes suppressed phosphorylation of ERK and JNK, which are critical signaling components stimulating proliferation, migration, and invasion of NASH-driven HCC.35 Consistently, we observed lower proliferative, migratory, and invasive capacity, as well as epithelial-mesenchymal transition in TAOK1-deficient hepatoma-derived cells. Interestingly, the inhibition of hepatic JNK activity has also been shown to increase fatty acid oxidation and decrease lipogenesis, thus alleviating liver steatosis.49 Hence, reduced JNK signaling in TAOK1-deficient hepatocytes may have contributed to the observed alterations in lipid metabolism; however, this possibility has not been further investigated in the current study. Similarly to TAOK1, a closely related STE20 kinase TAOK3 (also known as MAP3K18, JIK, or DPK) has been demonstrated to associate with intrahepatocellular LDs, promoting ectopic fat storage and aggravating oxidative/ER stress.16 Interestingly, both TAOK1 and TAOK3 interact with another LD-coating STE20-type kinase—STK25 (Figure 8E, F,16). Our recent studies in cultured human hepatocytes and mouse models have revealed that STK25 deficiency protects against liver steatosis by shifting the metabolic balance from lipid anabolism towards lipid catabolism.7–9,11,14,15 This raises the possibility that TAOK1/TAOK3 and STK25 function in the same signaling pathway and that interaction with STK25 may play a part in the molecular mechanism of action of TAOK1/TAOK3 in the regulation of hepatocellular lipotoxic milieu. Notably, in addition to TAOK1 and TAOK3, the human GCKVIII subfamily of STE20-type kinases also includes TAOK2 (also known as MAP3K17 or PSK1), which shares about $90\%$ amino acid identity with TAOK1/TAOK3 in the N-terminal protein kinase domain and displays about $60\%$–$70\%$ similarity in the central serine-rich and C-terminal regulatory domains. To date, it is not known whether the subcellular localization and function of TAOK2 in hepatocytes are similar or different compared with TAOK1 and TAOK3. We found that, in addition to hepatocytes, TAOK1 was abundantly expressed in liver nonparenchymal cells including HSCs and macrophages. This is interesting in light of recent evidence demonstrating that macrophages and HSCs are susceptible to lipotoxic damage characterized by excessive fat storage and oxidative stress, and causally contribute to NASH initiation and progression by stimulating inflammation and fibrogenesis.40,50 Notably, in this study, we did not find any evidence that the silencing of TAOK1 would reduce lipid storage or oxidative stress in liver nonparenchymal cells. The present study does have some limitations. Primarily, all the in vitro experiments in this report were performed using immortalized human cell lines, which may not be representative of in vivo conditions. To this end, further investigations using mouse models and human primary cells are warranted. Importantly, although this report provides mechanistic insight into the regulatory role of TAOK1 in the control of liver lipotoxicity, we have not yet fully characterized the hepatocellular mode of action of TAOK1, including its upstream activators or downstream substrates, which will be the focus of our future studies. In conclusion, our data shows for the first time that LD-binding STE20-type kinase TAOK1 modulates hepatocellular lipid homeostasis and, through control of the lipid channeling between anabolic and catabolic pathways, its deficiency breaks the vicious cycle of excessive lipid storage and oxidative/ER stress within hepatocytes. ## AUTHOR CONTRIBUTIONS Ying Xia generated the bulk of the results and wrote the manuscript. Emma Andersson, Sumit K. Anand, Emmelie Cansby, Mara Caputo, and Sima Kumari contributed to the research data. Syam Nair offered assistance with the Seahorse assay. Rando Porosk, Kalle Kilk, and Hanns-Ulrich Marschall provided expertise and contributed to the discussion. Matthias Blüher carried out qRT-PCR in human liver biopsies. Margit Mahlapuu directed the project, designed the study, interpreted the data, and wrote the manuscript. ## ACKNOWLEDGMENTS The authors thank Prof. Stephan Urban, Department of Infectious Diseases, University Hospital Heidelberg, for providing HepG2-NTCP (clone A3). ## FUNDING INFORMATION This work was supported by grants from the Swedish Research Council, the Swedish Cancer Society, the Novo Nordisk Foundation, the Swedish Heart-Lung Foundation, the Swedish Diabetes Foundation, the Å. Wiberg Foundation, the Adlerbert Research Foundation, the I. Hultman Foundation, the F. Neubergh Foundation, the Prof. N. Svartz Foundation, the L. and J. Grönberg Foundation, the W. and M. Lundgren Foundation, the I.-B. and A. Lundberg Research Foundation, the Erling-Persson Foundation, the Wenner-Gren Foundation, and by the Swedish state under the agreement between the Swedish Government and the county councils, the ALF-agreement. ## CONFLICT OF INTEREST Nothing to report. ## References 1. Loomba R, Friedman SL, Shulman GI. Mechanisms and disease consequences of nonalcoholic fatty liver disease. Cell (2021) 184 2537-64. PMID: 33989548 2. Powell EE, Wong VWS, Rinella M. Non-alcoholic fatty liver disease. Lancet (2021) 397 2212-24. PMID: 33894145 3. Ioannou GN. Epidemiology and risk-stratification of NAFLD-associated HCC. J Hepatol (2021) 75 1476-84. PMID: 34453963 4. Mashek DG. Hepatic lipid droplets: a balancing act between energy storage and metabolic dysfunction in NAFLD. Mol Metab (2020) 50 101115. PMID: 33186758 5. Abul-Husn NS, Cheng X, Li AH, Xin Y, Schurmann C, Stevis P. A protein-truncating HSD17B13 variant and protection from chronic liver disease. New Engl J Med (2018) 378 1096-106. 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--- title: Mobile health lifestyle intervention program leads to clinically significant loss of body weight in patients with NASH authors: - Jonathan G. Stine - Gloriany Rivas - Breianna Hummer - Andres Duarte-Rojo - Christine N May - Nathaniel Geyer - Vernon M. Chinchilli - David E. Conroy - Ellen Siobhan Mitchell - Meaghan McCallum - Andreas Michealides - Kathryn H. Schmitz journal: Hepatology Communications year: 2023 pmcid: PMC10027041 doi: 10.1097/HC9.0000000000000052 license: CC BY 4.0 --- # Mobile health lifestyle intervention program leads to clinically significant loss of body weight in patients with NASH ## Background & Aims: Lifestyle intervention remains the foundation of clinical care for patients with NASH; however, most patients are unsuccessful in enacting sustained behavioral change. There remains a clear unmet need to develop lifestyle intervention programs to support weight loss. Mobile health (mHealth) programs offer promise to address this need, yet their efficacy remains unexplored. ### Approach & Results: We conducted a 16-week randomized controlled clinical trial involving adults with NASH. Patients were randomly assigned (1:1 ratio) to receive Noom Weight (NW), a mHealth lifestyle intervention program, or standard clinical care. The primary end point was a change in body weight. Secondary end points included feasibility (weekly app engagement), acceptability (>$50\%$ approached enrolled), and safety. Of 51 patients approached, 40 ($78\%$) were randomly assigned (20 NW and 20 standard clinical care). NW significantly decreased body weight when compared to standard clinical care (-5.5 kg vs. -0.3 kg, $$p \leq 0.008$$; -$5.4\%$ vs. -$0.4\%$, $$p \leq 0.004$$). More NW subjects achieved a clinically significant weight loss of ≥$5\%$ body weight ($45\%$ vs. $15\%$, $$p \leq 0.038$$). No adverse events occurred, and the majority ($70\%$) of subjects in the NW arm met the feasibility criteria. ### Conclusions: This clinical trial demonstrated that NW is not only feasible, acceptable, and safe but also highly efficacious because this mHealth lifestyle intervention program led to significantly greater body weight loss than standard clinical care. Future large-scale studies are required to validate these findings with more representative samples and to determine if mHealth lifestyle intervention programs can lead to sustained, long-term weight loss in patients with NASH. ## Abstract ## INTRODUCTION In the absence of an approved drug therapy or cure, lifestyle intervention with the goal of modest body weight loss continues to be the foundation of clinical management in patients with NAFLD, a leading cause of chronic liver disease worldwide.1–3 However, most patients are unsuccessful in enacting sustained behavioral change or achieving a clinically significant body weight loss of $5\%$ or greater.4,5 Self-reported patient barriers to successful lifestyle change include a lack of time, understanding, or access to lifestyle intervention resources.4,5 Routine lifestyle modification counseling from health care providers continues to be very low in the real-world setting, presenting a further barrier to patients in leading a healthy lifestyle.6 Moreover, patients who are overweight or obese experience stigma about their body weight, which negatively impacts their willingness to participate in a lifestyle intervention program centered around body weight loss.5,7 There is a clear unmet need to develop effective lifestyle intervention programs that can address each of these barriers and empower patients with NAFLD to lead a healthy lifestyle. Such intervention programs need to consider the limitations of patients with NASH, the more severe type of NAFLD. Over the past several years, and forged out of necessity from the COVID-19 pandemic, telehealth and telemedicine have emerged at the forefront of clinical care. Each has been used to help improve the delivery of quality health care to patients with chronic liver disease.8,9 In patients with NAFLD, intervention with fitness activity trackers10,11 and directly supervised exercise training programs through real-time secure audio-visual technology12 have been successfully utilized in several small pilot studies. While these technologies appear feasible and safe, their impact on clinical outcomes, including body weight, remains unclear as <$20\%$ of individuals achieved clinically significant body weight loss.10,12 Mobile health (mHealth) lifestyle intervention programs, including those that are exclusively smartphone app-based, such as Noom Weight (NW), have been successful in achieving significant loss of body weight in the general population13 and in improving clinical outcomes in patients with prediabetes and diabetes.14,15 Yet, they remain largely unexplored in patients with NAFLD outside of a recent study in Singapore with the Nutritionist Buddy (HeartVoice Pte Ltd) mHealth application.16 For these reasons, we conducted a randomized controlled pilot study to determine if a commercially available mHealth-delivered lifestyle intervention program can lead to clinically significant body weight loss in patients with NASH. We also determined the feasibility, acceptability, and safety of this widely accessible application. ## Patients and study design This trial was a 16-week single-center, randomized controlled pilot study (NCT04872777) designed to enroll 40 adult patients with NASH and a smartphone. The study was conducted at Penn State Milton S. Hershey Medical Center and the Penn State College of Medicine in the US. NASH was defined by either [1] a historical liver biopsy with evidence of steatohepatitis [NAFLD Activity Score (NAS) >4]17 or [2] an imaging study (eg, ultrasound, CT, or MRI) showing hepatic steatosis and one of the following: (i) Fibrosis-4 (FIB-4) Index ≥1.45 or (ii) vibration-controlled transient elastography (FibroScan, Echosens) with liver stiffness measurement >8.2 kPA or FAST score >0.35.18,19 Patients were excluded if they were unable to operate a smartphone; participated in a lifestyle intervention program (including a body weight-loss program) within the preceding 90 days; were actively using a body weight-loss supplement; had cirrhosis; had other chronic liver diseases (eg, viral hepatitis); had a secondary cause of hepatic steatosis, including significant alcohol consumption, which was defined as >30 g/d for men and >20 g/d for women; and had severe medical comorbidities or psychiatric illness that would prevent study participation or were unable to provide informed consent. Patients were randomized 1:1 to intervention with NW, an mHealth-delivered comprehensive lifestyle intervention program that combines smartphone application self-management with human coaching to create and sustain lasting behavioral change (Figure S1, http://links.lww.com/HC9/A184), or standard clinical care using a computer-generated randomization schema (REDCap, Vanderbilt University) in blocks of 10.20 Both study groups received standard counseling from an academic hepatologist and in accordance with the best NASH clinical practices, which included Mediterranean-based dietary counseling and a recommendation to complete 150 min/week of moderate-intensity physical activity. Every patient (regardless of study group assignment) was also provided an electronic scale (Fitbit Aria Air, Fitbit Inc, San Francisco, CA), with Bluetooth capabilities that could seamlessly interact with a smartphone (and the NW application for providing feedback on body weight for the NW group). Pertinent clinical data within 28 days before enrollment were captured through a chart review of the electronic medical record where available, and included [1] liver enzymes, [2] blood glucose, [3] hemoglobin A1c, [4] cholesterol, and [5] clinical decision aids [eg, NAFLD Fibrosis Score (NFS) and FIB-4 Index (FIB-4)]. This study was conducted according to the guidelines of the Declaration of Helsinki, Good Clinical Practice guidelines, and local regulatory requirements and was approved by the Penn State College of Medicine Institutional Review Board (No. 17544). It was designed and conducted by the principal investigator in collaboration with the study sub-investigators. Formal written consent was obtained from each participant. The principal investigator collected the data and monitored the conduct of the study. All authors had access to the data, participated in the data interpretation, and can vouch for the accuracy and completeness of the data as well as the fidelity of the trial to the protocol. The final manuscript was reviewed and approved by all authors. ## The Noom Weight application and Noom Coach NW is an mHealth lifestyle change program that has been shown to promote clinically significant body weight loss and behavior change.21 The NW app includes self-monitoring and feedback features for food, exercise, and body weight, as well as digital access to a 1:1 behavior change coach, a support group facilitated by a health coach, and a curriculum delivered through daily articles focused on nutrition, physical activity, and sustainable behavioral change. NW’s approach is informed by cognitive behavioral therapy, acceptance and commitment therapy, and dialectical behavior therapy, all of which aid in behavior change and body weight management.22–24 Components of these approaches and motivational interviewing are incorporated into the 1:1 coaching and NW’s curriculum. For example, interactive daily articles introduce the framework (eg, What is cognitive behavioral therapy?), describe its components (eg, What are cognitive distortions?), and provide practical tips and applicable examples for the users to incorporate into their life (eg, step-by-step identification and reappraisal of a participant’s cognitive distortion). NW’s curriculum also encourages other dimensions of lifestyle changes that aid in total wellness and weight management by providing relevant lessons regarding other key dimensions of health (eg, paying attention to one’s sleep), physical activity (eg, ideas for integrating exercise into one’s daily life), and stress management (eg, examples of coping skills). In addition to following the curriculum, NW users are also encouraged to log their meals and body weight daily, although the program does allow users who do not wish to weigh in daily to skip that step. Meals are logged according to a tiered system that categorizes foods based on energy density in terms of high, medium, and low energy density. NW users are provided with information on the foods they log, including portion size, calories, caloric density, and other nutritional information. Previous studies have shown that adherence to NW’s color system is associated with greater weight loss over 18 months.25 While physical activity counseling is not included in the NW program, the application can access smartphone-based step counts and be used in conjunction with other health and exercise applications (eg, FitBit). ## End points The primary study end point was a change in body weight as measured by the FitBit Aria Air Bluetooth scale. Secondary end points included feasibility, acceptability, and safety. Across the mHealth literature, there is significant heterogeneity in study design and no agreed-upon standard of feasibility. For these reasons, we defined feasibility according to user engagement.26 Following previous studies using NW,27 engaged users were defined as those who complete at least 1 meaningful in-application action per week (eg, weight log, food log, exercise log, or article read). Smartphone app utilization data were recorded by Noom’s backend server. Again drawing from our previous experience with lifestyle intervention programs28 and from others in patients with chronic liver disease,29 acceptability was defined as >$50\%$ enrollment of subjects approached. Standard definitions for adverse events, including serious adverse events, were utilized in accordance with local institutional review board policies and definitions, and any known occurrence of serious adverse events in study participants or formal complaints about the intervention were recorded. Exploratory secondary efficacy end points were calculated by capturing pertinent clinical data within 28 days of study completion where available to include similar data to those that were recorded at the time of study enrollment (listed above). No significant modifications were made to the original study protocol. ## Statistical power and analysis Because NW has not been studied in patients with NASH, sample size estimates were derived from previous Noom mHealth interventions in patients with diabetes.15 The study sample size was based on the assumption that a 2.5 kg body weight loss would be observed for the lifestyle intervention condition and no change in body weight for the standard clinical care condition (SD of 2.5 kg for each group). With these assumptions and after accounting for $15\%$ subject drop-out28,30 and a 1:1 enrollment ratio, a total study enrollment size of 40 subjects (20 per condition) would be needed to evaluate the primary end point with $80\%$ statistical power using a 2-sided, 2-sample t test and a significance level (alpha) of 0.05. The analyses included all patients who were randomized. The analyses of the primary end point were performed with the use of both between-group comparisons (2-sample t tests) and within-group comparisons (paired t tests). Two-sided p values of < 0.05 indicated statistical significance, without adjustments for multiple comparisons due to the nature of our study. SAS (Cary, NC) Version 9.4 was used for all statistical analyses. The secondary clinical end points are presented as means with SD. Both between-group and within-group comparisons were performed. Categorical end points were analyzed with chi-squared and Fisher’s exact test where appropriate. Using similar statistical methods, clinically important subgroup analyses were performed to compare NW responders who lost clinically significant body weight (≥$5\%$) to those who did not. ## Patient characteristics From June 2021 through January 2022, 51 patients were approached for this study, 40 ($78\%$) of whom were enrolled (Fig. 1). Twenty patients were randomized and assigned to intervention with NW and 20 patients to standard clinical care. In total, 33 patients ($83\%$) completed the trial (eg, provided a body weight measurement at week 16 through phone call or through self-report in the NW application), with 5 patients in the NW group (1 subject discontinued using NW after wk10 and another subject after wk12, each of whom gained body weight amounts of $3\%$ and $2\%$, respectively) and 2 patients in the standard clinical care group not completing the trial. Intention-to-treat analysis was performed for all 40 patients assigned to the original groups, where data were available for each of the outcomes of interest. FIGURE 1: Trial CONSORT Diagram. The mean age of the study participants was 52±13 years (range 24–74 y) and $75\%$ ($$n = 30$$) were female. The majority of participants were Caucasian ($$n = 38$$) and $43\%$ ($$n = 17$$) had a college degree. The mean baseline body mass index (BMI) was 35.5 kg/m2 (range 24.7–50.0 kg/m2) and the mean baseline body weight was 101.3 kg (range 66.5–148.8 kg). Metabolic comorbidities were common; $63\%$ ($$n = 25$$) had hypertension, $60\%$ ($$n = 24$$) had hyperlipidemia, and $45\%$ ($$n = 18$$) had type 2 diabetes. Mean hemoglobin A1c was $6.8\%$ (range 5.1–$8.4\%$). For NASH disease staging, $40\%$ ($$n = 16$$) of the patients had a historical liver biopsy. The other $60\%$ ($$n = 24$$) had noninvasive disease assessment. In aggregate, individual liver fibrosis stages were as follows: stage F0/F1 ($$n = 17$$, $43\%$), stage F2 ($$n = 20$$, $40\%$), and stage F3 ($$n = 3$$, $17\%$). At baseline, $40\%$ ($$n = 16$$) of subjects were on stable doses of drug therapies for NASH, including 9 who were taking Vitamin E, 5 receiving a glucagon-like peptide (GLP-1) agonist (semaglutide $$n = 4$$, liraglutide $$n = 1$$), and 2 who were prescribed pioglitazone. There were no significant differences in baseline demographic or clinical characteristics between the NW and standard clinical care groups (Table 1). Specifically, the groups were well matched when comparing body weight, BMI, laboratories, or NASH staging/treatment. Almost all patients ($90\%$) in the NW group were sedentary (<5000 steps/d)31 and completed an average of 2239 steps/d the week before beginning the NW program based on smartphone-measured step counts. TABLE 1 \| Unnamed: 0 \| Noom weight (n=20) \| Standard clinical care (n=20) \| p value \| \| --- \| --- \| --- \| --- \| \| Demographics \| Demographics \| Demographics \| Demographics \| \| Age, y \| 53.3 (13.3) \| 50.4 (12.1) \| 0.467 \| \| Female sex, n (%) \| 12 (60) \| 17 (85) \| 0.077 \| \| Race/ethnicity, n (%) \| Race/ethnicity, n (%) \| Race/ethnicity, n (%) \| Race/ethnicity, n (%) \| \| White \| 20 (100) \| 20 (100) \| 0.487 \| \| Hispanic \| 0 (0) \| 2 (10) \| — \| \| Education, n (%) \| Education, n (%) \| Education, n (%) \| Education, n (%) \| \| Less than HS \| 0 (0) \| 0 (0) \| 0.613 \| \| HS \| 3 (15) \| 5 (25) \| — \| \| Some college \| 5 (25) \| 5 (25) \| — \| \| College degree \| 8 (40) \| 9 (45) \| — \| \| Other/Unknown \| 4 (20) \| 1 (5) \| — \| \| Smoking status, n (%) \| Smoking status, n (%) \| Smoking status, n (%) \| Smoking status, n (%) \| \| Active \| 4 (20) \| 4 (20) \| 0.066 \| \| Former \| 1 (5) \| 6 (30) \| — \| \| Never \| 15 (75) \| 10 (50) \| — \| \| Metabolic risk \| Metabolic risk \| Metabolic risk \| Metabolic risk \| \| Comorbidities, n (%) \| Comorbidities, n (%) \| Comorbidities, n (%) \| Comorbidities, n (%) \| \| Diabetes \| 10 (5) \| 8 (40) \| 0.525 \| \| Hyperlipidemia \| 14 (70) \| 10 (50) \| 0.197 \| \| Hypertension \| 16 (80) \| 11 (55) \| 0.091 \| \| PCOS \| 0 (0) \| 1 (5) \| 1.000 \| \| Medication use, n (%) \| Medication use, n (%) \| Medication use, n (%) \| Medication use, n (%) \| \| Cholesterol lowering \| Cholesterol lowering \| Cholesterol lowering \| Cholesterol lowering \| \| Statin \| 8 (40) \| 6 (30) \| 0.507 \| \| Fibrate \| 2 (10) \| 0 (0) \| 0.487 \| \| Antihyperglycemic \| Antihyperglycemic \| Antihyperglycemic \| Antihyperglycemic \| \| Metformin \| 4 (20) \| 8 (40) \| 0.168 \| \| GLP-1 \| 3 (15) \| 2 (10) \| 1.000 \| \| Sulfonylurea \| 2 (10) \| 2 (10) \| 1.000 \| \| SGLT2 \| 1 (5) \| 0 (0) \| 1.000 \| \| Antihypertensive \| Antihypertensive \| Antihypertensive \| Antihypertensive \| \| ACE/ARB \| 7 (35) \| 6 (30) \| 0.736 \| \| Diuretic \| 5 (25) \| 3 (15) \| 0.699 \| \| BB \| 4 (20) \| 3 (15) \| 0.677 \| \| CCB \| 1 (5) \| 2 (10) \| 1.000 \| \| NASH drugs (all) \| NASH drugs (all) \| NASH drugs (all) \| NASH drugs (all) \| \| Vitamin E \| 4 (20) \| 5 (25) \| 1.000 \| \| Pioglitazone \| 1 (5) \| 1 (5) \| 1.000 \| \| Blood pressure, mm Hg \| Blood pressure, mm Hg \| Blood pressure, mm Hg \| Blood pressure, mm Hg \| \| SBP \| 131 (13.5) \| 125 (10.9) \| 0.177 \| \| DBP \| 83 (6.8) \| 82 (8.3) \| 0.729 \| \| NASH phenotyping \| NASH phenotyping \| NASH phenotyping \| NASH phenotyping \| \| Fibrosis stage, n (%) \| Fibrosis stage, n (%) \| Fibrosis stage, n (%) \| Fibrosis stage, n (%) \| \| 0/1 \| 8 (40) \| 9 (45) \| 0.748 \| \| 2 \| 10 (50) \| 10 (50) \| — \| \| 3 \| 2 (10) \| 1(5) \| — \| ## Efficacy of Noom Weight After 16 weeks, NW significantly decreased body weight when compared to standard clinical care (-5.5±5.8 kg vs. -0.3±4.6 kg, $$p \leq 0.008$$; -5.4±$5.0\%$ vs. -0.4±$4.5\%$, $$p \leq 0.004$$), with the majority of body weight loss observed by the end of week 12 (Fig. 2). More NW subjects achieved a clinically significant weight loss of ≥$5\%$ body weight ($45\%$ vs. $15\%$, $$p \leq 0.038$$) (Fig. 3). NW also decreased BMI when compared to standard clinical care (-1.5±1.9 kg/m2 vs. -0.1±1.6 kg/m2, $$p \leq 0.037$$; -4.5±$5.4\%$ vs. -0.5±$4.5\%$, $$p \leq 0.031$$). While not statistically significant, NW marginally increased the baseline physical activity by $35\%$ (774 steps/d measured for a 1-week period at wk 16). No clinically significant changes in readily available clinical outcomes were found, including a change in noninvasive tests, such as liver biochemistries or clinical decision aids (eg, NFS and FIB-4), which were available in up to $40\%$ of subjects (Table 2). There was however a significant reduction in platelet count in the NW group when compared to standard clinical care (-28 vs. -5.7 ×109, $$p \leq 0.038$$ (Table 3)). Moreover, no significant between-group differences in baseline characteristics were observed when comparing NW responders who lost clinically significant body weight (≥$5\%$) to those who did not (Table 3). FIGURE 2: Change in body weight comparing Noom Weight with Standard Clinical Care. (A) After 16 weeks, NW significantly decreased body weight when compared to standard clinical care (-5.4±$5.0\%$ vs. -0.4±$4.5\%$, $$p \leq 0.004$$). (B) The majority of weight loss was seen by the end of Week 12. FIGURE 3: Percent of patients achieving clinically significant body weight loss comparing Noom Weight with Standard Clinical Care. Three times more NW subjects achieved a clinically significant weight loss of >$5\%$ baseline body weight compared to subjects receiving standard clinical care ($45\%$ vs. $15\%$, $$p \leq 0.038$$). TABLE_PLACEHOLDER:TABLE 2 TABLE_PLACEHOLDER:TABLE 3 ## Noom Weight utilization and engagement Overall utilization of the NW application was quite high; $83\%$ of the available weeks had measurable subject interaction with the smartphone application. Over time, NW opening rates each week remained relatively constant until week 16 (Figure S2, http://links.lww.com/HC9/A185), where there was a $15\%$ decline in app opening. Peak NW utilization and engagement occurred at week 4, which coincided with the greatest rate of body weight change (Fig. 2 & S2, http://links.lww.com/HC9/A185). Average weekly NW engagement per subject was as follows for self-directed activities: 17 meal logs; 15 content reviews; 4 weigh-ins; 2 exercises, and 1 coach message. Self-directed activity choice changed weekly (Figure S3, http://links.lww.com/HC9/A186). The social component of the application was utilized much less frequently. Average NW engagement per subject for social activities included interaction with 1 coaching message and 1 group post/comment/like. A behavioral shift was observed between week 1 and week 16. Over time, NW participant engagement became more self-initiated, with a greater percentage of application engagement spent with meal logging ($29.2\%$ week 1 vs. $49.0\%$ week 16, $$p \leq 0.024$$) or weigh-ins ($7.1\%$ week 1 vs. $15.1\%$ week 16, $$p \leq 0.180$$) rather than interactions with the Noom coach ($4.4\%$ week 1 vs. $1.7\%$ week 16, $$p \leq 0.064$$) or educational content ($54.5\%$ week 1 vs. $32.9\%$ week 16, $$p \leq 0.016$$) (Fig. 4). FIGURE 4: Temporal changes in Noom Weight engagement. Over time, engagement with the Noom Weight app became more self-directed, and participants spent a greater percentage of their time with meal logging (yellow) or weigh-ins (green). ## Feasibility, acceptability, and safety of Noom Weight The majority ($70\%$) of subjects met the a priori definition of feasibility by interacting with the NW smartphone application at least once each week. The a priori definition of acceptability was also met because $78\%$ of the patients approached decided to enroll in this clinical trial. Importantly, no adverse events were reported. ## DISCUSSION This is the first study to examine an mHealth-delivered lifestyle intervention program in adults with NASH. Among patients with NASH, 16 weeks of NW was superior to standard clinical care with respect to body weight loss because patients who completed NW lost 5 times more body weight overall, with nearly half of the NW patients achieving clinically significant body weight loss of $5\%$ or greater. This was seen independent of baseline body weight, BMI, individual components of the metabolic syndrome (eg, diabetes and hypertension), or NASH severity. Moreover, the NW lifestyle intervention program is feasible, acceptable, and safe as the a priori definition of each was met at the conclusion of this pilot clinical trial. Taken together, these data provide novel information that NW is not only feasible, acceptable, and safe but also highly efficacious in that this mHealth-delivered lifestyle intervention program led to significantly greater body weight loss than standard clinical care. Because at this time there is no known cure or regulatory agency-approved drug therapy for patients with NASH, an effective lifestyle intervention program will be crucial in improving patient-oriented outcomes, including the reduction of major adverse liver outcomes, major adverse cardiovascular events, and primary liver as well as extrahepatic cancers. Clinical guidelines from multiple leading academic societies1,32 focus broadly on lifestyle modification with dietary change and increased physical activity for all patients with NAFLD, including those with NASH; yet, they do not specify how such change is to be carried forth. Our findings suggest that mHealth is an effective platform by which to deliver a lifestyle intervention program and that the combination of smartphone application self-management with human coaching created and sustained lasting behavioral change through the NW program in patients with NASH. Based on the findings of Vilar Gomez et al,33 current clinical guidelines suggest a body weight loss of at least $5\%$ to improve not only liver fat but also the histologic features of NASH. Importantly, $45\%$ of patients who participated in the NW program achieved $5\%$ body weight loss or greater. This rate is much greater than that of the $30\%$ of patients with NASH who achieved this threshold of weight loss published by Vilar Gomez et al,33 who received in-person instruction to consume a hypocaloric diet and complete at least 200 min/week of habitual physical activity followed by individual in-person instruction every 8 weeks. Also, while this pilot study did not directly compare a supervised in-person lifestyle intervention program, such as the one used by Vilar Gomez et al. ,33 it is nonetheless intriguing and promising to see this indirect comparison in which NW outperforms historical intervention data in achieving rates of clinically significant body weight loss. Moreover, NW appears to be at least as effective in body weight loss when compared to other less widely available mHealth apps that have reported $25\%$ of subjects achieving a clinically significant weight loss.15 We also found a shift in behavior with the NW smartphone application. Over the duration of the 16-week program, participants initiated more interactions with the NW mobile application and spent a greater percentage of their time engaged with the application in self-monitoring and feedback activities such as meal logging or weigh-ins. This is important because self-monitoring has long been thought to be a central component of the behavioral treatment for weight loss,34 including successful long-term weight management.35 These data suggests that over time, NW may create the self-regulatory skills and feedback required for long-term success and create an intriguing avenue for follow-up study. Our study has multiple strengths inherent to the study design (eg, in the use of a commercial mHealth smartphone app-delivered lifestyle intervention program that incorporates human coaching, NW is efficacious in other populations with metabolic disease), study population (eg, a well phenotyped cohort of patients with NASH), possibly cost (eg, a monthly NW subscription may be less costly than a fitness center membership, personal training, or dietary counseling with a Registered Dietitian),36 and convenience factor as smartphone-based apps allow for program access from anywhere and are not tied to a physical location, such as a gym or wellness center. Limitations include the sample size, a largely Caucasian and female population, which may limit the generalizability, lack of allocation concealment, inability to capture NASH-relevant clinical end points in each patient (this was a pilot study), lack of histologic outcome (body weight reduction was used as a surrogate for histologic response), and no long-term follow-up. We also excluded patients with cirrhosis, including those without clinically significant portal hypertension, where body weight loss may still be helpful, offering an important avenue for future study. Future studies with large sample sizes, blinded assessors, and longer follow-up are necessary to confirm the results of this pilot study and to ascertain the sustainability of the findings. However, the present study demonstrates that clinically significant body weight loss is possible with only a short-term mHealth smartphone app-delivered lifestyle intervention. This finding is meaningful because it provides the basis for future large-scale use of smartphone-based lifestyle intervention for the clinical management of patients with NASH. ## CONCLUSION This randomized controlled clinical trial demonstrated that NW is not only feasible, acceptable, and safe but also highly efficacious in that this mHealth-delivered lifestyle intervention program led to significantly greater body weight loss than standard clinical care and at rates much greater than previous mHealth-based lifestyle intervention programs. Moreover, in a short time period, NW led to the establishment of self-monitoring behaviors necessary for long-term weight management success. Future large-scale studies with long-term follow-up are required to determine if mHealth-delivered lifestyle intervention programs can lead to sustained, long-term weight loss and improvement in routine clinical outcomes in patients with NAFLD and NASH. ## ACKNOWLEDGMENTS The authors thank Darcy Proctor and Paige Blanco for their contributions as NW coaches. No manuscript writing assistance was used in the preparation of this work. ## Funding information Research reported in this publication was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under Award Number K23DK131290 (Stine). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This project was also funded in part by Noom, Inc. ## CONFLICT OF INTEREST Jonathan G. Stine receives or has received research support from Astra Zeneca, Galectin, Grifols, Inc., Noom, Inc., and Novo Nordisk. Christine N May, Ellen Siobhan Mitchell, Meaghan McCallum, and Andreas Michealides are employees of Noom, Inc. Andres Duarte-Rojo consults for and receives grants from Axcella Health. The remaining authors have nothing to disclose. ## References 1. Chalasani N, Younossi Z, Lavine JE, Charlton M, Cusi K, Rinella M. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology (2018) 67 328-357. PMID: 28714183 2. Younossi ZM, Corey KE, Lim JK. AGA clinical practice update on lifestyle modification using diet and exercise to achieve weight loss in the management of nonalcoholic fatty liver disease: expert review. Gastroenterology (2021) 160 912-918. PMID: 33307021 3. Thorp A, Stine JG. 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--- title: 'Food and Nutrition Surveillance System (SISVAN) coverage, nutritional status of older adults and its relationship with social inequalities in Brazil, 2008-2019: an ecological time-series study' authors: - Brena Barreto Barbosa - Valéria Troncoso Baltar - Rogério Lessa Horta - Jackeline Christiane Pinto Lobato - Luiza Jane Eyre de Souza Vieira - Caroline de Oliveira Gallo - Antonio Augusto Ferreira Carioca journal: 'Epidemiologia e Serviços de Saúde : Revista do Sistema Unico de Saúde do Brasil' year: 2023 pmcid: PMC10027046 doi: 10.1590/S2237-96222023000100003 license: CC BY 4.0 --- # Food and Nutrition Surveillance System (SISVAN) coverage, nutritional status of older adults and its relationship with social inequalities in Brazil, 2008-2019: an ecological time-series study ## Abstract ### Objective: to analyze the temporal trend of Food and Nutrition Surveillance System (Sistema de Vigilância Alimentar e Nutricional - SISVAN) coverage and the nutritional status of older adults, and its correlation with indicators of social inequality in Brazil between 2008-2019. ### Methods: this was an ecological study using records from SISVAN, related to the population aged 60 years and older; the temporal trend of coverage and the correlation between indicators of social inequality and increment rate of nutritional status were analyzed; slope index of inequality and concentration index were used to measure absolute and relative inequalities. ### Results: 11,587,933 records were identified; national coverage increased from $0.1\%$ [2008] to $2.9\%$ [2019], with a statistically significant upward trend; a moderate inverse correlation with an annual increment rate of overweight between human development index and gross domestic product per capita, was found. ### Conclusion: there was an increasing trend in SISVAN coverage; the increase in overweight was associated with social inequality. ## Objetivo: analisar a tendência temporal da cobertura do Sistema de Vigilância Alimentar e Nutricional (SISVAN) e do estado nutricional de idosos, e sua correlação com indicadores de desigualdade social no Brasil, no período 2008-2019. analizar la tendencia temporal de cobertura del Sistema de Vigilancia Alimentaria y Nutricional (SISVAN), y el estado nutricional de adultos mayores, correlacionándolos con indicadores de desigualdad social, en el período 2008-2019. ## Métodos: estudo ecológico, sobre registros do SISVAN relativos à população na idade de 60 anos ou mais; analisaram-se a tendência temporal da cobertura e a correlação entre indicadores de desigualdade social e taxa de incremento do estado nutricional; os índices angular e de concentração foram utilizados para medir desigualdades absolutas e relativas. estudio ecológico mediante registros del SISVAN sobre la población ≥60 años. Se realizaron análisis de correlación entre indicadores de desigualdad social y la tasa de incremento del estado nutricional y análisis de desigualdades absolutas y relativas para obtener el índice de desigualdad angular y el índice de concentración. ## Resultados: foram identificados 11.587.933 registros de idosos; a cobertura nacional evoluiu de 0,$1\%$ [2008] para 2,$9\%$ [2019], com tendência de aumento estatisticamente significativa; foi encontrada correlação inversa moderada com taxa de incremento anual de sobrepeso, para índice de desenvolvimento humano e produto interno bruto per capita. se identificaron 11.587.933 registros. La cobertura nacional evolucionó del 0,$1\%$ en 2008 al 2,$9\%$ en 2019, con una tendencia ascendente estadísticamente significativa. Se encontró una correlación inversa moderada con la tasa de incremento anual de sobrepeso para IDH y PIB per cápita. ## Conclusão: houve tendência de crescimento da cobertura do SISVAN; o aumento de sobrepeso esteve associado à desigualdade social. ## Conclusión: hubo una tendencia de crecimiento en la cobertura del SISVAN. El aumento del sobrepeso se asoció con la desigualdad social. ## Main results The national coverage rose from $0.1\%$ [2008] to $2.9\%$ [2019], a significant upward trend. Moderate inverse correlation with annual increment rate of overweight between human development index and gross domestic product per capita, was found. ## Implications for services The low percentage of coverage results in insufficient data for the development and adjustment of public policies aimed at older adults. Regions with the worst social indicators may present a larger population of adults who are overweight, affecting health services. ## Perspectives The increase in the coverage of the nutritional status of older adults by SISVAN is essential for planning health actions. It can be seen the need to incorporate the actions of SISVAN into the routine Primary Health Care, as a way to boost its coverage. ## INTRODUCTION Food and Nutrition Surveillance (Vigilância Alimentar e Nutricional - VAN) is one of the guidelines of the National Food and Nutrition Policy (Política Nacional de Alimentação e Nutrição - PNAN), and allows the description and prediction of trends in the food and nutritional status of the Brazilian population, aiming at health promotion. VAN is carried out through the Food and Nutrition Surveillance System (Sistema de Vigilância Alimentar e Nutricional - SISVAN), operated by the Primary Health Care (PHC) with the objective of monitoring the dietary pattern and nutritional status of users of the Brazilian National Health System (Sistema Único de Saúde - SUS). 1 Created in 2008, the SISVAN online platform (SISVAN Web) has enabled the monitoring of the food consumption and nutritional status and the identification of population groups at risk for nutritional problems. 2 Nutritional status monitoring, using data from SISVAN, is performed by calculating body mass index (BMI), based on anthropometric measurements (body weight; height) of SUS users from different population strata: preschoolers and students, adolescents, adults, pregnant women and older adults. 3 However, the highest frequency of records in the system is for preschoolers, students, adolescents and pregnant women, 4 due to the criteria of the former Family Income Transfer Program (Programa Bolsa Família), currently Programa Auxílio Brasil, which is the main source of information for SISVAN. These programs present, as one of the conditionalities in the health sector, the nutritional monitoring of children under 7 years of age and prenatal care for pregnant women, aimed at preventing or reducing problems such as malnutrition, childhood obesity and maternal and infant mortality. 5 There have been positive changes in access to health services and in the reduction of socioeconomic inequalities in the country over the past 40 years, leading to a decrease in infant mortality and maternal mortality, partly attributed to conditional cash transfer programs, such as Programa Bolsa Família. 6 The accelerated aging process of the Brazilian population, in recent decades, the consequent increase in life expectancy and, at the same time, the increase in the occurrence of chronic non-communicable diseases, leading causes of death and disability in the country, demanded greater health care to the health conditions of the older adult population. 7 Brazil is among the most unequal countries in terms of economic and social status, which is one of the main determinants of malnutrition among the population. Inequality has worsened recently, as indicated by the increasing trend of the Gini index, from 0.506 in 2019 to 0.519 in 2022. The Gini index predicts outcomes on a scale of zero to 1, where numbers closer to zero indicate greater equality. These inequalities have been deepened as a result of the pandemic caused by the novel coronavirus, which started in 2020. 8 The investigation and monitoring of the nutritional status of older adults is important for early identification of risk factors for nutritional problems, enabling adjustments to nutritional intervention measures, aiming at preventing or reducing damage to health in this population. 9 However, the coverage of nutritional status by SISVAN has been lower in this age group, 4 representing a factor that has contributed to worsening food security among older adults. Knowledge on the coverage of nutritional status of older adults by SISVAN and its relationship with the indicators of social inequality is important for improving PNAN and monitoring food and nutrition indicators, based on data from the System. Thus, the objective of this study was to analyze the temporal trend of SISVAN coverage and the nutritional status of older adults, correlating it with indicators of social inequality in Brazil between 2008 and 2019. ## METHODS Study design This was an ecological time-series study, based on secondary data available on SISVAN Web platform, for the period 2008 to 2019. The units of analysis corresponded to Brazil, its macro-regions (North; Northeast; South; Southeast; Midwest) and the Federative Units (FUs). Data were extracted from the System on December 21, 2020. Setting The first version of SISVAN was made available by the Ministry of Health in 2004. In 2008, its new platform, SISVAN Web, was released, and it is available on the internet. This new version allowed the registration and access to anthropometric assessment and food consumption information of the entire population receiving PHC services within the SUS. 2 In 2017, SISVAN, version 3.0, was released, which optimized its integration with e-SUS Primary Care (e-SUS Atenção Básica - e-SUS AB) and it is remotely accessed (https://sisaps.saude.gov.br/sisvan/). Open access annual reports are available on SISVAN Web platform. They consolidate all types of follow-ups, registered by health professionals during the (VAN) actions in the PHC, and those carried out by e-SUS AB and the Bolsa Família Program Management System, which periodically, migrate automatically to SISVAN platform. 2 Programa Auxílio Brasil replaced Bolsa Família Program in November 2021, maintaining its functionality as a source of information for SISVAN. This name change occurred after the data collection period of this research, which is why reference is made to Bolsa Família Program in the text. Participants We analyzed the elderly registered on SISVAN and monitored on the system, and information on the nutritional status of this population, by consulting consolidated reports, publicly accessible, available in the SISVAN Web platform (https://sisaps.saude.gov.br/sisvan/relatoriopublico/index). For this study, we selected the stage of the life cycle related to the elderly, whose age group includes individuals aged 60 years or older, according to the SISVAN classification. 3 Study variables Nutritional status, based on BMI, was classified according to the World Health Organization (WHO) recommendation, using the standard formula: weight in kilograms (kg) divided by the square of height in meters (m²). The following BMI cutoff points, specific for older adults, were used in the categorization of this index: underweight (BMI < 22kg/m²); normal weight (BMI between 22kg/m² and 27kg/m²); and overweight (BMI > 27kg/m²). 10 Regarding correlation analysis, the following continuous variables and their descriptions were used: human development index (HDI), an indicator comprised of data on education, income and life expectancy; 11 Gini index, used to measure the degree of income concentration; 12 low individual monthly income [proportion (%) of poor individuals, representing the proportion of people with household income per capita below the poverty line]; and low household income [proportion (%) of poor households, representing the proportion of households with household income per capita below the poverty line]. 13 The dependent variables were the temporal trends of coverage and distribution of nutritional status categories, and the independent variables corresponded to the region, the reference year and indicators of social inequalities. Data source and analysis Data were extracted from the SISVAN website and organized in Excel spreadsheet®. The database, its compilation and analysis, and the preparation of tables and graphs were performed using the Power BI platform and the Power BI visualization on a web page. 14 All records available on the platform were used in the analyses. The temporal trend of SISVAN coverage was analyzed by calculating the total coverage, represented by the percentage of individuals monitored via SISVAN Web. The percentage of coverage was calculated by dividing the number of records of older people (age ≥ 60 years) with nutritional status information on the SISVAN Web, divided by the population in this same age group defined as SUS users, multiplied by 100. 4 This calculation was based on data on the total resident population, made available by the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística - IBGE), 15 and the data on the population using the SUS, available from the National Regulatory Agency for Private Health Insurance and Plans (Agência Nacional de Saúde Suplementar - ANS). 16 The same criterion was used by previous studies that evaluated the national coverage of nutritional status and food intake by SISVAN. 4, 17 Coverage and prevalence of nutritional status (underweight, normal weight or overweight) were calculated according to the national macro-region, Brazil as a whole and the reference year (independent variable). This information was used to evaluate the temporal change in SISVAN coverage and the distribution of nutritional status categories (dependent variable), at a $95\%$ confidence interval ($95\%$CI). The temporal trend was analyzed using Prais-Winsten regression models, a recommended approach for ecological studies, to control the self-correction of regression residuals among the years analyzed. 18 The average annual coverage change and each category of nutritional status were calculated using the following formula: [-1 + (10β)] x 100 where β is logarithm to base 10, resulting from the Prais-Winsten regression. Non-significant p-values (p ≥ 0.05) indicated a trend of stability, while significant p-values ($p \leq 0.05$), indicated rising or decreasing trend, according to positive or negative annual change, respectively. Correlation coefficients (r) of social inequality indicators (HDI; Gini index) with annual increment rates of nutritional status classifications of older people (underweight; normal weight; overweight) were estimated using Pearson’s correlation test, and p-value < 0.05 was considered significant, with the units of analysis comprised of the 26 Brazilian states and the Federal District. Analyses of absolute and relative inequalities related to nutritional status were performed, according to the social inequality indicators described, and thus, the slope index of inequality (SII) and the concentration index (CIX) were obtained. 19 In order to calculate the CIX, the variables HDI, Gini index, GDP per capita and number of households and poor individuals were classified into quintiles. For the significance level, p-value ≤ 0.05 was considered. All statistical analyses were performed using Stata software, version 11.2 (Stata Corp, College Station, TX, USA). ## RESULTS A total of 11,587,933 records of older adults were identified on SISVAN system during the study period. Between 2008 and 2019, the percentage of SISVAN coverage was less than $3\%$, nationally and among macro-regions; with the exception of the South region, which showed percentages of coverage greater than $3\%$ in 2017 ($3.3\%$), 2018 ($3.9\%$) and 2019 ($5.5\%$), and the Southeast region, with $3.1\%$ coverage in 2019. It could be seen a statistically significant and marked increase in temporal trend of SISVAN coverage in all macro-regions, with 2019 showing the highest national coverage in all macro-regions (Table 1). At the national level, the percentage of SISVAN coverage among older adults ranged from $0.1\%$ in 2008 to $2.9\%$ in 2019. The South and Southeast macro-regions presented the highest percentages of coverage in the years analyzed, with the highest value recorded in the South region in 2019 ($5.5\%$). The average annual change for the country ($38.4\%$; $95\%$CI 28.0;49.7) and for all national macro-regions was positive and statistically significant, showing an increase in the system coverage in the period studied. The lowest annual coverage change was identified in the Midwest ($32.2\%$; $95\%$CI 21.3;44.3) and Southeast ($33.8\%$; $95\%$CI 27.7;40.1), while the highest annual change was observed in the North ($44.4\%$; $95\%$CI 27.9;63.0) and Northeast ($45.2\%$; $95\%$CI 26.2;66.9) regions (Table 1). Table 1- Temporal trend of nutritional status coverage of older adults registered on the Food and Nutrition Surveillance System, Brazil, 2008-2019Brazil and macro-regionsAnnual coverage of nutritional status (%) Annual change (%)a $95\%$CIb p-value Trend200820092010201120122013201420152016201720182019BRAZIL0.10.20.20.30.30.30.51.52.02.32.62.938.428.0;49.7< 0.001RisingNorth0.10.20.10.20.20.20.41.62.12.12.32.544.427.9;63.0< 0.001RisingNortheast0.10.10.10.10.10.10.31.51.81.71.92.245.226.2;66.9< 0.001RisingSouth0.10.30.30.30.30.30.31.72.63.33.95.540.822.9;61.9< 0.001RisingMidwest0.10.20.20.30.20.30.31.01.41.62.02.132.221.3;44.3< 0.001RisingSoutheast0.10.40.30.40.40.60.71.61.92.52.93.133.827.7;40.1< 0.001Risinga) Annual increment rate, calculated using the formula [-1+(10^β)]×100, in which β is the coefficient resulting from the Prais-Winsten regression; b) $95\%$CI: $95\%$ confidence interval. Regarding the classification of nutritional status among older adults registered on SISVAN, an increasing trend in the prevalence of overweight was found at the national level and in all macro-regions. At the national level, overweight among older adults showed a percentage increase of $8.3\%$ in the period from 2008 to 2019, with an annual change of $1.8\%$ ($95\%$CI 1.5;2.2). The Southern region presented the highest percentage of overweight prevalence in all years analyzed, when compared to the other macro-regions. However, the highest annual increase was identified in the North region: $3.1\%$ (Table 2). Table 2- Temporal trend of the prevalence of overweight in older adults registered on the Food and Nutrition Surveillance System, Brazil, 2008-2019Brazil and macro-regionsAnnual prevalence of overweight (%) Annual change (%)a $95\%$CIb p-value Trend200820092010201120122013201420152016201720182019BRAZIL43.141.943.945.046.245.145.248.748.949.750.951.41.81.5;2.2< 0.001RisingNorth36.936.839.339.639.942.242.647.747.348.049.249.53.12.6;3.6< 0.001RisingNortheast38.936.640.140.341.338.742.544.144.145.046.646.32.11.6;2.5< 0.001RisingSouth50.549.850.654.255.156.256.257.655.757.158.558.61.50.8;2.1< 0.001RisingMidwest40.943.043.145.147.446.548.352.852.151.352.252.92.41.8;3.1< 0.001RisingSoutheast42.140.543.044.145.444.044.448.349.048.749.750.51.91.5;2.4< 0.001Risinga) Annual increment rate, calculated using the formula [-1+(10^β)]×100, in which β is the coefficient resulting from the Prais-Winsten regression; b) $95\%$CI: $95\%$ confidence interval. On the other hand, the prevalence of underweight showed a decreasing temporal trend in Brazil and in the five macro-regions. At the national level, the percentage of underweight ranged from $18.1\%$ in 2008 to $12.2\%$ in 2019, with a negative annual change of $3.9\%$ ($95\%$CI -4.7;-3.0). Among the macro-regions, the Northeast showed the highest percentage of underweight in all years analyzed, except for the highest results in the North region related to the years 2011 ($18.8\%$) and 2012 ($19.0\%$) (Table 3). Table 3- Temporal trend of the prevalence of underweight in older adults registered on the Food and Nutrition Surveillance System, Brazil, 2008-2019Brazil and macro-regionsAnnual prevalence of underweight (%) Annual change (%)a $95\%$CIb p-value Trend 200820092010201120122013201420152016201720182019BRAZIL18.118.817.316.515.716.616.613.913.613.212.512.2-3.8-4.7;-3.0< 0.001DecreasingNorth20.120.318.518.819.017.217.513.814.013.412.712.5-4.8-5.9;-3.8< 0.001DecreasingNortheast20.421.619.518.617.719.817.615.615.414.813.913.9-4.0-4.8;-3.1< 0.001DecreasingSouth13.113.712.610.910.610.611.39.610.59.78.98.9-3.6-4.5;-2.6< 0.001DecreasingMidwest19.217.417.415.614.715.515.213.012.812.812.211.6-4.2-4.8;-3.5< 0.001DecreasingSoutheast19.620.018.217.416.417.417.014.414.014.113.513.0-3.9-4.6;3.1< 0.001Decreasinga) Annual increment rate, calculated using the formula [-1+(10^β)]×100, in which β is the coefficient resulting from the Prais-Winsten regression; b) $95\%$CI: $95\%$ confidence interval. It could be seen a decreasing temporal trend in the prevalence of older adults with nutritional status classified as adequate, at the national level and in the five macro-regions. At the national level, the percentage of older adults with nutritional status classified as adequate ranged from $38.7\%$ in 2008 to $36.4\%$ in 2019, representing a negative annual change of $0.7\%$ ($95\%$CI -0.8;-0.5). In all macro-regions, the lowest prevalence of adequate nutritional status was found in 2019, with the exception of the Midwest and Northeast regions, which showed their lowest percentages in 2016 ($35.1\%$) and 2018 ($39.6\%$), respectively (Table 4). Table 4- Temporal trend of the prevalence of normal weight in older adults registered on the Food and Nutrition Surveillance System, Brazil, 2008-2019Brazil and macro-regionsTemporal trend of the prevalence of normal weight (%) Annual change (%)a $95\%$CIb p-value Trend 200820092010201120122013201420152016201720182019BRAZIL38.739.438.838.638.138.338.337.537.537.136.636.4-0.7-0.8;-0.5< 0.001DecreasingNorth43.042.942.241.641.240.539.938.538.738.538.138.1-1.2-1.5;-1.0< 0.001DecreasingNortheast40.741.840.441.141.041.540.040.340.540.239.639.8-0.4-0.5;-0.20.001DecreasingSouth36.436.536.835.034.333.232.532.833.833.232.632.5-1.1-1.8;-0.50.004DecreasingMidwest39.939.739.539.337.938.036.534.335.135.935.635.5-1.2-1.9;-0.60.002DecreasingSoutheast38.339.538.838.538.238.638.637.337.037.236.836.5-0.6-0.8;-0.4< 0.001Decreasinga) Annual increment rate, calculated using the formula [-1+(10^β)]×100, in which β is the coefficient resulting from the Prais-Winsten regression; b) $95\%$CI: $95\%$ confidence interval. With regard to the social inequality indicators analyzed in the correlation with the annual increment rate according to nutritional status, only HDI and GDP per capita presented statistically significant correlation coefficient (r). A moderate positive correlation with annual increment rate of underweight between HDI (p-value = 0.003; $r = 0.556$) and GDP per capita (p-value < 0.001; $r = 0.681$) was found. A moderate inverse correlation with annual increment rate of overweight between HDI (p-value = 0.002; r = -0.565) and GDP per capita (p-value = 0.007; r = -0.508) was found. The analyses of slope index of inequality confirmed the correlation between HDI, GDP per capita and nutritional status of older adults (underweight, normal weight or overweight). Regarding underweight, the slope index values were positive for HDI (p-value = 0.003) and GDP per capita (p-value < 0.001). As for overweight, the values were negative for HDI (p-value = 0.002) and GDP per capita (p-value = 0.007). The analyses of concentration index presented negative values, that is, the corresponding concentration curve lies above the diagonal, showing that the least favored FUs (in relation to HDI and GDP per capita) accumulated the highest rates of underweight and overweight when compared to the FUs with the highest values of HDI and GDP per capita (p-value < 0.05) (Table 5). Table 5- Absolute and relative inequalities in nutritional status according to indicators of social inequality in older adults registered on the Food and Nutrition Surveillance System, Brazil, 2008-2019Indicators of social inequalitySII: slope index of inequalitya UnderweightNormal weightOverweightHuman Development Index (HDI)26.77 (0.003)3.80 (0.372)-17.48 (0.002)Gini index15.98 (0.108)7.44 (0.090)-6.36 (0.327)Gross domestic product (GDP) per capita 0.0001 (<0.001)0.000 (0.448)-0.0001 (0.007)Low individual monthly income (proportion of poor people)-0.07 (0.075)-0.001 (0.930)0.05 (0.058)Low household income (proportion of poor households)-0.09 (0.066)0.001 (0.981)0.06 (0.056) CIX: concentration indexa UnderweightNormal weightOverweightHuman Development Index (HDI)-0.15 (0.018)-0.05 (0.672)-0.19 (0.007)Gini index-0.08 (0.184)-0.14 (0.242)-0.06 (0.394)Gross domestic product (GDP) per capita -0.14 (0.024)0.02 (0.871)-0.15 (0.040)Low individual monthly income (proportion of poor people)0.11 (0.098)0.05 (0.676)0.13 (0.070)Low household income (proportion of poor households)0.12 (0.083)0.05 (0.670)0.13 (0.068)a) Statistically significant index values ($p \leq 0.05$). ## DISCUSSION It could be seen a low coverage of the nutritional status of older adults by SISVAN, with a rising and significant temporal trend at the national level and in the five national macro-regions. Regarding the classification of nutritional status, there was an increase in the prevalence of overweight, which was inversely related to HDI and GDP per capita, at the same time there was a decreasing temporal trend in the prevalence of normal weight and underweight, the latter directly related to HDI and GDP per capita, at national level and in the five macro-regions. The analyses of concentration index showed negative values, demonstrating that the least favored FUs (in relation to HDI and GDP per capita) accumulated highest increment rates of underweight and overweight in relation to the FUs with the highest values of HDI and GDP per capita. The low coverage values found and the lack of knowledge about the criteria that define the proportions of coverage verified, lead to the observation that estimates of prevalence of nutritional status are limited to the group of people covered by SISVAN, and it is not recommended to extrapolate these data to the general population. The maternal-infant population has been the greatest representativeness of SISVAN, since the implementation of its online version, given that the conditionalities of the Bolsa Família Program do not include the follow-up of adults and older adults. 4 It seems seriously compromising, in terms of reliability, that a nationwide system, aimed at monitoring the nutritional status of the Brazilian population, does not offer data that can be read as representative of the general population. However, this point highlights the estimated rates of positive change for coverage estimates throughout the country and its macro-regions, pointing to the mobilization and directing efforts of teams from local health networks in order to put the proposal for surveillance of nutritional status of the older adults into practice. Despite the difficulties, SISVAN has been constantly strengthened and expanded, having as one of its main challenges entering data and the incorporation of the system itself into the routine primary health care services, which is responsibility of managers and health professionals. The low coverage of the nutritional status of older adults by SISVAN, with a significant increasing trend in coverage, was identified in an analysis performed using stratification of life cycles, during the first six years of implementation of SISVAN Web, from 2008 to 2013. 4 The results of this study confirm the increasing in coverage expected for the age group of 60 years and older. Nevertheless, the data found remain below the data expected for this group, when the predominance of follow-up of other stages of life is observed, such as those of children, adolescents and pregnant women. For example, over a five-year period [2008-2012], the national coverage of the nutritional status of preschoolers by SISVAN ranged from $17.7\%$ to $27.9\%$, while that of older adults ranged from only $0.4\%$ to $1.2\%$ during the same period. 4 The low coverage of the nutritional status of older adults by SISVAN is worrisome, because they are the fastest growing segment of the Brazilian population due to the process of demographic transition, caused, among other factors, by declining birth rates and increased life expectancy. 20 Along with rapid population aging, the epidemiological transition is taking place, changing the profile of morbidity and mortality. As such, we could say that the Brazilian elderly population lives longer, but not necessarily better. At the age of 60, the emergence of chronic non-communicable diseases (NCDs) and their consequences are more evident, and may reduce or hinder the independence and autonomy of people in this age group. 21 The prevalence of normal weight showed the lowest annual change observed in the country, with a decreasing trend. As mentioned before, the coverage verified in the period is so low that it is impossible to extrapolate the estimated prevalence for the total population considered in the study. However, a decreasing trend in the prevalence of normal weight is even more worrisome, if we take into consideration that the aging process can cause changes in body composition of older adults, including an increase and redistribution of fat mass and concomitant reduction of lean mass and bone density, factors that are independent of changes in body weight and BMI. 21 Regional differences in the distribution of nutritional status classification of older adults were also observed in a study that evaluated individuals aged 60 years or older, taking part in the 2008-2009 Family Budget Survey (Pesquisa de Orçamentos Familiares - POF), conducted by IBGE. A higher prevalence of underweight was found in residents of rural areas and in the Northeast and Midwest regions. 12 Underweight has historically been related to socioeconomic problem in Brazil 23 and, in this study, its annual increment rate was directly related to HDI and GDP per capita of the FUs. These results, however, should be interpreted with caution, given that the low coverage of the nutritional status of older adults may have interfered with this correlation. However, the analyses of concentration index found that the FUs with the worst socioeconomic profile had the highest increment rates of underweight and overweight. Indicators of social inequality are often directly associated with better living conditions. According to a systematic review, HDI was directly related to a better gait speed (considered a marker of overall health status in older adults), suggesting that education, income and life expectancy affect this marker performance. 24 Similarly, GDP growth in China was associated with greater physical fitness of the elderly, a result attributed to increased financial investments in public sports and health services in that country. 25 Unexpectedly, the increment rate of overweight was inversely related to HDI and GDP per capita of the Brazilian FUs. According to another systematic review, dedicated to investigating the nutritional status of older adults in Africa, overweight was positively related to HDI, with a higher prevalence found in countries with better socioeconomic conditions. 26 In Brazil, overweight is more prevalent in older adults living in the South and Southeast regions, 11 a fact generally attributed to the economic and social differences historically present in the configuration of Brazilian regions, which include inequalities in income, schooling, basic sanitation and housing conditions, with South and Southeast regions showing better rates. These socioeconomic inequalities influence the availability of and access to goods and services, affecting the quality of life and health conditions of the general population. 27 However, it is worth considering that the greatest change in coverage proportions on SISVAN occurred in the North and Northeast regions; in turn, the highest coverage observed corresponded exactly to the Southern region, in the last year analyzed, and it is possible that the different proportions of coverage in the system are also related to trends in nutritional status indicators, which would explain the highest prevalence of overweight in Southern Brazil. A greater detection of overweight may possibly be related to a greater search for people with this profile by health services, or to a greater demand for health services for these people. Therefore, there may be some selection bias in the generation of the estimated data. The increase in the prevalence of overweight and the decreasing trend of underweight and normal weight may indicate the nutritional transition for the older adult population, as initially verified among the adult population. This process, which has been underway for 40 years in the country, 28 is characterized by a decrease in the prevalence of malnutrition and an increase in the occurrence of overweight. Initially, a higher prevalence of overweight and obesity was observed in Brazilian regions with the best socioeconomic status. 23 In the last decade, the occurrence of overweight has been increasing among the low-income adult population. 28 This fact was observed in the increase in the prevalence of overweight among older adults followed by SISVAN at the national level, taking into consideration that most of the population using the SUS has lower income, when compared to people with private health insurance plans. 29 This finding can help clarify the inverse correlations between the annual increment rate of overweight and the indicators of social inequality - HDI and GDP per capita, found in this study. Despite the increasing trend in the coverage found, SISVAN has not been used to its full potential since the creation of its web platform. Although there is collection and entry of weight and height information, those responsible for the system, in general, do not use the data generated for the planning, management and evaluation of food and nutrition actions. 30 Some of the main reasons given for this gap include the complexity of the system, professional training and work overload. 4 It is noteworthy that the results of this study do not allow conclusions at the individual level, since it is an investigation of ecological and aggregate analysis, which is one of the factors capable of interfering in the interpretation of the findings. The use of secondary data is also a restriction, because they come from different sources and, consequently, inconstancy in the credibility of the information, which is susceptible to errors during the collection, typing and under-recording, among others. However, given the lack of studies on the subject, these findings can contribute to generate more hypotheses about the relationship between social inequalities and the nutritional status of older adults. The low percentage of coverage, with a significant increasing trend for the older adult population, observed in the first 12 years of SISVAN, indicates that its use is in an adaptation process, resulting in the production of insufficient data to support the development and adjustment of public policies for the prevention of diseases/health conditions, as well as health promotion and maintenance aimed at this population. And, in view of the new needs of nutritional care identified by demographic and epidemiological transitions, the increase in coverage of Food and Nutrition Surveillance in this stage of the life cycle is also characterized as preferential and indispensable for planning health actions for the older adult population. Regional inequalities were identified in the distribution of nutritional status classifications, with the highest rates of overweight in the South region, while the highest rates of underweight were found in the North and Northeast regions. The increasing trend in the prevalence of overweight and the decrease in the occurrence of underweight and normal weight in all Brazilian macro-regions suggest the occurrence of the nutritional transition process for the older adult population, similarly to what was identified for the Brazilian adult population in the 1970s, 1980s and 1990s. 23 It could be seen the need to incorporate the actions of SISVAN into the routine primary health care services, as a way to boost the coverage of the system. Therefore, it is essential to raise awareness among professionals and managers about the importance of data collection and use of information, in addition to the structural support and use of the Brazilian SIVAN, for the situational diagnosis of food and nutrition in all stages of the life cycle. Such actions can positively impact the coverage and data quality, benefiting the population through effective monitoring of their nutritional health. ## References 1. 1 Brasil. Ministério da Saúde. Secretaria de Atenção à Saúde. Departamento de Atenção Básica Política Nacional de Alimentação e Nutrição2 edBrasília, DF[internet]20132 fev 2021Disponível em: http://bvsms.saude.gov.br/bvs/publicacoes/politica_nacional_alimentacao_nutricao.pdf. Política Nacional de Alimentação e Nutrição (2013) 2. 2 Brasil. Ministério da Saúde. Secretaria de Atenção à Saúde. 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Brasília (DF): Instituto Brasileiro de Geografia e Estatística [internet] (2021) 17. 17 Agência Nacional de Saúde Suplementar Dados e Indicadores do Setor: beneficiários de planos privados de saúde [internet]202113 jan 2021Disponível em: http://www.ans.gov.br/perfil-do-setor/dados-e-indicadores-do-setor. Dados e Indicadores do Setor: beneficiários de planos privados de saúde [internet] (2021) 18. Prais SJ, Winsten CB. Trend estimators and serial correlation (1954) 19. Schneider MC, Castillo-Salgado C, Bacallao J, Loyola E, Mujica OJ, Vidaurre M. Métodos de mensuração das desigualdades em saúde. Rev Panam Salud Pública (2002) 17 1-16 20. Camargos MCS, Gonzaga MR, Costa JV, Bomfim WC. Estimativas de expectativa de vida livre de incapacidade funcional para Brasil e Grandes Regiões, 1998 e 2013. Ciênc Saúde Coletiva (2019) 24 737-747. DOI: 10.1590/1413-81232018243.07612017 21. 21 World Health Organization (WHO) Noncommunicable diseases. Key Facts; [internet]20219 fev 2022Disponível em: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases#:~:text=Key%20facts,%2D%20and%20middle%2Dincome%20countries. Noncommunicable diseases. Key Facts; [internet] (2021) 22. Ponti F Santoro A, Mercatelli D Gasperini C, Conte M Martucci. Aging and imaging assessment of body composition: from fat to facts. Front Endocrinol (2020) 10 861-878. DOI: 10.3389/fendo.2019.00861 23. Batista M, Rissin A. A transição nutricional no Brasil: tendências regionais e temporais. Cad Saude Publica (2003) 19 S181-S191. DOI: 10.1590/S0102-311X2003000700019 24. Junior RCF, Pieruccini-Faria F, Montero-Odasso M. Are Human Development Index dimensions associated with gait performance in older adults? A systematic review. Exp gerontol (2018) 102 59-68. DOI: 10.1016/j.exger.2017.12.001 25. Liu Z, Agudamu TB, Akpinar S, Jabucanin B. The Association Between the China’s Economic Development and the Passing Rate of National Physical Fitness Standards for Elderly People Aged 60-69 From 2000 to 2020. Front Public Health (2022) 10 1-8. DOI: 10.3389/fpubh.2022.857691 26. Mabiama G, Adiogo D, Millimono T, Fayemendy P, Vernier T, Boumediene F. Undernutrition, overweight and obesity among elderly living in communities in Africa: a systematic review. Proc Nutrition Society (2021) 80. DOI: 10.1017/S0029665121002810 27. Geib LTC. Determinantes sociais da saúde do idoso. Cienc Saude Coletiva (2012) 17 123-133. DOI: 10.1590/S1413-81232012000100015 28. Melo SPSC, Cesse EAP, Lira PIC, Ferreira LCCN, Rissin A, Batista M. Sobrepeso, obesidade e fatores associados aos adultos em uma área urbana carente do Nordeste Brasileiro. Rev Bras Epidemiol (2020) 23. DOI: 10.1590/1980-549720200036 29. Silva ZP, Ribeiro MCSA, Barata RB, Almeida MF. Perfil sociodemográfico e padrão de utilização dos serviços de saúde do Sistema Único de Saúde (SUS), 2003-2008. Cienc Saude Coletiva (2011) 16 3807-3816. DOI: 10.1590/S1413-81232011001000016 30. Rolim MD, Lima SML, Barros DC, Andrade CLT. Avaliação do SISVAN na gestão de ações de alimentação e nutrição em Minas Gerais, Brasil. Cienc Saude Coletiva (2015) 20 2359-2369. DOI: 10.1590/1413-81232015208.00902015
--- title: 'Hyaluronan in liver fibrosis: basic mechanisms, clinical implications, and therapeutic targets' authors: - Jieun Kim - Ekihiro Seki journal: Hepatology Communications year: 2023 pmcid: PMC10027054 doi: 10.1097/HC9.0000000000000083 license: CC BY 4.0 --- # Hyaluronan in liver fibrosis: basic mechanisms, clinical implications, and therapeutic targets ## Abstract Hyaluronan (HA), also known as hyaluronic acid, is a glycosaminoglycan that is a critical component of the extracellular matrix (ECM). Production and deposition of ECM is a wound-healing response that occurs during chronic liver disease, such as cirrhosis. ECM production is a sign of the disease progression of fibrosis. Indeed, the accumulation of HA in the liver and elevated serum HA levels are used as biomarkers of cirrhosis. However, recent studies also suggest that the ECM, and HA in particular, as a functional signaling molecule, facilitates disease progression and regulation. The systemic and local levels of HA are regulated by de novo synthesis, cleavage, endocytosis, and degradation of HA, and the molecular mass of HA influences its pathophysiological effects. However, the regulatory mechanisms of HA synthesis and catabolism and the functional role of HA are still poorly understood in liver fibrosis. This review summarizes the role of HA in liver fibrosis at molecular levels as well as its clinical implications and discusses the potential therapeutic uses of targeting HA in liver fibrosis. ## INTRODUCTION The extracellular matrix (ECM) maintains physical tissue and organ architecture under physiological conditions. Elevated production and accumulation of ECM in liver tissues are closely associated with the progression of fibrosis, and the concentrations of ECM in liver tissues and blood serve as biomarkers of fibrotic diseases, such as cirrhosis. The ECM plays a more complex role in regulating cellular biology and behavior through the activation of receptor-dependent intracellular signaling.1,2 The 2 major molecular classes of ECM are fibrous proteins (eg, collagen, fibronectin, elastin, and laminin) and glycosaminoglycans [eg, hyaluronan (HA) and heparan sulfate]. Among glycosaminoglycans, HA is a highly anionic and unbranched molecule without sulfate and core protein. HA is commonly known as hyaluronic acid in hepatology clinics and is used as a biomarker that is elevated in the blood of patients with cirrhosis.1,3 Until recently, it was unclear whether HA is a bystander component of deposited ECM or a biologically active component. Unlike the lung fibrosis and cancer research fields, the molecular mechanisms of HA-mediated disease progression of liver fibrosis are poorly understood.3,4 In this review, we summarize the current knowledge of HA biogenesis and catabolism, regulatory mechanisms, molecular size-dependent functions, clinical implications, and the therapeutic potential of targeting HA in liver fibrosis. ## HA as a biomarker for liver fibrosis HA is used as a noninvasive biomarker of liver fibrosis. Elevated serum levels of HA correlate well with disease progression in liver fibrosis and cirrhosis. Moreover, fibrosis scores, including the enhanced liver fibrosis score, HepaScore, and Fibrospect II, utilize HA in their algorithms.5 Serum HA levels are higher in HCV patients with cirrhosis compared with patients without cirrhosis and are even higher in HCV patients with HCC compared with HCV patients with cirrhosis.6 HCV patients who achieve sustained response after treatment have reduced serum HA levels.7 Similarly, serum HA levels in HBV patients with fibrosis are high, whereas serum HA levels after anti-viral therapies are low.8–11 In NAFLD and alcohol-associated liver disease (ALD) patients, serum HA can differentiate between patients with and without cirrhosis. These reports indicate that noninvasive measures of serum HA are useful for the diagnosis of cirrhosis and as an indicator of fibrosis resolution after treatment, independent of the etiology of liver fibrosis. However, some reports suggest the changes in the HA levels slightly vary depending on their etiologies. The sensitivity and specificity of HA are high for HBV-associated fibrosis ($90.9\%$ and $98.1\%$, respectively),12 but they are moderate for ALD-mediated fibrosis ($82.8\%$ and $69\%$, respectively).13 Also, serum HA levels are high in alcohol-associated cirrhosis when compared with those in NAFLD cirrhosis.14 When compared with other biomarkers, the sensitivity of HA alone is greater than type 4 collagen 7S (T4C7S), N-terminal propeptide of type III collagen (PIIINP), TIMP1, laminin, but not M2BPGi, Pro-C3, FIB-4, APRI, and enhanced liver fibrosis score; the specificity is higher than T4C7S, laminin, M2BPGi, Pro-C3, FIB-4, APRI, enhanced liver fibrosis score, but not PIIINP and TIMP1 in HBV fibrosis patients.9,15,16 In ALD, the studies reported HA, as a single marker, is better than PIIINP, APRI, and FIB-4 score.14,17 However, to diagnose early-stage fibrosis in NAFLD, HA is not as good as type 4 collagen and T4C7S.18 Serum HA levels are good for differentiating between F2 and F3 but not sensitive to differentiating F1 and F2 fibrosis or diagnosing mild liver fibrosis.15,19 The limitations of HA alone may be overcome by the combination of HA with other noninvasive biomarkers (eg, PIIINP, Pro-C3, FIB-4) and imaging systems (eg, magnetic resonance elastography, Fibroscan). HA may complement current screening tests for suspected liver disease patients. For example, HA can identify asymptomatic HCV-infected patients from blood donors.20 The combination of HA with cytokeratin-18 M30 improves to differentiate NAFLD patients with fibrosis from those without fibrosis.21 Addition of HA to the currently available fibrosis scores to improve the prediction and diagnostic accuracy of cirrhosis and HCC is also of special interest. Although FIB-4, one of the current fibrosis scores, predicts future HCC development,22 it is not as accurate in patients with type 2 diabetes compared with nondiabetic patients. A combination of FIB-4 and HA during risk stratification for incident cirrhosis and HCC reduces the false-positive rate without increasing the false-negative rate in diabetes patients.23 Thus, the addition of HA to the current diagnostic system may improve the prediction and diagnostic accuracy of liver disease, contributing to the reduction of the liver disease burden in the future. ## Serum HA levels are regulated by LSECs HA is constantly produced and is present in almost all tissues, including the skin, surrounding blood vessels, lung bronchioles, and circulation.24 The half-life of HA is only 2–5 minutes, highlighting the rapid turnover of circulating HA. One-third of circulating HA is replaced daily. LSECs are responsible for the uptake, degradation, and elimination of $90\%$ of HA.25–28 LSECs express CD44 and Stabilin-2, also known as the HA receptor for endocytosis. These receptors are the major clearance receptors for circulating HA. Under physiological conditions, HA contents are low in circulation (<50 ng/mL) and very low (~1.5–2 μg/g) in normal liver tissues.28 In contrast, serum HA contents are increased to >100 ng/mL in patients with liver fibrosis or cirrhosis (≥F3).4,15 One of the mechanisms responsible for elevated serum HA levels in patients with cirrhosis stems from the inability of LSECs to eliminate circulating HA. LSEC elimination of HA is disrupted in cirrhosis, which may involve the “capillarization” of LSECs and the downregulation of CD44 in LSECs, preventing HA endocytosis29 (Figure 1). As a result, circulating HA accumulates in cirrhosis. Acetaminophen-induced acute liver injury causes LSEC dysfunction and elevation of circulating HA.30,31 Based on these findings, serum HA levels are used as a marker of LSEC dysfunction.29 Because the HA clearance-LSEC dysfunction hypothesis is reasonable, other mechanisms, such as hypersynthesis of HA, have not been carefully considered in the liver fibrosis field for a long time. FIGURE 1: Schematic illustration of the dynamic role of hyaluronan (HA) during the progression of liver disease. When the liver is healthy, low molecular mass form of hyaluronic acid (LMM-HA) are found in circulation. Fenestrated LSECs are responsible for HA clearance by eliminating circulating HA through CD44 and Stabilin-2. Quiescent HSCs (qHSCs) hardly express hyaluronan synthase (HAS2) responsible for HA production in the liver. After the liver injury, the serum and hepatic HA level is increased by LSEC dysfunction and HA hypersynthesis. In acetaminophen-induced liver injury or cirrhosis, LSEC clearance of HA is disrupted with LSEC capillarization. As a result, circulating HA accumulates in the blood. In the injured liver of hepatitis B and C, alcoholic liver disease, or non-alcoholic fatty liver disease, qHSCs transdifferentiate into activated HSCs (aHSCs) that upregulate HAS2 and produce HMM-HA. These highly proliferative aHSCs overexpress HAS2 and HA, leading to HA accumulation. In the presence of inflammation, a major hallmark of liver disease progression, HMM-HA is converted to a LMM-HA. This profibrogenic LMM-HA promotes HSC activation and extracellular matrix (ECM) production through CD44 and TLR4, resulting in liver fibrosis. ## HA is actively synthesized during liver fibrosis progression HA is composed of repeating disaccharides D-glucuronate and N-acetyl-D-glucosamine, indicating that HA is a sugar-based molecule, not a protein. There is no direct antibody to detect HA in tissue staining, so HA-binding protein is commonly used as a surrogate. Based on HA-binding protein staining (Figure 2), HA is almost undetectable in the healthy or uninjured liver (F0). HA accumulation began in early fibrosis (F1–2) and dramatically increased in F3–F4 fibrosis (Figure 2). HA deposition is observed mainly in fibrous bands but not in the liver parenchyma. HSCs are the precursors of ECM-producing myofibroblasts in liver fibrosis and the main cellular source of HA in the liver.4,32 HA is synthesized by a membrane-bound enzyme, HA synthase (HAS).3 HAS synthesizes HA using uridine diphosphate (UDP)-α-D-glucuronate (UDP-GlcUA) and UDP-α-N-acetyl-D-glucosamine (UDP-GlcUAc) as substrates (Figure 3A). HAS has 3 isozymes: HAS1, HAS2, and HAS3.33 Of the 3 HAS enzymes, HAS2 is the most powerful and critical. Global knockout of HAS2 in mice is embryonic lethal due to failed cardiac development, whereas HAS1 and HAS3 knockout mice are viable.34–36 Both HAS1 and HAS2 synthesize high molecular mass (HMM) HA (>2×106 Da); however, the enzymatic activity of HAS1 is not as strong. In contrast, HAS3 synthesizes lower molecular mass HA (2×105–2×106 Da).33 These isozymes synthesize HA differently depending on cytoplasmic sugar availability. HAS1 is inactive without a UDP-sugar supply, but HAS3 does not depend on UDP-sugar levels. HAS2 activity increases with UDP-sugars content.37 This suggests that HAS1 may significantly affect HA production with increased sugar availability in diabetes and obesity. FIGURE 2: Staining for hyaluronan (HA) in human NASH tissues. HA staining in liver sections of patients with NASH-mediated liver fibrosis (F0-F4). HA-binding protein (HABP) was used for staining HA. Negative controls are stained with HABP following treatment of the tissue sections with 1 mg/mL Streptomyces hyalurolyticus hyaluronidase for 1 hour at 37 °C. FIGURE 3: Mechanism of hyaluronan (HA) synthesis and degradation. (A) Mechanism of synthesis of hyaluronan (HA) by hyaluronan synthase (HAS). HA biosynthesis is catalyzed by HAS at the inner surface of the plasma membrane. HAS uses 2 cytosolic substrates, UDP-glucuronic acid (UDP-GlcUA) and UDP-N-acetyl-glucosamine (UDP-GlcNAc), and extrudes the growing polymer through the membrane to form elongated HA. The HAS family includes three isoforms (HAS1, HAS2, and HAS3) that produce different molecular mass HA. HAS1 and HAS2 synthesize high molecular mass (HMM) HA (>2×106 Da), and HAS3 generates slightly lower molecular mass HA (2×105–2×106 Da). (B) The endolytic mechanism of degradation of HMM-HA (1×106–107 Da) by hyaluronidase (HYAL). HYAL1 and HYAL2 are the major HYALs that degrade HA. The process of HA cleavage is initiated by HYAL2, a membrane-anchored protein that acts in cooperation with CD44. HYAL2 degrades HMM-HA into fragments of approximately 1–2×104 Da. These fragments are then internalized into endosomes and further cleaved to oligosaccharides by HYAL1 in lysosomes. Transmembrane protein 2 (TMEM2), a type II transmembrane protein with intrinsic HYAL activity at neutral PH, acts as a cell-surface HYAL to break down HMM-HA into low molecular mass (LMM)-HA. HAS3 knockout mice develop liver fibrosis similar to wild-type mice,38 suggesting that HAS1 or HAS2 is more likely to regulate liver fibrosis. In the normal liver, levels of the HAS enzymes and HA are very low. Upon HSC activation, HAS2 is the most inducible isozyme, and its expression is dramatically elevated, facilitating HA synthesis (Figure 1). In contrast, HAS1 and HAS3 upregulation after HSC activation are not as dramatic.4 HSCs are responsible for HAS2 expression and HA production in liver fibrosis in a study of mice lacking HAS2 expression in their HSCs.4 Mice lacking HAS2 expression in their HSCs exhibit reduced HSC activation and fibrosis, along with abolished HA deposition in the liver and no upregulation of circulating HA compared with baseline. An in vivo unbiased targeted genomic screening strategy demonstrates HAS2 as one of the 5 most important and novel profibrogenic genes regulating collagen production in carbon tetrachloride-induced liver fibrosis.39 HSCs with knockdown or knockout of HAS2 show reduced collagen production, HSC migration, and proliferation in vitro.4,40 Also, HAS2 is crucial for HA production, cancer growth in cholangiocarcinoma, and liver metastasis caused by colorectal and pancreatic cancers.41,42 These findings indicate that HAS2 is the isozyme responsible for HA production in liver fibrosis and liver malignancy; HAS2 and HA are primarily expressed in HSCs; and HAS2 and HA contribute to HSC activation, liver fibrosis, and fibrosis-mediated cancer progression. These studies further suggest that reduced HA clearance and active HA synthesis determine the level of circulating HA in liver fibrosis (Figure 1). ## Molecular mass determines the biological function of HA HA is synthesized by HAS enzymes and produced as HMM forms (~2×106 Da). HMM-HA is involved in organ development and tissue protection. As such, HMM-HA has been used for cosmetic purposes and for relieving joint pain through local injection. However, once inflammation occurs, HA is fragmented into low molecular mass (LMM)-HA (~1–3×105 Da). HA fragmentation occurs non-enzymatically through reactive oxygen species and enzymatically through hyaluronidases (HYALs).3 HYAL1 and HYAL2 play a major role in HA catabolism. HYAL2 is a cell membrane-anchored protein and cleaves HMM-HA to (intermediate) LMM-HA (~20 kDa) in collaboration with CD44. The HMM-HA binding to CD44 induces an acidic extracellular environment by promoting Na+/H+ exchanger 1(NHE1) activity, allowing HYAL2 to degrade HA to small fragments (~20 kDa). These HA fragments are then taken up to the intracellular acidic endosomal-lysosomal vesicles for further degradation by HYAL1.43 Cell migration-inducing protein is also an HYAL that degrades HMM-HA into both intermediate-sized fragments of between 35 and 50 kDa as well as LMM-HA.44 PH20, another HYAL, is mainly expressed in sperm cells and is well known for its essential role in fertilization.3 Recent studies demonstrate that transmembrane protein 2, a membrane-bound enzyme, can degrade HA in the extracellular environment. Transmembrane protein 2 has intrinsic HYAL activity at neutral pH and is expressed widely in adult mouse tissues, including the lymph nodes and liver.45,46 Elevated levels of HA in liver fibrosis are attributed to impaired HYAL activity. Interestingly, serum HYAL activity increases in acute hepatitis C, whereas it decreases in chronic hepatitis C.47 Transmembrane protein 2 expression decreases in patients with chronic hepatitis B compared with healthy controls.48 These reports suggest that HYALs play a role in HA turnover and liver fibrosis. However, the underlying mechanisms of HYAL activity and HA conversion into LMM forms in liver fibrosis still require investigation. In healthy populations, the molecular size of circulating HA is relatively low (100–300 kDa).28,46,49 In contrast to HMM-HA, LMM-HA is proinflammatory, profibrogenic, and involved in tissue remodeling. Because HA turnover is very short, and the level of circulating HA is low, circulating LMM-HA in a healthy population is not harmful. However, the levels of circulating total and LMM-HA dramatically increase during the progression of liver fibrosis.4 Likewise, in fibrotic liver tissues, LMM-HA levels increase.4 In current clinical practice, total HA concentrations in blood are measured. LMM-HA is the dominant form of HA in both blood and liver tissues in liver fibrosis.4 Given that LMM-HA levels reflect inflammation and HSC activation, measurement of LMM-HA or the ratio of HMM-HA to LMM-HA may be a more sensitive biomarker for liver fibrosis than total HA. Large cohort studies followed by validation studies are required to develop these biomarker recommendations in liver fibrosis. Both LMM-HA and the ratio of HMM-HA to LMM-HA are promising diagnostic and prognostic markers of liver fibrosis and may also reflect the effectiveness of antifibrotic drugs. ## HA receptors and HA-mediated biological functions HA receptors include CD44, Toll-like receptor 4 (TLR4), TLR2, the receptor for HA-mediated motility expressed protein (RHAMM), and Stabilin-2. CD44 is a classical HA receptor that also binds collagen, fibronectin, and osteopontin. CD44 is widely expressed in immune cells, such as T cells, and is a T-cell activation marker. In acute lipopolysaccharide (LPS)-induced liver injury, HA and CD44 are required for neutrophil recruitment to the injured site.50 CD44 blockade inhibits neutrophil recruitment and reduces liver injury, indicating that CD44 promotes LPS-induced liver injury. CD44 is also a marker of cancer stem cells. Normal hepatocytes do not express CD44, whereas transformed HCC cells express CD44 at high levels. Mice with hepatocytes lacking CD44 showed reduced development of HCC, suggesting CD44 is not only cancer stem cells marker but also a functional molecule that contributes to HCC development. In addition to HA binding to CD44 in HCC development, CD44 acts as a co-receptor for EGF receptor and inhibits p53 through AKT and mouse double minute 2. Inhibition of p53 activity results in hepatocyte escape from p53-dependent apoptosis and promotes the reprogramming of hepatocytes into HCC progenitor cells.51 CD44 promotes NASH and NASH-associated HCC in mice.52,53 These reports indicate that CD44 promotes M1 polarization of liver macrophages in NASH and the CD44-HA interaction mediates KC-dependent intrahepatic platelet accumulation in NASH-HCC. HSCs express CD44 and TLR4, and HSC activation upregulates the expression of CD44 in liver fibrosis. Both CD44 and TLR4 promote HSC proliferation and invasion induced by HSC-derived HA (Figure 1). However, these receptors regulate distinct gene expression patterns in HSCs. CD44 and TLR4 regulate type I and IV collagens and TIMP1; TLR4 regulates type III collagen; and CD44 regulates type VI collagen.4 TLR4 is a crucial receptor for LPS and translocated intestine-derived LPS promotes HSC activation and liver fibrosis.54 Similarly, HA-mediated TLR4 activation contributes to liver fibrosis progression.4 In HSCs, CD44 contributes to Notch1 activation. HA overexpression and activated CD44 upregulate Notch1 receptor and its ligand Jagged-1 in HSCs.4 Briefly, activated CD44 proteolytically cleaves the CD44 intracellular domain, which leads to nuclear translocation of CD44 intracellular domain and upregulation of Notch1 expression (Figure 4).55,56 CD44-mediated Notch1 activation results in HSC invasion, proliferation, and activation.4 Hence, HA promotes fibrogenic, proliferative, and invasive phenotypes of HSCs through the activation of CD44 and TLR4. The different sizes of HA bind CD44 but often lead to other biological actions.57 The binding of HMM-HA to CD44 suppresses yes-associated protein (YAP)-mediated cell growth, while the LMM-HA promotes the CD44-YAP-dependent cell growth in vascular smooth muscle cells, fibroblasts, or breast cancer cells. Also, LMM-HA, but not HMM-HA, induces the interaction between CD44 and TLR$\frac{2}{4}$ in breast tumor cells to promote invasion and cytokine production.58,59 In HSCs, LMM-HA induces HSC activation through CD44 and TLR4, whereas HMM-HA suppresses or has less effect on HSC activation.4 More studies are required to determine molecular size-dependent HA, its receptor binding, and signaling-mediated actions in liver disease. FIGURE 4: Hyaluronan (HA) promotes HSC activation through CD44-mediated Notch1 transcription. Activation of HSCs and inflammation is the primary driver of liver fibrosis. During the progression of liver fibrosis, hyaluronan synthase 2 (HAS2), the critical hepatic HA synthase, is overexpressed in activated HSCs and produces HA. HA generated as high molecular mass (HMM) forms are converted into low molecular mass (LMM)-HA in the presence of inflammation. LMM-HA binds to and activates CD44, the major HA receptor. CD44 has three domains: an extracellular domain (ECD), a transmembrane domain (TMD), and an intracellular domain (ICD). Activated CD44 proteolytically cleaves CD44-ICD and releases CD44-ICD into the cytoplasm. CD44-ICD translocates to the nucleus and initiates transcription of Notch1. Notch1 receptor interacts with its ligand Jagged-1 (JAG1), derived from nearby liver resident KCs and/or HSCs, to activate profibrogenic Notch1 signaling. HA-mediated CD44-Notch1 activation promotes HSC activation and liver fibrosis. Less is known about the remaining HA receptors RHAMM and Stabilin-2. A few reports show that HA–RHAMM binding promotes cancer cell motility in HCC and liver metastasis,60–62 but further studies are necessary to define the mechanistic role of RHAMM in liver disease. Stabilin-2 is a scavenger receptor expressed mainly in LSECs and can bind to HA63 (Figure 1). Stabilin-2 knockout mice exhibit high levels of circulating HA, underscoring the contribution of Stabilin-2 expression in LSECs to HA endocytosis and clearance. Interestingly, elevated circulating HA protects against melanoma lung metastasis.63 Given that HMM-HA inhibits YAP-mediated tumor growth through CD44 and that LMM-HA enhances YAP-mediated tumor growth by binding to CD44,59 increased circulating HA after Stabilin-2 ablation may be due to the tumor-suppressive properties of HMM-HA. However, this hypothesis requires additional investigation. Collectively, HA receptors play major roles in disease-regulating signaling pathways in immune cells, cancer cells, HSCs, and LSECs. ## Regulatory mechanisms of HAS2 expression in HSCs HSCs are responsible for HAS2 expression and HA synthesis in liver fibrosis.4 Quiescent HSCs do not express HAS2 or produce HA.4 Upon HSC activation, HAS2 expression and HA production are dramatically upregulated.4 Like ECM-producing lung fibroblasts, TGF-β is a potent inducer of HSC transdifferentiation to myofibroblasts and HAS2 overexpression. The HAS2 promoter does not contain conserved binding sites for Smad transcription factors, which are activated by TGF-β, suggesting an alternative transcriptional mechanism for the upregulation of HAS2 in response to TGF-β.24 T-box 4, a transcription factor upregulated by TGF-β in lung fibroblasts,64 is not induced by TGF-β in HSCs (unpublished observations, Y.M. Yang and E. Seki). Instead, the HAS2 promoter contains 3 putative binding sites for Wilms tumor 1 (WT1) (Figure 5).4 WT1 is a transcription factor expressed in cells originating from the mesoderm and is also expressed in HSCs.65 TGF-β induces the upregulation of both WT1 and HAS2, and WT1 knockdown abolishes TGF-β-induced HAS2 induction, indicating that TGF-β-mediated WT1 transcriptionally regulates HAS2 expression in HSCs.4 In contrast, another study demonstrated that WT1 negatively regulates HSC activation in carbon tetrachloride–induced liver fibrosis.66 Although different experimental approaches are used (eg, other Cre mice selected to target HSCs and different liver fibrosis models), future studies are necessary to unveil the precise mechanisms of HSC activation related to WT1 and HAS2. FIGURE 5: Regulation of hyaluronan synthase 2 (HAS2) in HSCs. In HSCs, TGF-β initiates intracellular signaling by binding and activating 2 TGF-β receptor I (TGF-βRI) and II (TGF-βRII) and activated TGF-β receptor signaling induces Wilms tumor 1 (WT1) expression. Subsequently, WT1 binds to the promoter region of HAS2. All 3 WT1 binding sites in the HAS2 promoter (–2057, –1002, and –636 bp from the transcription start site) are required for activation. HAS2 is also post-transcriptionally regulated by miR-200c binding to the 3’-UTR of HAS2 mRNA. HAS2 expression is also regulated epigenetically.40 For example, an epigenetic regulator methyl-CpG binding protein 2 regulates the expression of profibrogenic genes through phosphorylation at serine 80 in HSCs. HAS2 is one of the profibrogenic genes regulated by methyl-CpG binding protein 2. A post-transcriptional regulation of HAS2 was also reported. miR-200c is downregulated by liver fibrosis, binds to the 3’-UTR of HAS2, and post-transcriptionally regulates HAS2 expression in HSCs (Figure 5).67 Hedgehog (Hh) signaling is another important pathway in HSC activation and liver fibrosis. In Hh signaling, the interaction of sonic Hedgehog ligand and the cell surface receptor Patched releases and activates Smoothened, leading to nuclear localization of glioblastoma family transcription factors (Glis) that regulate the expression of cell-specific target genes. Although not reported in the liver, HAS2 is a direct target of Gli transcriptional regulation during early mouse limb development.68 Gli3 binds to the HAS2 promoter region, and HAS2 is required to establish joint patterning within digits as an important downstream effector of sonic Hedgehog ligand signaling. Although there is no direct evidence for Hh-mediated HAS2 regulation in the liver, a few reports suggest Hh signaling interacts with HA production in NAFLD.69 Therefore, further studies are required to define the connection between HAS2 and Hh signaling. Many questions still remain regarding the regulatory mechanism of HAS2 in the liver because the importance of HAS2 in the liver has only recently attracted interest. ## Targeting HA as a therapeutic strategy for liver disease There are 2 approaches to target HA in liver disease: inhibition of HA synthesis and targeted degradation of HA. The coumarin derivative (7-hydroxy-4-methylcoumarin), 4-methylumbillifelone (4-MU), also known as hymecromone, is used to treat biliary spasm in Asia and Europe and inhibit HA synthesis. Mechanistically, 4-MU competes with UDP-GlcUA as a substrate for UDP-glucuronosyltransferase, effectively inhibiting the synthesis of HA (Figure 6). 4-MU also suppresses HAS2 and HAS3 transcription70 to inhibit HA synthesis. In HSCs, 4-MU treatment inhibits HA production and collagen expression and suppresses liver fibrosis induced by cholestasis, carbon tetrachloride, and NASH in mice.4,71,72 Because HA contributes to HCC development, 4-MU treatment inhibits HCC growth in mice.73 Although previous reports suggest that a high dose of 4-MU is required due to its rapid clearance and low systemic bioavailability (<$3\%$), a recent study revealed that 4-methylumbelliferyl glucuronide (4-MUG), the main metabolite of 4-MU, also has bioactivity. 4-MUG inhibits HA synthesis and further converts back into 4-MU in vivo.74 This suggests that the bioavailability of 4-MU is higher than what we previously thought. Hence, further pharmacokinetics and pharmacodynamics studies for 4-MU and 4-MUG are still needed.75 Several clinical trials using 4-MU were conducted and reported it is a safe and well-tolerated oral medication that decreases HA levels in the serum and sputum of healthy subjects.70,76 This suggests its efficacy in pulmonary disease. Future studies are required to reveal its efficacy in liver disease, including liver fibrosis. At the same time, we must be cautious of the systemic effect of 4-MU outside the liver. In addition to 4-MU, etoxazole, a chitin synthesis inhibitor, was reported as an HA inhibitor. Etoxazole reduces HA production and prevents collagen accumulation in carbon tetrachloride-induced liver fibrosis.77 Importantly, etoxazole has a half-maximal inhibitory concentration (IC50) of HA deposition in a lower micromolar range (4.21±3.82 μΜ) than 4-MU (IC50=8.68±1.6 μΜ), suggesting that etoxazole has therapeutic potential to inhibit HA at a lower dose than 4-MU. FIGURE 6: Postulated mechanism of the inhibitory effect of 4-methylumbillifelone (4-MU) on hyaluronan (HA) synthesis. HA synthesis is inhibited by the small molecule inhibitor 4-MU, a coumarin derivative. Functionally, 4-MU is a competitive substrate for uridine diphosphate (UDP)-glucuronosyltransferase (UGT), an enzyme involved in HA synthesis. (A) In the absence of 4-MU, HA is produced by hyaluronan synthase (HAS) from UDP-N-acetyl-glucosamine (GlcNAc) and UDP-glucuronic acid (GlcUA). These substrates are generated by the transfer of a UDP residue to Glc-NAc or Glc-UA via UGT. Thus, the availability of UDP-GlcNAc and UDP-GlcUA limits HA synthesis. (B) In the presence of 4-MU, 4-MU binds to GlcUA and depletes the pool of UDP-GlcUA, which is required for HA synthesis. Thus, 4-MU restricts the availability of an essential substrate for HA synthesis. Degradation of HA by HYALs is an alternative approach to reducing local HA abundance. In colorectal cancer liver metastasis, chemoresistance, and HA deposition are often observed after anti-VEGF therapy. Accumulation of HA in cancer is associated with increased tumor stiffness and prevents chemotherapeutics from reaching cancer cells. In a preclinical mouse model, PEGylated HYAL PH20 administration degrades accumulated HA in liver metastasis, allowing anti-cancer chemotherapeutics to reach cancer cells and resulting in increased efficacy of anti-VEGF plus chemotherapy.78 The recent phase III clinical trial reported that PEGylated HYAL PH20 did not improve overall and progression-free survivals in pancreatic cancer patients with nab-paclitaxel/gemcitabine treatment.79 However, approaches to degrade HA may still be applied to treat liver malignancy, including HCC with cirrhosis, which often has strong fibrous capsules surrounding tumors. As mentioned previously, the molecular mass of HA impacts biological function. HA35, the 35 kDa form of HA, has anti-inflammatory effects, distinct from most proinflammatory LMM-HA. HA35 inhibits TLR4 signaling by decreasing importin α5 and increasing Tollip, which results in the inhibition of alcohol-induced liver injury in rodents.80,81 Thus, HA35 may be a treatment option for TLR4-mediated liver disease progression. However, the stability of HA35 must be carefully considered because degradation of HA35 to smaller molecular mass could be pro-inflammatory and difficult to control. Given that cancer cells and cancer stem cells overexpress CD44, HA-based drug, and nucleotide delivery systems may be an effective treatment strategy. HA-paclitaxel conjugate, doxorubicin, and gemcitabine conjugated to an HA backbone, and lipid nanoparticles containing paclitaxel and targeted with HA are currently tested for the treatment of bladder, ovarian, and breast cancer as well as melanoma.82 HA-based nanoparticles can deliver specific microRNAs (eg, miR-29, miR-125, miR-155) to macrophages, polarizing them from pro-cancer M2 to anti-cancer M1 states.82 Because liver cancer cells, liver macrophages, and activated HSCs express CD44 at high levels, HA-based delivery systems may be an effective treatment for liver cancer, ALD, NASH, and liver fibrosis. However, HA components may stimulate proinflammatory, profibrotic, and procancer CD44 signaling, limiting the application of this approach. Additional studies are necessary to investigate the therapeutic potential of HA-based CD44 targeting systems in liver disease. Obesity is a systemic disease that affects NASH progression and insulin resistance. Obesity can be treated using 4-MU, which acts on brown adipose tissues. A recent report demonstrates that 4-MU treatment inhibits diet-induced weight gain and attenuates insulin resistance independent of HA.83 Briefly, 4-MU treatment changes the metabolic activity of brown adipose tissues. Sugar precursors (UDP-GlcUA and UDP-GlcUAc) are no longer used for HA synthesis, leading to increased glycolysis and mitochondrial respiration in brown adipose tissues and mitigation of obesity and diabetes. This alternative pathway is suggested as the mechanism by which HA synthesis affects disease progression.83 ## Concluding remarks In hepatology research and clinical practice, HA is a classic biomarker of cirrhosis. Although disruption of HA clearance is recognized for its role in cirrhosis, HA hypersynthesis has not been thoroughly investigated. Additional studies are still needed to elucidate regulatory mechanisms and downstream effectors of HA. A recent study established a connection between enhanced HA synthesis and HAS2 overexpression in activated HSCs during liver fibrosis. The bidirectional regulation between HA and HSCs is crucial for HSC activation, ECM deposition, and fibrosis. HSC-derived HA also affects surrounding liver cells, including hepatocytes, KCs, and LSECs, further facilitating HSC activation and fibrosis. Although a recent intriguing report shows that inhibition of HA synthesis causes previously underappreciated HA-independent biological mechanisms,83 targeting HA synthesis can be an attractive treatment approach for fibrotic liver disease as well as liver malignancy. Repurposing 4-MU or additional development of new 4-MU derivatives or small molecules should also be considered. The effects of different molecular mass HA are validated. Although further studies of the mechanisms of HA catabolism are desired, the detection of different molecular mass HA may become a valuable diagnostic tool. New diagnostic tools will also help to validate the effectiveness of novel antifibrotic drugs. Targeting HA is a valuable approach for diagnosing and treating liver fibrosis. ## FUNDING INFORMATION This work is supported by NIH grants R01DK085252, R01AA027036, and P01CA233452 as well as by the Cedars-Sinai Medical Center through the Cedars-Sinai Cancer Project Acceleration Award. ## CONFLICT OF INTEREST The authors have no conflicts to report. ## References 1. Matsuda M, Seki E. The liver fibrosis niche: novel insights into the interplay between fibrosis-composing mesenchymal cells, immune cells, endothelial cells, and extracellular matrix. Food Chem Toxicol (2020) 143 111556. PMID: 32640349 2. Matsuda M, Seki E. Hepatic stellate cell-macrophage crosstalk in liver fibrosis and carcinogenesis. Semin Liver Dis (2020) 40 307-20. PMID: 32242330 3. Jiang D, Liang J, Noble PW. Hyaluronan as an immune regulator in human diseases. Physiol Rev (2011) 91 221-64. PMID: 21248167 4. 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--- title: 'DEPRESSIVE AND EATING DISORDERS IN PATIENTS POST-BARIATRIC SURGERY WITH WEIGHT REGAIN: A DESCRIPTIVE OBSERVATIONAL STUDY' authors: - Thiago de Almeida Furtado - Marcelo Gomes Girundi - Cláudio de Oliveira Chiari Campolina - Sofia Cunha Mafra - Alice Marina Osório de Oliveira - Maria Luiza Patrão Dias dos Santos - Sarah Ferreira Lopes - Mariana Alvarenga Freire journal: 'Arquivos Brasileiros de Cirurgia Digestiva : ABCD' year: 2023 pmcid: PMC10027068 doi: 10.1590/0102-672020230002e1725 license: CC BY 4.0 --- # DEPRESSIVE AND EATING DISORDERS IN PATIENTS POST-BARIATRIC SURGERY WITH WEIGHT REGAIN: A DESCRIPTIVE OBSERVATIONAL STUDY ## ABSTRACT ### BACKGROUND: Although bariatric surgery is today’s gold standard treatment for obesity, weight regain affects the success rate of the procedure. Recent studies have identified psychiatric and neurological factors as possible causes. ### AIMS: The aim of this study was to evaluate the influence of psychiatric diseases on the outcome and long-term success of bariatric surgeries and find a weight regain threshold that has an acceptable sensibility to mental health-related issues to be used in research and clinical studies. ### METHODS: This is a observational study of bariatric patients submitted to Roux-en-Y bypass or sleeve gastrectomy, with a postoperative time of 2–10 years to access weight regain, depression, and binge-eating disorder. ### RESULTS: Of 217 patients studied, 163 were women and 54 were men, with an average postoperative time of 5.2±2.6 years. Weight regain was experienced in $35\%$ of the patients, binge-eating disorder in $24.9\%$, and depression in $24\%$. The greater weight before surgery, body mass index (BMI), percentage increase to maximum weight loss, and time postoperatively all have a significant positive correlation with weight regain ($$p \leq 0.045$$, $$p \leq 0.026$$, $p \leq 0.001$, and $p \leq 0.001$, respectively). A significant association between binge-eating disorder, depression, and anxiety with weight regain ($$p \leq 0.004$$, $$p \leq 0.008$$, and $$p \leq 0.001$$, respectively) was found. ### CONCLUSIONS: The significant weight regain rates with significant impact on psychiatric disorders highlight the need for continuous postoperative monitoring focused on the psychiatric aspects of obesity to aid surgeries’ long-term success. ## INTRODUÇÃO: Embora a cirurgia bariátrica atualmente é considerada o tratamento padrão ouro para a obesidade, o reganho de peso afeta a taxa de sucesso do procedimento. Estudos recentes identificaram fatores psiquiátricos e neurológicos como possíveis causas. ## OBJETIVOS: Avaliar a influência de transtornos psiquiátricos no resultado a longo prazo das cirurgias bariátricas; testar a sensibilidade e correlação das fórmulas de reganho de peso e de seus respectivos pontos de corte para questões relacionadas à saúde mental. ## MÉTODOS: Estudo observacional de pacientes pós bariátricos submetidos à by-pass em Y de Roux ou gastrectomia vertical com pós-operatório de 2 a10 anos avaliados quanto a reganho de peso, depressão e transtorno da compulsão alimentar. ## RESULTADOS: Foram avaliados 217 pacientes, 163 mulheres e 54 homens com pós-operatório de 5,2±2,6 anos. O reganho de peso foi registrado em $35\%$ dos pacientes, o transtorno da compulsão alimentar (TCA) foi encontrado em 24,$9\%$ e depressão em $24\%$. O ganho de peso pré-operatório, o índice de massa corporea (IMC), o aumento percentual para perda máxima de peso e tempo de pós-operatório, apresentaram correlação positiva significativa com o reganho de peso ($$p \leq 0$$,045), ($$p \leq 0$$,026), (<0,001), (<0,001). Foi encontrada associação significativa entre TCA, depressão e ansiedade com reganho de peso ($$p \leq 0$$,004), ($$p \leq 0$$,008) e ($$p \leq 0$$,001). ## CONCLUSÕES: As taxas significativas de reganho de peso associado ao impacto dos transtornos psiquiátricos reforçam a necessidade de acompanhamento pós-operatório contínuo focado nos aspectos psiquiátricos da obesidade, para sucesso do tratamento cirúrgico em longo prazo. ## INTRODUCTION According to the World Health Organization (WHO), obesity is the most serious health problem in the world, with almost 1.9 billion patients suffering from it. It is a chronic disease with a multifactorial cause: genetic, environmental, socioeconomic, endocrine, metabolic, and psychiatric. Bariatric surgery has been a revolution in the management of obesity since its establishment. The procedure leads to substantial improvements in comorbidities in up to $80\%$ of patients and has been quickly accepted as a useful tool for weight loss, with almost 580,000 procedures performed worldwide every year 1. Previous studies have shown a higher prevalence of binge-eating disorders (BEDs), depression, and other psychiatric illnesses in obese patients when compared to the overall population 28. Despite being considered the gold standard in care, suboptimal rates of weight loss and subsequent recurrence are observed in up to $30\%$ of patients 26. This proves that the method does not cover all aspects of the disease’s pathogenesis, which explains the long-term limitations of the procedure 26. Moreover, its worst long-term outcomes, mainly in psychiatric patients, may suggest that surgery alone may not be sufficient for this group of patients. Additionally, the correlation between weight regain and mood disorders has not yet been described in a clear and consistent way to support such changes in the way surgeons approach obese psychiatric patients or to request the need for more rigorous guidelines 10. Limitations in the current literature are mainly the lack of standardized guidelines for assessing weight regain, a subjective and not yet standardized psychiatric evaluation for this specific population and length time bias 18. Although the preoperative psychiatric evaluation is recommended by existing guidelines, its low rigor and specificity, as well as the lack of perspective by the medical community on the impact of their use on surgical results, lead to a weak and inaccurate adherence to them, especially regarding the psychiatric prerequisites 3. The aim of the present study was to investigate the influence of psychiatric diseases on the outcome and long-term success of bariatric surgeries, to determine a weight regain (WR) threshold that has an acceptable sensibility to mental health-related issues to be used in research and clinically, and finally, to suggest the need for bariatrics’ protocols revision so as to include a more rigorous psychiatric evaluation and treatment with comparable importance on the procedure’s outcome to the surgical part itself. ## METHODS This observational cross-sectional study was approved by the Ethics and Research Committee of the Faculty of Medical Sciences of Minas Gerais (nº 27053519.8.0000.513). Data were collected from medical records and online questionnaires of 3136 patients, submitted to bariatric surgery at two private obesity clinics in Belo Horizonte (MG), after having met the following inclusion criteria: age 18–65 years, submitted to a primary bariatric surgery with either Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG), from 2010 to 2018. A pilot study was carried out with 30 patients of the target population of 3136 patients from both bariatric centers to estimate the percentage of WR in the population. With significance of $$p \leq 0.05$$, $95\%$ confidence interval and $20\%$ recurrence ratio determined a sample of 229 patients needed for the study. Out of 3136 patients, 1174 answered the phone and were invited to participate, and 455 agreed to receive the survey via email and authorized the consent form to have their medical records accessed. From these, only 235 answered the questions. Data searched in the medical records were preoperative weight and body mass index (BMI), psychiatric medications used before surgery, depression, binge eating, and other comorbidities reported by the patient before surgery, and the surgical technique used. ## Instruments The survey contained the consent form, identification data, questions regarding weight data, the binge-eating disorder scale (BES), and the Hospital Depression Scale (HADS) with questions only from the depression subscale (HADS-D), both diagnoses according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) 14,20,26,28. The weight data accessed were presurgical weight, nadir weight, time to nadir weight, and current weight. The HADS-D is a self-administration screening scale. The severity of depression is classified as normal (0–7), mild (8–10), moderate (11–15), and severe (16–21), with Cronbach’s internal consistency alpha coefficient of 0.90. The BES is a self-administered screening tool questionnaire that uses a Likert scale, and the individual can be classified as the studies of community-acquired pneumonia (CAP), employing without CAP (0–17), moderate CAP (18–26), and severe CAP (>27) 14,20,27,28. Both questionnaires were chosen for their international usage that allows the comparison of the results with those from other countries, as well as for the possibility of classifying the magnitude of the disease in each patient. ## Statistical Analysis For statistical analysis, the R software version 4.0.3 and a significance level of $5\%$ were considered. Initially, the adherence of data to the normal parameters was accessed. Numerical variables were assessed by the chi-square and Fisher’s exact tests, and the comparison between groups was assessed by the Mann-Whitney U test. The categorical variables were presented as absolute and relative frequencies and the numerical variables as mean ± standard deviation and median (1st quartile-3rd quartile). From 235 patients’ samples, 2 were excluded for answering the questionnaire two times and 12 for having less than 24 months of postoperative time. In total, 217 patients with at least 2 years of surgery were evaluated. WR was calculated using both the nadirs postsurgical weight considering a cutoff point of $15\%$ and the percentage of maximum weight loss considering a cutoff point of $20\%$ 4,5. ## RESULTS We evaluated 163 women ($75.1\%$) and 54 men ($24.9\%$); 128 ($71.9\%$) of them underwent RYGB and 50 ($28.1\%$) SG. Patients had an average age of 42.2±10.0 years and a postoperative time of 5.2±2.6 years. Weight and BMI before the procedure were 116.4±17.7 kg and 40.0±4.2, respectively. The mean lowest weight achieved was 70.7±14.9 kg after 13.8±7.6 months after the procedure. The current weight, at the time that the research was performed, was 80.4±18.2 kg, which was 14.0±$11.7\%$ greater than the nadir weight. With a cutoff point of $15\%$ of nadir weight, $35\%$ of patients ($$n = 76$$) regained weight. Values ≥ $20\%$ were used for the cutoff point of WR when the percentage of maximum weight loss was used. Our sample averaged 21.9±$17.6\%$ of maximum weight loss, and $46.5\%$ of patients ($$n = 101$$) obtained values ≥$20\%$ (Table 1). Table 1 \| Unnamed: 0 \| Weight Regain* \| Weight Regain.1 \| p-value \| \| --- \| --- \| --- \| --- \| \| \| Yes n=76 (%) \| No n=141 (%) \| p-value \| \| Preoperative weight in the patient chart (n=180) \| 109.3 (100.0–126.0) \| 105.6 (98.0–116.5) \| 0.045M \| \| Preoperative weight referred by the patient \| 115.0 (107.0–132.8) \| 112.0 (102.0–122.0) \| 0.019M \| \| BMI before surgery (n=178) \| 40.1 (39.0–43.0) \| 39.8 (37.0–41.0) \| 0.026M \| \| Time since surgery (years) \| 7.0±2.4 \| 4.9±2.4 \| <0.001M \| \| Nadir weight \| 69.0 (58.0–80.0) \| 68.0 (60.0–78.0) \| 0.884M \| \| Time after the procedure to nadir (n=213) \| 12.9±5.7 \| 14.3±8.3 \| 0.455M \| \| Current weight \| 86.5 (77.2–98.2) \| 72.0 (64.0–84.0) \| <0.001M \| \| Percentage increase in weight in relation to the lowest weight achieved \| 22.4 (17.5–31.1) \| 7.0 (3.8–10.4) \| <0.001M \| \| Minimum–maximum \| 15.1–63.5 \| 0.0–14.7 \| – \| The preoperative data concerning psychological aspects that were found in the medical charts were as follows: 10 patients used psychiatric drugs, $6.9\%$ had depression, $8.8\%$ binge eating, and $2.7\%$ anxiety. Regarding the postoperative psychological assessment through the questionnaires BEDS and HADS, 34 patients screened positively for moderate BED and 20 for severe BED, both of which accounted for $24.9\%$ of patients. In total, 26 were screened as light depression, 18 moderate, and 8 severe, which accounts for $24\%$ of the patients. In contrast, when asked about current comorbidities, depression was referred by 43 ($19.8\%$) patients, anxiety by 108 ($49.8\%$), and none referred BED as a current disorder. WR was significantly associated with greater presurgical weight and BMI, greater postoperative time, and a greater percentage increase in minimum weight (0.045, 0.026, <0.001, and <0.001, respectively) (Table 1). Neither gender, age, surgical technique nor nadir weight was related to a greater WR ($$p \leq 0.23$$; $95\%$CI 0.10–0.13, and 0.88, respectively). Patients who underwent SG were younger (38.9±10.8 vs. 43.2±9.5; $$p \leq 0.013$$) and had a lower BMI before surgery than RYGB [37.0 (34.0–40.5) vs. 40.0 (38.8–42.2); $p \leq 0.001$]. Although a higher BMI was associated with WR, BMI itself was not associated with either gender or surgical technique. However, men reached the lowest weight faster than women (10.8±5.2 vs. 14.8±8.0; $p \leq 0.001$). Most of the patients that regained weight were submitted to RYGB, but this was not statistically significant ($$p \leq 0.13$$). No psychiatric diagnosis at preoperative time had a positive association with postoperative WR in either formula (Table 2). Postoperative BED positivity for $24.9\%$ of patients was significantly associated with WR at both thresholds ($$p \leq 0.004$$ and $p \leq 0.001$). Depression screened at the HADS-S questionnaire did not have a positive correlation with WR considering nadir weight (0.521), but current depression and anxiety reported by the patient did show a positive association ($$p \leq 0.008$$ and $$p \leq 0.001$$). Anxiety reported by the patient impacted WR when the percentage of maximum weight loss was used ($$p \leq 0.04$$). Depression almost had a significant relationship ($$p \leq 0.06$$) with the same formula. Patients who reported current anxiety and depression are younger (40.4±9.2 vs. 44.1±10.5; $$p \leq 0.009$$, $$p \leq 0.042$$) and female ($$p \leq 0.041$$, $$p \leq 0.009$$). Table 2* \| Unnamed: 0 \| Unnamed: 1 \| Weight regain >15% of nadir weight \| Weight regain >15% of nadir weight.1 \| Weight regain >15% of nadir weight.2 \| Weight regain > 20% of percentage maximum weight loss \| Weight regain > 20% of percentage maximum weight loss.1 \| Weight regain > 20% of percentage maximum weight loss.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| Yes \| No \| p-value \| Yes \| No \| p-value \| \| Preoperative depression (medical chart) \| Preoperative depression (medical chart) \| – \| – \| 0.63 F \| – \| – \| >0.99 F \| \| \| Yes \| 4 (26.7) \| 11 (73.3) \| – \| 7 (6.9) \| 8 (6.9) \| – \| \| \| No \| 72 (35.6) \| 130 (64.4) \| – \| 94 (93.1) \| 108 (93.1) \| – \| \| Preoperative BED \| Preoperative BED \| – \| – \| 0.67 Q \| – \| – \| 0.425 Q \| \| \| Yes \| 8 (42.1) \| 11 (57.9) \| – \| 11 (10.9) \| 8 (6.9) \| – \| \| \| No \| 68 (34.3) \| 130 (65.7) \| – \| 90 (89.1) \| 108 (93.1) \| – \| \| BED \| BED \| – \| – \| 0.004 Q \| – \| – \| <0.001 Q \| \| \| Without BED \| 47 (28.8) \| 116 (71.2) \| – \| 63 (62.4) \| 100 (86.2) \| – \| \| \| Moderate BED \| 18 (52.9) \| 16 (47.1) \| – \| 26 (25.7) \| 8 (6.9) \| – \| \| \| Severe BED \| 11 (55.0) \| 9 (45.0) \| – \| 12 (11.9) \| 8 (6.9) \| – \| \| HADS \| HADS \| – \| – \| 0.521 M \| – \| – \| 0.585 M \| \| \| Normal \| 54 (32.7) \| 111 (67.3) \| – \| 73 (72.3) \| 92 (79.3) \| – \| \| \| Light \| 10 (38.5) \| 16 (61.5) \| – \| 13 (12.9) \| 13 (11.2) \| – \| \| \| Moderate \| 8 (44.4) \| 10 (55.6) \| – \| 10 (9.9) \| 8 (6.9) \| – \| \| \| Severe \| 4 (50.0) \| 4 (50.0) \| – \| 5 (5.0) \| 3 (2.6) \| – \| \| Current comorbidities referred by the patient \| Current comorbidities referred by the patient \| – \| – \| – \| – \| – \| – \| \| \| Depression \| 23 (53.5) \| 20 (46.5) \| 0.008 Q \| 26 (25.7) \| 17 (14.7) \| 0.061 Q \| \| \| Anxiety \| 50 (46.3) \| 58 (53.7) \| <0.001 Q \| 58 (57.4) \| 50 (43.1) \| 0.049 Q \| ## DISCUSSION This study has yielded valuable data regarding WR after bariatric surgery. It reinforces previous results that indicate the influence of psychopathologies on bariatric procedures’ outcomes 31. A Brazilian perspective on weight outcomes and its possible correlations have a great impact on scientific literature since Brazil currently holds second place in the worldwide ranking of bariatric procedures 7. This study was performed only with private healthcare system patients. Therefore, results may differ from public healthcare system and international centers due to the different pre- and postoperative guidelines adopted. Such influence can be noticed at the $79.1\%$ of patients submitted to RYGB, while in the public system, SG is the most performed technique 24. Despite this peculiarity, the sample is a consistent portrait of the population that met criteria to be submitted to the procedure: $75\%$ women of 41 years of age (36–48) with mean preoperative BMI of 40 (37–42). Thresholds for WR were chosen according to the results of King [2018], who compared the performance of the WR formulas with mental health decline and satisfaction with surgery. A $15\%$ of nadir weight and $20\%$ of maximum weight loss performed well with these outcomes ($$p \leq 0.03$$ and $$p \leq 0.09$$; $p \leq 0.008$ and $p \leq 0.001$). The results of 35 and $46.5\%$ of patients in the present study for each formula, respectively, were consistent with the variations of literature: 27–$50.2\%$ 22,32. Greater preoperative weight, BMI, and postoperative time 7.4 (5.6–8.9) were the main weight outcomes to influence WR. Most patients started regaining weight after reaching nadir weight (2–4 years) but continued until 10 years postoperatively with a significant variation in the percentage of weight regained between patients (0–$63\%$). Therefore, it is not possible to say how much weight patients will regain, even though the weight predictors described above may designate that a patient requires a closer, stricter, and longer follow-up 9,16,17,24. WR was significantly influenced by both mood and eating pathologies, but some controversies must be discussed. Neither depression nor binge eating preoperatively impacted WR, a topic that needs agreement within the literature 2,9,10,21,23. These data must be seen cautiously since preoperatively, psychiatric comorbidities found in medical charts were available for only 43 out of 217 patients, with $6.9\%$ ($$n = 15$$) experiencing depression, $8.8\%$ ($$n = 19$$) binge eating, and $2.7\%$ ($$n = 6$$) anxiety. Almost $50\%$ of bariatric surgery candidates have a current or past psychiatric disorder. These data question the low prevalence of preoperative psychiatric disorders among our sample 14. Since this is documented information, it is possible to question a patient’s failure to report mental illness, either due to embarrassment or due to fear of procedure’s denial. A surgeon’s failure to investigate these comorbidities may also be considered here. In contrast, postoperative BED was significantly associated with both WR measures ($$p \leq 0.004$$ and $p \leq 0.001$), which confirms data from recent literature 5,10,13,20. Although some studies have inferred some improvement in BED after the surgery 9,16, our research found the contradictory results. Since we did not access lifetime eating pathology at baseline, it is not possible to conclude if these 54 patients may have had it before surgery or developed again after the procedure 15,16,29. It is important to note that this study only considered the DSM-V definition of BED. However, recent studies signal other maladaptive eating behaviors that are generally neither screened nor documented, such as grazing, loss of control eating, picking and nibbling, and night eating 8,17. Considering these other behaviors, the prevalence of eating disorders could have been much greater than the one found in the present research. These other habits may be equally or more relevant than BED itself postoperatively. As a response to external stressors, eating small portions of high-caloric foods rather than huge amounts still stimulates the brain’s reward system, since these neuropsychiatric pathways are not altered with the surgery 12. Hanvold et al. 13 found that smokers had a significantly lower WR, which supports the hypotheses that finding ways to feed neuropsychological pathways is a major cause of WR maintenance 19,30. Interestingly, while $24.9\%$ of patients screened positive for binge eating, none of them reported it when asked about current diseases, but instead, $49.8\%$ reported anxiety. A possible hypothesis is that patients may refer to having anxiety rather than BED, because one of the origins of BED comes from anxiety. On the contrary, anxiety itself may be another mood disorder that should be screened and evaluated in future research. As a result, these data bring up the need of making patients aware of the impact of both formal diagnosis and subthreshold maladaptive eating behaviors that can put them at risk for worse outcomes 16. As opposed to BED, the understanding of depression’s impact on WR is still controversial 2,16, despite the evidence that each condition is a risk factor for the future development of the other 6. Although the results of the HADS-D subscale did not impact WR, possibly due to its screening rather than diagnostic characteristic, the current depression reported by the patient had a significant impact on WR ($$p \leq 0.008$$ and $$p \leq 0.06$$). Self-reported depression was found mostly in female ($$p \leq 0.041$$) and young patients ($$p \leq 0.042$$) 6, supporting the results of King [2019]. Depression’s impact on surgical outcomes is not only limited to the statistical results but also it impairs cooperativeness 11 and impedes motivation, both of which interfere with overall adherence to protocols, diet 4, physical activity 25, behavioral change, and long-term follow-up adherence 8. All of these are considered critical protectors for WR, long-term surgical success, and quality of life 9,31,32. Therefore, the need for the implementation of a standardized follow-up program, such as the Ontario Bariatric Network, is clear, which includes a strict mental health aid to patients for protocol adherence and behavioral change 1. WR often means the return of medical comorbidities that the surgery’s weight loss was able to remit 10,33, with no significant difference from the preoperative baseline ($$p \leq 0.67$$). The confirmation of the rate of WR, its interaction with time, and clinical outcomes shows that it can determine the success of the procedure rather than weight loss alone. Therefore, it should be a major concern for both the surgeon and the patient during follow-ups. As many patients largely believe that the surgery alone will cure obesity, it is important to raise awareness of patients’ active treatment participation to maintain the weight acquired with surgery, since returning to old habits undermines surgical, anatomical, and hormonal changes. 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--- title: 'BAROS PROTOCOL IN A UNIVERSITY HOSPITAL: WHAT IS THE IMPORTANCE IN THE POSTOPERATIVE RESULTS OF BARIATRIC SURGERY?' authors: - João Evangelista - José Henrique Cardoso Ferreira da Costa - Johnnes Henrique Vieira Silva - Murilo Pimentel Leite Carrijo - Pedro Castor Batista Timóteo da Silva - Daniel Felipe Morais Vasconcelos - e Pedro Cavalcanti de Albuquerque journal: 'Arquivos Brasileiros de Cirurgia Digestiva : ABCD' year: 2023 pmcid: PMC10027073 doi: 10.1590/0102-672020230002e1726 license: CC BY 4.0 --- # BAROS PROTOCOL IN A UNIVERSITY HOSPITAL: WHAT IS THE IMPORTANCE IN THE POSTOPERATIVE RESULTS OF BARIATRIC SURGERY? ## ABSTRACT ### BACKGROUND: Although bariatric surgery is highly effective for the treatment of obesity and its comorbidities, preoperative weight loss has an impact on its results. ### AIMS: The aim of this study was to correlate preoperative weight loss with the outcome of bariatric surgery using the Bariatric Analysis and Reporting Outcome System scores. ### METHODS: This is a cross-sectional, observational study with 43 patients undergoing bariatric surgery that compared a group of 25 patients with a percentage of preoperative excess weight loss ³$8\%$ with a group of 18 patients with a percentage of preoperative excess weight loss <$8\%$ or with weight gain. The research took place at the bariatric surgery outpatient clinic of the Oswaldo Cruz University Hospital with patients 1 year after the surgery. ### RESULTS: Patients had a mean age of 40.8 years (42.7 percentage of preoperative excess weight loss ≥$8\%$ vs. 38.2 percentage of preoperative excess weight loss <$8\%$, $$p \leq 0.095$$). No significant difference was found between the two groups regarding preoperative comorbidities and body mass index at entry into the program. Higher preoperative body mass index (48.69 vs. 44.0; $$p \leq 0.029$$) was observed in the group with percentage of preoperative excess weight loss <$8\%$. No significant difference was found regarding the percentage of excess weight loss (71.4±$15.4\%$; percentage of preoperative excess weight loss ≥$8\%$ vs. $69.47\%$±14.5 percentage of preoperative excess weight loss <$8\%$; $$p \leq 0.671$$), the result of the surgery according to the Bariatric Analysis and Reporting Outcome System scores protocol, the resolution of comorbidities, the quality of life, and the surgical complications between the two groups. ### CONCLUSIONS: Based on the available data, it is reasonable that bariatric surgery should not be denied to people who have not achieved pre-established weight loss before surgery. ## RACIONAL: Apesar da cirurgia bariátrica ser altamente eficaz para o tratamento da obesidade e suas comorbidades, ainda não está bem estabelecido o impacto da perda de peso pré-operatória em seus resultados. ## OBJETIVOS: Correlacionar a perda de peso pré-operatória com o resultado da cirurgia bariátrica pelos escores do método Bariatric Analysis and Reportig Outcome System. ## MÉTODOS: Estudo observacional transversal com 43 pacientes submetidos a cirurgia bariátrica que comparou um grupo de 25 pacientes com percentual de perda do excesso de peso pré-operatória igual ou maior a $8\%$ com um grupo de 18 pacientes com percentual de perda do excesso de peso pré-operatória menor a $8\%$ ou com ganho de peso. A pesquisa ocorreu no ambulatório de Cirurgia Bariátrica do Hospital Universitário Oswaldo Cruz com pacientes após um ano da cirurgia. ## RESULTADOS: Os pacientes tinham uma média de idade de 40,8 anos (42,7 percentual de perda do excesso de peso pré-operatória ≥$8\%$ vs 38,2 percentual de perda do excesso de peso pré-operatória <$8\%$, $$p \leq 0.095$$). Não foram encontradas diferenças significativas entre os dois grupos em relação às comorbidades pré-operatórias e o IMC na entrada do programa. Foi observado maior IMC pré-operatório (48,69 vs 44,0; $$p \leq 0$$,029) no grupo com percentual de perda do excesso de peso pré-operatória <$8\%$. Não foram encontradas diferenças significativas em relação ao percentual de perda do excesso de peso (71,4±15,$4\%$; percentual de perda do excesso de peso pré-operatória ≥$8\%$ vs 69,47±14,$5\%$ percentual de perda do excesso de peso pré-operatória <$8\%$, $$p \leq 0$$,671), ao resultado da cirurgia pelo protocolo Bariatric Analysis and Reportig Outcome System, a resolução das comorbidades, a qualidade de vida e as complicações cirúrgicas entre os dois grupos. ## CONCLUSÕES: Com base nos dados disponíveis é condizente que a cirurgia bariátrica não seja negada a pessoas que não atingiram uma perda de peso pré-estabelecida antes da cirurgia. ## INTRODUCTION Obesity is a chronic disease with different factors for its occurrence and is genetically related to an excessive accumulation of body fat. Excess weight gain causes an increased risk of several diseases, particularly cardiovascular diseases, diabetes, and cancer 11. The indication for surgical treatment of morbid obesity is related to the ineffectiveness of clinical treatment and the high risk of mortality from untreated severe obesity 18. Bariatric surgery is the most effective treatment for obesity, which provides 20–$35\%$ of initial body weight loss between 12 and 18 months after surgery 10. Preoperative weight loss aims not only to facilitate the surgical procedure but also to transform the patient’s quality in relation to diet with the objective of adapting their eating habits to the postoperative period, correcting vitamin deficiencies, improving insulin resistance, and decreasing obesity-related low-grade systemic inflammation 7,6. Some insurance companies in the United States require percent preoperative excess weight loss (PPEWL) in the range of 5–$10\%$ before approving surgery. Some bariatric centers successfully prescribe mandatory adherence to weight loss programs before accepting patients for operation 15. However, potential negative effects associated with a preoperative low-calorie program include patient discomfort, increased costs, treatment denial, increased morbidity in surgery associated with the catabolic state, and a possible delay in the treatment that is required 3,20. Oria et al. published the Bariatric Analysis and Reporting Outcome System (BAROS) protocol, which, using a point scale, standardized a set of instruments for evaluating the results obtained with patients undergoing surgery worldwide. This protocol evaluates four main areas, namely, percentage of excess weight loss, changes in medical conditions, quality of life, and postoperative complications 13. Although there are flaws in the constitution of BAROS, it is still considered a standard method for evaluating bariatric surgery 12. In this context, carrying out a survey that correlates preoperative weight loss with the result of bariatric surgery using an instrument standardized worldwide will show if preoperative weight loss will imply not only post-operative weight loss but also the overall result of the surgery. The objective was to correlate preoperative weight loss with the result of bariatric surgery using the scores of the BAROS method. ## METHODS This is an observational, cross-sectional, retrospective study with 43 patients undergoing bariatric surgery, which compared a group of 25 patients with a percentage of preoperative excess weight loss ³$8\%$ with a group of 18 patients with a percentage of preoperative excess weight loss of <$8\%$ or with weight gain. The results of the BAROS protocol were compared in order to correlate preoperative weight loss with the results of bariatric surgery. The cutoff point of $8\%$ was adopted in relation to the percentage of preoperative excess weight loss based on previous studies and because it is used as a goal in this bariatric surgery service. The inclusion criteria used in the research were as follows: The exclusion criterion in the research was as follows: The percentage of preoperative excess weight loss was calculated by subtracting the patient’s weight on the day of surgery from the patient’s weight when he entered the bariatric surgery program, and the result was divided by the patient’s ideal weight loss. All study participants were monitored by the multidisciplinary team of the Bariatric Surgery Service at Oswaldo Cruz University Hospital, composed of an endocrinologist, nutritionist, psychologist, social assistant, and bariatric surgeon, aiming at significant preoperative weight loss. Data collection was carried out at the hospital’s outpatient clinic, applying the BAROS protocol, validated in Brazil, which was applied in-person while waiting for the medical consultation 1 year after Roux-en-Y gastric bypass surgery, after the participants signed the Free Consent Form. The data collection period was from January 1, 2020, to September 30, 2021. A total of 227 patients were interviewed, and 177 did not have a year after surgery. Of the remaining 50 patients, 2 underwent sleeve-type bariatric surgery and 5 had insufficient data to complete the BAROS protocol, leaving 43 patients in the research, as shown in Figure 1. Figure 1: Research protocol. Data were retrieved from the medical records of patients regarding surgical summaries, patient profiles, pre-surgical weight loss, evolution of weight loss after surgery, complications, and changes in comorbidities after gastroplasty. The BAROS protocol is composed of four domains, namely, percentage of excess weight loss, quality of life, changes in comorbidities, and post-surgical complications. For each domain, a certain score is added, except for the last domain, in which it is reduced from the total score. The answers were evaluated according to a score presented in the table proposed by Oria et al., resulting in a classification with the following possible results: “insufficient,” “acceptable,” “good,” “very good,” and “excellent” 13. The diagnosis of hypertension was considered as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥ 90 mmHg, and its resolution as blood pressure below these values only with diet and/or use of diuretics and improvement in blood pressure control with the use of these medications. The diagnosis of type 2 diabetes mellitus (DM2) was considered as fasting blood glucose above 125 mg/dL, blood glucose above 200 mg/dL 2 h after the oral glucose tolerance test, or glycated hemoglobin above $6.4\%$. Its resolution was considered when there was normalization of these values only with diet and physical exercise. Its improvement was when there was glycemic control only with oral antidiabetics and without the use of insulin. The diagnosis of dyslipidemia (DLD) was considered as total cholesterol above 200 mg/dL, HDL cholesterol below 35 mg/dL, and/or triglycerides above 250. Its resolution was considered when there was normalization of these values without the use of medication, and the improvement was when there was normalization of these values with the use of medication. The diagnosis of osteoarthritis (OA) was considered in the presence of radiological signs and typical symptomatology. Its resolution was considered in the absence of symptoms without the use of medication and its improvement in overcoming the symptoms with the use of medication. The diagnosis of gastroesophageal reflux disease (GERD) was considered in the presence of typical symptoms with an upper digestive endoscopy ruling out other differential diagnoses. Its resolution was considered in the absence of symptoms without the use of medication and its improvement in overcoming the symptoms with the use of medication. Data were entered into the Microsoft Excel program, and for all statistical tests, significance was determined at p≤0.05. The descriptive analysis was presented in absolute and percentage frequencies. The paired “t” test was used to compare mean age, body mass index (BMI) at entry into the surgery program, preoperative BMI, BMI after 1 year of surgery, and percentage loss of excess weight. To compare genders, preoperative comorbidities, BAROS protocol results, postoperative complications, quality of life (variable “much better”), and resolution of comorbidities, the chi-square test was used. ## RESULTS A sample of 43 patients with a mean age of 40.8±10.11 years were interviewed. In total, $76.7\%$ of the participants were women; the mean BMI at the moment they arrived at the University Hospital’s obesity program was 48.76±5.75; mean preoperative BMI was 45.9±6.5; and the mean BMI at the interview moment was 31.3±5.15. The patient characteristics are described in Table 1. Table 1 \| Unnamed: 0 \| Unnamed: 1 \| All patients \| ≥8% \| <8% \| p-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| n \| n \| 43 \| 25 \| 18 \| \| \| Age, years \| Age, years \| 40.8±10.1 \| 42.7±10.2 \| 38.2±9.4 \| 0.095 \| \| Sex, n (%) \| Sex, n (%) \| \| \| \| 0.895 \| \| \| Male \| 10 (23.2) \| 6 (24) \| 4 (22.2) \| \| \| \| Female \| 33 (76.7) \| 19 (76) \| 14 (77.7) \| \| \| \| BMI – beginning \| 48.76±5.75 \| 49.3±5.1 \| 47.9±6.5 \| 0.481 \| \| \| BMI preoperative \| 45.9±6.5 \| 44.0±5.0 \| 48.69±7.41 \| 0.029 \| \| \| BMI current \| 31.3±5.15 \| 30.3±4.18 \| 32.6±6.1 \| 0.161 \| \| \| %EWL \| 70.62±14.8 \| 71.4±15.4 \| 69.47±14.5 \| 0.671 \| \| Comorbidities (%) \| Comorbidities (%) \| \| \| \| \| \| \| HBP \| 25 (58.1) \| 15 (60) \| 10 (55.5) \| 0.77 \| \| \| DM2 \| 13 (30.2) \| 6 (24) \| 7 (38.8) \| 0.295 \| \| \| DLD \| 13 (30.2) \| 6 (24) \| 7 (38.8) \| 0.295 \| \| \| GERD \| 13 (30.2) \| 6 (24) \| 7 (38.8) \| 0.295 \| \| \| OA \| 14 (32.5) \| 9 (36) \| 5 (27.7) \| 0.568 \| \| Result BAROS (%) \| Result BAROS (%) \| \| \| \| \| \| \| Excellent \| 14 (32.5) \| 15 (60) \| 10 (55.5) \| 0.77 \| \| \| Great \| 22 (50.1) \| 6 (24) \| 7 (38.8) \| 0.295 \| \| \| Good \| 5 (11.6) \| 6 (24) \| 7 (38.8) \| 0.295 \| \| \| Fair \| 2 (4.6) \| 6 (24) \| 7 (38.8) \| 0.295 \| \| \| Excellent \| 14 (32.5) \| 9 (36) \| 5 (27.7) \| 0.568 \| \| \| Great \| 22 (50.1) \| 15 (60) \| 10 (55.5) \| 0.77 \| \| Complications (%) \| Complications (%) \| \| \| \| \| \| \| Cholelithiasis \| 4 (9.3) \| 3 (12) \| 1 (5.5) \| 0.471 \| \| \| Incisional hernia \| 8 (18.6) \| 4 (16) \| 4 (22.2) \| 0.602 \| \| \| Intestinal Obstruction \| 1 (2.3) \| 0 \| 1 (5.5) \| 0.226 \| \| \| Pneumonia \| 1 (2.3) \| 1 (4) \| 0 \| 0.392 \| \| \| DVT \| 1 (2.3) \| 1 (4) \| 0 \| 0.392 \| \| \| Abdominal wall infection \| 1 (2.3) \| 1 (4) \| 0 \| 0.392 \| \| \| Anemia \| 9 (20.9) \| 6 (24) \| 3 (16.6) \| 0.2866 \| \| \| Vitamins deficiency \| 24 (55.8) \| 13 (52.2) \| 11 (61.1) \| 0.552 \| \| \| Hair loss \| 32 (74.4) \| 17 (68) \| 15 (83.3) \| 0.255 \| \| Resolution (%) \| Resolution (%) \| \| \| \| \| \| \| HBP \| 21 (84) \| 13 (86.6) \| 8 (80) \| 0.656 \| \| \| DM2 \| 11 (84.6) \| 6 (100) \| 5 (71.4) \| 0.154 \| \| \| DLD \| 10 (76.9) \| 5 (83.3) \| 5 (71.4) \| 0.61 \| \| \| GERD \| 12 (92.3) \| 5 (83.3) \| 7 (100) \| 0.261 \| \| \| OA \| 8 (57.1) \| 6 (66.6) \| 2 (40) \| 0.334 \| More than a half of patients presented with hypertension (HBP), $30.2\%$ had DM2, $30.2\%$ had DLD, $30.2\%$ had GERD, and $32.5\%$ had OA. The most common postoperative complications were vitamin deficiency ($55.8\%$) and hair loss ($74.4\%$), followed by anemia ($20.9\%$), incisional hernia ($18.6\%$), and cholelithiasis ($9.3\%$). There was only a single case of intestinal obstruction, pneumonia, deep venous thrombosis (DVT), and abdominal wall infection. Most of the results evaluated by the BAROS protocol were classified as very good ($50.1\%$), followed by excellent ($30.2\%$) and good ($11.6\%$). The percentage loss of excess weight was 70.62±$14.8\%$, and there was a high rate of resolution of comorbidities: $84\%$ of arterial hypertension (HBP); $84.6\%$ of DM2; $92.3\%$ of GERD; $76.9\%$ of DLD; and $57.1\%$ of OA. Most patients considered the quality of life parameters to be much better: self-esteem ($88.3\%$), readiness for physical activity ($72\%$), social relationships ($55.8\%$), work ($79\%$), and interest in sex ($55.8\%$). The quality of life questionnaire results are described in Table 2. Table 2 \| Unnamed: 0 \| Unnamed: 1 \| All patients \| ≥8% \| <8% \| p-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| n \| n \| 43 \| 25 \| 18 \| \| \| Self-esteem (%) \| Self-esteem (%) \| \| \| \| \| \| \| Much better \| 38 (88.3) \| 21 (84) \| 17 (94) \| 0.29 \| \| \| Better \| 3 (6.97) \| 3 (12) \| 0 \| \| \| \| Unaltered \| 1 (2.32) \| 0 \| 1 (5.55) \| \| \| \| Worse \| 0 \| 0 \| 0 \| \| \| \| Much worse \| 1 (2.32) \| 1 (4) \| 0 \| \| \| Physical activity willingness (%) \| Physical activity willingness (%) \| \| \| \| \| \| \| Much better \| 31 (72) \| 18 (72) \| 13 (72.2) \| 0.986 \| \| \| Better \| 7 (16.2) \| 4 (16) \| 3 (16.6) \| \| \| \| Unaltered \| 3 (6.97) \| 2 (8) \| 1 (5.55) \| \| \| \| Worse \| 2 (4.65) \| 1 (4) \| 1 (5.55) \| \| \| \| Much worse \| 0 \| 0 \| 0 \| \| \| Social relationship ability (%) \| Social relationship ability (%) \| \| \| \| \| \| \| Much better \| 24 (55.8) \| 13 (52) \| 11 (61.1) \| 0.552 \| \| \| Better \| 8 (18.6) \| 5 (20) \| 3 (16.6) \| \| \| \| Unaltered \| 8 (18.6) \| 5 (20) \| 3 (16.6) \| \| \| \| Worse \| 3 (6.97) \| 2 (8) \| 1 (5.55) \| \| \| \| Much worse \| 0 \| 0 \| 0 \| \| \| Work willingness (%) \| Work willingness (%) \| \| \| \| \| \| \| Much better \| 34 (79) \| 18 (72) \| 16 (88.8) \| 0.179 \| \| \| Better \| 4 (9.3) \| 3 (12) \| 1 (5.55) \| \| \| \| Unaltered \| 2 (4.65) \| 2 (8) \| 0 \| \| \| \| Worse \| 2 (4.65) \| 1 (4) \| 1 (5.55) \| \| \| \| Much worse \| 0 \| 0 \| 0 \| \| \| Sex interest (%) \| Sex interest (%) \| \| \| \| \| \| \| Much better \| 24 (55.8) \| 12 (48) \| 12 (66.6) \| 0.223 \| \| \| Better \| 4 (9.3) \| 2 (8) \| 2 (11.1) \| \| \| \| Unaltered \| 14 (32.5) \| 10 (40) \| 4 (22.2) \| \| \| \| Worse \| 1 (2.32) \| 1 (4) \| 0 \| \| \| \| Much worse \| 0 \| 0 \| 0 \| \| Among the 43 patients included in the survey, $58.1\%$ ($$n = 25$$) had a PPEWL ≥$8\%$, while $41.8\%$ ($$n = 18$$) did not. Patients had a mean age of 40.8 years (42.7 PPEWL ≥$8\%$ vs. 38.2 PPEWL <$8\%$, $$p \leq 0.095$$). No significant difference was found in relation to preoperative comorbidities and BMI at entry into the bariatric surgery program (49.3±5.1 PPEWL ≥$8\%$ vs. 47.9±6.5 PPEWL <$8\%$; $$p \leq 0.481$$). A higher preoperative BMI (48.69±7.41 vs. 44.0±5.0; $$p \leq 0.029$$) was observed in the group with PPEWL <$8\%$. No significant differences were found in relation to the percentage loss of excess weight (71.4±$15.4\%$ – PPEWL ≥8 vs. 69.47±$14.5\%$ PPEWL <$8\%$; $$p \leq 0.671$$) and the result of surgery by the BAROS protocol, surgical complications, the quality of life questionnaire, and the resolution of comorbidities between the two groups. ## DISCUSSION Most of the evaluated patients were female ($76.7\%$), according to the findings of several national and international studies 1,15,18. The present study demonstrated that 38 ($88.3\%$) of the 43 patients had some comorbidity, demonstrating that obesity is a clinical condition that acts as a risk factor for the onset of other diseases. The most common comorbidities were high blood pressure (HBP) ($58.1\%$), DM2 ($30.2\%$), DLD ($30.2\%$), GERD ($30.2\%$), and OA ($32.5\%$) 15. Analyzing the relationship between bariatric surgery and its impact on weight loss, it was noted that, as we have done before, this procedure was proved to be quite effective 14. The mean BMI before and after surgery was 45.9 and 31.3 kg/m 2, respectively, which implies a significant reduction in cardiovascular mortality and all-cause mortality associated with excess weight 11. After surgery, the patients achieved a significant improvement in relation to their obesity class, going from class III to class I. The mean percentage loss of excess weight was $70.62\%$, proving that there was success in relation to bariatric surgery, for which the minimum required value is above $50\%$ 4. Regarding the evaluation of bariatric surgery results by the BAROS protocol, they were “excellent” in $32.5\%$ of the cases, “very good” in $50.1\%$, “good” in $11.6\%$, and “fair” in $4.6\%$ %, consistent with literature data 15. Our data did not demonstrate that a PPEWL ≥$8\%$ is related to a better outcome of bariatric surgery evaluated by the BAROS protocol, percentage loss of excess weight, post-surgical complications, quality of life, and resolution of comorbidities. A cohort study published in 2020 with 480,075 patients undergoing bariatric surgery demonstrated that even modest weight loss before bariatric surgery was associated with a lower risk of mortality within 30 days after the procedure. Compared with patients without preoperative weight loss, patients with weight loss greater than $0\%$ to less than $5.0\%$, 5.0–$9.9\%$, and $10.0\%$ and greater had 24, 31, and $42\%$, respectively, lower risk of mortality in 30 days 19. A study on weight loss before surgery with 20,564 patients undergoing gastric bypass from the Scandinavian Obesity Registry showed that preoperative weight loss was associated with increased weight loss, with the greatest effect seen with BMI >45.7 kg/m2 at 1 year postoperatively (OR 2.39; $95\%$CI 2.10–2.72; $p \leq 0.001$). In contrast to these findings, a study by Horwitz et al, evaluating compulsory insurance for preoperative weight loss, found no difference between mandatory preoperative weight loss and none at 1 and 2 years postoperatively 5. A study published in 2021 that analyzed 2,061 patients who underwent gastric bypass found that patients who achieved $5\%$ preoperative weight loss had similar rates of complications (4.2 vs. $5.1\%$; $$p \leq 0.288$$) and reoperation (3.0 vs. $3.4\%$; $$p \leq 0.800$$) compared to those who lost less or no weight. Patients who achieved $10\%$ preoperative weight loss had increased complications (6.6 vs. $3.7\%$; $$p \leq 0.017$$) and reoperation rates (4.5 vs. $2.7\%$; $p \leq 0.001$) 21. A meta-analysis published in 2021 of three randomized clinical trials that compared a group with a structured preoperative weight loss regimen compared with standard care found no differences in percentage weight loss between the two groups (SMD 0.007; $95\%$CI -0.561–0.546; $$p \leq 0.98$$). Also, a meta-analysis was performed in this study with prospective and retrospective cohort studies that compared, after 12 months of surgery, a group with preoperative weight loss and another group without preoperative weight loss, which did not show any difference in the percentage of weight loss between the two groups (SMD 0.035; $95\%$CI -0.163–0.233; $$p \leq 0.73$$) 8. In 2022, a cohort study was published that analyzed 427 patients undergoing bariatric surgery and observed that greater preoperative weight loss was related to a decrease in hospitalization time (1.8 vs. 1.3 days) but was not associated with a reduction in operative time, overall complication rates, ICU admissions, or intraoperative complications 17. The Bariatric Surgery Clinical Practice Guideline [2019], supported by several American Medical Societies, recommends that preoperative weight loss should not be an impediment to performing bariatric surgery, based on conflicting data in the literature regarding the benefit of preoperative weight loss and the potential harm of not having bariatric surgery 9. Among this study limitations, it should be underlined the small sample of patients, also it comes to a retrospective study in a single center and without randomization. The analysis performed was only of patients who attended the outpatient consultation, which may have masked the results of the study due to the possible non-inclusion of patients with less satisfactory results. The study analyzed the short-term results of surgery (1 year postoperatively). ## CONCLUSION The percentage of excess weight loss in the preoperative period ≥$8\%$ is not associated with the difference in percentage loss of excess weight in the postoperative period, surgical complications, quality of life, and results of bariatric surgery as evaluated by the protocol BAROS after 1 year of surgery, taking into account various limitations of this study. Therefore, through well-established protocols, it is possible to state that bariatric surgery is not contraindicated for individuals who have not achieved a pre-established weight loss before surgery. ## References 1. Driscoll S, Gregory DM, Fardy JM, Twells LK. Long-term health-related quality of life in bariatric surgery patients: a systematic review and meta-analysis. Obesity (2016) 24 60-70. DOI: 10.1002/oby.21322 2. Gerber P, Anderin C, Gustafsson UO, Thorell A. 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--- title: 11β-HSD1 determines the extent of muscle atrophy in a model of acute exacerbation of COPD authors: - Justine M. Webster - Kelsy Waaijenberg - Wouter R. P. H. van de Worp - Marco C. J. M. Kelders - Sara Lambrichts - Claire Martin - Frank Verhaegen - Brent Van der Heyden - Charlotte Smith - Gareth G. Lavery - Annemie M. W. J. Schols - Rowan S. Hardy - Ramon C. J. Langen journal: American Journal of Physiology - Lung Cellular and Molecular Physiology year: 2023 pmcid: PMC10027082 doi: 10.1152/ajplung.00009.2022 license: CC BY 4.0 --- # 11β-HSD1 determines the extent of muscle atrophy in a model of acute exacerbation of COPD ## Abstract Muscle atrophy is an extrapulmonary complication of acute exacerbations (AE) in chronic obstructive pulmonary disease (COPD). The endogenous production and therapeutic application of glucocorticoids (GCs) have been implicated as drivers of muscle loss in AE-COPD. The enzyme 11 β-hydroxysteroid dehydrogenase 1 (11β-HSD1) activates GCs and contributes toward GC-induced muscle wasting. To explore the potential of 11βHSD1 inhibition to prevent muscle wasting here, the objective of this study was to ascertain the contribution of endogenous GC activation and amplification by 11βHSD1 in skeletal muscle wasting during AE-COPD. Emphysema was induced by intratracheal (IT) instillation of elastase to model COPD in WT and 11βHSD1/KO mice, followed by vehicle or IT-LPS administration to mimic AE. µCT scans were obtained prior and at study endpoint 48 h following IT-LPS, to assess emphysema development and muscle mass changes, respectively. Plasma cytokine and GC profiles were determined by ELISA. In vitro, myonuclear accretion and cellular response to plasma and GCs were determined in C2C12 and human primary myotubes. Muscle wasting was exacerbated in LPS-11βHSD1/KO animals compared with WT controls. RT-qPCR and western blot analysis showed elevated catabolic and suppressed anabolic pathways in muscle of LPS-11βHSD1/KO animals relative to WTs. Plasma corticosterone levels were higher in LPS-11βHSD1/KO animals, whereas C2C12 myotubes treated with LPS-11βHSD1/KO plasma or exogenous GCs displayed reduced myonuclear accretion relative to WT counterparts. This study reveals that 11β-HSD1 inhibition aggravates muscle wasting in a model of AE-COPD, suggesting that therapeutic inhibition of 11β-HSD1 may not be appropriate to prevent muscle wasting in this setting. ## INTRODUCTION Chronic obstructive pulmonary disease (COPD), characterized by remodeling of the airways (bronchitis) and destruction of the lung parenchyma (emphysema), is predicted to be the third leading cause of death worldwide by 2030 [1]. Skeletal muscle wasting is a severe extrapulmonary complication of COPD, particularly in patients with emphysema [2, 3], which contributes to frailty and poor disease outcomes, and is an independent predictor of mortality [4]. To date, therapeutic strategies designed to prevent or reverse the acute process of muscle wasting in COPD patients are limited, and primarily restricted to exercise and nutritional intervention, with limited efficacy in the acute exacerbation disease phase [5, 6]. Acute exacerbations of COPD (AE-COPD), typically secondary to acute pulmonary and systemic inflammation with infection, accelerate muscle loss in COPD [7]. Glucocorticoids (GCs), such as cortisol (corticosterone in mice), are a class of endogenous anti-inflammatory steroids that are elevated in response to systemic inflammation [8]. This increase in circulating cortisol is mediated by the inflammatory activation of the hypothalamic-pituitary-adrenal axis (HPA) resulting in increased adrenal output [8]. Synthetic therapeutic glucocorticoids possess potent anti-inflammatory immunomodulatory properties and are frequently utilized in the management of COPD patients during inflammatory exacerbation [9]. Both elevated endogenous and therapeutic GCs are associated with increased GC signaling within skeletal muscle [10], and contribute to muscle atrophy in human disease, through the activation of proteolytic pathways, and suppression of anabolic signaling and myonuclear turnover (11–13). The enzyme 11 β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) converts inactive endogenous GCs precursors (cortisone in humans, 11-dehydrocorticosterone in mice) into their active form (cortisol and corticosterone respectively), where it amplifies GC signaling and determines peripheral tissue exposure to GCs [14]. Its expression is potently upregulated in muscle in response to proinflammatory mediators present during acute exacerbation of COPD (including cytokines such as IL-1β and TNF-α), as well as by alternative factors implicated in COPD muscle wasting such as hypoxia and fasting (15–17). Several studies have revealed a critical role for 11β-HSD1 in determining local endogenous and therapeutic GC levels within the muscle, which in turn modulate anti-anabolic and catabolic muscle metabolism in the primary cell and ex vivo tissue cultures and in vivo models [15, 16]. However, although therapeutic inhibitors of 11β-HSD1 have been widely explored in Phase II clinical trials in the management of metabolic diseases such as hypertension, osteoporosis, and insulin resistance, their application in the management of muscle wasting in inflammatory diseases such as COPD remains poorly defined (18–25). The objective of this study was to ascertain the contribution of endogenous corticosteroid activation and amplification by 11β-HSD1 toward the pathophysiology of skeletal muscle wasting during an acute exacerbation of emphysematous-COPD and test the hypothesis that 11β-HSD1 inhibition can prevent GC induced muscle wasting in this context. To achieve this, we used a murine model of AE-COPD with global transgenic deletion of 11β-HSD1 (11βHSD1/KO), in combination with in vitro approaches to assess the contribution of circulating endocrine mediators by exposing cultured muscle cells to plasma of WT and 11β-HSD1/KO AE-COPD mice. Our data show aggravated skeletal muscle wasting during AE-COPD in mice with transgenic deletion of 11β-HSD1 compared with wild-type counterparts. This coincides with an elevation of circulating endogenous GC levels and an attenuation of anabolic recovery mechanisms in these 11βHSD1/KO animals following the acute exacerbation. These data offer new insights into the role of endogenous GCs metabolism in muscle atrophy and the efficacy of targeting 11β-HSD1 in the management of muscle wasting in AE-COPD. This study shows that 11β-HSD1 inhibition does not prevent but aggravates muscle wasting during an acute exacerbation of COPD. ## Animal Models To determine the role of 11β-HSD1 in skeletal muscle wasting during an acute exacerbation of COPD, WT, and global 11β-HSD1 knockout (11βHSD1/KO) mice were used. Global 11βHSD1/KO mice were achieved using Cre-loxP technology, generating a Tri-loxed 11β-HSD1 allele by flanking exon 5 with LoxP sites, as previously described and maintained on a C57BL/6J background [26]. Male WT and 11βHSD1/KO mice (aged 14–20 wk, $$n = 33$$) received 2 weekly intratracheal instillations (IT; D0, D7), with 3 U of porcine pancreas elastase (E; Elastin Products Company, Missouri) dissolved in 50 μL of sterile PBS−/− to induce emphysema. The later adult stage of development was selected so as to remove complications in the interpretation of data arising from high anabolic growth evident at earlier stages of development. [ 27]. Presence of emphysema was determined by micro–Computed Tomography (μCT) analysis (D12). WT and 11βHSD1/KO animals were then divided randomly into two subgroups ($$n = 7$$–9), receiving a single bolus (IT) of 2 μg/g mouse of LPS (Escherichia coli, 055:B5, Sigma, Dorset, UK) dissolved in sterile PBS−/− or PBS−/− alone (v.c.) to evoke a pulmonary inflammatory response (AE) [28, 29]. Animals were euthanized 48 h post-LPS or PBS IT-instillation and hind limb muscles [gastrocnemius, soleus, tibialis anterior, extensor digitorum longus (EDL), and plantaris] dissected, weighed, and snap frozen in liquid nitrogen and stored at −80°C for biochemical analysis. Bloods were collected from abdominal vena cava to obtain plasma. Right lungs lobes were rinsed with Hank’s balanced salt solution (HBSS) to obtain bronchoalveolar lavage fluid (BALf), left lung lobes were collected and snap frozen for biochemical analysis. All animals were socially housed ($$n = 2$$–4/cage) in standard conditions (12 h/12h dark-light cycle, temp 21 ± 1°C) with ad libitum access to standard chow and water. All experiments were carried out at the central animal facility at Maastricht University. Protocols and procedures involving mice were approved by the Institutional Animal Care Committee of Maastricht University and the Central authority for scientific procedures on animals (CCD; AVD1070020198766). The group size was determined by power calculations based on previous experience with this model. All analyses derived from animal experiments were analyzed blinded. ## μCT Imaging and Assessment of Emphysema and Muscle Mass Animals were anesthetized using a mixture of air and isoflurane ($4\%$ induction, $2\%$ maintenance) and scanned using a micro cone beam CT (µCBCT) scanner (XRAD-225Cx, Precision X-Ray, North Branford) at an X-ray tube potential of 50 kVp and an X-ray current of 5.6 mA, giving an imaging dose of 0.3 Gy. The µCBCT projection data were reconstructed using the Feldkamp back-projection algorithm with a voxel dimension of 100 × 100 × 100 µm3 [30]. A mouse-mimicking CT calibration phantom with 12 cylindrical tissue-mimicking inserts of 3.5 mm diameter (SmART Scientific Solutions, Maastricht, the Netherlands) [31] was used to also calibrate the µCBCT scanner in mass density (g/cm3) units. Next, the lung volumes were manually segmented on every reconstructed µCBCT using the SmART-ATP software (version 1.3.6, Precision X-ray Inc.) [32], and the density of every segmented lung voxel was plotted in a mass density histogram. Low attenuation area (LAA) threshold was set from −871 (0.21 g/cm3) to −610 (0.45 g/cm3) Hounsfield units (HU). Muscle volumes were established using the convolutional neural network as previously described [33]. ## Lung Histology Lungs were fixed in formalin and embedded in paraffin. Sections 4 μm in thickness were cut with a microtome. Slides were dewaxed with xylene, rehydrated, and stained with Mayer’s hematoxylin and eosin (both VWR International B.V.). Stained slides were dehydrated and mounted with glass coverslips using a xylene-based mounting medium (DPX, Sigma-Aldrich). The sections were examined by light microscopy using a Nikon Eclipse E800 microscope (Nikon Instruments Inc.) with ZEN 3 software (Carl Zeiss Microscopy GmbH) to assess inflammatory infiltrate. ## ELISA Analysis Plasma corticosterone (R&D Systems Parameter KGE009, Minneapolis) and IL-6 (R&D Systems Quantikine M6000B, Minneapolis) levels were determined using a commercially available ELISA assay in accordance with the manufacturer’s instructions, and optical density was determined at 450 nm using a microplate reader. ## RNA Isolation and Analysis of Gene Expression Lung and muscle (gastrocnemius) RNA was extracted by mechanical suspension and lysis of powdered tissue in TRIzol reagent (Sigma-Aldrich Chemie B.V., the Netherlands). Phase separation and RNA precipitation were performed with the addition of isopropanol (Sigma-Aldrich Chemie B.V., the Netherlands) and glycogen (Invitrogen 10814). RNA precipitates were washed in $70\%$ ethanol and reconstituted in RNA storage solution (Invitrogen AM700) and stored at −80°C. Lung cDNA was generated using the Tetro cDNA Synthesis Kit (GC biotech) according to the manufacturer’s instructions. Expression of specific genes was assessed by real-time Polymerase chain reaction (PCR) on a LC480 software (v. 2014.1) and relative DNA starting quantities of the samples were derived using LinRegPCR software (v. 2014.0, Ruijter). Expression of genes of interest (Table 1) was normalized using GeNorm software by geometric average of three reference genes (Cyclophilin, RPLP0, and YWHAZ). Table 1. \| Gene \| Forward Primer (5′ to 3′) \| Reverse Primer (5′ to 3′) \| \| --- \| --- \| --- \| \| Cyclophilin \| TTCCTCCTTTCACAGAATTATTCCA \| CCGCCAGTGCCATTATGG \| \| RPLP0 \| GGACCCGAGAAGACCTCCTT \| GCACATCACTCAGAATTTCAATGG \| \| YWHAZ \| TGCTGGTGATGACAAGAAAGGAA \| AACACAGAGAAGTTGAGGGCCA \| \| CXCL2 \| CCCTGGTTCAGAAAATCATCCAAA \| TTTGGTTCTTCCGTTGAGGGAC \| \| IL-1RA \| GCCAGGACCGCTCAGAGA \| TGCCTCGACTGTTAGTCAAGCA \| \| GILZ \| GGGGCAGAGATGGGAGAGAT \| CCCAAAGCTGTAACCCCACA \| \| FoxO1 \| AAGAGCGTGCCCTACTTCAAGGATA \| CCATGGACGCAGCTCTTCTC \| Muscle cDNA was generated using the Multiscribe reverse transcription kit (Thermo Fisher Scientific, Loughborough, UK) according to the manufacturer’s instructions. Expression of specific genes was assessed by real-time PCR using TaqMan Gene Expression Assays (Thermo Fisher Scientific, Loughborough, UK) on an ABI7500 system (Applied Biosystems, Warrington, UK). Expressions of genes of interest (Thermo Fisher Scientific, Loughborough, UK) Fbxo32 (Atrogin-1, Mm00499523), Trim63 (Murf-1, Mm01185221), CXCL1 (Mm04207460), IκBα (Mm004777798), and IL-1β (Mm00434228) were normalized using GeNorm software by geometric average of three reference genes (GAPDH, HPRT, and YWHAZ). All data were expressed as arbitrary units (AU) using the calculation; DDCt = DCt(experimental group) − DCt(control group) and reported as fold-change = 2DDCt. ## Western Blot and Analysis of Protein Powdered gastrocnemius muscle and lysed in ice-cold IP-lysis buffer containing protease inhibitors (Complete; Roche Nederland, Woerden, the Netherlands), using a rotating blade tissue homogenizer (Polytron homogenizer, Kinematica). The total protein concentration of the supernatant was determined with a BCA protein assay kit (Pierce Biotechnology, Cat. No. 23225, Rockford, IL) according to the manufacturer’s instructions. Alternatively, the cell pellet obtained from BAL fluid was collected following centrifugation (10’, 1,500 g, 4°C) and stored at −80°C. Lysates were prepared and processed for Western blot analysis. Proteins were denatured in Laemmli buffer 100°C for 5 min. Ten micrograms of protein were separated on a Criterion XT Precast $4\%$–$12\%$ or $12\%$ Bis-Tris gel (Bio-Rad Laboratories, Veenendaal, the Netherlands) and transferred onto nitrocellulose transfer membrane (Bio-Rad Laboratories). The membrane was stained with Ponceau S solution ($0.2\%$ Ponceau S in $1\%$ acetic acid; Sigma-Aldrich Chemie) to control for protein loading. After blocking in $5\%$ nonfat dried milk, membranes were incubated at 4°C overnight with primary antibodies (Table 2). All antibodies were diluted 1:1,000 in Tris Buffered Saline (TBS)-Tween plus $5\%$ Bovine serum albumin (BSA) or $3\%$ skimmed milk. Signal detection used horseradish peroxidase-conjugated secondary antibody (1:10,000 in nonfat fried milk; Vector Laboratories, Burlingame, CA) and visualized with chemiluminescence (Supersignal West Pico or Femto Chemiluminescent Substrate; Pierce). Membranes were imaged (Amersham Imager 600, GE Life Sciences) and quantified using Image Quant software. Table 2. \| Antibody \| Cat. No. (Cell Signaling, UK, Unless Otherwise Stated) \| \| --- \| --- \| \| p-ULK1 (S757) \| 6888 \| \| ULK1 \| 8054 \| \| p-FoxO1 (S256) \| 9461 \| \| FoxO1 \| 2880 \| \| p-Akt (S473) \| 9271 \| \| Akt \| 9272 \| \| p-S6 (S235/236) \| 4858 \| \| S6 \| 2217 \| \| 4E-BP1 \| 9452 \| \| MyLC-3 (F310) \| AB 531863 (from DSHB, Iowa) \| \| LC3B \| 2775 \| \| F4/80 \| 123102 (from BioLegend, San Diego) \| ## Assessment of Postnatal Myonuclear Accretion Blood was centrifuged and plasma was collected. Cre-C2C12 (Cre-IRES-PuroR) cells were differentiated for 4 days, and myotube damage was induced on day 4 by incubating with HBSS in DM ($50\%$). After 25 h, LV-floxed-Luc (LV-flox-luc) myoblasts were added cells to Cre-C2C12 myoblasts and further incubated with culture medium ($5\%$ serum) for 3 days. Proliferating Cre-C2C12 myoblasts were cultured as above with the absence of myotube damage on day 4. On day 5, LV-flox-Luc cells were added to C2C12 myoblasts. Following a 6-h incubation, myotubes were incubated with corticosterone (250 nM; Sigma-Aldrich Chemie B.V, Netherlands, C2505) or dexamethasone (10 μM; Sigma-Aldrich Chemie B.V, Netherlands, D8893) dissolved in DMSO for 3 days. Luminescence was determined using a luminometer (Berthold Lumat LB9507, Belgium) and corrected for protein content. ## Human Myotube Culture Reagents were obtained from Sigma (Gillingham, UK) unless stated otherwise. Primary myoblasts obtained from healthy donors (CC‐2580; Lonza, Slough, UK) were maintained in-house in Skeletal Muscle Basal Medium‐2 (Lonza; CC‐3244 and CC‐3246) containing $0.1\%$ human epidermal growth factor, $2\%$ l‐glutamine, $10\%$ fetal bovine serum (FBS), and $0.1\%$ gentamicin/amphotericin‐B in the absence of GCs. Confluent myotubes were differentiated in Dulbecco’s modified Eagle’s medium (DMEM) containing $2\%$ horse serum (HS) for 120 h. Media were replaced every 2–3 days as previously reported [16]. ## Statistical Analysis Data are shown as means ± SE. Comparisons were computed using GraphPad Prism [34]. Following the assessment of Gaussian distribution, significance assessment was analyzed by unpaired t test, one-way and two-way ANOVA with Tukey’s post hoc analysis or Pearson correlation analysis, or nonparametric equivalent tests as appropriate. Statistical significance was defined as P value < 0.05 ($P \leq 0.05$; $P \leq 0.01$; *$P \leq 0.001$; no asterisk or NS, $P \leq 0.05$). ## Confirmation of AE-COPD Model in WT and 11βHSD1/KO Mice To investigate the role of 11β-HSD1 in muscle wasting during AE-COPD, WT and 11βHSD1/KO mice were intratracheally instilled with elastase, followed by instillation of LPS or vehicle control to evoke a pulmonary inflammatory response in an emphysematous background (Supplemental Fig. S1) [29, 35]. To determine emphysema development in both WT and 11βHSD1/KO animals following intratracheal instillation of elastase, μCBCT scans were taken (Fig. 1A) and lung density histograms were made for each mouse lung (Fig. 1B). In line with the left-ward shift suggesting a decrease in the density of the lung tissue, increased LAA of the lungs on day 12 (D12) compared with any prior elastase treatments on D–1 confirmed the presence of emphysema in WT and 11βHSD1/KO mice (fold-change WT = 1.5, $P \leq 0.001$; 11βHSD1/KO = 1.9, $P \leq 0.001$), with no significant differences seen between animal genotypes (Fig. 1D). Emphysema was also apparent from the enlarged alveolar space observed in all animals (Fig. 1G). Subsequently, emphysematous WT and 11βHSD1/KO animals were intratracheally instilled with either LPS, to evoke a pulmonary inflammatory response or PBS. Inflammatory* gene expression markers IL-1RA (fold-change WT = 15.3, $$P \leq 0.07$$; 11βHSD1/KO = 8.2, $$P \leq 0.07$$; Fig. 1C) and CXCL2 (fold-change WT = 55, $$P \leq 0.04$$; 11βHSD1/KO = 41, $$P \leq 0.07$$; Fig. 1E) in lung tissue of mice treated with LPS were increased compared with WT counterparts for CXCL2, although only showed statistical significance in WT animals. Figure 1.: WT and 11β-HSD1/KO mice intratracheally instilled with elastase and LPS developed emphysema and pulmonary inflammation, respectively. μCT scans were obtained 1 day before (D1) and 12 days following (D12) intratracheal instillation of elastase in WT (n = 12) and 11βHSD1/KO (n = 11) mice (A) and were analyzed for LAA, with a LAA threshold of −871 (0.21 g/cm3) to −610 (0.45 g/cm3) Hounsfield units (HU; B–D). Male mice then received a single bolus of PBS or LPS (n = 7–9/group) and lungs and BALf collected after 48 h for mRNA and protein analysis. Gene expression (AU) of Lung IL-1RA (C) and CXCL2 (E) were determined and expressed as fold-change compared with control. Total protein concentrations of BALf pellet (F) were determined and expressed as fold-change compared with control. Histological analysis of lung cross-sections (magnification ×400) were analyzed for macrophage and granulocyte infiltration following PBS and LPS treatment in WT and 11βHSD1/KO animals (black arrowhead: alveolar macrophage; white arrowhead: infiltrating granulocyte; G). Body weights were taken before euthanasia (H) expressed as means ± standard error. Statistical significance was determined using two-way analysis with Tukey’s post hoc analysis and unpaired t test. P < 0.05, **P < 0.001. AU, arbitrary units; BALf, bronchoalveolar lavage fluid, 11βHSD1/KO, 11βHSD1 global genetic deletion; Em, enlarged alveolar space; LAA, low attenuation area; WT, wild type. In addition, total protein obtained from bronchoalveolar lavage fluid (BALf) pellet increased (WT: P ≤ 0.05) or tended to increase (11βHSD1/KO = $$P \leq 0.051$$) in response to LPS, suggestive of inflammatory cell infiltration in the lumen of the lungs (Fig. 1F). This was supported by an observed increase in levels of the macrophage marker F$\frac{4}{80}$ in BALf cell pellets (Supplemental Fig. S2, A and B), although this was not significant in 11βHSD1/KO, and increased alveolar macrophage and granulocyte infiltration apparent in lung tissue of WT and 11βHSD1/KO animals treated with LPS (Fig. 1G). To assess the systemic impact of AE-COPD, body weights were assessed 48 h postinstallation (Fig. 1H). Both WT and 11βHSD1/KO animals showed a significant reduction in body weights following LPS treatment compared with PBS controls (−$12\%$, $P \leq 0.05$ and −$13\%$, $P \leq 0.05$, respectively; Supplemental Fig. S3B), with no significant differences seen between LPS-treated WT and 11βHSD1/KO animals. Combined, these data demonstrate that the lung inflammatory response and body weight loss as a systemic consequence of AE-COPD are preserved in emphysematous 11βHSD1/KO mice. ## Muscle Wasting Is More Pronounced in LPS-Treated Emphysematous 11βHSD1/KO Mice To investigate the involvement of 11β-HSD1 in the impact of lung inflammation on muscle wasting in this model [28, 29], M. gastrocnemius wet weights were assessed, showing a significant reduction in LPS-treated compared with PBS control animals of both genotypes (−$7\%$, $P \leq 0.005$ and −$13\%$, $P \leq 0.0001$ respectively; Fig. 2B). In addition, hind limb muscle mass was calculated using noninvasive μCBCT scans (Fig. 2, A and C) of hind limb muscles 48 h post-LPS instillation (Supplemental Fig. S3B) Importantly, calculating individual muscle mass changes derived from μCBCT scans obtained from animals pre- and post-PBS/LPS instillation, revealed a stronger reduction in muscle mass WT compared with 11βHSD1/KO animals treated with LPS (−$3.4\%$ vs. −$9.3\%$, $P \leq 0.0005$; Fig. 2C). Figure 2.: LPS-treated emphysematous 11βHSD1/KO mice show exacerbated muscle wasting compared with WT controls. μCT scans were taken pre- and posttreatment of LPS for 48 h in male WT and 11βHSD1/KO mice (n = 7–9/group; A) and changes in muscle mass of total hind limb (C) were determined. Gastrocnemius wet muscle weights were measured and expressed relative to the start body weight (B). Protein levels of MyLC-3 (D) were assessed, normalized to Tubulin, and expressed as fold-change compared with control with representative western blot images. Gene expression (AU) of homogenized gastrocnemius Atrogin-1 (E) and MuRF-1 (F) levels were determined and expressed as fold-change compared with control. Values are expressed as means ± standard error. Statistical significance was determined using two-way analysis with Tukey’s post hoc analysis and unpaired t test. P < 0.05, P < 0.005, P < 0.001, **P < 0.0001. 11βHSD1/KO, 11βHSD1 global genetic deletion; BW, body weight; M. Gastroc., gastrocnemius; WT, wild type. Accordingly, a significant decrease ($P \leq 0.05$) in MyLC3 protein content was observed following LPS in the muscle of 11βHSD1/KO mice, whereas in WT mice only a trend toward a decrease ($$P \leq 0.09$$) was observed (Fig. 2D). To determine the effects of AE-COPD on muscle wasting in WT and 11βHSD1/KO animals we examined Atrogin-1 and MuRF-1 expression levels as these E3 ligases are involved in muscle proteolytic responses [36]. LPS treatment significantly induced Atrogin-1 mRNA levels in the gastrocnemius muscle after 48 h in both WT and 11βHSD1/KO mice (fold-change WT = 2.5, $P \leq 0.05$; 11βHSD1/KO = 5.1, $P \leq 0.005$; Fig. 2E). Although the LPS-induced increase in Atrogin-1 mRNA levels appeared more pronounced (∼ twofold) in the muscle of 11βHSD1/KO compared with WT animals, this was not significantly different. In contrast, only in 11βHSD1/KO muscle MuRF-1 levels tended to increase in response to LPS (fold-change 11βHSD1/KO = 3.2, $$P \leq 0.07$$), whereas no changes were present in the muscle of WT animals (Fig. 2F). Combined, these data show more pronounced muscle wasting in 11βHSD1/KO animals following AE-COPD. ## Suppression of Catabolic Signaling Is Reduced in the Muscle of 11βHSD1/KO Compared with WT Animals in Response to LPS For further insight into catabolic processes in the gastrocnemius muscle, we examined protein levels and phosphorylation status of markers of the autophagy lysosomal pathway using Western blot analysis (Fig. 3, A–L). LC3B lipidation, resulting in an altered migration pattern (LC3B-II), is an important step in autophagy. Although LC3B-II abundance was not significantly altered (Fig. 3A), LC3B-I levels were significantly elevated following LPS in both WT and 11βHSD1/KO mice (Fig. 3B). Importantly, the LC3BII/I ratio, indicative of autophagic flux [37], was significantly reduced in LPS-treated WT but not 11βHSD1/KO animals (Fig. 3, A–D). In addition, ULK1 (Ser757) phosphorylation inhibits autophagosome formation [38], and was increased in WT but not 11βHSD1/KO mice in response to LPS (fold-change WT = 1.4, $P \leq 0.05$; 11βHSD1/KO = 1.3, $$P \leq 0.193$$; Fig. 3E), suggesting attenuation of autophagic signaling in WT animals although this increase was not reflected in p-ULK1/Total ULK1 ratios (Fig. 3G). The transcriptional activity of FoxO1 is also involved in control of autophagy and is inactivated through phosphorylation on Ser256 [39]. Treatment with LPS increased phosphorylation of FoxO1 (Ser256; fold-change WT = 3.9 $P \leq 0.001$; 11βHSD1/KO = 3.5 $P \leq 0.001$; Fig. 3I) and Total FoxO1 (fold-change WT = 1.9 $P \leq 0.005$; 11βHSD1/KO = 2 $P \leq 0.001$; Fig. 3J) protein levels across both groups. Despite this, FoxO1 p/total ratio clearly increased in WT (fold-change WT = 1.9 $P \leq 0.005$) or tended to increase in 11βHSD1/KO mice (fold-change 1.7 $$P \leq 0.057$$) following LPS treatment (Fig. 3K). Figure 3.: Catabolic signaling in the muscle of WT and 11βHSD1/KO animals in response to LPS. 48 h after PBS or LPS treatment in male WT and 11βHSD1/KO mice (n = 7–9/group), gastrocnemius muscle was collected for mRNA and protein analysis. Gastrocnemius protein levels of LC3B-II (A) and LC3B-I (B) were assessed, normalized to Tubulin, and expressed as fold-change compared with control, with representative western blot images (D). Phosphorylated ULK1(S575) (E) and FoxO1(S256) (I), and total ULK1 (F) and FoxO1 (J) were assessed, normalized to Ponceau staining and expressed as fold-change compared with control with representative western blot images (H and L). Ratios LC3B-II/C3B-I (C), and phosphorylated and total ULK1 (G) and FoxO1 (K) were assessed and shown as fold-change compared with control. Statistical significance was determined using two-way analysis with Tukey’s post hoc analysis and unpaired t test. P < 0.05, P < 0.005, P < 0.001. AU, arbitrary units; 11βHSD1/KO, 11βHSD1 global genetic deletion; WT, wild type. Combined, these data show a coherent suppression of catabolic autophagy signaling following LPS, which is apparent at multiple levels in muscle of WT animals but not in 11βHSD1/KO mice. ## Anabolic Signaling Is Activated in the Muscle of WT Compared with 11βHSD1/KO Emphysematous Mice Following LPS We next determined whether recovery mechanisms following of AE-COPD-induced muscle atrophy extended to anabolic signaling in the gastrocnemius muscle of WT and 11βHSD1/KO animals. Akt was investigated as an upstream regulator of protein synthesis (Fig. 4, A–C and H) [39]. In LPS-treated WT mice, a significant increase in phosphorylation of Akt was observed (fold-change = 1.5, $P \leq 0.005$; Fig. 4A), which was not apparent in 11βHSD1/KO animals. Although this differential increase was also observed for p/total Akt ratio, it was not significant (fold-change =1.3, $$P \leq 0.118$$; Fig. 4C) due to small changes in total Akt levels (Fig. 4B). In addition, phosphorylation status of S6 (Ser$\frac{235}{236}$), which determines mRNA translation in protein synthesis, was examined (Fig. 4, D–F, and H). Both WT and 11βHSD1/KO animals treated with LPS showed increases in p-S6, with the former having a significant increase (fold-change WT = 1.7, $P \leq 0.005$; 11βHSD1/KO = 1.7, $$P \leq 0.112$$; Fig. 4D). This pattern also apparent for p/total S6 ratios (fold-change WT = 1.9, $P \leq 0.05$; 11βHSD1/KO = 1.8, $$P \leq 0.187$$; Fig. 4F) and hyper-phosphorylation of 4E-BP1 (fold-change WT = 2.9, $P \leq 0.01$; 11βHSD1/KO = 4, $P \leq 0.05$; Fig. 4, G–H). Figure 4.: Anabolic signaling in the muscle of WT and 11βHSD1/KO animals in response to LPS. 48 h after PBS or LPS treatment in male WT and 11βHSD1/KO mice (n = 7–9/group), gastrocnemius muscle was collected for mRNA and protein analysis. Gastrocnemius protein levels of phosphorylated Akt (S475) (A) and S6 (s235/236) (D), and total Akt (B) and S6 (E) were assessed, normalized to Ponceau staining and expressed as fold-change compared with control with representative western blot images (H). Ratios of phosphorylated and total Akt (C) and S6 (F) were assessed and shown as fold-change compared with control. Hyper-phosphorylation of 4E-BP1 was assessed (G). Statistical significance was determined using two-way analysis with Tukey’s post hoc analysis and unpaired t test. P < 0.05, *P < 0.005. AU, arbitrary units; 11βHSD1/KO, 11βHSD1 global genetic deletion; WT, wild type. Taken together, these data may suggest that 11βHSD1/KO mice treated with LPS have a delayed reactivation of protein synthesis signaling and reduced initiation of a recovery response in skeletal muscle compared with WT counterparts. ## Plasma Corticosterone Levels Are Increased in LPS Treated 11βHSD1/KO Animals and Suppress In Vitro Myonuclear Accretion To investigate whether altered muscle recovery responses in WT and 11βHSD1/KO mice reflect the actions of circulating mediators, we examined the impact of the plasma of these animals in a C2C12 model of myonuclear accretion, a process essential for skeletal muscle growth and repair. Mouse plasma was added to damaged C2C12-Cre myotubes cocultured with LV-flox-Luc C2C12 myoblasts and postnatal myonuclear accretion was assessed (Fig. 5A). Only in cultures incubated with the plasma of LPS-treated WT mice a trend to significant increase ($$P \leq 0.051$$) in myonuclear accretion was observed (Fig. 5B). Instead, plasma corticosterone levels were increased but only in LPS-treated 11βHSD1/KO mice compared with WT controls (fold-change = 1.4, $P \leq 0.05$; Fig. 5C) and direct comparison between LPS treated WT and 11βHSD1/KO mice showed plasma corticosterone levels significantly higher in 11βHSD1/KO than WT animals (fold-change = 1.5, $P \leq 0.005$). Importantly, increased gastrocnemius mRNA levels of Gilz, a glucocorticoid responsive gene, were only observed in LPS-treated 11βHSD1/KO mice (fold-change WT = 1.2 $$P \leq 0.756$$; 11βHSD1/KO = 1.8 $P \leq 0.05$; Fig. 5D), indicative of CORT-induced signaling in affected skeletal muscle. Human primary cultures treated with corticosterone showed significant increases in Gilz and FoxO1 mRNA expression (Fig. 5, E and F). We then examined the impact of elevated corticosterone levels in a murine primary myotube and C2C12 culture. In vitro investigation of the effects of GCs on postnatal myonuclear accretion showed significant inhibition by both corticosterone and synthetic GC dexamethasone (fold-change CORT = 0.5 $P \leq 0.05$; DEX = 0.3 $P \leq 0.005$; Fig. 5G). Figure 5.: Glucocorticoid levels in plasma and muscle LPS treated WT and 11βHSD1/KO animals and in vitro analysis of myonuclear accretion when treated with glucocorticoids. In vitro postnatal myonuclear accretion of damaged C2C12 myotubes incubated with mouse plasma (n = 7–9/group) was determined by luciferase activity (A and B). Plasma corticosterone levels determined by ELISA (C; n = 7–9/group). Gene expression (AU) of GILZ from homogenized gastrocnemius expressed as fold-change compared with control (D). Gene expression (AU) of GILZ (E) and FoxO1 (F) of in vitro human primary myotubes treated with cortisol (100 nM; n = 3). Effects of corticosterone (250 nM) or dexamethasone (10 μM; 72 h) on postnatal myonuclear accretion in vitro (n = 3; G), relative luciferase activity (RLU/protein). Statistical significance was determined using two-way analysis with Tukey’s post hoc analysis and unpaired t test. P < 0.05, *P < 0.005. AU, arbitrary units; 11βHSD1/KO, 11βHSD1 global genetic deletion; Cort, corticosterone; Dex, dexamethasone; WT, wild type. [Image created with BioRender.com and published with permission.] These data demonstrate that 11βHSD1/KO animals have a greater GC response to LPS than WT counterparts in vivo. In addition, GCs can impair myonuclear accretion in vitro, therefore may impair muscle recovery following damage. ## DISCUSSION Both enhanced systemic inflammation and elevated glucocorticoid responses have been implicated as important drivers of muscle wasting (17, 40–43) in acute exacerbations of COPD (AE-COPD) and pulmonary inflammation-induced muscle loss. 11 β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) is a prereceptor regulator and gatekeeper of GC action, and its role in this context remains poorly defined. The aim of this study was to determine the contribution of 11β-HSD1 in muscle atrophy associated with acute exacerbations of COPD and test the hypothesis that its transgenic deletion in a mouse model of AE-COPD can abrogate GC-induced muscle wasting in this disease model setting. Although the majority of AE-COPD is associated with a respiratory infection [44], the findings in our model utilizing the bacterial component LPS to induce pulmonary and systemic inflammation, require confirmation in models using other potential triggers of AE, such as viral pathogens to expand translatability to AE-COPD patients. We observed increased muscle atrophy in animals with global deletion of 11β-HSD1 following AE-COPD relative to their wild-type counterparts, characterized by increased measures of Ubiquitin 26S-proteasome (UPS) mediated degradation and suppressed markers of muscle anabolism and recovery. These findings were contrary to the original hypothesis based on murine models of corticosteroid excess, where we postulated that global deletion of 11β-HSD1 would protect from the anti-anabolic/catabolic actions of corticosteroids in muscle [45, 46]. Instead, they more closely reflected observations of exacerbated muscle wasting previously reported in murine models of chronic inflammation with global deletion of 11β-HSD1, where the loss of local glucocorticoid reactivation fueled increased catabolic wasting [16]. The most evident cause of the discrepancy between these studies may relate to the dosing of corticosteroid exposure, where 11β-HSD1 deletion protected from muscle wasting at higher therapeutic doses and exacerbated wasting when endogenous corticosteroid levels were examined. In this study, the increased muscle wasting observed in animals with transgenic deletion of 11β-HSD1 correlated with a marked increase in circulating endogenous corticosteroids and muscle GR signaling in response to AE-COPD. Endogenous circulating GCs are significantly upregulated through the hypothalamic-pituitary-adrenal (HPA) axis, in response to various acute inflammatory stressors, including sepsis and the acute exacerbation of COPD [47, 48]. Although elevated levels of endogenous circulating GCs support the suppression of systemic inflammation and improve survival outcomes [49], the increased activation of GC signaling in skeletal muscle drives proteolysis and reduced anabolic signaling. Previous studies have implicated the importance of GC signaling in mediating muscle atrophy in murine models of sepsis and inflammation-associated muscle wasting [50]. Here, the targeted deletion of GR in mouse muscle showed prevention of muscle wasting in models of LPS-induced sepsis and in cancer-associated muscle wasting [50]. Similarly, atrogene expression in rat muscle was blunted in response to LPS with inhibition of GR using RU-486 [51], further emphasizing the importance of GC signaling in skeletal muscle with inflammatory-associated muscle wasting. The enzyme 11β-HSD1 mediates cellular GC action through its conversion of inactive GCs to their active counterparts [52]. In prior studies, its transgenic deletion in models of corticosterone excess resulted in marked protection from GC-induced muscle wasting (including in models of inflammatory polyarthritis), preventing the activation of GC-induced catabolic pathways including the transcription factor FoxO1 and muscle-specific E3 ligases Atrogin-1 and MuRF-1 [45, 46]. In contrast, its transgenic deletion in chronic models of TNF-α driven inflammatory polyarthritis in the absence of exogenous therapeutic GCs exacerbates inflammation-induced muscle wasting [16]. These studies reveal the duality of the roles of 11β-HSD1 in muscle in inflammatory disease, where it mediates the muscle-protective anti-inflammatory properties of endogenous GCs, as well as mediating direct GC-induced muscle wasting in response to exogenous therapeutic GCs [16]. Although activation of the NF-κB pathway within muscle has been shown to be an important component of muscle wasting in following pulmonary inflammation [13], transgenic deletion of 11β-HSD1 did not enhance inflammatory signaling within the muscle (Supplemental Fig. S4) [13]. These results would indicate that 11β-HSD1 appears to play a limited role in suppressing local muscle inflammation, and inflammatory muscle wasting in this model of AE-COPD. Systemic markers of inflammation showed a similar trend across both WT and 11BKO animals, with levels of IL-6 in response to LPS showing a comparable induction in both groups, suggesting that the severity of AE inflammation did not appear to mediate the differences in muscle wasting observed between WT and 11βHSD1/KO animals in this model (Supplemental Fig. S5). However, although measures of inflammation were comparable between WT and 11βHSD1/KO animals, circulating levels of endogenous GCs showed a significant divergence with corticosterone being significantly elevated in 11βHSD1/KO animals following AE-COPD. This was accompanied by a significant increase in measures of GC signaling in muscle, with a marked increase in the response gene Gilz in 11βHSD1/KO animals [53]. Together these results suggest that the muscles during AE-COPD, from 11βHSD1/KO animals are subjected to increased catabolic and antianabolic GC signaling as a result of elevated circulating GC exposure. Previously, elevated activation of the HPA axis and GC signaling within muscle have been shown to contribute to muscle atrophy in LPS-induced inflammation [8]. This increased atrophy and GC response indicated that the HPA axis plays a pivotal role in driving inflammatory muscle catabolism. Proinflammatory mediators such as IL-6 and TNF-α are potent drivers of HPA axis activation and corticosteroid release [54]. This process of HPA axis upregulation by proinflammatory mediators is in turn negatively regulated by circulating GCs in a negative feedback loop that facilitates the resolution of circulating GCs levels. 11β-HSD1 has been shown to be highly expressed, and dynamically regulated within the hypothalamus, with its local amplification of GCs playing a role in facilitating negative feedback of circulating corticosteroids in the HPA axis [55]. This loss of 11β-HSD1 within the hypothalamus in 11βHSD1/KO animals may be one factor resulting in the elevated levels of circulating GCs and muscle wasting during AE-COPD in 11βHSD1/KO animals. GCs induce muscle atrophy through several pathways, driving antianabolic [56, 57], and catabolic signaling primarily through activation of the UPS [36]. Upon exposure to GCs, transcription factor FoxO1 increases in expression and activity [58], in turn, activating atrogenes such as Atrogin-1 and MuRF-1 [59]. Increased FoxO1 transcript levels in primary human myotubes in response to cortisol confirm these effects of GCs, and the increases in FoxO1 protein levels in WT and 11βHSD1/KO muscle reflect preceding transcriptional GR actions induced by GCs. FoxO1 activity is subject to the regulation of its nuclear export following phosphorylation by Akt [39], and the kinetics of this inhibitory phosphorylation inversely correlate with muscle mass loss in response to pulmonary inflammation [60]. We propose that increased circulating GCs may drive reduced inhibitory FoxO1 phosphorylation in AE-COPD in LPS-11βHSD1/KO animals contributing to the aggravated muscle loss we observe. In line with previous observations in the related model of pulmonary inflammation, protein synthesis signaling through the IGF-1/Akt/mTOR pathway is reactivated 48 h following LPS in the muscle of WT animals, which signified initiation of muscle mass recovery in that study [60]. Although mTOR activity was not directly measured in this study, increased phosphorylation of its indirect and direct downstream targets, i.e., S6, 4E-BP1, and ULK1 suggests activation of mTOR signaling in WT muscle. As the phosphorylation of Akt and the downstream mTOR targets is consistently lower or absent, this suggests attenuated protein synthesis signaling in muscle of 11βHSD1/KO compared with WT animals following AE-COPD. Such anti-anabolic effects of GCs on muscle by antagonism of the IGF-1/Akt/mTOR pathway have been well established [56, 57]. In vivo treatment of methylprednisolone, a synthetic GC, in male rats showed significant reductions in IGF-1 mRNA expression in the gastrocnemius [61]. It has been previously demonstrated that Akt signaling is impaired by nongenomic actions of GR by GCs, which block IRS-1-PI3K interactions, resulting in reduced Akt phosphorylation and suppression of downstream mTOR signaling [62]. Although phosphorylation of S6 kinase and 4E-BP1 are instrumental for initiating mRNA translation, GC treatment of myoblasts significantly reduces S6 phosphorylation, illustrating the direct inhibitory actions of GCs on protein synthesis [56]. Taken together, we speculate attenuated anabolic signaling in LPS-11βHSD1/KO mice compared with WT counterparts reflects the impact of residual elevations in circulating GCs and contributes to reduced muscle mass. Further measures of muscle anabolism, such as assessment of protein synthesis using puromycin incorporation are required to effectively validate these observations [63]. Another process involved in muscle mass maintenance and recovery is postnatal myogenesis, in which satellite cells differentiate and fuse with existing fibers [64]. Myogenesis has been shown to be inhibited by systemic cues, including inflammatory cytokines and GCs [65], resulting in reduced satellite cell proliferation and differentiation [66], and myonuclear accretion [67]. We modeled postnatal myogenesis-driven muscle recovery in vitro to assess the presence of circulating mediators in plasma that impact this process. Myonuclear accretion in response to a standardized atrophic stimulus has a trend toward attenuation in presence of plasma from LPS-11βHSD1/KO mice compared with WT counterparts. Although no difference in inflammatory cytokine IL-6 levels is present (Supplemental Fig. S5), the significant increases in corticosterone plasma levels suggest GCs directly interfere with myonuclear accretion. In line with this notion, treatment with corticosterone or dexamethasone is sufficient to inhibit myonuclear accretion. Although not assessed in a postnatal myogenesis set-up as done here, recent studies have shown inhibition of myogenesis in C2C12 cells following dexamethasone and cortisone treatment, in line with our findings [68]. Combined, the elevated circulating GCs levels present in LPS-11βHSD1/KO mice may be responsible for suppressed protein synthesis signaling and impaired myonuclear accretion, contributing to exaggerated muscle wasting. In this study, we deployed a murine model of AE-COPD driven by repeated elastase instillation and LPS induction of pulmonary inflammation, characterized by emphysema, pulmonary and systemic inflammation, and muscle atrophy [29]. Global deletion of 11β-HSD1 allows for translational extrapolation to the use of therapeutic 11β-HSD1 inhibitors. However, our study indicated that this approach does not prevent muscle wasting elicited by AE-COPD. However, a targeted, muscle-specific 11β-HSD1/KO approach would help disentangle deleterious and positive actions of both local and systemic actions of 11β-HSD1. This study highlights the important role of 11β-HSD1 in mediating muscle wasting during an acute exacerbation of COPD. We show that with the transgenic deletion of 11β-HSD1 during AE-COPD, there is a compensatory increase in circulating corticosterone during the resolution phase of pulmonary and systemic inflammation, which correlates with sustained activation of UPS-mediated proteolysis and reduced muscle anabolic signaling. Based on these findings, the use of therapeutic 11β-HSD1 inhibitors during an acute exacerbation of COPD may not be appropriate in this setting. This study shows that 11β-HSD1 inhibition does not prevent but aggravates muscle wasting during an acute exacerbation of COPD. ## DATA AVAILABILITY Data will be made available upon reasonable request. ## GRANTS This research was supported by Versus Arthritis Grants (Reference: 19859 and 20843). ## DISCLOSURES No conflicts of interest, financial or otherwise, are declared by the authors. ## AUTHOR CONTRIBUTIONS F.V., G.G.L., A.M.W.J.S., R.S.H., and R.C.J.L. conceived and designed research; J.M.W., K.W., W.R.P.H.v.d. W., M.C.J.M.K., S.L., C.M., and R.C.J.L. performed experiments; J.M.W., K.W., M.C.J.M.K., C.M., B.V.d. H., C.S., R.S.H., and R.C.J.L. analyzed data; J.M.W., K.W., C.M., R.S.H., and R.C.J.L. interpreted results of experiments; J.M.W., K.W., B.V.d. H., R.S.H., and R.C.J.L. prepared figures; J.M.W. drafted manuscript; J.M.W., W.R.P.H.v.d. W., M.C.J.M.K., S.L., C.M., F.V., B.V.d. 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--- title: 11β-HSD1 inhibition does not affect murine tumour angiogenesis but may exert a selective effect on tumour growth by modulating inflammation and fibrosis authors: - Callam T. Davidson - Eileen Miller - Morwenna Muir - John C. Dawson - Martin Lee - Stuart Aitken - Alan Serrels - Scott P. Webster - Natalie Z. M. Homer - Ruth Andrew - Valerie G. Brunton - Patrick W. F. Hadoke - Brian R. Walker journal: PLOS ONE year: 2023 pmcid: PMC10027213 doi: 10.1371/journal.pone.0255709 license: CC BY 4.0 --- # 11β-HSD1 inhibition does not affect murine tumour angiogenesis but may exert a selective effect on tumour growth by modulating inflammation and fibrosis ## Abstract Glucocorticoids inhibit angiogenesis by activating the glucocorticoid receptor. Inhibition of the glucocorticoid-activating enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) reduces tissue-specific glucocorticoid action and promotes angiogenesis in murine models of myocardial infarction. Angiogenesis is important in the growth of some solid tumours. This study used murine models of squamous cell carcinoma (SCC) and pancreatic ductal adenocarcinoma (PDAC) to test the hypothesis that 11β-HSD1 inhibition promotes angiogenesis and subsequent tumour growth. SCC or PDAC cells were injected into female FVB/N or C57BL6/J mice fed either standard diet, or diet containing the 11β-HSD1 inhibitor UE2316. SCC tumours grew more rapidly in UE2316-treated mice, reaching a larger ($P \leq 0.01$) final volume (0.158 ± 0.037 cm3) than in control mice (0.051 ± 0.007 cm3). However, PDAC tumour growth was unaffected. Immunofluorescent analysis of SCC tumours did not show differences in vessel density (CD31/alpha-smooth muscle actin) or cell proliferation (Ki67) after 11β-HSD1 inhibition, and immunohistochemistry of SCC tumours did not show changes in inflammatory cell (CD3- or F$\frac{4}{80}$-positive) infiltration. In culture, the growth/viability (assessed by live cell imaging) of SCC cells was not affected by UE2316 or corticosterone. Second Harmonic Generation microscopy showed that UE2316 reduced Type I collagen ($P \leq 0.001$), whilst RNA-sequencing revealed that multiple factors involved in the innate immune/inflammatory response were reduced in UE2316-treated SCC tumours. 11β-HSD1 inhibition increases SCC tumour growth, likely via suppression of inflammatory/immune cell signalling and extracellular matrix deposition, but does not promote tumour angiogenesis or growth of all solid tumours. ## Introduction Glucocorticoids are vital modulators of the physiological stress response, exerting myriad effects across a range of tissues [1]. Their potent anti-inflammatory and immunosuppressive effects have also been exploited clinically for more than half a century; synthetic glucocorticoids are commonly used to treat chronic inflammatory conditions such as rheumatoid arthritis, to suppress the immune system before organ transplant, and in the treatment of leukemia [2]. The adverse consequences of chronic glucocorticoid excess are exemplified in people with Cushing’s syndrome, who develop increased central adiposity, dyslipidemia, muscle wasting, loss of memory, hyperglycaemia and insulin resistance [1]. Reducing glucocorticoid action in key target tissues, such as liver, adipose and brain, may therefore be clinically desirable, but targeting the hypothalamic-pituitary-adrenal (HPA) axis risks compromising the systemic coordination of the stress response. Glucocorticoids are subject to tissue-specific pre-receptor regulation by the 11β-hydroxysteroid (11β-HSD) isozymes; 11β-HSD2 converts cortisol or corticosterone to inert 11-keto metabolites (cortisone or 11-dehydrocorticosterone, respectively) to allow selective access of aldosterone to mineralocorticoid receptors (MR), while 11β-HSD1 re-activates glucocorticoids by catalyzing the reverse reductase reaction in target tissues [3], including liver, adipose, brain and the blood vessel wall [4]. Preclinical investigations have demonstrated that 11β-HSD2 inhibition can reduce myocardial fibrosis in a rat (uni-nephrectomy) model [5] whilst inhibition of 11β-HSD1 reduced hepatic steatosis in mice fed a high fat diet [6]. Targeting 11β-HSD1 offers a novel therapeutic avenue to reduce glucocorticoid action. Clinical trials of 11β-HSD1 inhibitors have shown moderate improvements in glycaemic control in patients with type II diabetes [7], and more recently have shown promise in the treatment of cognitive decline [8]. Glucocorticoids also exert potent angiostatic effects, an activity first shown over 30 years ago but the mechanism of which remains uncertain [9]. Inhibition or deletion of 11β-HSD1 promotes angiogenesis in vitro and in vivo, enhancing wound healing, reducing intra-adipose hypoxia and, most strikingly, enhancing recovery after myocardial infarction in mice [10–14]. Whilst presenting a potential clinical opportunity, these findings have also raised concerns that 11β-HSD1 inhibitors could exacerbate conditions characterised by pathological angiogenesis, such as proliferative diabetic retinopathy and solid tumour growth [15]. Whereas 11β-HSD1 inhibition or deletion was recently shown not to promote angiogenesis in a model of proliferative retinopathy [16], there is evidence to suggest it could influence tumour growth [17]. Moreover, not only might 11β-HSD1 inhibitors act in vascular cells to promote tumour angiogenesis, but they might also directly influence tumour cells as well as other cells in the tumour microenvironment, including fibroblasts, and tumour-associated immune cells [18]. The only study to address this topic thus far demonstrated that overexpression of 11β-HSD1 in hepatocellular carcinoma cells reduced tumour growth and angiogenesis [17]. No study has yet examined the effects of 11β-HSD1 inhibition on tumour growth. Of note, expression of 11β-HSD1 and the glucocorticoid receptor (GR) are particularly high in squamous cell carcinoma (SCC) [19], highlighting this tumour type as potentially glucocorticoid-sensitive. The present study tested the hypothesis that 11β-HSD1 inhibition promotes the growth of subcutaneously-implanted SCC and pancreatic ductal adenocarcinoma (PDAC) tumours in mice, as a result of increased tumour angiogenesis. ## Animals In total, 18 C57BL6/J and 18 FVB/N mice were purchased from Envigo (Blackthorn, UK) or Charles River (Elphinstone, UK), respectively. All experimental animals were female and aged 9–14 weeks and sacrificed by cervical dislocation. Groups were age-matched. All procedures were approved by the institutional ethical committee and carried out by a licensed individual and in strict accordance with the Animals (Scientific Procedures) Act 1986 and the EU Directive $\frac{2010}{63}$ and under project licence $\frac{70}{8897}$ or $\frac{60}{4523.}$ ## Cell culture Studies made use of two immortalised murine cancer cell lines. SCC cells [20] were generated in-house by Dr Alan Serrels using a two-stage 7,12-Dimethylbenz[a]anthracene (DMBA)/TPA chemical carcinogenesis protocol [21]. A PDAC cell line, Panc043, was provided by the Beatson Institute in Glasgow; these cells were originally derived from tumours developed using the LSL-KrasG12D/+;LSL-Trp53R172H/+;Pdx-1-Cre (KPC) model [22]. Panc-043 cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with $10\%$ FCS. SCC cells were maintained in Glasgow Minimum Essential Medium (GMEM) supplemented with $10\%$ FCS, 2mM L-Glutamine, 1mM sodium pyruvate, MEM non-essential amino acids (Thermo-Fisher) and MEM vitamins. Tumour cells (SCC or Panc043) were cultured in 96-well plates (Bio-Greiner; 5000 cells per well) and treated with 25-300nM UE2316 or corticosterone. Plates were imaged and confluence determined using the Incucyte ZOOM Live-cell analysis system (over 72 hours; Essen BioScience). An alamarBlue assay (Thermo-Fisher) was also performed as per manufacturer’s instructions to provide a secondary measure of viable cell number. ## Drugs and corticosteroids The 11β-HSD1 inhibitor UE2316 ([4-(2-chlorophenyl-4-fluoro-1-piperidinyl][5-(1H-pyrazol-4-yl)-3-thienyl]-methanone) was synthesised by High Force Ltd (Durham, UK) [23]. For in vivo studies, UE2316 was delivered ad libitum to animals added to a RM1 diet (175mg/kg UE2316) prepared by Special Diet Services (Essex, UK). 11-Dehydrocorticosterone and corticosterone were from Steraloids (Newport, USA). Tritiated steroids ([1,2,6,7]-3H4-corticosterone and [1,2,6,7]-3H4-cortisone) were from PerkinElmer (Wokingham, UK). ## Tumour model In vivo studies used an established model of subcutaneous tumour development [24]. SCC or PDAC cells were injected subcutaneously (1x106 cells/flank) into FVB/N or C57BL6/J mice, respectively, fed either control or UE2316 diet for 5 days in advance of injection and throughout the remainder of the experiment ($$n = 6$$-9/group). Diet was weighed regularly to monitor consumption, which did not differ between diets. SCC tumours were grown for 11 days, PDAC tumours were grown for 14 days, and both were measured using calipers every 2–3 days. Tumour volume was calculated as the volume of an ellipsoid (0.5lengthbreadth2). Animals were weighed and checked for signs of unexpected ill health every 2–3 days. Weight loss of >$20\%$ between measurements was considered grounds for humane termination, as were any tumour-related limitations in the animal’s normal behavioural repertoire (e.g., impeded movement) or a body conditioning score of 2 being reached. Animals were to be culled prior to the pre-defined experimental endpoint if tumours reached maximum permissible size (15 mm diameter), or if signs of ulceration were evident. Mice were culled by cervical dislocation. ## Vessel staining Paraffin-embedded tumour sections underwent rehydration and heat-based antigen retrieval. Sections were permeabilised ($0.4\%$ Triton-X, 15 min) and blocked ($1\%$ normal goat serum, 30 min; Biosera, Nuaille, France), incubated with primary CD31 antibody ($\frac{1}{300}$ dilution, 18h, 4°C, Ab28364; Abcam, Cambridge, UK), rinsed with PBS and incubated with secondary antibody and primary conjugated α-smooth muscle actin antibody ($\frac{1}{1000}$ dilution, 1 hour, room temperature, A-11034; Molecular Probes, Eugene, USA. C6198; Sigma) before counterstaining with DAPI (5 min) and mounting using Fluoromount G (SouthernBiotech, Cambridge, UK). Slides were imaged with an Axioscan. Z1 (Zeiss) digital slide scanner. Higher magnification images were obtained using a LSM710 confocal microscope (Zeiss). Vessels were manually counted by a blinded observer across 10 randomly selected 0.1mm2 fields of view, from two tumour sections spaced 50 μm apart. CD31-positive/α-SMA-negative vessels and CD31/α-SMA-positive vessels were both quantified to allow the ratio of vessels with smooth muscle coverage to be calculated. As a secondary measure of vessel density, sections stained for CD31 were quantified by Chalkley count, as described [25]. One section (three hotspots) was quantified per tumour. ## In vivo tumour cell proliferation Tumour sections were stained with Ki67 antibody (proliferation marker, $\frac{1}{100}$ dilution, Ab155580; Abcam) as above. 2 sections/tumour were scanned at 200x magnification, the most proliferative region selected by eye, and this region then imaged at 400x magnification. Ki67-positive cells were then quantified manually per hotspot. ## Immune/Inflammatory cell staining F$\frac{4}{80}$ ($\frac{1}{300}$, 14–4801; eBiosciences) and CD3 ($\frac{1}{100}$, Sc-20047; Santa-Cruz) staining were performed using the Leica BOND-III automated staining system and the Leica refine detection kit as per manufacturer’s instructions (Leica). Trypsin-based antigen retrieval was used for F$\frac{4}{80}$ staining, and heat-based antigen retrieval for CD3 staining. Dehydrated sections were mounted with DPX and imaged using the slide scanner. Images were segmented and stain percentage area was quantified automatically using ImageJ software. ## Enzyme activity assays A BioRad protein DC assay (BioRad, Hemel-Hempsted, UK) was performed as per manufacturer’s instructions. ## Dehydrogenase activity assay Homogenized tumour samples were diluted in assay buffer (63g glycerol, 8.77g NaCl, 186mg ethylenediaminetetraacetic acid (EDTA), 3.03g Tris, made up to 500mL with distilled H2O and pH adjusted to 7.7). 3H4-Corticosterone (250nM) and NADP+ (2mM; Cambridge Bioscience) were added before incubation in a shaking water bath (37°C). After incubation, samples were extracted with ethyl acetate (10:1), dried under nitrogen and dissolved in 65:15:25 water/acetonitrile/methanol. ## Reductase activity assay C57BL6/J mouse liver was excised and sectioned. Liver pieces (5-20mg, $$n = 6$$/group) were cultured in 1mL DMEM-F12 medium containing 12.5nM 3H4-cortisone and 1μM cold cortisone with either 300nM UE2316 or vehicle (final DMSO concentration $0.3\%$). Plates were incubated for 24 hours ($5\%$ CO2, 37°C). Media was extracted on Sep-Pak C-18 (360mg) cartridges (Waters, Elstree, UK), dried under nitrogen, resuspended in 200μL HPLC-grade H2O added, and extracted with ethyl acetate (10:1) to remove phenol red contamination, dried under nitrogen and dissolved in 60:40 water/methanol. ## Second harmonic generation imaging Type I collagen was visualized in SCC and PDAC tumours ($$n = 6$$/group) by Second Harmonic Generation (SHG) microscopy. A pump laser (tuned to 816.8 nm, 7 ps, 80 MHz repetition rate; 50 mW power at the objective) and a spatially overlapped second beam, termed the Stokes laser (1064 nm, 5–6 ps, 80 MHz repetition rate, 30 mW power at the objective; picoEmerald (APE) laser) was inserted into an Olympus FV1000 microscope coupled with an Olympus XLPL25XWMP N.A. 1.05 objective lens with a short-pass 690 nm dichroic mirror (Olympus). The Second Harmonic Generation signal was filtered (FF552-Di02, FF$\frac{483}{639}$-Di01 and FF$\frac{420}{40}$) and images quantified using Image J. ## qPCR Frozen tissue was homogenized in Qiazol reagent (Qiagen), allowed to settle at room temperature for 5 min, vortexed in chloroform and left to settle for 2 min before centrifugation (12000 RCF x 15 min at 4°C). The resultant aqueous phase was mixed with an equal volume of $70\%$ ethanol. All subsequent on-column steps were performed as per the RNeasy manufacturer’s protocol. RNA concentration and integrity were assessed using the Nanodrop 1000 (Thermo-Fisher Scientific). cDNA was generated from RNA using the QuantiTect Reverse Transcription Kit (Qiagen) as per manufacturer’s protocol. For the PCR reaction, samples were incubated at 42° for 15 min followed by 95° for 3 min in a Thermal cycler (Techne-Cole-Palmer, Staffordshire, UK). cDNA was diluted $\frac{1}{40}$ in RNase-free water and a standard curve constructed by serial dilution of a pooled sample. In triplicate on a 384-well plate, 2μL of sample were combined with 5μL of Lightcyler 480 Probes Master mastermix (Roche), primers (0.1μL/sample Forward and Reverse), probe (0.1μl/sample) and RNase-free water to make up to 10μL total volume. Plates were spun (420 RCF x 2 min on LCM-3000 plate centrifuge (Grant Instruments, Royston, UK) before analysis on the Light Cycler 480 (Roche). Samples were run for 50 cycles (10s at 95°C and 30s at 60°C). All data were normalised to the average of two housekeeping genes (Gapdh and Tbp). ## RNA sequencing RNA from SCC tumours, extracted as described above, was sequenced by GATC Biotech (Constance, Germany). Raw data were processed using Tophat2 [26], which was used to map reads to the mouse mm10 reference genome. Differential gene expression was analysed using Cuffdiff [27]. DEseq2 was used to perform a Principle Component Analysis (PCA) to assess variance between samples. Gene ontology analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8. ## Data analysis and statistics All statistics were performed using Prism software v$\frac{6}{7}$ (Graphpad). Data are presented as mean ± S.E. Outliers were identified using Grubbs’ test and excluded appropriately. All data sets were tested for a parametric distribution and transformed/analysed appropriately. N refers to the number of animals per group in an experiment, with the exception of cell culture studies in which N refers to biological repeats on separate days using the same cell line. $P \leq 0.05$ was considered significant. ## 11β-HSD1 is expressed in SCC but not PDAC tumour cell lines When comparing 11β-HSD1 dehydrogenase activity between tumour types, SCC tumours showed a considerably higher rate of product formation than PDAC (Fig 1A) and showed higher GR expression (Fig 1B). 11β-HSD2 was not detected in either tumour type. Fig 1: SCC tumours show greater 11β-HSD1 activity and express more GR than PDAC tumours.A) SCC tumours had greater 11β-HSD1 dehydrogenase activity than PDAC tumours. ** P<0.001. N = 6/group. B) GR transcript levels were greater in SCC tumours than PDAC tumours. N = 5-6/group. * P<0.05. Data were compared by independent sample t-test.* ## 11β-HSD1 inhibition enhances SCC tumour growth UE2316 accelerated the growth of SCC tumours from day 4 onwards (Fig 2A) but had no effect on the growth of PDAC tumours (Fig 2B). UE2316 and control diet fed groups consumed similar quantities of diet and did not differ in weight throughout the experiment (Fig 2C). The estimated dosage achieved in the present studies (based on diet consumed per cage per 2–3 days) was 25-30mg/kg/mouse/day. Ki67 staining revealed a trend towards reduced tumour cell proliferation in UE2316-treated SCC tumours compared to control tumours, but this did not reach significance (Fig 2D–2F). Fig 2: The 11β-HSD1 inhibitor UE2316 enhances SCC but not PDAC tumour growth.A) UE2316 enhanced tumour growth from day 4 onwards in mice injected with SCC cells. N = 9/group. B) UE2316 did not affect PDAC tumour growth in mice injected with Panc043 cells. N = 6/group. C) Neither tumour cell injection (day 5) nor UE2316 diet introduction affected mouse weight. N = 6-9/group. * P<0.01. Data were compared by 2-Way ANOVA. D) The proportion of cells staining positive for proliferation marker Ki67 showed a trend towards being reduced (P = 0.07) in tumours from UE2316-treated mice but this did not reach significance. N = 6/group. Data were compared by independent samples t-test. Representative images of hotspots from Ki67-stained squamous cell carcinoma (SCC) tumours from control (E) and UE2316 treated (F) mice are shown. Hotspots were typically near the periphery of the tumour. Scale bar = 50μm.* ## 11β-HSD1 inhibition does not promote angiogenesis in SCC tumours The effect of 11β-HSD1 inhibition on vessels in tumours was assessed by CD31/α-SMA-positive staining (Fig 3A and 3B). In SCC and PDAC tumours, UE2316 did not affect the number of blood vessels per field of view (Fig 3C) or the vessel number determined by Chalkley counts (Fig 3E). In SCC tumours, UE2316 did not affect the proportion of immature vessels lacking smooth muscle coverage, assessed by CD31 staining in the absence of α-SMA staining (Fig 3D). UE2316 also did not affect mRNA levels for angiogenic factors Vegfa and Vegfr2 in either tumour type (Fig 3F). Fig 3: UE2316 does not affect angiogenesis in tumours.A) Tumour tissue from SCC tumours; endothelial cells are stained green (CD31 visualised with Alexa-Fluor 488), smooth muscle cells are stained red (α-SMA visualised with Cy3) and nuclei are stained blue (DAPI). Tumours had densely packed nuclei. 200x magnification. Scale bar 50μm. B) CD31 was also visualised with diaminobenzidine (DAB) for counts. 200x magnification. Scale bar 50μm. C) UE2316 did not affect vessel density in either SCC or PDAC tumours. D) UE2316 did not affect the proportion of vessels lacking smooth muscle coverage in SCC tumours (i.e. CD31 positive but α-SMA negative). (E) UE2316 did not affect Chalkely counts in SCC tumours. 1 section/tumour, N = 5–6 animals/group. F) mRNA levels for Vegfa and Vegfr2 in SCC tumours were unaffected by UE2316. Data were compared by independent samples t-test for panels C/D, Mann-Whitney U test for Panel E. ## Neither corticosterone nor UE2316 affect SCC cell proliferation in vitro SCC cells in culture were imaged using the Incucyte ZOOM live cell imaging system to investigate the effects of glucocorticoids and UE2316 on cell growth and morphology. Addition of increasing concentrations of corticosterone (Fig 4A) or UE2316 (Fig 4B) had no effect on the growth of SCC cells over 72 hours. Neither corticosterone (Fig 4C) nor UE2316 (Fig 4D) affected cell viability at any concentration, assessed after 72 hours using an alamarBlue assay. Fig 4: Neither corticosterone nor UE2316 affect SCC cell growth or viability in vitro.The confluence of SCC cells imaged over 72 hours using the Incucyte was unaffected by exposure to either corticosterone (CORT, panel A) or the 11β-HSD1 inhibitor UE2316 (panel B). 300nM STS was included in all experiments as a positive cytotoxic control. N = 5 (technical repeats, treatments in sextuplet). SCC viability, as determined by the alamarBlue assay, was unaffected by the addition of corticosterone (panel C) or the 11β-HSD1 inhibitor UE2316 (panel D). AU = Arbitrary units. N = 4 (technical repeats, treatments in sextuplet). Data were compared by one-way ANOVA. ## 11β-HSD1 inhibition does not alter F4/80- or CD3- positive cell infiltration into SCC tumours To quantify inflammatory cell content, sections from SCC tumours from control (Fig 5A) and UE2316-diet-fed mice (Fig 5B; $$n = 6$$/group) were labelled with F$\frac{4}{80}$ antibody, a macrophage marker. The antibody produced a cytoplasmic stain, present across the tumour but concentrated at the tumour periphery and in regions near the centre of the tumour. There was no significant difference in F$\frac{4}{80}$-positive area in tumours from RM-1 and UE2316 diet-fed mice, despite a trend towards a decrease in UE2316-treated tumours (Fig 5C). To quantify infiltrating T-cells, SCC tumours from control and UE2316-diet-fed mice ($$n = 5$$/group) were labelled with anti-CD3 to identify CD3-positive cells. There was no significant difference in CD3-positive area in tumours from RM-1 and UE2316 diet-fed mice (Fig 5D); representative images are shown in Fig 5E and 5F. Fig 5: F4/80 and CD3 positive cell number in SCC tumours were unaffected by UE2316.Representative images of squamous cell carcinoma (SCC) tumours from control (A) and UE2316-treated (B) mice are shown, with DAB immunoreactivity to anti-F4/80 antibody shown in brown and haematoxylin-counterstained nuclei in blue. C) Immunostaining did not reveal a difference in F4/80-positive stain area between tumour from control and UE2316-treated mice (P = 0.17). N = 5-6/group. Data were compared by independent samples t-test. Scale bar = 50μm. Immunostaining found no difference in CD3-positive stain area between SCC tumours from control and UE2316-treated mice, assessed by whole section analysis. N = 5/group (D). Representative images of CD3 labelled SCC tumour sections from control (E) and UE2316-treated (F) squamous cell carcinoma (SCC). DAB immunoreactivity shown in brown and haematoxylin-counterstained nuclei in blue. Data were compared by independent samples t-test, N = 5/group. ## 11β-HSD1 inhibition reduces type 1 collagen deposition in SCC tumours To determine whether tumour collagen deposition was altered by 11β-HSD1 inhibition, Second Harmonic Generation (SHG) microscopy was performed on SCC tumours ($$n = 6$$/group; Fig 6A and 6B). Automatic quantification of % collagen area from SHG images revealed a reduced amount of type I collagen in tumours from mice fed UE2316-diet compared to tumours from mice fed normal diet (Fig 6C). This difference was also apparent at a transcriptional level (Fig 6D). Fig 6: Type I collagen is reduced in SCC tumours from UE2316-treated mice.Second Harmonic Generation imaging showed type I collagen (white signal) in SCC tumours from UE2316-treated (B) and control mice (A). Scale bar = 100μm. C) Type I collagen was reduced in tumours from UE2316-treated mice. P<0.001. N = 5/group. D) Col1a1 mRNA was reduced in SCC tumours from UE2316-treated mice compared to control mice. AU = Arbitrary units. P<0.01. N = 5-6/group. Data were compared by independent samples t-test.* ## 11β-HSD1 inhibition influences immune and inflammatory signaling in SCC tumours Genes found to be differentially expressed (DE) between control and UE2316-treated SCC tumours by RNA sequencing were analysed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8. 674 genes were found to be differentially regulated between treatment groups. Significantly relevant ($P \leq 0.05$) biological processes are listed in Table 1. Given the importance of local glucocorticoids in regulating inflammation, and the chronic inflammatory state of the tumour microenvironment, genes associated with the inflammatory response and immune response ($6.1\%$ and $4.1\%$ of DE genes respectively, as identified by DAVID) and their relative expression in UE2316-treated tumours (as identified by RNA-seq) are shown in Fig 7A and 7B. mRNA coding for a large number of pro-inflammatory cytokines were reduced after UE2316 treatment, while mRNA for cytokine receptors, toll-like receptor and mast cell protease transcript was increased. Fig 7: UE2316 affects inflammatory and immune response genes in SCC tumours; analysed using Gene Ontology analysis.Differentially-expressed genes identified by RNA-sequencing were defined as being related to the inflammatory response (A) or immune response (B) by Gene Ontology analysis. $$n = 4$$-5/group. A modified Fisher Exact test was used to determine whether the proportion of genes in a given list was significantly associated with a biological process compared to the murine genome: $P \leq 0.05$ for all the above. Data represent mean values with black bars representing genes that are down-regulated in the UE2316-treated tumours and open bars those that are up-regulated. TABLE_PLACEHOLDER:Table 1 ## Discussion The data generated in this investigation demonstrate that 11β-HSD1 inhibition can promote SCC tumour growth in mice. This effect was not seen in PDAC tumours, which expressed lower levels of both GR and 11β-HSD1 than SCC. The present findings are in agreement with other studies that report SCC express particularly high levels of GR [28,29], suggesting that SCC may be a more glucocorticoid-sensitive tumour type than PDAC. 11β-HSD1 inhibition did not alter vessel density or in vitro tumour cell proliferation, but immune and inflammatory signalling pathways were altered at the transcriptomic level, as was 11β-HSD1 itself. Immune and inflammatory cell content did not differ between control and UE2316-treated SCC tumours, suggesting perhaps that cell behaviour (cytokine environment/activation state) is altered by UE2316. Fluorescence-Associated Cell Sorting of tumours would be required to more elegantly investigate this question in future studies. Generation of Type 1 collagen was reduced at both the transcriptomic and protein level in UE2316-treated SCC tumours; whether this change relates to the altered inflammatory environment remains uncertain. The only previous study to directly manipulate 11β-HSD1 expression in a solid tumour model demonstrated that 11β-HSD1 overexpression reduced the growth of hepatocellular carcinoma (HCC) tumours in Balb/C nude mice [17], an effect which was apparent over a similar time course as the effect of 11β-HSD1 inhibition shown here (i.e. 3–5 days after cell injection). While the present study supports a role for 11β-HSD1 and local glucocorticoid metabolism in regulating tumour growth from an early stage, a different mechanism may be responsible; the study in HCC identified a significant reduction in tumour angiogenesis, attributed to reduced glycolysis, in tumours overexpressing 11β-HSD1 compared to controls. No evidence of such a process was seen in SCC tumours. The present study made use of a murine tumour cell line able to grow in mice with a functional immune system, a significant strength given that 11β-HSD1 deletion reduces T-cell infiltration in some inflammatory models [30–32] and is likely to influence the tumour microenvironment [33]. Interestingly, both tumour types used in these separate studies (SCC and HCC) were derived from tissues in which 11β-HSD1 is known to play a regulatory role (skin and liver) [34–38]. However, the present study found no evidence of enhanced angiogenesis after 11β-HSD1 inhibition. Enhanced angiogenesis and recovery post-myocardial infarction have been demonstrated consistently in 11β-HSD1 knockout mice [10,11,13,14] and following exposure to the 11β-HSD1 inhibitor UE2316 [39]. The reparative response to myocardial infarction is characterised by increased neutrophil and macrophage recruitment into the myocardium after 11β-HSD1 inhibition [11,14], an effect absent in SCC tumours. Angiogenesis after induced myocardial infarction in rodents is a beneficial process and distinct from the aberrant non-resolving hypoxia-driven angiogenesis seen in tumours [40], which may be mediated by different mechanisms and explain the context-specific effects of 11β-HSD1 inhibition. Given the lack of evidence that exaggerated angiogenesis promotes SCC tumour growth following 11β-HSD1 inhibition, we considered other mechanisms. The absence of a glucocorticoid-mediated effect on SCC cell proliferation, or any direct effect of UE2316 in vitro, strongly suggests that direct proliferative effects on tumour cells are not relevant. However, based on the gene ontogeny analysis of SCC tumours, the immune and inflammatory responses are likely to be of mechanistic importance. SCC tumours from UE2316-treated mice showed reduced expression of a range of pro-inflammatory cytokine and chemokine genes. These changes were accompanied by an increase in the expression of several members of the Tlr and Tnfrsf families, and Csf1r, suggesting reduced pro-inflammatory ligand binding. Furthermore, expression of a number of interferon-γ (IFN-γ) inducible genes was reduced in tumours from UE2316-treated animals. As TLR activation can stimulate the production of IFNs, interleukins and TNF by myeloid and lymphoid cells [18], the evidence points towards reduced inflammatory and immune cell signalling within tumours from UE2316-treated mice. The reduced expression of Ccl and Cxcl chemokines would predict reduced migration of eosinophils, neutrophils and T-cells into tumours, whilst the reduced expression of 11β-HSD1 itself after UE2316 treatment is indicative of reduced immune/inflammatory cell infiltration and activation as the enzyme is expressed in macrophages and lymphocytes and upregulated by immune cell activation [3]. The role of inflammation in tumour progression is controversial in that it can both promote tumour progression (including via stimulation of angiogenesis) and inhibit tumour progression (via anti-tumour immunosurveillance). In the present model, 11β-HSD1 inhibition appears to decrease inflammatory signalling whilst enhancing tumour growth, raising the intriguing possibility that UE2316 dampens the anti-tumour immune response. This requires confirmation at the cellular level. 11β-HSD1 inhibition has been shown to influence inflammation previously, but its effects are context-dependent and may vary between acute or chronic inflammation. Similar to induced myocardial infarction, 11β-HSD1 deficiency increases acute inflammation in models of arthritis, peritonitis and pleurisy [41,42]. In obese adipose tissue and atherosclerotic plaques from 11β-HSD1 deficient animals, however, inflammatory and immune cell infiltration is attenuated [30,31]. Arguably, the chronic, non-resolving inflammation and hypoxia seen in obese adipose tissue and atheroma are more similar to the tumour microenvironment than to the ischaemic myocardium; thus mechanistically the latter models may be more relevant. There is analogous evidence from other models that 11β-HSD1 influences the same inflammatory pathways as we observed here. Wamil et al. [ 30] reported that 11β-HSD1 deletion reduces similar cytokines (including members of the CCL, CXCL and TNF families) in adipose tissue from high-fat diet-fed mice, associated with decreased CD8+ T-cell infiltration and macrophage infiltration in adipose tissue. Michailidou et al. [ 12] found decreased fibrosis in adipose tissue from 11β-HSD1 knockout animals. Furthermore, 11β-HSD1 deletion reduces macrophage and T-cell infiltration into atherosclerotic plaques [32]. Several of the key gene expression changes seen in the present study have also been seen in atherosclerotic plaques after 11β-HSD1 inhibition [31], including reductions in interleukins, toll-like receptors, STAT family members, and several chemokines. The selective 11β-HSD1 inhibitor BVT-2733 was previously shown to improve symptoms of collagen-induced arthritis by reducing the expression of pro-inflammatory cytokines, including TNF, IL-1β and IL6, and reducing inflammatory cell infiltration into joints [43]. Furthermore, the beneficial effects of 11β-HSD1 inhibition in the synovium have been linked to reduced glucocorticoid action in synovial fibroblasts and osteoclasts resulting in a net reduction in damaging inflammation [44]. Although we found effects of 11β-HSD1 inhibition on transcripts in SCC tumours, these were not reflected in demonstrable differences in cell content. Staining for the T cell marker CD3 did not identify a difference between SCC tumours from control and UE2316-treated mice. Likewise, F$\frac{4}{80}$ staining did not reveal a marked difference in macrophage numbers between treatment groups, yet key transcripts for markers of macrophage polarisation were altered in whole tumour homogenates, suggesting a more subtle effect of 11β-HSD1 inhibition on macrophage content or polarisation. Cancer-associated fibroblasts and extracellular matrix (ECM) deposition can also influence tumour progression [45–47]. The reduced type 1 collagen seen in SCC tumours mirrors the reduced fibrosis in obese adipose tissue from 11β-HSD1 deficient mice [15], which also showed decreased alpha-smooth muscle actin expression, suggesting reduced fibroblast numbers. Reduced fibrosis and reduced expression of Col1a1, Col1a2, Col14a1, stromal-cell derived factor 1 (Sdf1) and Lox are all suggestive of reduced fibroblast activity [45,46]. Since fibroblasts can promote anti-tumour immune cell infiltration into tumours [45,47], suppression of fibroblasts by UE2316 could explain the potentially dampened anti-tumour immune response in SCC tumours. Conversely, inflammatory cells are also able to recruit fibroblasts into SCC tumours [48] and this enhanced recruitment can promote SCC growth suppression via the deposition of a fibrotic ECM that constrains tumour cell proliferation and invasiveness [49,50], so the effect of UE2316 could be primarily on inflammatory cells or on tumour cells releasing pro-inflammatory signals, with secondary effects on fibroblasts. Given that only one of the two tumour types examined responded to UE2316 treatment, predicting which tumour types may be more at risk will be important if 11β-HSD1 inhibitors are to be used in at-risk patients. Review of cancer genomics data sets available via the cBioPortal for Cancer Genomics [51] reveals amplification of HSD11B1 expression in 8–$10\%$ of breast and hepatobiliary cancer studies, while around $8\%$ of cutaneous melanomas show either mutation ($4\%$) or amplification ($4\%$) of the gene. Altered expression of HSD11B1 is also apparent in around $5\%$ of studies on endometrial cancers, non-Hodgkin lymphomas, non-small cell lung cancers and melanomas. Extra vigilance is recommended if 11β-HSD1 use is indicated in patients with such HSD11B1-expressing tumours. In summary, to the best of our knowledge this is the first study to investigate the possibility that pharmacological inhibition of 11β-HSD1 could promote tumour growth by increasing the angiogenic growth of blood vessels. 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--- title: Sources of PM2.5‐Associated Health Risks in Europe and Corresponding Emission‐Induced Changes During 2005–2015 authors: - Yixuan Gu - Daven K. Henze - M. Omar Nawaz - Hansen Cao - Ulrich J. Wagner journal: GeoHealth year: 2023 pmcid: PMC10027220 doi: 10.1029/2022GH000767 license: CC BY 4.0 --- # Sources of PM2.5‐Associated Health Risks in Europe and Corresponding Emission‐Induced Changes During 2005–2015 ## Abstract We present a newly developed approach to characterize the sources of fine particulate matter (PM2.5)‐related premature deaths in Europe using the chemical transport model GEOS‐Chem and its adjoint. The contributions of emissions from each individual country, species, and sector are quantified and mapped out at km scale. In 2015, total PM2.5‐related premature death is estimated to be 449,813 (257,846–722,138) in Europe, $59.0\%$ of which were contributed by domestic anthropogenic emissions. The anthropogenic emissions of nitrogen oxides, ammonia, and organic carbon contributed most to the PM2.5‐related health damages, making up $29.6\%$, $23.2\%$, and $16.8\%$, respectively of all domestic anthropogenic contributions. Residential, agricultural, and ground transport emissions are calculated to be the largest three sectoral sources of PM2.5‐related health risks, accounting for $23.5\%$, $23.0\%$, and $19.4\%$, respectively, of total anthropogenic contributions within Europe. After excluding the influence of extra‐regional sources, we find eastern European countries suffered from more premature deaths than their emissions caused; in contrast, the emissions from some central and western European regions contributed premature deaths exceeding three times the number of deaths that occurred locally. During 2005–2015, the first decade of PM2.5 regulation in Europe, emission controls reduced PM2.5‐related health damages in nearly all European countries, resulting in 63,538 (46,092–91,082) fewer PM2.5‐related premature deaths. However, our calculation suggests that efforts to reduce air pollution from key sectors in some countries can be offset by the lag in control of emissions in others. International cooperation is therefore vitally important for tackling air pollution and reducing corresponding detrimental effects on public health. ## Key Points Residential, agricultural, and ground transport emissions were the largest sources of the PM2.5‐related health burden in EuropeEastern Europe experienced more premature deaths than their own emissions caused, making them net importers of the pollution health burdenEmission reductions, most from transport, energy, and industrial sources, reduced pollution health damages in Europe during 2005–2015 ## Introduction Outdoor air pollution has been a top global health concern since the 1970s (Crippa et al., 2016; Fenger, 2009; McKitrick, 2007), and has been a leading cause of the global disease burden for decades (Burnett et al., 2018; Cohen et al., 2005; C. J. L. Murray et al., 2020). Long‐term exposure to outdoor pollution, mostly by exposure to fine particulate matter (with an aerodynamic diameter smaller than 2.5 μm; PM2.5), was calculated to lead to 3.3 million premature deaths worldwide in 2010 (Lelieveld et al., 2015), and quantifications of ambient PM2.5‐related health risks implied an annualized growth rate of $1.46\%$ during 2010–2019 (C. J. L. Murray et al., 2020). Epidemiologic cohort studies have provided increasing evidence that PM2.5 exposure increases the risk of premature death from health outcomes including chronic obstructive pulmonary disorder (COPD), ischemic heart disease (IHD), lower respiratory illnesses (LRI), lung cancer (LC), type‐II diabetes (T2D), and stroke (Anderson et al., 2012; Pinault et al., 2017; Thurston et al., 2016; Yin et al., 2017). Owing to this, developing effective strategies for reducing the burden of disease attributable to PM2.5 exposure is a sustainability goal shared by countries worldwide. During the past two decades, the European Union (EU) has enacted multiple policies to reduce air pollution. In 2005, a cap of 25 μg m−3 for the annual average exposure to PM2.5 was first proposed to reduce the exposure of the population in addition to the existing controls on PM10 (with aerodynamic diameter less than 10 μm), and a uniform reduction target of $20\%$ was proposed for all member states to be attained between 2010 and 2020 (COM [2005] 0446 final, 2005). After that, PM2.5 exposure reduction targets were set at the national level by a series of directives, aiming to reduce the annual mean PM2.5 concentrations to 25 μg m−3 in 2015 and 20 μg m−3 in 2020 (Directive, $\frac{2008}{50}$/EC, 2008). Emission caps were set for PM2.5 and its precursors, like sulfur dioxide (SO2), nitrogen oxides (NO x), ammonia (NH3), and non‐methane volatile organic species, in each member state (Directive, $\frac{2001}{81}$/EC, 2001). As a result, emissions of PM2.5, SO2, NO x, and NH3 fell by $29\%$, $76\%$, $36\%$, and $8\%$, respectively in 2019 compared to those in 2005 in 27 member states of the EU (EU‐27) (EEA, 2021). The World Health Organization (WHO) established a new air quality guideline (AQG) level of 5 μg m−3 for long‐term PM2.5 exposure in 2021 (WHO, 2021). This new guideline represents the lowest exposure level of PM2.5 above which there could be an increase in adverse health impacts. Even in 2020, when anthropogenic emissions were reduced due to the coronavirus pandemic, over $96\%$ of the EU urban population was still exposed to PM2.5 concentrations exceeding the AQG level (EEA, 2022). In line with this, Tarín‐Carrasco et al. [ 2022] estimated that PM2.5 accounted for 725,000–1,056,000 annual excess premature deaths across Europe in 2010, and predicted that the number of PM2.5‐related deaths would keep increasing in the next 50 years. To reduce the deleterious impacts of PM2.5 pollution on public health, there is a continued need to evaluate the sources of PM2.5‐related health impacts in Europe. Chemical transport models (CTMs) are frequently used for such studies due to their unique capabilities of simulating non‐linear processes of atmospheric chemistry, unlike other source apportionment approaches that are limited to primarily linear relationships (Thunis et al., 2019). The effects of emission changes on PM2.5‐associated health impacts have previously been characterized by comparing simulated results under different emission scenarios (Andersson et al., 2009; Anenberg et al., 2014; Crippa et al., 2019; Im et al., 2018; Lelieveld et al., 2015, 2019; Silva, Adelman, et al., 2016; Silva, West, et al., 2016; Tarín‐Carrasco et al., 2022). For example, Lelieveld et al. [ 2015] quantified the contributions of seven source categories to PM2.5‐related premature deaths in 2010 by removing their emissions one at a time from ECHAM5/MESSy atmospheric chemistry (EMAC) model simulations and found that agricultural sources could be a leading source category in Europe. The multi‐model results of Im et al. [ 2018] suggested that a $20\%$ reduction of European anthropogenic emissions could avoid a total of 47,000 premature deaths in Europe. In addition to these brute‐force finite difference calculations, CTM tagging approaches, where emissions of certain species are marked (“tagged”) in the computation so that they can be tracked from particular source categories or locations, are also capable of doing source apportionment. Incorporating the tagging method into an integrated model system, Economic Valuation of Air pollution, Brandt et al. [ 2013] estimated that emissions from power plant, agricultural, road transport, and non‐industrial combustion plant sources contributed $24\%$, $25\%$, $18\%$, and $10\%$, respectively of the total health‐related external costs in Europe in 2000. Both these approaches provide valuable insights, but they can be computationally expensive when fine temporal or spatial detail is needed regarding sources, and hence are frequently limited in terms of the resolution of sources that can be considered (Henze et al., 2007, 2009). Adjoint models provide an alternative approach to efficiently calculate the response of a particular receptor function (e.g., PM2.5 concentration, PM2.5‐related premature deaths) to a large number of sources, with which detailed contributions from all kinds of emissions can be mapped out in health assessment studies (Lee et al., 2015; Malley et al., 2021; Nawaz & Henze, 2020; Nawaz et al., 2021; Pappin & Hakami, 2013). Compared to traditional model calculations of changes in the final state (e.g., concentrations) induced by a perturbation in model parameters (e.g., emissions), the adjoint method is a receptor‐oriented approach which calculates the sensitivities of the final state to a series of model parameters by transforming the changes in the final state backward in time (Henze et al., 2007, 2009). Based on the adjoint sensitivity calculation, Lee et al. [ 2015] examined the response of global PM2.5‐related mortality to changes in different local emissions in 2005, suggesting that 1 kg km−2 yr−1 decrease in NH3 and carbonaceous aerosol emissions could lead to the largest reductions in global mortality. For the European region, air pollution sensitivity studies and data assimilation have been conducted for over two decades using regional adjoint modeling but without a focus on health impacts (e.g., Elbern & Schmidt, 2001; Elbern et al., 2000; Menut et al., 2000; Vautard et al., 2000). Previous adjoint‐based health impact source attribution studies with respect to this region have been conducted as part of global simulations with a horizontal resolution of 2° × 2.5° in Lee et al. [ 2015] and Malley et al. [ 2021]. The coarse spatial distributions contribute to significant uncertainties in the health assessments, especially in polluted or populated areas (Li et al., 2016; Punger & West, 2013). Li et al. [ 2016] quantitatively examined the influence of model resolution on estimates of PM2.5‐related premature mortality, suggesting that the calculated national mortality from a coarse‐resolution (2° × 2.5°) simulation could be $8\%$ lower than that from the fine‐resolution (0.5° × 0.666°) model simulation in the United States (US); uncertainty in ascribing this health burden to specific sources of PM2.5 precursor emissions would be even higher. From this point of view, approaches to reduce the uncertainty of adjoint simulations are needed to obtain details of the sources of pollution‐associated mortality in Europe. Here we present a high‐resolution adjoint calculation in Europe by conducting a nested‐grid simulation using the CTM GEOS‐Chem and its adjoint to examine the response of total PM2.5‐related premature deaths to various emissions. Remote sensing derived surface‐level PM2.5 concentrations are incorporated into the adjoint sensitivity analyses to correct for model biases and to characterize km‐scale spatial variability. The unique contributions of emissions from individual countries, species and sectors to PM2.5‐related premature deaths in Europe are characterized at a 0.1° × 0.1° spatial resolution on a monthly basis in 2015. The emission‐induced changes in the sources of PM2.5‐related premature deaths are also investigated by comparing source attribution results for 2005 and 2015, during the first 11‐year period of PM2.5 regulation in the EU, with the aim of better characterizing (e.g., with finer temporal and spatial details) the sources of regional pollution‐associated health risks and to provide associated implications for environmental policies. ## Surface PM2.5 Concentrations In situ observations from 972 monitoring sites are used to evaluate the performance of the model simulation of surface PM2.5 concentrations in Europe. The observed hourly or daily PM2.5 concentrations in 2015 are obtained from the E1a data set, and collected via the EEA Air Quality e‐reporting database (https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm, accessed on: 14 February 2023). The E1a data are validated assessment data annually reported to EEA by each EU member state and have been successfully tested by automated quality control. Information on station type, station area, measurement type, method, equipment as well as data quality are available for each monitoring site via the EEA's air quality portal (https://discomap.eea.europa.eu/App/AirQualityMeasurements/index.html, accessed on: 14 February 2023). Annual mean concentrations are calculated for each monitoring site (with a data capture rate higher than $90\%$) to compare with the model results. Remote‐sensing derived surface PM2.5 concentrations are incorporated into the adjoint simulations to further improve the agreement between the simulation and observation (Text S1 in Supporting Information S1). We use the latest high‐resolution (0.01° × 0.01°) satellite‐based estimates (V5.GL.02) from van Donkelaar et al. [ 2021] for the base simulation year of 2015. Combining satellite retrievals of aerosol optical depth, chemical transport modeling, and ground‐based measurements, these hybrid PM2.5 estimates exhibit general consistency with ground‐based observations. After incorporating the satellite data, the simulated site‐averaged annual mean PM2.5 concentration in Europe increases from 12.77 to 14.08 μg m−3, which is closer to the observed level (14.98 μg m−3), and the R 2 between the simulated and observed PM2.5 concentrations increases from 0.34 to 0.80. Detailed evaluations of simulated PM2.5 exposure in Europe are provided in Text S2 in Supporting Information S1. ## GEOS‐Chem Forward Model A nested‐grid capability of the GEOS‐Chem CTM (http://www.geos-chem.org, accessed on: 11 October 2022) is used to simulate the ambient concentrations of aerosols over Europe. The model is driven by assimilated meteorology from the Goddard Earth Observing System (GEOS‐FP) of the NASA Global Modeling and Assimilation Office, which are down‐sampled to a resolution of 0.25° × 0.3125° for the European domain (32.75°–61.25°N, −15°–40°E). Fourty‐Seven vertical layers are included in the model, extending from the surface to 0.01 hPa. To better estimate aerosol concentrations, a new Secondary Organic Aerosol (SOA) scheme is incorporated into the model following Nault et al. [ 2021] and Nawaz et al. [ 2021]. Other gas‐phase chemistry and aerosol treatments are described in Text S3 in Supporting Information S1. PM2.5 is calculated as the total mass of aerosol‐phase sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), organic carbon (OC), black carbon (BC), SOA, and fine mode mineral dust (aerodynamic diameter less than 1.8 μm). Chemical boundary conditions are provided by a global simulation at a horizontal resolution of 2° × 2.5°, and updated in the nested‐grid region every 3 hours. The base year of the simulation is 2015, when the air quality standards for PM2.5 had been introduced for over 10 years. ## Emissions A newly released anthropogenic emission inventory in support of Hemispheric Transport of Air Pollution (HTAPv3 mosaic, https://edgar.jrc.ec.europa.eu/dataset_htap_v3, accessed on: 11 October 2022) is used in the model, obtained from Emissions Database for Global Atmospheric Research. The emission inventory includes monthly emissions of SO2, NO x, carbon monoxide (CO), NMVOCs, NH3, PM10, PM2.5, OC, and BC at the global scale, with a resolution of 0.1° × 0.1° covering the period 2000–2018. In Europe, the HTAPv3 mosaic emissions are from European Monitoring and Evaluation Program—Copernicus Atmosphere Monitoring Service regional inventory (CAMS‐REG, v5.1), built from officially reported emission data provided to Centre of Emission Inventory and Projection (CEIP) by each member state. The inventory covers eight main sectors including shipping, aviation, energy, industry, ground transport, waste, agricultural, and residential emissions. Each main sector is further divided into several detailed sectors (Table S1 in Supporting Information S1), which provides comprehensive information on the sources of air pollutants. NMVOC emissions are lumped into model‐ready emissions for the GEOS‐Chem (Text S4 in Supporting Information S1) and anthropogenic emissions of SOA precursors (SOAP) are calculated following Nault et al. [ 2021] as described in Text S5 in Supporting Information S1. In addition to anthropogenic emissions, emissions from biogenic (Guenther et al., 2006), biomass (van der Werf et al., 2010), dust (Zender et al., 2003), lightning NO x (L. T. Murray et al., 2012), soil NO x (Hudman et al., 2012), as well as other natural sources are also including in the model calculation. ## Adjoint Sensitivity Calculation The GEOS‐Chem adjoint (Henze et al., 2007) v35n is used for sensitivity analyses in the European domain, with the same model resolution and processes as in the forward model described in Section 2.2. Sensitivity analyses begin with the definition of a response (the cost function, JPM2.5); in this study this is defined as the total number of PM2.5‐related premature deaths from COPD, IHD, LRO, LC, T2D, and stroke in all the European countries listed in the Global Health Data Exchange (GHDx, https://ghdx.healthdata.org/, accessed on: 11 October 2022) over the targeted receptor region (shaded areas in Figure 1) in 2015 (Text S6 in Supporting Information S1). After one year of spinning up of the forward model, twelve 1‐month adjoint simulations are conducted in 2015, in which gradients of the cost function with respect to emissions of each PM2.5 precursors in each grid cell and month are calculated. With these so‐called adjoint sensitivities λE, km‐scale (0.1° × 0.1°) contributions from emissions of 6 species and 16 sectors defined in the HTAP v3 emission inventory can be quantified at a monthly basis (Text S7 in Supporting Information S1). By comparing the different emission contributions, we further quantify the emission induced changes in PM2.5‐related health impacts in Europe during 2005–2015, which is the first stage of EU PM2.5 regulation. To obtain similar results as these would require 1,403,136 sensitivity simulations if using forward‐modeling or other source‐oriented methods. Though the time required by a single adjoint sensitivity calculation might be approximately 10 times the computational cost of a single forward run (Henze et al., 2007), the adjoint approach can still be more than 11,692 times faster than the forward‐modeling based calculations. Figure 1: The spatial distributions of the (a) annual mean PM2.5 exposure, (b) population, and (c) PM2.5‐related premature deaths in 2015. ## Premature Deaths Attributable to PM2.5 Exposure We present the calculated spatial distributions of the annual mean PM2.5 exposure, population, and PM2.5‐related premature deaths in Figure 1. The total number of PM2.5‐related premature deaths (i.e., the cost function) are calculated to be 449,813 out of a population of 598.97 million over the receptor region in 2015. Considering the uncertainty introduced by the forward and adjoint model calculations as well as the data and method chosen for the health assessment, we calculated lower and upper bounds for these health impacts which we discuss in more detail in Section 3.5. Table S2 in Supporting Information S1 lists estimates of total PM2.5‐related premature deaths in Europe obtained from other recent model studies. Our estimate, although slightly lower than that of Lelieveld et al. [ 2019] in 2015, agrees well with the magnitudes of model calculations in these studies. The spatial distribution of the estimated premature deaths is also consistent with previous literature studies (e.g., Im et al., 2018). As Figure 1 shows, most deaths were from populated regions (e.g., central Europe) or areas where the PM2.5 levels or the baseline mortality rates are high (e.g., eastern Europe, Figure S3 in Supporting Information S1). Compared to western and central Europe, inhabitants in eastern European countries experienced higher risks associated with PM2.5 pollution, and the mortality per grid cell exhibited much higher values. The main causes of the estimated health risks were IHD and stroke, accounting for $44.8\%$ and $23.5\%$, respectively, to the total PM2.5‐related premature deaths over the receptor region. ## Source Attribution of PM2.5‐Related Premature Deaths Using the adjoint sensitivities (λE, Section 2.4), contributions of anthropogenic emissions from distinct species and sector groups to total PM2.5‐related premature deaths over the receptor region are calculated at the 0.1° × 0.1° resolution of the HTAPv3 emission inventory. Table S3 in Supporting Information S1 summarizes the annual source contributions aggregated over various precursor species and sector groups. Anthropogenic emissions of NO x, NH3, SO2, OC, BC, and SOAP over the nested model domain are calculated to contribute 265,328 PM2.5‐related premature deaths in the receptor region in 2015, accounting for approximately $59.0\%$ of the total premature deaths from all PM2.5 in the region. The results suggest that a majority of the PM2.5‐related health risks in Europe were associated with domestic emissions of these species within Europe, yet there is still a large proportion of contributions from other anthropogenic, natural or external sources. In this section, we mainly focus on the source attribution of the domestic anthropogenic contributions. The contributions from other emission sources are discussed in Section 3.3 below. ## Source Contributions to PM2.5‐Related Premature Deaths Figure 2a presents the relative contribution from each sector group to the total PM2.5‐related premature deaths attributable to the anthropogenic emissions of NO x, NH3, SO2, OC, BC, and SOAP within the nested model domain. As the source attribution results suggest, residential, agricultural, and ground transport emissions were the major sources of the regional PM2.5‐related health risks, accounting for $13.9\%$, $13.6\%$, and $11.4\%$ of the total burden of PM2.5‐related premature deaths in Europe in 2015. The results are consistent with earlier works in Europe (e.g., Crippa et al., 2019; Lelieveld et al., 2015; Silva, Adelman, et al., 2016), that found that annual PM2.5 concentrations and corresponding health effects stemmed mainly from the agricultural and residential sectors, followed by the transport sector. In our estimate, however, residential contributions are higher than the contributions from agricultural emissions, making residential emissions the largest anthropogenic source category. This difference between our study and previous works can be explained by the different treatments of SOA. Limited by the availability of emission inventories of SOA precursor gases, no explicit treatment of anthropogenic SOA was considered in previous calculations, whereas our study incorporates the newly developed SOA scheme (Nault et al., 2021) into the model and includes SOA contributions when estimating PM2.5 concentrations. The inclusion of SOA leads to increased contributions from sectors with large VOC emissions, like residential, transport, and industry. Agriculture‐livestock and road transport emissions are calculated to be the major sources of agricultural and ground transport contributions, respectively, contributing $69.9\%$ and $77.4\%$, respectively to the sector contributions. Figure 2: Annual source apportionment of the PM2.5‐related health risks contributed by the anthropogenic emissions within Europe. The pie charts indicate the (a) sectoral and (b) species apportioning, respectively, of the PM2.5‐related premature deaths over the nested European domain in 2015. The number in parentheses is the percentage of the contributions from each category in the total PM2.5‐related premature deaths induced by anthropogenic emissions within Europe. For species contributions (Figure 2b), emissions of NO x, NH3, and OC were the top 3 ranked contributors, making up $29.6\%$, $23.2\%$, and $16.8\%$, respectively of the total anthropogenic contributions within Europe. Most NO x ‐contributed PM2.5‐related premature deaths were associated with the transport emissions, with ground transport, shipping, and aviation making up $59.0\%$ of the total NO x contributions. Agricultural activities (e.g., crops, livestock, and waste) were related with $81.7\%$ of the NH3 contributions and $59.0\%$ of the OC contributions were from residential sources. Though energy and industry emissions might not play as dominant a role as the other sectors, they were still the second largest sources of NO x and OC contributions, respectively, and made up $79.2\%$ of the contributions from SO2 emissions. To further analyze the source regions of the contributions associated with the anthropogenic emissions within Europe, Figure 3 displays the spatial distributions of the major species contributions at the resolution of the HTAP v3 emissions (0.1° × 0.1°). With the adjoint calculated fine‐resolution sensitivity, source regions of the contributions can be easily identified even from individual point sources and transport systems. As is shown in Figure 3, road transport contributions originated mainly from central European countries (e.g., Benelux, Germany, and Italy), while residential contributions were concentrated in southern and southeastern European countries (e.g., Italy, Hungary, and Romania). Contributions from agricultural sources were less spatially confined, exhibiting high values in northwest and southeast Germany, north Italy, Czechia, Hungry, Serbia, Poland, and Ukraine. To better understand the roles of different countries in influencing the PM2.5‐associated health risks in Europe, we discuss more details about the contributions at the country level in Section 3.3. Compared to the contributions from transport, agricultural, and residential sources, contributions of emissions from industry and energy sectors were more from point sources, originating mainly from the United Kingdom (UK), Germany, Poland, Romania, and the European part of Russia. Figure 3: The spatial distributions of the annual contributions from anthropogenic emissions of NO x , NH3, and organic carbon to the total PM2.5‐related premature deaths over the receptor region. The first row shows the total contributions from each species and the subsequent rows show the spatial distributions of the two sectors with the largest contributions for each species. The spatial distributions are presented at the fine resolution (0.1° × 0.1°) of the HTAPv3 emission inventory. ## Monthly Source Attribution In addition to the source apportionment at the annual time scale, we also characterize the monthly changes in contributions from different species and sectoral emission sources in Figure 4. Given the seasonality of the meteorology, emissions, as well as the physical and chemical processes in the atmosphere, the monthly source attribution results provide additional details that enable policy‐makers to make better decisions compared to those made solely based on annual results. As Figure 4a shows, the monthly contributions exhibited different changes among various species. Emissions of OC and BC were associated with more damages to the public health in winter (December, January, and February), contributing $215.5\%$ and $259.2\%$, more premature deaths, respectively, than those in summer (June, July, and August). The results are consistent with the seasonal changes in residential contributions (Figure 4b), which accounted for over $70\%$ of the OC and BC contributions in winter, and only $20\%$–$40\%$ of those in summer. As Text S7 in Supporting Information S1 explains, the contributions are determined by two parameters: the adjoint sensitivities and emissions. To illustrate the seasonal changes of source contributions, Figure S2 in Supporting Information S1 displays the normalized monthly variations of mean sensitivities, and total emissions for each species and sector over the receptor region. In addition to the increased residential emissions, the sensitivities of the PM2.5‐related health risk to the OC and BC emissions in winter also exhibited values $37.4\%$–$39.2\%$ higher than those in summer, further contributing to the seasonal differences. In contrast, emissions of SO2 and SOAP had more adverse impacts on the public health in summer, contributing to 3.9 and 1.2 times, respectively, more premature deaths than those during wintertime. Similar patterns are found in the contributions of emissions from the energy and industry sectors, where approximately $78.1\%$ of the SO2 contributions originated. In Figure S2 in Supporting Information S1, the emissions from energy and industry sector show similar decreases, though relatively smaller, as the residential emissions in summer. Along with their major sources, the emissions of SO2 and SOAP in summer also exhibited values $23.6\%$ and $19.4\%$ lower than those in winter, respectively. In contrast, the summertime sensitivities for SO2 and SOAP emissions were $326.4\%$ and $73.7\%$ higher, respectively, than those during wintertime, as the photo‐chemical oxidation needed for the formation of sulfate and SOA increases. The results suggest that the influence of the sensitivity changes overcame that impacts of the emission changes, which determined the seasonality of the contributions from SO2 and SOAP emissions. Figure 4: Monthly variations of (a) species and (b) sectoral contributions to the PM2.5‐related health risks aggregated over the nested model domain. Normalization for each month is done by dividing each source category contribution by the total anthropogenic contribution during the same month. The colors in each bar match the ordering provided in the legend. Figure 4a also shows that contributions from NO x emissions exhibited peak values during February to April, accounting for $47.2\%$ of the annual total contributions from NO x emissions. As discussed in previous studies, the wintertime sensitivities for NO x and NH3 are usually higher due to the favorable formation conditions of ammonium nitrate (Guo et al., 2019; Nawaz et al., 2021). In this study, even BC, which is barely involved in chemical reactions, exhibited higher sensitivities during the early spring (Figure S2 in Supporting Information S1), indicating that the meteorological conditions (e.g., low surface wind speeds) can be more beneficial for the accumulation of surface aerosols during that time. In addition to the increased NO x sensitivities, NH3 emissions show significant increases during the same period as the emissions from agricultural crops and waste sources, which contributed to $66.5\%$–$80.8\%$ of all NH3 emissions during February to April, increased before the start of the growing season. The increased NH3 emissions and NO x sensitivities provide extremely favorable conditions for the formation of ammonium nitrate, resulting in relatively large contributions of emissions from NO x ‐rich sector groups. ## Country‐Level Source Attribution In this section, we examine the contributions from anthropogenic emissions at the country level in order to characterize the roles of different countries in influencing the PM2.5‐associated health risks in Europe. Tables S4 and S5 in Supporting Information S1 list the sectoral and species contributions, respectively, from each European country in 2015, while corresponding relative contributions to the total nationwide contributions are displayed in Figure 5. Ukraine, Germany, Poland, Italy, Russia (though only partially included in the model calculation, with approximately $30\%$ of its total population), and France were the top six source countries, contributing to over $56.7\%$ of the total anthropogenic PM2.5‐related premature deaths within Europe; this emphasizes the importance of regulating anthropogenic sources in these key source countries. However, as Figure 5 shows, the sectoral and species contributions exhibited strong variability at the country level. For western and central European countries (e.g., Germany, France, Benelux, Switzerland), ground transport emissions were the dominant anthropogenic sources of PM2.5‐related health risk, accounting for $25\%$–$40\%$ of the nationwide contributions. Consequently, anthropogenic NO x emissions were the most important sources of the premature deaths, making up over $35\%$ of the nationwide contributions. In Mediterranean and Eastern countries (like Italy, Spain, Poland, and Romania), the anthropogenic contributions were mainly from the residential sector emissions, which can even make up over $50\%$ (e.g., Croatia $53.2\%$) of the total deaths contributed by the nationwide emissions. Correspondingly, in these countries the influence of carbonaceous aerosol emissions is substantial. Due to targeted pollution regulations, emissions from energy and industry sectors were usually not the dominant contributors in European countries, but for countries which were greatly influenced by point sources (e.g., Serbia), they still accounted for large proportions of the nationwide contributions. For example, energy contributions made up about one third of the total nationwide PM2.5‐related premature deaths in Serbia, which was 1.2 and 5.7 times higher than the contributions from its domestic residential and ground transport emissions. Figure 5: Relative contributions from emissions associated with specific sectors and species to the total health burden from anthropogenic emissions from each European country (region). Countries (regions) are listed in descending order from left to right according to their contributions to the total PM2.5‐associated premature deaths over the receptor region in 2015. The name followed by an asterisk indicates a country that only lies partially within our nested model domain. The colors in each bar match the ordering provided in the legend. In Figure 6 we present more detailed annual source appointments of the nationwide contributions from the top six ranked contributing countries in 2015. The results allow us to identify which species from which emission sector in these countries contributed most to the adverse impacts on public health in Europe, providing more practical implications for policy making. For example, emissions from Ukraine contributed 34,581 premature deaths in Europe, most of which were contributed by emissions from industry ($19.6\%$), energy ($19.3\%$), agriculture livestock ($18.0\%$), and residential ($14.9\%$) sources. Emissions of OC, SO2, and NH3 were the dominant sources of industry, energy, and agriculture livestock contributions, respectively, accounting for $27.5\%$, $79.4\%$, and $90.6\%$, respectively of each sector contributions. In contrast, emissions from ground transport ($25.3\%$), agriculture livestock ($18.8\%$), and industry ($16.5\%$) sectors were the main sources of the premature deaths contributed by anthropogenic emissions in Germany. Unlike in Ukraine, SOAP emissions made up most ($42.8\%$) of the industrial contributions from Germany, suggesting that the local industrial structure might lead to large differences in the country‐level source contributions, even from the same sector. Differences across source contributions can also be found within the energy sector. In Poland, emissions from residential and energy sectors were associated with $27.0\%$ and $19.1\%$, respectively of the total nationwide contributions. $50.1\%$ of the energy contributions were from SO2 emissions and $44\%$ of those were from NOx emissions, which was quite different from Germany and Ukraine where the dominant contributions in the energy sector came from NO x ($66.0\%$) and SO2 ($79.4\%$) emissions, respectively. For premature deaths contributed by emissions from Italy, $60.5\%$ were from the residential and road transport sectors. Similar source attribution results are found in France, where emissions from the road transport and residential sectors made up $24.7\%$ and $22.6\%$, respectively of the nationwide contributions. For all six countries, the source attributions of contributions from these two sectors were more consistent compared to those from industry and energy sources, with NO x emissions making up a majority of the nationwide transport contribution and OC emissions making up a large proportion of the nationwide residential contributions. Figure 6: Sector‐specific anthropogenic contributions from Ukraine, Germany, Poland, Italy, Western Russia (European parts), and France. The number in parentheses in the title refers to the total PM2.5‐related premature deaths contributed by the anthropogenic emissions from each country (region). The name followed by an asterisk indicates a country that only lies partially within our nested model domain. ## Local and Regional Contributions to the PM2.5‐Related Premature Deaths In Section 3.2, we quantify the contributions of anthropogenic emissions from each individual country, species, and detailed sector to PM2.5‐related premature deaths within Europe. In this prior analysis, we only consider the anthropogenic sources within our nested European domain and thus the results explain only $59\%$ of the total estimated premature deaths. The remaining $41\%$ of the premature deaths are attributable to emissions from natural sources within Europe and emissions from outside Europe. Those sources aside, contributions from domestic anthropogenic emissions can be largely influenced by strong transboundary transport among European countries. The study of Crippa et al. [ 2019] suggested that transboundary air pollution contributed $25\%$–$75\%$ to PM2.5 pollution in European counties. Here we discuss the contributions to PM2.5‐related premature deaths from extra‐regional sources (Section 3.3.1) as well as the redistribution of the PM2.5‐related premature deaths contributed by the anthropogenic emission within Europe (Section 3.3.2). The results can help us learn about the premature deaths attributable to sources other than the domestic European anthropogenic emissions discussed in the previous section. This provides further understanding of the limitations of local policies in reducing the pollution related regional health risks within Europe. ## Contributions From Extra‐European Sources To understand the influence of sources of PM2.5‐related premature deaths beyond anthropogenic emissions within Europe, we consider contributions from extra‐regional sources in this section. For the nested simulation, the boundary conditions are generated by a first, global simulation at a horizontal resolution of 2° × 2.5°, the inputs from which can be considered as the influence of sources from areas outside the nested‐grid region, and are updated every 3 hours in the simulation. Therefore, we conduct a perturbation experiment by reducing all inputs of the boundary conditions by $20\%$ (BC‐20), and the contributions of the extra‐regional emissions are calculated based on the differences between the estimations in the base model run (BASE) and the BC‐20 scenario run. To quantify the effects of a $20\%$ decrease in boundary conditions, we only apply the satellite downscaling to the premature death estimation in both BASE and BC‐20 computations, since the satellite rescaling would correct the bias induced by the perturbation. The contribution resulting from extra‐European emissions in each grid cell is then estimated to first order by multiplying the corresponding difference induced by the $20\%$ perturbation by five. In Figures 7a and 7b we present the relative contributions from external sources to population‐weighted PM2.5 concentrations and PM2.5‐related premature deaths in 2015. The perturbation results suggest that the impact of extra‐regional emissions decreased from the near‐boundary areas to the central regions, and the extra‐regional emissions contributed to $31.4\%$ of the PM2.5 exposure and $25.1\%$ of the premature deaths, respectively, averaged over the European region. The calculated contributions of extra‐regional sources to PM2.5‐related health burden in Europe are comparable to those (∼$22\%$) found in previous studies (e.g., Anenberg et al., 2014; Crippa et al., 2019; Im et al., 2018; Liang et al., 2018). This suggests that PM2.5 and its associated health burden in Europe were mainly attributable to domestic sources, yet the extent of domestic influences varied due to several factors including the definition of the receptor region, the inclusion of natural components, the target year, the perturbation method, and differences in model‐setups (e.g., resolution, physical and chemical mechanisms) used in the calculation. Contributions from extra‐regional emissions (Figure 7c) are calculated by multiplying the estimated premature deaths (Figure 1c) by the relative contributions as displayed in Figure 7b. In our receptor region, the extra‐regional emissions contributed approximately 113,087 premature deaths, leading to more adverse impacts in hotspots near the boundaries (e.g., south UK, Benelux, and east Ukraine). The premature deaths caused by sources within the nested‐grid region (Figure 7d) are then obtained according to the differences between the estimated total premature deaths and the calculated external emission contributed deaths. Thus, domestic anthropogenic emissions of NO x, NH3, SO2, OC, BC, and SOAP, as mentioned in Section 3.2, lead to 265,328 premature deaths, accounting for approximately $78.8\%$ of the total contributions of emissions within nested domain. The previous literature emphasizes that natural sources (e.g., dust) also contributed strongly to mortality, making up about one‐sixth of global air pollution induced premature deaths (Lelieveld et al., 2015). In this study, we consider fine mode dust particles as a component of PM2.5, and assume that they are equally toxic as other fine particulate matter species (e.g., SO42−, NO3−, NH4+, OC, BC). The nested‐grid region includes not only large ocean areas but also northern Africa and even parts of the Middle East where natural sources contribute $15\%$–$92\%$ of the local premature mortality. Therefore, the remaining deaths other than those contributed by domestic anthropogenic sources can be largely attributable to the contributions of natural sources including dust, sea salt or large‐scale biomass burning, as well as to the initial pollution conditions inherited from the previous months, accounting for $15.9\%$ of the estimated premature deaths in Europe, or $21.2\%$ of the total contributions of emissions from within the domain. Figure 7: The relative contributions of the extra‐regional emissions to (a) the surface PM2.5 concentrations (population‐weighted) and (b) the PM2.5‐related premature deaths in Europe. (c, d) Are the calculated PM2.5‐related premature deaths contributed by emissions outside and within the nested‐grid region. ## The Redistribution of the Local Contributions Within Europe There are complex links between emissions and PM2.5 concentrations, since the pathways of emitted air pollutants depend on not only sources, but also meteorological conditions, geographical features, and their chemical properties. In addition to the pollution level, PM2.5‐associated premature deaths in a specific region are also related to the exposed population and even the medical conditions that determine the disease mortalities, which raises the complexity of the relationship between the magnitude of emission and the number of premature deaths that occur locally. To examine this further, we define the contribution ratio (CR) as the contribution of anthropogenic emissions to premature deaths anywhere in Europe divided by the premature deaths occurring in each grid cell. Figure 8 displays the spatial distributions of CRs and the absolute differences between the two types of premature deaths in 2015. Here we consider the actual premature deaths as the total estimated deaths in Figures 8a and 8b and the estimated deaths excluding the influence of the extra‐regional emissions (Figure 7d) in Figures 8c and 8d, respectively. The former provides us an overall view of the health risks contributed by local emissions relative to the total health burden experienced by the local population, and the latter helps us better understand the redistribution of anthropogenic contributions controllable within the nested‐grid region. As is shown in Figure 8a, the emissions from some western and central European regions caused more premature deaths than those that occurred locally. The “over‐contributing” regions, that is, those whose emissions contributed to more premature deaths than were incurred locally (Figure 8b), mainly occurred in northeast Spain, central UK, northeast France, Luxembourg, east Germany, and Austria, where the estimated number of premature deaths from anthropogenic emissions exceeded the number of deaths that occurred locally by a factor of three. In a sense, these regions are net “exporters” of air pollution health impacts, given that their emissions cause more premature deaths than they alone experience. In contrast, most eastern European countries, except for regions where large point sources were located, suffered from more premature deaths than were caused by their own throughout Europe; they are net “importers” of health damages in this sense. For example, the anthropogenic emissions from Greece only caused an estimated 2,224 premature deaths, while 7,356 premature deaths occurred there. After the impacts from extra‐regional sources are excluded in Figures 8c and 8d, the redistribution of the contributions by sources within Europe exhibits a distinct pattern of the adverse pollution‐related health risks being transferred from the west to the east. The most “over‐burdened” counties/regions (with a within‐region CR of less than 0.42, Table S6 in Supporting Information S1) included Greece, Bulgaria, Andorra, and Cyprus. The results are consistent with the calculation of Crippa et al. [ 2019] who indicated that the PM2.5 concentrations in these regions could be caused more by extra‐regional sources than the domestic emissions. Figure 8: The spatial distributions of (a) the contribution ratio (the contribution of anthropogenic emissions to premature deaths anywhere in Europe divided by the premature deaths occurring in each grid cell.) and (b) the absolute difference between the anthropogenic emission contributions to premature deaths anywhere in Europe and the estimated premature deaths in each grid cell. (c, d) Are similar to (a, b), but the estimated actual premature deaths are replaced by those caused by emissions only within Europe. The local PM2.5‐related premature deaths are determined by the population, mortality rates of pollution‐associated health outcomes, and the exposure level (Text S6 in Supporting Information S1). As displayed in Figure 1a and discussed in previous works (e.g., Ciarelli et al., 2019; Kiesewetter et al., 2015), eastern European countries can be characterized as pollution hotspots where the population was exposed to higher concentrations of PM2.5 than in western or central European countries. The relative health risk was thus higher according to the exposure response relationship following the GBD 2019 study (C. J. L. Murray et al., 2020), which means a larger proportion of the premature deaths would be attributed to PM2.5 exposure. Additionally, the baseline mortality rates associated with pollution‐related diseases (IHD, COPD, LRI, LC, T2D, and STROKE, Figure S3 in Supporting Information S1) were higher in eastern European countries. Taking Ukraine as an example, the average death rate across the six diseases was 1,025 deaths per 100K population in 2015 according to the GBD results, which was 3.5 times the number in France (∼295 deaths per 100K population). The high mortalities might be related to poor medical conditions or low socio‐economic status. A concern is that the high mortalities result in more premature deaths responding to per unit increases in PM2.5 concentrations, further aggravating the detrimental health impacts of pollution. As indicated in previous source appointments (e.g., Crippa et al., 2019; Im et al., 2018) and our results (see Sections 3.2.1 and 3.2.3), eastern European countries (like Ukraine, Poland, and Romania) were estimated to be among the major sources of the burden of pollution‐related disease in Europe due to the burning of solid fuels for domestic heating and industry. However, according to the discussions above, we find that people living in some of these regions experienced even greater harmful air pollution effects than their local sources caused, since they were not only more susceptible to the adverse health effects of severe PM2.5 pollution but also received PM2.5 emanating from western and central Europe. This redistribution further increases the heterogeneity of the pollution related health risks in Europe and might lead to larger social inequalities in health and other socio‐economic aspects. ## Emission‐Induced Contribution Changes Between 2005 and 2015 As discussed above, domestic anthropogenic emissions are the most important sources of PM2.5‐related health risks in Europe. To tackle air pollution and protect human health and the environment, the EU has implemented a comprehensive clean air policy during the past two decades. Air quality standards (e.g., Directive $\frac{2008}{50}$/EC) and national reduction commitments (e.g., Directive (EU) $\frac{2016}{2286}$) for air pollutants (e.g., SO2, NO x, NMVOCs, NH3, and PM2.5) were introduced and updated in different stages. In this section, we consider the first stage of the PM2.5 regulation in Europe, which lasted from 2005, when the cap of 25 μg−3 for annual mean PM2.5 concentrations was first put forward, until 2015, when the limit value was met (European Commission, https://environment.ec.europa.eu/topics/air/air-quality/eu-air-quality-standards_en, accessed on: 11 August 2022). We quantify how such policy‐induced decreases in aerosol and its precursor emissions contributed to health benefits within Europe. To exclude the influence of other factors (e.g., meteorology, natural sources, and socio‐economic conditions), we assume that the adjoint sensitivity for each species in 2005 is consistent with that in 2015, and calculate the corresponding contributions of emissions from each individual country, species, and sector according to Text S7 in Supporting Information S1. Figure 9 shows the total contribution changes for each species and sectoral source within Europe between 2005 and 2015. Reductions in anthropogenic emissions during this 11‐year period can account for 63,538 fewer PM2.5‐related premature deaths. Avoided deaths were primarily attributable to decreased contributions from NO x, SO2, and SOAP emissions, accounting for $40.6\%$, $24.6\%$, and $20.0\%$, respectively, of the total decreases in the premature deaths. Most sectoral sources contributed to the decreases, albeit to varying degrees. Consistent with the large reductions in NO x contributions, ground transport emission reductions contributed more than half ($53.1\%$) of the total deaths avoided by the emission control. Decreases in energy and industrial emissions resulted in $29.0\%$ and $12.8\%$, respectively, of the total emission‐induced decreases in premature deaths, which were the other two major sources of the health benefits. SO2 contributions exhibited the second largest decreases ($36.8\%$) during 2005–2015, $69.6\%$ of which were from decreased emissions from energy sources. In contrast to the large decreases in contributions from ground transport and energy emissions, premature deaths from residential and agricultural sources, which were the largest two sources of PM2.5‐related premature deaths in Europe (Figure 2) in 2015, exhibited very slight changes, with corresponding decreases accounting for only $6.6\%$ and $2.0\%$, respectively of the total emission‐induced decreases in the premature deaths. Further, contributions of emissions from agriculture waste burning and agriculture crops even increased by $5.1\%$ and $5.3\%$, respectively in 2015 compared those in 2005. Similar increases occurred in contributions from sources like waste, international shipping and aviation, among which premature deaths contributed by international aviation emissions increased most ($26.2\%$) in 2015, leading to 1,453 more deaths compared to the beginning of the first stage. It should be noted that we consider only the impacts of anthropogenic emission changes on the health benefits here. The GBD results (https://vizhub.healthdata.org/gbd-results/, accessed on: 11 October 2022) suggested that the total number of deaths from COPD, IHD, LRO, LC, T2D, and stroke decreased by 383,601 (330,238–445,449) in Europe during 2005–2015, and the changes accounted for $9.5\%$ of the total deaths in 2015. If the demographic changes are considered, the values of relative emission‐induced changes, depicted as the total PM2.5‐related premature deaths in 2015 minus those in 2005, should be smaller than what we present here. However, those differences are likely very small compared to those induced by health impact assessment uncertainty, which we discuss with more details in Section 3.5. Figure 9: Changes in the PM2.5‐related premature deaths contributed by (a) species and (b) sectoral emission changes during 2005–2015 over the receptor region. The percentages indicate the relative changes in 2015 compared to the number in 2005. Pie charts in panels (c and d) show the proportions of the species and sectoral contributions in the total premature death changes contributed by the anthropogenic emission reductions. The number in parentheses is the percentage of the contribution changes from each category in the total anthropogenic emission‐induced changes in PM2.5‐related premature deaths within Europe. As for changes at the country level, the results displayed in Figure 10 suggest that nearly all European countries decreased their contributions to the PM2.5‐associated health risks in the first stage of emission controls, though the magnitude and attribution of changes varies widely. For example, anthropogenic emission reductions in France, Italy, and Poland were the leading contributors to health benefits, accounting for $13.3\%$, $10.5\%$, and $9.5\%$, respectively, of the total deaths avoided in Europe. Emission reductions in road transport, residential, and industry sectors drove the bulk of the calculated decreases in France. In Poland, however, those decreases were dominated by energy emission reductions, accounting for $48.3\%$ of the total decreases nationwide. In Italy, premature deaths contributed by road transport emissions exhibited significant decreases, but this benefit was largely offset by increased contributions from Italian residential emissions which led to 2,462 more deaths in 2015 compared to the number in 2005. Similar increases in contributions from residential emissions occurred in many other countries (e.g., United Kingdom, Romania, Spain, Bulgaria, Czechia, and Hungary), which explains the relatively small change in the total contribution of residential emissions during the study period. Figure 10 also indicates that while in general there were large decreases, there were still some contributions that exhibited only slight decreases or even increases. For example, premature deaths contributed by energy and industrial emissions increased by 1,856 and 770, respectively in Ukraine, and by 15 and 23, respectively in Latvia, in contrast to the average decreasing trend in Europe. Compared to the contributions from other sources, premature deaths attributed to agricultural activities exhibited very slight decreases, and, for some countries, even large increases, especially for those related to crop production. Figure 10: The attribution of country (region)‐specific changes in the PM2.5‐related premature deaths contributed by anthropogenic emissions within the receptor region during 2005–2015. The name followed by an asterisk indicates a country that only lies partially within our nested model domain. ## Uncertainty Analysis and Limitations The uncertainty in our analysis arises from several sources: the satellite‐derived PM2.5 concentrations used for calculation of PM2.5 exposure, the estimation of the health impacts associated with this exposure, the adjoint model calculation of exposure sensitivities to emissions, the application of these sensitivities using a first‐order linear approximation of source contributions, and the magnitude of the emissions themselves. Previous source attribution studies have shown that the estimation of exposure‐associated health impacts is usually the largest source of uncertainty in health impact assessments (Nawaz & Henze, 2020; Nawaz et al., 2021). While we discuss the uncertainties from sources other than this in the following paragraph, we treat them separately and only consider uncertainties caused by the estimation of exposure‐associated health impacts in determining the uncertainty bounds, since the covariance between the health impact calculation and other types of uncertainties remains to be investigated. We apply satellite downscaling and rescaling (Text S1 and S2 in Supporting Information S1) to the simulated surface concentrations before the values are passed to the adjoint calculation. Artifacts in the calculated exposure can be discerned by comparing the corrected concentrations to the measured PM2.5 values. In the selected 972 monitoring sites, the simulated PM2.5 concentrations after the satellite correction have a low bias of $6.0\%$ in 2015 (Figure S1b in Supporting Information S1). We assume these biases can then lead to slight underestimation of the exposure and related health impacts. Additionally, the satellite product itself also contributes to the uncertainty, in which the annual mean PM2.5 concentrations exhibits overall uncertainties of −$8\%$ to +$13\%$ in Europe (van Donkelaar et al., 2021), influencing the relative uncertainty in our estimation. Uncertainty in the adjoint model's PM2.5 source‐receptor sensitivities can be associated with uncertainties in meteorology as well as the chemical and physical processes represented by the GEOS‐Chem model. Such kinds of uncertainty can be accessed by comparing the original model results (without satellite rescaling and downscaling) to the observations. It should be noted that the evaluated model performance is also coupled with the uncertainty in emissions, which is difficult to separate from the model uncertainties. As Figure S1a in Supporting Information S1 shows, the overall total bias induced by these two factors combined is approximately −2.21 μg m−3, which translates into about −$15\%$ underestimation in the PM2.5 levels and related calculations in Europe. The adjoint model sensitivities are also merely tangent linear gradients, the application of which is likely to be reasonable over a limited range of perturbations (Henze et al., 2007). For OC, BC, and primary species, their response to the emission perturbations is linear, so that errors from a first‐order linear approximation are close to zero. In contrast, for NH3, NO x, SO2, and SOA, the first‐order linear approximation neglects higher order sensitivities, giving rise to relatively larger errors in exposure and related health impacts when large emission perturbation occurs. During 2005 to 2015, the overall anthropogenic emissions of NH3, NO x, SO2, and SOA changed by −$1.3\%$, −$19.3\%$, −$34.9\%$, and −$26.8\%$, respectively over the studied region. These magnitudes are still within the perturbation range for which first‐order linear approximation is applicable (Henze et al., 2007; Koo et al., 2013). For PM2.5, the first‐order sensitivities to emissions of NO x, NH3, SO2 and VOCs were calculated to be one or two orders of magnitude higher than the higher‐order sensitivities (Koo, 2011), indicating that the first‐order linear approximation can be considered accurate even though small uncertainties might result from ignoring the impacts of higher‐order sensitivities. The health impact assessment uncertainty arises from uncertainty in the estimates of population, mortality rates of health outcomes, and the exposure response relationships used to calculate pollution‐related premature deaths from health outcomes. The latter is usually considered to be the major source of uncertainty in the health impact analysis, and is typically used in determining the uncertainty bounds (Lee et al., 2015). Uncertainties in the gridded population data used here from CIESIN [2018] are not explicitly available. However, by comparing the total population of each European country to the estimates provided by the GBD 2019 results (https://vizhub.healthdata.org/gbd-results/, accessed on: 11 October 2022), we can calculate an uncertainty range as the percent difference comparing the model population to both of the GBD bounds, of −$20\%$ to +$54\%$ for the country‐level population over the studied region. The GBD results provide explicit uncertainty bounds for mortalities and relative risks. By considering the range of all three of these factors, we estimate that the total number of PM2.5‐related premature deaths has bounds ranging from 257,846 to 722,138. These lower and upper bound values are $57\%$ and $161\%$, respectively, of the mean estimate [449,813]. Correspondingly, the uncertainty of the contribution of each individual anthropogenic source to the PM2.5‐associated health impacts ranges from −$37.6\%$ to +$72.1\%$ in this study. These are likely larger uncertainty ranges than those induced by the exposure estimate, source attribution modeling, or emissions alone as discussed in the previous paragraph. Apart from uncertainties introduced by technical limitations, our source attribution results might still be limited to some extent when applied in practice. For example, policy makers have a strong interest in designing air quality regulations that are both cost effective and equitable. When regulating polluting industries that are important drivers of economic growth, policy trade‐offs arise which call for careful, quantitative assessments of the economic costs of a specific emission control action, its (monetized) public health benefits, and the geographical distribution of those benefits. This requires an alternative analytical approach that not only identifies where most air pollution‐related health burden comes from but that links policy impacts on the source of emissions to health benefits. To enable this kind of research, future studies will aim to integrate relevant economic and econometric modeling with non‐linear atmospheric chemistry models such as the one we have proposed here. Apart from broadening the scope of the modeling, improving the quality of model inputs would strongly benefit this line of research. First, more detailed mortality data at the sub‐national level, especially for countries covering large areas with dense population, will allow more accurate source attribution and health benefit estimates. Second, advances in the estimation of the concentration‐exposure‐health responses (e.g., Burnett et al., 2018) will reduce biases and large uncertainties in the estimation of health benefits. ## Discussion and Conclusions In this study, we present a newly developed approach to characterize the sources of PM2.5‐related health risks in Europe in 2015 and quantify corresponding changes induced by the anthropogenic emission changes during the first stage of the EU PM2.5 objectives from 2005 to 2015, using the CTM GEOS‐Chem and its adjoint. In 2015, the total PM2.5‐related premature death is estimated to be 449,813 (257,846–722,138) out of a total population of 598.97 million over the European region considered in this study. Our estimate is slightly lower than that of Lelieveld et al. [ 2019] since the latter study calculated the PM2.5‐associated premature deaths over a larger European region using the Global Exposure Mortality Model, which accounts for a larger range of PM2.5 exposure by including new cohort data from China, and providing larger hazard ratio predictions for nearly all concentrations than the GBD estimates (Burnett et al., 2018). IHD and stroke were estimated to be the top two causes of premature death attributable to PM2.5 exposure, which is similar to the calculation results reported in recent studies over Europe (Tarín‐Carrasco et al., 2022), China (Zheng et al., 2021), and the US (Kazemiparkouhi et al., 2022). We find that anthropogenic emissions within Europe contributed $59.0\%$ of the total estimated premature deaths, which is the largest sources of the PM2.5‐related health risks in Europe. Due to heterogeneous distributions of precursor species (NO x, NH3, SO2, OC, BC, and SOAP), the domestic anthropogenic contributions differed greatly by sector in 2015. Residential and agricultural emissions were the most important contributing sectors, accounting for $23.5\%$, and $23.0\%$, respectively, of the total burden of PM2.5‐related premature deaths induced by anthropogenic emissions within Europe. Our estimate of residential contributions is likely higher than earlier works owing to the inclusion of SOA. Monthly source apportionment suggests the domestic residential emissions were associated with more premature deaths in winter due to the high emission rates and the high sensitivity for carbonaceous aerosols, while the agricultural emissions led to more premature deaths during February to April when emissions from agricultural crops and waste sources significantly increased before the start of the growing season. The country‐level source attribution results have multiple policy implications with respect to air quality and public health in Europe. For western and central European countries, anthropogenic emissions from ground transport sectors made up a majority of nationwide contributions to PM2.5‐associated premature deaths, while in Mediterranean and Eastern countries, residential emissions were the dominant source of the health risks. However, even for contributions from the same sector, the dominant species can vary by location. The largest diversity is found in industrial and energy sectors, suggesting that the local industrial/energy structures and policies further increase the source complexity of the PM2.5‐related health risks. Thus, more detailed source attribution results should be provided at least at the country level so that emissions controls can be better informed and more effective. Additionally, our calculations suggest that there were redistributions of the anthropogenic contributions within Europe, further increasing the heterogeneity of the pollution related health risks. After excluding the influence of extra‐regional sources, the eastern European countries suffered from more premature deaths than their emissions caused; in contrast, the emissions from some central and western European regions contributed premature deaths exceeding three times the number of deaths that occurred locally. For people living in eastern European countries such as Ukraine, Poland, Romania, they experienced even greater harmful air pollution effects, since they were not only more susceptible to the adverse health effects of the severe local PM2.5 pollution but also experience the consequences of emissions from western and central parts of Europe simultaneously, resulting in larger social inequalities with respect to health and other socio‐economic aspects in Europe. During 2005–2015, emissions controls promoted decreases in the PM2.5‐related health risks in nearly all European countries. The anthropogenic emission changes during the 11‐year period resulted in 63,538 (46,092–91,082) fewer PM2.5‐related premature deaths in 2015 compared to 2005. Most of the decreases were associated with decreased contributions from ground transport, energy, and industrial sources, making up $53.1\%$, $29.0\%$, and $12.8\%$, respectively, of the total decreases in the premature deaths. This result indicates that the control strategies for these sectors effectively mitigated the detrimental effects of PM2.5 pollution on public health in Europe during the first emission control stage. However, there were several sectoral source changes, for example, those in residential, agricultural, waste, shipping, and aviation sources, that had little impact on the estimated decreases and even some that led to an increase in premature deaths during these years. When examining the health impact changes in individual country, we find that countries progress at their own pace in reducing the adverse impacts of air pollution on the public health. Decreases in the contributions from sectoral emissions in some countries can be offset by the increased contributions in others, reducing the benefit of the emission control strategies in some regions. Overall, compared to a focus on local emission reduction policies and actions alone, international cooperation on transboundary air pollution can also be an important part in tacking air pollution and increasing the effectiveness of the EU policies throughout the continent. ## Conflict of Interest The authors declare no conflicts of interest relevant to this study. ## Data Availability Statement The GEOS‐Chem adjoint model used in this study is an open‐access model which is publicly available online (http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_Adjoint, accessed on: 11 October 2022). 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--- title: Risk and protection factors for self-reported hypertension and diabetes in João Pessoa, Brazil. The VIGITEL survey, 2014. A cross-sectional study authors: - Ana Paula Leite Moreira - Deborah Carvalho Malta - Rodrigo Pinheiro de Toledo Vianna - Patrícia Vasconcelos Leitão Moreira - Alice Teles de Carvalho journal: São Paulo Medical Journal year: 2017 pmcid: PMC10027241 doi: 10.1590/1516-3180.2017.0044250517 license: CC BY 4.0 --- # Risk and protection factors for self-reported hypertension and diabetes in João Pessoa, Brazil. The VIGITEL survey, 2014. A cross-sectional study ## ABSTRACT ### CONTEXT AND OBJECTIVE: Chronic diseases are the main cause of death among adults and are responsible for most outpatient and hospital care expenses in Brazil. The objective here was to determine the prevalence of hypertension and diabetes and to analyze the associations with risk and protection factors among adults. ### DESIGN AND LOCAL: Cross-sectional study in a state capital in northeastern Brazil. ### METHODS: Data on adults of both sexes aged ≥ 45 years who were interviewed in the Vigitel telephone survey in 2014 were analyzed. Prevalence ratios were estimated using Poisson regression, to identify associated factors. ### RESULTS: Among women, the prevalence of hypertension was $48.4\%$ and of diabetes, $12.7\%$; among men, the prevalences were $41.9\%$ and $13.8\%$, respectively. Multivariate analysis showed that for women, age group ≥ 65 years, overweight, self-assessed poor health and dyslipidemia remained associated with higher prevalence of hypertension. For men, overweight and self-assessed poor health remained associated with higher prevalence of hypertension. Regarding diabetes, in the multivariate model for women, age group 55-64 years, schooling level between zero and four years and no regular consumption of beans remained associated with higher prevalence. For men, age groups 55-64 years and ≥ 65 years and being married or in a stable partnership were associated with higher prevalence of diabetes. ### CONCLUSIONS: The results indicated that the prevalences of hypertension and diabetes were high and that preventable factors were associated with this situation, thus providing support for public policies aimed towards coping with this. ## INTRODUCTION The four major noncommunicable diseases are cardiovascular diseases, diabetes, neoplasms and chronic respiratory diseases. These diseases have several common risk factors, which can be classified as modifiable and non-modifiable. The modifiable risk factors include smoking, abusive consumption of alcoholic beverages, excess body weight, unhealthy eating habits, sedentary lifestyle and metabolic abnormalities such as dyslipidemias. The non-modifiable risk factors are heredity, race, sex and age.1 The Global Burden of Disease (GBD)2 study, coordinated by the Institute of Metrics and Health Assessment (IHME) of the University of Washington (United States) showed that in Brazil, between 1990 and 2010, there were changes in the rankings among the ten leading causes of death. Among these causes of years of life lost due to premature death (YLLs), diabetes and hypertension increased by more than $40\%$ over this period. The five leading causes of years of life lost due to death or disability (DALYs) among women were depression, ischemic heart disease, low back pain, cerebrovascular disease and diabetes; and among men, homicide, ischemic heart disease, car accidents, low back pain and cerebrovascular disease. Also according to this study,2 the risk factors that most contributed towards premature death and loss of health among men and women in Brazil in 2010 were inadequate diet, high blood pressure, overweight and altered fasting glycemia. In the northeastern region of Brazil, data from 2014 on the major groups of causes of death that were reported by the Department of Informatics of the Brazilian National Health System (DATASUS)3 showed that diseases of the circulatory and endocrine systems and nutritional and metabolic diseases corresponded, respectively, to proportions of deaths of $27.56\%$ and $7.52\%$, in comparison with the total number of deaths from all causes. In João Pessoa, the state capital of Paraíba, which was the subject of the present study, data from 2014 also reported by DATASUS3 showed that diseases of the circulatory and endocrine systems and nutritional and metabolic diseases corresponded, respectively, to proportions of deaths of $27.48\%$ and $6.67\%$, in comparison with the total number of deaths from all causes. Diabetes is a highly incapacitating disease that can cause diabetic retinopathy, amputations, nephropathies, cardiovascular and encephalic complications, among other conditions. It can impair individuals’ functional capacity, autonomy and quality of life, thus resulting in high social and financial costs for society and for these individuals and their families.4 Hypertension is considered to be both a chronic disease and a risk factor for other diseases and chronic conditions, such as chronic kidney disease and diabetes, among others. This gives it greater prominence as these individuals’ health conditions worsen, thus contributing towards loss of quality of life, early lethality of diseases and high costs for social and healthcare systems. It has a multifactorial nature, with an asymptomatic course in many cases, which means that this diagnosis is neglected and, consequently, so is treatment. In addition, hypertension is highly prevalent in Brazil and in the world, thus representing a great challenge for public health.5,6 A study carried out on the adult population of Campinas,7 state of São Paulo, showed that there were significant differences in the prevalences of risk and protection factors for chronic diseases according to gender. The prevalences of smoking, former smokers, alcohol abuse, overweight, obesity and free-time physical activity were higher among men; among women, healthier eating habits and dyslipidemia were more prevalent. Chronic diseases are responsible for the greatest proportion of the burden of diseases diagnosed in Brazil and present significant modifiable risk factors. The impact of these diseases and their risk factors varies according to gender and the level of development of the different regions of the country. Moreover, chronic diseases are highly prevalent among people aged 45 years and over. Because very few studies on hypertension and diabetes have been conducted in João Pessoa, the aims of the present study were to ascertain the prevalence of these diseases and to identify and measure the independent effects of risk and protection factors relating to the presence of previous medical diagnoses of these diseases, as reported by adults in this municipality. ## OBJECTIVE The objectives of this study were to ascertain the prevalence of hypertension and diabetes and to identify the relationships of sociodemographic and behavioral characteristics, food consumption characteristics and health indicators towards the presence of previous medical diagnoses of these two chronic diseases, as reported by adults, stratified according to sex, in a state capital in northeastern Brazil. ## METHODS This was a cross-sectional, population-based, epidemiological study that used data from the Surveillance of Risk and Protection Factors for Chronic Diseases by Telephone (Vigitel) survey. The project for implementing Vigitel was approved by the National Ethics Committee for Research on Human Beings (CONEP) of the National Health Council (CNS), Ministry of Health, under report no. 355.590, of June 26, 2013, and under the certificate of presentation for ethics assessment (CAAE) no. 16202813.2.0000.0008. Since the project related to telephone interviews, the free and informed consent document was replaced by verbal consent that was obtained by the Ministry of Health at the time of the interview. To conduct the present study, the coordination office for non-transmissible diseases and health hazards of the Secretariat for Health Surveillance, Department of Health Situation Analysis, Ministry of Health, made data from Vigitel 2014 relating to João Pessoa (capital of the state of Paraíba) available to us. Adults aged 45 years and over who were living in households in João Pessoa served by at least one landline telephone in the year 2014 were included. During that year, 1,517 adults aged 18 and over living in this city were interviewed as part of the Vigitel survey.8,9 Out of this total, 867 interviews were conducted with adults aged 45 years and over, i.e. the target audience of the present study. These individuals comprised 566 females ($65.28\%$) and 301 males ($34.71\%$). This sample of 867 adults was weighted according to sex, age and schooling, in accordance with the methodology established for Vigitel, using the “rake” method,8,9,10,11thus making the data of this sample representative of the total adult population of this capital. Details of the sampling process and the weighting of Vigitel estimates, along with other details of the methodology used by this system can be seen in other published papers.8,9 The electronic questionnaire used at the time of the interviews is available in the annual publication of results from Vigitel.9 The dependent variables analyzed in this study were the prevalence of hypertension and the prevalence of diabetes, as reported by adults who had previously received these medical diagnoses. They were defined by the percentages of adults who reported having a prior medical diagnosis as positive answers to the following questions, respectively: “Has any doctor ever told you that you have high blood pressure?”; and “Has any doctor ever told you that you have diabetes?”. The independent variables analyzed in this study were selected based on their importance for determining the total burden of disease, as estimated by the World Health Organization for the Americas region.12 They consisted of risk and protection indicators selected from Vigitel, and were grouped into: sociodemographic, behavioral, food consumption and health indicators. The sociodemographic variables were: age group (45-54, 55-64 or ≥ 65 years); marital status (single, married/stable partnership, widowed or separated/divorced); schooling level (0-4, 5-8, 9-11 or ≥ 12 years of study); and possession of health insurance (yes or no). The behavioral categories included: smokers (adults who reported being current smokers, regardless of the number of cigarettes, frequency and duration of smoking); former smokers (adults who reported being former smokers, regardless of time elapsed); and physically inactive individuals (adults who had not exercised during their free time within the last three months, were not performing any intense physical efforts at work, were not going to work/school on foot or by bicycle with a minimum journey time of 20 minutes and were not performing heavy cleaning in their homes). The food consumption variables were: regular consumption of fruits, vegetables and greens (on five or more days of the week); recommended consumption of fruits, vegetables and greens (five servings daily, on five or more days of the week); regular consumption of beans (on five or more days of the week); consumption of meat with excess fat (habit of consuming red meat with visible fat and/or chicken with skin); consumption of milk with full fat content (habit of consuming whole milk fat); regular consumption of soda or artificial juice (on five or more days of the week); regular consumption of sweets (on five or more days per week); replacement of lunch or dinner by snacks (seven or more times a week); and high salt intake (adults who considered that their salt intake was high or too high). The health indicators analyzed the following conditions: overweight, in terms of the body mass index (BMI); defined as body weight (kg) divided by square of height (m2), for which self-reported information on weight and height was used to calculate BMI and adults who presented BMI ≥ 25 kg/m2 were considered to be overweight, as classified by the World Health Organization;13 self-assessed poor/very poor health (adults who assessed their health as poor or very poor, in answer to the question “Would you rate your health as: very good, good, normal, poor, or very poor?”); and dyslipidemia (adults who reported having a previous medical diagnosis of dyslipidemia: high cholesterol or triglycerides). All the analyses were performed using the Vigitel expansion factor, using the Stata survey procedure version 11 SE. Initially, Pearson chi-square association tests were performed to verify the existence of a statistical association between the independent variables and outcomes (P ≤ 0.05). Subsequently, the Poisson regression model was used to verify the existence of factors associated with arterial hypertension and diabetes. The variables that presented P ≤ 0.20 in the univariate analysis were considered for introduction into the multivariate model. The magnitudes of the associations found were measured using prevalence ratios (PR) with their respective $95\%$ confidence intervals ($95\%$ CI). ## RESULTS Data from 867 adults aged 45 years and over were analyzed. These comprised 566 women and 301 men, corresponding to $65.28\%$ and $34.72\%$ of the total sample, respectively. Another 271 adults were excluded because they were 18-44 years old. Most of these individuals were between 25-34 years old, had 9 to 11 years of educational attainment and had no health insurance. Previous medical diagnoses of hypertension and diabetes were reported by $45.8\%$ and $13.1\%$ of the population, respectively, and there were no significant differences between the genders. Table 1 describes the sociodemographic, behavioral, food consumption and health indicators of the study population and the presence of hypertension and diabetes, stratified according to gender. Among the women, there was higher prevalence of hypertension in the age group ≥ 65 years ($95\%$ CI: 50.6-66.2) and among those who did not consume whole milk ($95\%$ CI: 46.8-59.9); those who were overweight ($95\%$ CI: 52.2-65.4); those who self-rated their health as poor/very poor ($95\%$ CI: 48.1-85.2); and those who reported having a medical diagnosis of dyslipidemia ($95\%$ CI: 55.6-71.3). Among the men, there was statistically significant higher prevalence of hypertension in the age group ≥ 65 years ($95\%$ CI: 39.1-62.2) and among those who reported consuming meats with excessive fat ($95\%$ CI: 17.6-43.6); those who were overweight ($95\%$ CI: 39.0-56.6); those who self-assessed their health as poor ($95\%$ CI: 64.2-97.6); and those who reported having a medical diagnosis of dyslipidemia ($95\%$ CI: 42.0-67.4). Table 1:Prevalences of hypertension and diabetes in the population, according to gender and sociodemographic, behavioral, food consumption and health indicator characteristics. João Pessoa, Paraíba, Brazil [2014]VariablesHypertension Diabetes Female sex Male sex Total P (%)Female sex Male sex Total P (%)P (%)P-value † P (%)P-value † P (%)P-value † P (%)P-value † Sociodemographic indicators Age group45-54 years40.1 0.01031.90.03836.76.80.0114.30.0015.855-64 years51.550.050.917.324.420.2≥ 65 years58.650.755.617.218.517.7Marital statusSingle38.70.27836.90.89238.19.20.4010.60.0006.3Married/stable partnership48.642.745.712.117.414.7Widowed49.949.749.918.14.616.9Separated/divorced57.539.151.015.53.811.4Education≥ 12 years42.20.29835.00.54439.45.80.04815.10.8899.39-11 years45.446.545.810.213.211.35-8 years48.636.444.014.011.313.00-4 years56.245.351.219.115.617.5Possession of health insurance Yes45.50.33749.50.10547.010.40.27716.70.38712.7No50.537.745.214.012.213.2Behavioral indicatorsSmokersNo49.40.09242.00.95046.712.40.41413.30.68612.7Yes31.241.438.118.416.216.9Former smokersNo48.70.87542.40.87046.414.10.1979.80.04612.5Yes47.841.244.79.220.214.4Physically inactiveNo46.20.12938.40.11743.111.20.13013.80.98812.2Yes55.451.753.812.713.915.8Food consumption indicatorsRegular consumption of FVGYes45.50.26543.60.68744.811.90.62413.70.98112.5No51.440.746.713.513.913.7Recommended consumption of FVGYes47.20.77845.70.57146.711.00.51210.50.39810.8No48.840.845.513.314.813.9Regular consumption of beansYes47.40.52741.20.57244.610.60.04913.90.88012.1No50.946.449.718.012.916.7Meat with excessive fatNo49.30.32346.10.04148.112.00.25913.40.83512.5Yes40.428.933.218.714.916.3Milk with full fat contentNo53.40.02441.50.87548.615.20.07715.80.32015.4Yes41.042.641.69.010.99.8Regular consumption of soda or artificial juiceNo48.60.74643.70.18346.712.50.60914.40.45213.2Yes44.927.534.517.09.012.2Regular consumption of sweetsNo47.90.56843.50.09946.113.60.08714.50.20914.0Yes51.926.543.56.36.56.4Replacement of lunch or dinner with snackNo49.90.08342.90.28445.31.60.03214.40.16313.9Yes61.329.151.04.75.95.1High salt consumptionNo47.60.11342.70.57145.712.70.91614.70.42913.5Yes64.336.147.012.07.19.0Health indicatorsBMI - overweightNo32.40.00028.40.01331.18.60.0426.10.0097.7Yes59.047.754.015.417.116.1Self-assessed poor/very poor healthNo47.00.03939.20.00043.812.70.88712.90.12512.8Yes69.889.677.111.828.718.0Self-reported dyslipidemiaNo38.20.00036.10.01737.348.90.09213.20.70211.6Yes63.855.060.851.115.215.8Note: the results express the percentage for the population. * Prevalence; †Statistical analysis performed was Pearson’s chi-square test. BMI = body mass index; FVG = fruits, vegetables and greens. There was higher prevalence of diabetes among women over 55 years old ($95\%$ CI: 11.0-26.2); among those with 0-4 years of education ($95\%$ CI: 11.8-29.5); among those who did not consume beans regularly ($95\%$ CI: 11.9-26.2); among those who did not replace lunch or dinner with snacks ($95\%$ CI: 10.3-17.7); and among those who were overweight ($95\%$ CI: 11.2-20.8). There was higher prevalence of diabetes among men in the age group of 55-64 years ($95\%$ CI: 13.5-39.9); among those who were married or were in a stable partnership ($95\%$ CI: 11.8-24.8); among those who reported being former smokers ($95\%$ CI: 12.0-32.1); and among those who were overweight ($95\%$ CI: 11.3-25.1) (Table 1). Table 2 shows the crude and adjusted analyses on factors associated with hypertension in women and men. The adjusted analysis relating to the women’s data showed that the age group ≥ 65 years old, excessive body weight, self-rated poor health and a previous medical diagnosis of dyslipidemia remained independently associated with higher prevalence of hypertension. Consuming whole-fat milk remained associated with lower prevalence of hypertension in women. In the adjusted analysis for men, overweight and self-rated poor health were independently associated with hypertension. The results differed between the age groups of 55-64 years and ≥ 65 years. Presence of a previous medical diagnosis of dyslipidemia lost its statistical significance in the adjusted analysis. Table 2:Prevalence and prevalence ratios (crude and adjusted) for prior medical diagnosis of hypertension reported by women ($$n = 566$$) and men ($$n = 300$$), according to sociodemographic, behavioral, food consumption and health indicator variables. João Pessoa, Paraíba, Brazil [2014]VariablesWomen Men Crude PR* $95\%$ CIAdjusted † PR $95\%$ CICrude PR $95\%$ CIAdjusted † PR $95\%$ CISociodemographic indicatorsAge group45-54 years1.01.01.01.055-64 years1.2 (0.9-1.7)‡1.1 (0.9-1.5)1.5 (1.0-2.4)‡1.3 (0.9-2.0)≥ 65 ears1.4 (1.1-1.8)‡1.3 (1.0-1.7)§1.5 (1.0-2.3)‡1.3 (0.9-1.9)Marital statusSingle1.0-1.0-Married/stable partnership1.2 (0.8-1.8)-1.1 (0.5-2.2)-Widowed1.2 (0.8-1.8)-1.3 (0.5-3.3)-Separated/divorced1.4 (0.9-2.2)-1.0 (0.4-2.7)-Education≥ 12 years1.0-1.0-9-11 years1.0 (0.8-1.4)-1.3 (0.8-1.9)-5-8 years1.1 (0.8-1.5)-1.0 (0.6-1.7)-0-4 years1.3 (0.9-1.7)-1.2 (0.8-2.0)-Possession of health insuranceYes1.0-1.01No1.1 (0.8-1.3)-0.7 (0.5-1.0)‡0.8 (0.6-1.1)Behavioral indicatorsSmokersNo1.01.01.0-Yes0.6 (0.3-1.1)‡0.6 (0.4-1.1)0.9 (0.6-1.6)-Former smokersNo1.0-1.0-Yes0.9 (0.7-1.2)-0.9 (0.6-1.3)-Physically inactiveNo1.01.01.01.0Yes1.1 (0.9-1.5)‡0.9 (0.7-1.2)1.3 (0.9-1.9)‡1.1 (0.8-1.6)Food consumption indicatorsRegular consumption of FVGYes1.0-1.0-No1.1 (0.9-1.4)-0.9 (0.6-1.3)-Recommended consumption of FVGYes1.0-1.0-No1.0 (0.8-1.3)-0.8 (0.6-1.3)-Regular consumption of beansYes1.0-1.0-No1.0 (0.8-1.3)-1.1 (0.7-1.6)-Meats with excessive fatNo1.0-1.01.0Yes0.8 (0.5-1.2)-0.6 (0.3-1.0)‡0.7 (0.4-1.2)Milk with full fat contentNo1.01.01.0-Yes0.7 (0.6-0.9)‡0.7 (0.6-0.9)§1.0 (0.7-1.4)-Regular consumption of soda or artificial juiceNo1.0-1.01.0Yes0.9 (0.5-1.5)-0.6 (0.2-1.3)0.7 (0.4-1.4)Regular consumption of sweetsNo1.0-1.01.0Yes1.0 (0.8-1.4)-0.6 (0.3-1.1)‡0.7 (0.3-1.3)Replacement of lunch or dinner with snackNo1.01.01.0-Yes1.3 (0.9-1.7)‡1.2 (0.9-1.5)0.6 (0.3-1.4)-High salt intakeNo1.01.01.0-Yes1.3 (0.9-1.8)‡1.2 (0.9-1.7)0.8 (0.4-1.5)-Health indicatorsBMI - overweightNo1.01.01.01.0Yes1.8 (1.4-2.3)‡1.7 (1.3-2.2)§1.6 (1.0-2.6)‡1.7 (1.1-2.5)§Self-assessed poor/very poor healthNo1.01.01.01.0Yes1.4 (1.1-2.0)‡1.3 (1.0-1.8)§2.2 (1.7-2.9)‡1.9 (1.4-2.5)§DyslipidemiaNo1.01.01.01.0Yes1.6 (1.3-2.0)‡1.5 (1.2-1.8)§1.5 (1.0-2.1)‡1.2 (0.9-1.7)PR = prevalence ratio; CI = confidence interval; †Statistical analysis adjusted using Poisson regression, performed only on independent variables that presented significance ≤ 0.20 (P ≤ 0.20) in Pearson’s chi-square test; ‡P ≤ 0.20; §P ≤ 0.05. BMI = body mass index; FVG = fruits, vegetables and greens. Table 3 shows the crude and adjusted analyses on factors associated with diabetes in women and men. In the adjusted analysis relating to women, the age group of 55-64 years, educational level of 0-4 years and not consuming beans regularly remained independently associated with higher prevalence of diabetes. This differed from the age group ≥ 65 years and educational attainment of 5-8 years, which lost their association with the prevalence of diabetes in the adjusted analysis. Replacing meals with snacks remained associated with lower prevalence of diabetes in women. In the adjusted analysis for men, the age groups of 55-64 years and ≥ 65 years remained independently associated with diabetes, along with being married or in a stable partnership. Table 3:Prevalence and prevalence ratios (crude and adjusted) for prior medical diagnosis of diabetes reported by women ($$n = 566$$) and men ($$n = 297$$), according to sociodemographic, behavioral, food consumption and health indicator variables. João Pessoa, Paraíba, Brazil [2014]VariablesWomen Men Crude PR* $95\%$ CIAdjusted † PR $95\%$ CICrude PR $95\%$ CIAdjusted † PR $95\%$ CISociodemographic indicatorsAge group45-54 years1.01.01.01.055-64 years2.5 (1.1-5.4)‡2.0 (0.9-4.4)§5.6 (2.1-14.8)‡5.1 (1.9-13.4)§≥ 65 years2.5 (1.2-5.1)‡1.8 (0.8-3.9)4.2 (1.6-10.7)‡4.0 (1.4-10.9)§Marital statusSingle1.0-1.01.0Married/stable partnership1.3 (0.5-3.1)-27.8 (3.5-216.9)‡17.7 (2.0-153.0)§Widowed1.9 (0.8-4.7)-7.4 (0.6-91.4)‡3.7 (0.2-54.9)Separated/divorced1.6 (0.5-5.3)-6.1 (0.4-77.2)‡6.5 (0.4-97.3)Education≥ 12 years1.01.01.0-9-11 years1.7 (0.7-4.0)1.7 (0.7-4.1)0.8 (0.4-1.8)-5-8 years2.4 (0.9-5.8)2.0 (0.8-5.0)0.7 (0.2-2.1)-0-4 years3.3 (1.4-7.7)‡2.5 (1.0-6.3)§1.0 (1.4-2.5)-Possession of health insuranceYes1.0-1.0-No1.3 (0.7-2.3)-0.7 (0.3-1.4)-Behavioral indicators Smokers No1.0-1.0-Yes1.4 (0.5-3.7)-1.2 (0.4-3.1)-Former smokers No1.01.01.01.0Yes0.6 (0.3-1.2)‡0.7 (0.3-1.4)2.0 (1.0-4.2)‡1.6 (0.8-3.2)Physically inactiveNo1.01.01.0-Yes1.5 (0.8-2.6)1.0 (0.6-1.7)1.0 (0.4-2.2)-Food consumption indicatorsRegular consumption of FVGYes1.0-1.0-No1.1 (0.6-1.9)-1.0 (0.4-2.1)-Recommended consumption of FVGYes1.0-1.0-No1.2 (0.6-2.1)-1.4 (0.6-3.1)-Regular consumption of beansYes1.01.01.0-No1.6 (1.0-2.8)‡1.6 (1.0-2.7)§0.9 (0.3-2.5)-Meats with excessive fatNo1.0-1.0-Yes1.5 (0.7-3.2)-1.1 (0.4-2.8)-Milk with full fat contentNo1.01.01.0-Yes0.5 (0.3-1.0)‡0.6 (0.3-1.1)0.6 (0.3-1.4)-Regular consumption of soda or artificial juiceNo1.0-1.0-Yes1.3 (0.4-4.4)-0.6 (0.1-2.2)-Regular consumption of sweetsNo1.01.01.01.0Yes0.4 (0.1-1.1)‡0.5 (0.1-1.3)0.4 (0.1-1.7)‡0.5 (0.1-2.1)Replacement of lunch or dinner with snackNo1.01.01.01.0Yes0.3 (0.1-0.9)‡0.3 (0.1-1.0)§0.4 (0.1-1.5)‡0.6 (0.1-2.6)High salt intakeNo1.0-1.0-Yes0.9 (0.3-2.7)-0.4 (0.7-3.2)-Health indicatorsBMI - overweightNo1.01.01.01.0Yes1.7 (1.0-3.1)‡1.7 (0.9-3.0)2.8 (1.2-6.3)‡2.2 (0.9-4.9)Self-assessed poor/very poor healthNo1.0-1.01.0Yes0.9 (0.3-2.6)-2.2 (0.8-5.8)‡1.8 (0.7-4.4)DyslipidemiaNo111.0-Yes1.5 (0.9-2.6)‡1.3 (0.8-2.1)1.1 (0.5-2.3)-PR = prevalence ratio; CI = confidence interval; †Statistical analysis adjusted using the Poisson regression, performed only on independent variables that presented significance ≤ 0.20 (P ≤ 0.20) in Pearson’s chi-square test; ‡P ≤ 0.20; §P ≤ 0.05. BMI = body mass index; FVG = fruits, vegetables and greens. ## DISCUSSION The results from the present study identified factors associated with hypertension and diabetes in the study population. For women, hypertension remained associated with the age group ≥ 65 years old and with higher prevalence of overweight, self-rated poor/very poor health and dyslipidemia. For men, hypertension was associated with higher prevalence of overweight and self-rated poor/very poor health. Regarding diabetes, in women, the age group of 55-64 years, educational attainment of 0-4 years and regular non-consumption of beans were associated with higher prevalence of this chronic disease. In men, the age groups of 55-64 years and ≥ 65 years were associated with higher prevalence of diabetes, along with being married or in a stable partnership. The prevalence of hypertension identified in the present study was greater than that found through the Vigitel survey in João Pessoa (the state capital of Paraiba) in the years 201214 and 2013,15 when the self-reported frequencies of a medical diagnosis of this chronic disease among adults aged ≥ 18 years were $25.7\%$ and $24.4\%$, respectively. The prevalence of diabetes was similar to that found in a study conducted in Florianópolis,16 Santa Catarina, Brazil, in which data on elderly people aged ≥ 60 years with self-reported diagnoses of diabetes were evaluated. Moreover, the prevalence of diabetes identified in the present study was also greater than that found through the Vigitel survey in João Pessoa in the years 201214 and 2013,15 when the self-reported frequencies of a medical diagnosis of diabetes among adults were $5.9\%$ and $6.5\%$, respectively. In the present study, higher prevalences of hypertension and diabetes occurred with advancing age, in agreement with previous research. Higher prevalence of noncommunicable diseases with advancing age is an expected result because of the characteristics of these diseases and structural and physiological changes that occur in the body during aging.16,17 However, it is worth mentioning that, regarding diabetes, there was a slight decrease in prevalence with advancing age. This inverse relationship was also found in the ISA-SP18 project (Health Surveys in the State of São Paulo) and in the SABE19 study (Health, well-being and aging). One possible explanation might relate to survival bias, given the greater mortality among diabetics with increasing age, due to the great number of complications resulting from this disease.4,18 Self-assessed poor/very poor health was associated with hypertension in both genders. The literature generally indicates that health evaluations are worse among women, since they are the individuals who access healthcare services the most. Thus, women have greater concern for and perception of their health. On the other hand, men tend to self-evaluate their health as poor only in the presence of some disease.16 In a study carried out by Carvalho et al. ,20 the prevalence of self-assessed poor health was significantly higher among individuals with lower educational level, those with chronic disease (hypertension, diabetes or obesity) and women, both in northeastern Brazil and in Portugal. The present study also showed that dyslipidemia was significantly associated with hypertension among women. There is evidence of a correlation between lipid profile and systemic arterial pressure, as observed in metabolic syndrome.21 Regarding food consumption variables, despite the consensus in the literature that consumption of foods that are considered to be risk factors for non-communicable chronic diseases (such as high-fat foods) and replacement of meals by snacks (usually composed of snack foods and fast food) have direct relationships with occurrences of chronic diseases, the present study did not confirm these association. These results may have two explanations: the women involved might already have been undergoing treatment, with dietary reeducation to control hypertension and diabetes, thus forming a framework of reverse causality; or these women might also have distorted their reporting of some foods, if they already knew their beneficial or even harmful effects on health. Individuals with diagnoses of non-communicable chronic diseases are more likely to attend healthcare services, where they are advised to change their eating and healthcare behaviors. In such cases, the inverse association is a positive indicator.22,23 Starting with the 2013 Vigitel survey,24 questions regarding replacement of meals by snacks were included. In João Pessoa, interviewees in this situation who had been replacing meals with regional foods such as tapioca, couscous and other items that do not fit the definition of dinner may have been included. The questionnaire only became more specific after Vigitel 2015, through inclusion of positive responses regarding consumption of pizzas, sandwiches and other processed snacks, thus excluding items that are common in some regions, such as couscous and tacacá, among others. Not consuming beans regularly was associated with higher prevalence of diabetes among women. Beans are legumes that traditionally have formed part of the Brazilian diet and adequate consumption of beans has been strongly associated with protection against several diseases, since it is one of the foods with proportionally larger amounts of dietary fiber, compared with other foods and constitutes an important item within healthy food consumption.25 Some Brazilian studies on populations in the northeastern and southeastern regions have shown that bean consumption has beneficial effects at the population level, through providing protective effects against body weight gain.26 The present study also found an association between lower schooling level (0-4 years) and higher prevalence of diabetes among women. This was similar to the findings from a study carried out in the city of Viçosa, Minas Gerais,27 in which higher schooling levels were inversely associated with occurrences of diabetes among 621 elderly individuals aged 60 years and over. Likewise, in a study carried out in the municipality of Triunfo,28 in the backlands of the state of Pernambuco, Brazil, on a representative sample of 198 adults with a mean age of 57.7 years, all the cases of diabetes were among individuals who were illiterate or had only had elementary education. The fact that the higher the schooling level is, the lower the chances are that individuals will develop hypertension and/or diabetes, demonstrates that government investment in education is paramount. Low educational level can hinder access to healthcare information and limit understanding of the guidelines regarding prevention and/or treatment of diabetes.27 Regarding marital status, for men, being married or in a stable partnership showed a statistically significant association with occurrences of diabetes. This result was contrary to those of other studies, such as GAZEL,29 in which reports of non-communicable chronic diseases were more frequent among individuals living alone. That association seemed to result from greater exposure to behavioral risk factors for chronic diseases among single individuals. The results from the present study also demonstrated that overweight was a determinant strongly associated with occurrences of hypertension. Other studies on elderly individuals in the municipalities of Marques de Souza (Rio Grande do Sul)30 and Bauru (São Paulo)17 and from the Vigitel survey,31 conducted in all Brazilian state capitals, showed that between $20\%$ and $30\%$ of the prevalence of hypertension could be explained by an association between overweight and increased risk of developing this disease.31 Some limitations of the present study need to be pointed out. One limitation to be highlighted relates specifically to the methodology used in the Vigitel system: only individuals living in households that have a landline can be interviewed, which gives rise to the possibility of calibration bias. However, weighting factors through which it is sought through post-stratification to estimate the prevalence taking into account differences in the demographic characteristics of the Vigitel sample in relation to those of the entire population are used. Furthermore, the high response rate achieved through Vigitel contributes to the quality of the data. Another limitation relates to the use of self-reported data, which can be influenced by individuals’ access to medical diagnoses and their understanding of their health condition. The potential for information bias, with overestimation of height and underestimation of weight, cannot be discarded given that Vigitel provides self-reported and unmeasured weight and height. However, validation studies on some Vigitel indicators have been conducted in Brazil,32,33 showing agreement between the information reported through Vigitel and the information from household surveys. Vigitel has the advantage of being a non-invasive method in which it is easy to obtain data, at low cost. Since Vigitel has a cross-sectional methodological design, it is not possible to establish any temporal cause-and-effect relationship among the associations between outcomes and independent variables. Therefore, it cannot distinguish whether the factors associated with hypertension and diabetes are causes or consequences of illness. However, recognizing the risk factors associated with chronic diseases is essential for identifying groups with specific needs and for guiding public policies, through establishing appropriate monitoring of these risk factors. A further limitation of the present study relates to its extraction of Vigitel data, in which data from only one Brazilian state capital were separated out. This potentially reduced the statistical power of the tests through decreasing the sample size. Moreover, only adults aged 45 and over were included, thus hindering knowledge of the behavior of the population under 45 years old. Since hypertension and diabetes are chronic diseases, these factors may have changed over the course of life, thereby reducing the effect observed in this study. However, this is a limitation of the cross-sectional study design. Despite the limitations identified, some potentialities of this study stood out. Cross-sectional population-based studies on representative samples conducted through telephone surveys are of great relevance because they are fast and cost-effective alternatives. They thus constitute an important epidemiological tool for determining the dimensions of problems, through estimating indicators for health conditions, health-related behaviors and access to and use of healthcare and disease treatment services. Such studies provide support for actions that may be implemented to promote health and prevent non-communicable chronic diseases in the reference population or in others with similar characteristics. ## CONCLUSION The results obtained confirmed the importance of hypertension and diabetes as a public health problem and identified a list of factors associated with these chronic diseases, among which some would be susceptible to intervention. Thus, this study identified an urgent need for specific interventions in this population, with implementation of healthcare aimed towards minimizing the complications arising from these pathological conditions, as well as preventing the onset of other chronic diseases. These interventions should be conducted in such a way that they allow individuals to discuss issues relating to their chronic conditions and the risk factors involved, while at the same time enabling stimulation and providing conditions that encourage these individuals to adopt healthier lifestyles. ## References 1. 1 Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Análise de Situação de Saúde Plano de ações estratégicas para o enfrentamento das doenças crônicas não transmissíveis (DCNT) no Brasil 2011-2022 Brasília Ministério da Saúde 2011 Available from: http://bvsms.saude.gov.br/bvs/publicacoes/plano_acoes_enfrent_dcnt_2011.pdf Accessed in 2017 (Jul 28). Plano de ações estratégicas para o enfrentamento das doenças crônicas não transmissíveis (DCNT) no Brasil 2011-2022 (2011.0) 2. 2 Institute for Health Metrics and Evaluation Data visualizations Available from: http://www.healthdata.org/results/data-visualizations Accessed in 2017 (Jul 28). Data visualizations 3. 3 Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Coordenação Geral de Informações e Análises Epidemiológicas Informações de saúde. Sistema de informações sobre mortalidade (SIM) Available from: http://tabnet.datasus.gov.br/cgi/deftohtm.exe?sim/cnv/obt09pb.def Accessed in 2017 (Feb 22). Informações de saúde. Sistema de informações sobre mortalidade (SIM) 4. Francisco PMSB, Belon AP, Barros MBA. Diabetes auto-referido em idosos: prevalência, fatores associados e práticas de controle [Self-reported diabetes in the elderly: prevalence, associated factors, and control practices]. Cad Saúde Pública (2010.0) 26 175-184. PMID: 20209221 5. 5 World Health Organization World health statistics 2016: monitoring health for the SDGs Geneva World Health Organization 2016. World health statistics 2016: monitoring health for the SDGs (2016.0) 6. Weschenfelder Magrini D, Gue Martini J. Hipertensión arterial: principales factores de riesgo modificables en la estrategia salud de la familia [Hipertensão arterial: principais fatores de risco modificáveis na estratégia saúde da família]. Enferm Glob (2012.0) 11 344-353 7. Francisco PMSB, Malta DC, Segri NJ, Barros MBA. Desigualdades sociodemográficas nos fatores de risco e proteção para doenças crônicas não transmissíveis; inquérito telefônico em Campinas, São Paulo [Sociodemographic inequalities in non-communicable chronic disease risk and protection factors: telephone survey in Campinas, São Paulo, Brazil]. Epidemiol Serv Saúde (2015.0) 24 7-18 8. Moura EC, Monteiro CA, Claro RM. Vigilância de fatores de risco para doenças crônicas por inquérito telefônico nas capitais dos 26 estados brasileiros e no Distrito Federal (2006) [Surveillance of risk-factors for chronic diseases through telephone interviews in 27 Brazilian cities (2006)]. Rev Bras Epidemiol (2008.0) 11 20-37 9. 9 Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância de Doenças e Agravos não Transmissíveis e Promoção da Saúde Vigitel Brasil 2014: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico Brasília Ministério da Saúde 2015 Available from: http://bvsms.saude.gov.br/bvs/publicacoes/vigitel_brasil_2014.pdf Accessed in 2017 (Jul 28). Vigitel Brasil 2014: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico (2015.0) 10. Graham K. Compensating for missing survey data (1983.0) 11. Bernal RTI, Malta DC, de Araújo TS, Silva NN. Inquérito por telefone: pesos de pós-estratificação para corrigir vícios de baixa cobertura em Rio Branco, AC [Telephone survey: post-stratification adjustments to compensate non-coverage bias in city of Rio Branco, Northern Brazil]. Rev Saúde Pública (2013.0) 47 316-325. PMID: 24037359 12. 12 World Health Organization Preventing chronic diseases: a vital investment Geneva World Health Organization 2005. Preventing chronic diseases: a vital investment (2005.0) 13. 13 World Health Organization Obesity: preventing and managing the global epidemic Report of a WHO Consultation WHO Technical Report Series 894 Geneva World Health Organization 2000. Obesity: preventing and managing the global epidemic (2000.0) 14. 14 Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância de Doenças e Agravos não Transmissíveis e Promoção da Saúde Vigitel Brasil 2012: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico Brasília Ministério da Saúde 2013 Available from: http://bvsms.saude.gov.br/bvs/publicacoes/vigitel_brasil_2012_vigilancia_risco.pdf Accessed in 2017 (Jul 28). Vigitel Brasil 2012: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico (2013.0) 15. 15 Brasil. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Vigilância de Doenças e Agravos não Transmissíveis e Promoção da Saúde Vigitel Brasil 2013: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico Brasília Ministério da Saúde 2014 Available from: http://bvsms.saude.gov.br/bvs/publicacoes/vigitel_brasil_2013.pdf Accessed in 2017 (Jul 28). Vigitel Brasil 2013: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico (2014.0) 16. Malta DC, Iser BPM, Oliveira MM. Prevalência de fatores de risco e proteção para doenças crônicas não transmissíveis em adultos: estudo transversal, Brasil 2012 [Prevalence of risk and protective factors for chronic diseases in adult population: cross-sectional study, Brazil 2012]. Epidemiol Serv Saúde (2014.0) 23 609-622 17. Turi BC, Codogno JS, Fernandes RA, Monteiro HL. Frequência de ocorrência e fatores associados à hipertensão arterial em pacientes do Sistema Único de Saúde [Frequency and associated factors of hypertension in public health system patients]. Rev Bras Ativ Fís Saúde (2013.0) 18 43-52 18. Mendes TAB, Goldbaum M, Cesar CLG. Diabetes mellitus: fatores associados à prevalência em idosos, medidas e práticas de controle e uso dos serviços de saúde em São Paulo, Brasil [Diabetes mellitus: factors associated with prevalence in the elderly, control measures and practices, and health services utilization in São Paulo, Brazil]. Cad Saúde Pública (2011.0) 27 1233-1243. PMID: 21710020 19. Lebrão ML, Laurenti R. Saúde, bem-estar e envelhecimento: o estudo SABE no município de São Paulo [Health, well-being and aging: the SABE study in São Paulo, Brazil]. Rev Bras de Epidemiol (2005.0) 8 127-141 20. Carvalho AT, Malta DC, Barros MBA. Desigualdades na autoavaliação de saúde: puma análise para populações do Brasil e de Portugal [Inequalities in self-rated health: an analysis of the Brazilian and Portuguese populations]. Cad Saúde Pública (2015.0) 31 2449-2461. PMID: 26840823 21. Marte AP, Santos RD. Bases fisiopatológicas da dislipidemia e hipertensão arterial [Dyslipidemia and hypertension: physiopathology]. Rev Bras Hipertens (2007.0) 14 252-257 22. Azevedo ECC, Dias FMRS, Cabral PC, Diniz AS. Consumo alimentar de risco e proteção para as doenças crônicas não transmissíveis e sua associação com a gordura corporal: um estudo com funcionários da área de saúde de uma universidade pública de Recife (PE), Brasil [Risk and protection food consumption factors for chronic non-communicable diseases and their association with body fat: a study of employees in the health area of a public university in Recife in the state of Pernambuco, Brazil]. Ciênc Saúde Coletiva (2014.0) 19 1613-1622 23. Azevedo ECC, Monteiro JS, Cabral PC, Diniz AS. Padrão alimentar de risco para as doenças crônicas não transmissíveis e sua associação com a gordura corporal - uma revisão sistemática [Dietary risk patterns for non-communicable chronic diseases and their association with body fat - a systematic review]. Ciênc Saúde Coletiva (2014.0) 19 1447-1458 24. Malta DC, Campos MO, Oliveira MM. Prevalência de fatores de risco e proteção para doenças crônicas não transmissíveis em adultos residentes em capitais brasileiras, 2013 [Noncommunicable chronic disease risk and protective factor prevalence among adults in Brazilian state capital cities, 2013]. Epidemiol Serv Saúde (2015.0) 24 373-387 25. Velásquez-Meléndez G, Pessoa MC, Mendes LL. Tendências da frequência do consumo de feijão por meio de inquérito telefônico nas capitais brasileiras, 2006 a 2009 [Trends in frequency of consumption of beans assessed by means of a telephone survey in Brazilian state capitals between 2006 and 2009]. Ciênc Saúde Coletiva (2012.0) 17 3363-3370 26. Sichieri R, Castro JFG, Moura AS. Fatores associados ao padrão de consumo alimentar da população brasileira urbana [Factors associated with dietary patterns in the urban Brazilian population]. Cad Saúde Pública (2003.0) 19 S47-S53. PMID: 12886435 27. Vitoi NC, Ribeiro AQ, Franceschini SCC, Nascimento CM, Fogal AS. Prevalência e fatores associados ao diabetes em idosos no município de Viçosa, Minas Gerais [Prevalence and associated factors of diabetes in the elderly population in Viçosa, Minas Gerais, Brazil]. Rev Bras Epidemiol (2015.0) 18 953-965. PMID: 26982308 28. Lyra R, Silva RS, Montenegro RM. Prevalência de diabetes melito e fatores associados em população urbana adulta de baixa escolaridade e renda do sertão nordestino brasileiro [Prevalence of diabetes and associated factors in an urban adult population of low educational level and income from the Brazilian Northeast wilderness]. Arq Bras Endocrinol Metab (2010.0) 54 560-566 29. Metzger MH, Goldberg M, Chastang JF, Leclerc A, Zins M. Factors associated with self-reporting of chronic health problems in the French GAZEL cohort. J Clin Epidemiol (2002.0) 55 48-59. PMID: 11781122 30. Silveira J, Deitos A, Dal Bosco SM, Scherer F. Fatores associados à hipertensão arterial sistêmica e ao estado nutricional de hipertensos inscritos no programa Hiperdia [Factors associated with systemic hypertension and nutritional status of hypertensive enrolled in the program Hiperdia]. Cad Saúde Colet (Rio J.) (2013.0) 21 129-134 31. Muraro AP, Santos DF, Rodrigues PRM, Braga JU. Fatores associados à Hipertensão Arterial Sistêmica autorreferida segundo VIGITEL nas 26 capitais brasileiras e no Distrito Federal em 2008 [Factors associated with self-reported systemic arterial hypertension according to VIGITEL in 26 Brazilian capitals and the Federal District in 2008]. Ciênc Saúde Coletiva (2013.0) 18 1387-1398 32. Monteiro CA, Moura EC, Jaime PC, Claro RM. Validade de indicadores do consumo de alimentos e bebidas obtidos por inquérito telefônico [Validity of food and beverage intake data obtained by telephone survey]. Rev Saúde Pública (2008.0) 42 582-589. PMID: 18709237 33. Francisco PMSB, Barros MBA, Segri NJ, Alves MCGP. Comparação de estimativas de inquéritos de base populacional [Comparison of estimates of population-based surveys]. Rev Saúde Pública (2013.0) 47 60-68. PMID: 23703131
--- title: Evaluation of waist-to-height ratio as a predictor of insulin resistance in non-diabetic obese individuals. A cross-sectional study authors: - Giovana Jamar - Flávio Rossi de Almeida - Antonio Gagliardi - Marianna Ribeiro Sobral - Chao Tsai Ping - Evandro Sperandio - Marcelo Romiti - Rodolfo Arantes - Victor Zuniga Dourado journal: São Paulo Medical Journal year: 2017 pmcid: PMC10027251 doi: 10.1590/1516-3180.2016.0358280417 license: CC BY 4.0 --- # Evaluation of waist-to-height ratio as a predictor of insulin resistance in non-diabetic obese individuals. A cross-sectional study ## ABSTRACT ### BACKGROUND: Insulin resistance (IR) and progressive pancreatic β-cell dysfunction have been identified as the two fundamental features in the pathogenesis of obesity and non-insulin-dependent diabetes mellitus. We aimed to investigate correlations between anthropometric indices of obesity and IR in non-diabetic obese individuals, and the cutoff value from receiver operating characteristic (ROC) curve analysis. ### DESIGN AND SETTING: Cross-sectional study conducted in a private clinic. ### METHODS: We included obese individuals (body mass index, BMI ≥ 30 kg/m2) with no diabetes mellitus (fasting glucose levels ≤ 126 mg/dl). The participants were evaluated for the presence of cardiovascular risk factors and through anthropometric measurements and biochemical tests. Furthermore, IR was assessed indirectly using the homeostatic model assessment (HOMA)-IR and HOMA-β indexes. The area under the curve (AUC) of the variables was compared. The sensitivity, specificity and cutoff of each variable for diagnosing IR were calculated. ### RESULTS: The most promising anthropometric parameters for indicating IR in non-diabetic obese individuals were waist-to-height ratio (WHtR), waist circumference (WC) and BMI. WHtR proved to be an independent predictor of IR, with risk increased by $0.53\%$ in HOMA-IR, $5.3\%$ in HOMA-β and $1.14\%$ in insulin. For HOMA-IR, WHtR had the highest AUC value (0.98), followed by WC (0.93) and BMI (0.81). For HOMA-β, WHtR also had the highest AUC value (0.83), followed by WC (0.75) and BMI (0.73). The optimal WHtR cutoff was 0.65 for HOMA-IR and 0.67 for HOMA-β. ### CONCLUSION: Among anthropometric obesity indicators, WHtR was most closely associated with occurrences of IR and predicted the onset of diabetes in obese individuals. ## INTRODUCTION Insulin resistance (IR) is considered to be one of the main risk factors for cardiovascular disease (CVD). It is associated with several metabolic abnormalities such as impaired glucose tolerance, non-insulin-dependent diabetes mellitus (NIDDM), hypertension and dyslipidemia.1,2 Maintenance of normal blood glucose comes mainly from the ability of β-pancreatic cells to secrete insulin and the sensitivity of the target tissues to respond to normal levels of insulin in the bloodstream.3 The homeostasis model assessment (HOMA) is a widely validated clinical and epidemiological tool for estimating IR and β-cell function. It is derived from a mathematical assessment of the balance between hepatic glucose output and insulin secretion from fasting levels of glucose and insulin.4 HOMA-IR and HOMA-β have been adopted as an alternative to the gold standard method, i.e. the hyperinsulinemic-euglycemic clamp technique. Although use of HOMA indices requires an invasive access,5 it is inexpensive and easy to apply.6 One aspect of research on obesity that is currently attracting attention is the distribution of fat in the body. Diabetes, atherosclerosis and sudden cardiac death occur quite frequently among obese people, but when obesity is centralized in the abdominal region, the negative repercussions (both metabolic and cardiovascular) are more significant.7 Several studies have evaluated the correlation between IR and anthropometric indices of obesity such as body mass index (BMI), waist circumference (WC), neck circumference (NC) and hip circumference (HP). They have demonstrated that the distribution of visceral fat causes significant damage to the insulin-signaling pathway due to secretion of adipokines, e.g. C-reactive protein (CRP),2,8,9 thus leading to increased cardiometabolic risk.10 Therefore, obesity is the most prominent predictor of IR and diabetes.11 Anthropometry is considered to be a non-invasive tool for early diagnosis of the onset of NIDDM. In addition, it provides an alternative evaluation of IR at lower cost that is accessible for application in epidemiological studies and primary care within health services.8 However, there is no consensus regarding which anthropometric measurement is most indicative of IR in non-diabetic obese subjects, or regarding the cutoff values. ## OBJECTIVE We aimed to investigate the correlations between anthropometric indices of obesity and IR in non-diabetic obese individuals, and to identify the best cutoff values of these indices for predicting IR, through using receiver operating characteristic (ROC) curve analyses. ## Participants This study used a cross-sectional design. The participants were selected as a convenience sample of consecutive patients admitted between 2013 and 2015, when they presented the following inclusion criteria: BMI ≥ 30 kg/m2 and no diabetes mellitus (DM) (reported or fasting blood glucose ≤ 126 mg/dl).12,13 We enrolled 136 obese individuals, comprising 72 men and 64 women, at the Obesity Clinic of the Angiocorpore Institute of Cardiovascular Medicine, located in the city of Santos, São Paulo, Brazil. They had been referred for the examinations because of a variety of medical indications. This study formed part of a larger study assessing the determinants of exercise intolerance among obese individuals. All the participants agreed to participate, and none of them presented abnormalities during the examinations that would exclude them. The Ethics Committee for Research on Human Beings of the Federal University of São Paulo (Universidade Federal de São Paulo, UNIFESP) approved this study under the number 1.079.239. Furthermore, an informed consent statement was signed by all of these volunteers. ## Anthropometric obesity indices Body weight and height were measured by using a weighing scale with stadiometer that measured to precisions of the nearest 0.1 kg and 1 cm (Toledo, São Paulo, Brazil). The individuals were weighed without shoes. The neck (NC), waist (WC) and hip (HC) circumferences were measured in cm using an inelastic tape (Sanny) with precision of 1 mm. We measured NC at the midpoint of the neck; WC at the midpoint between the last rib and the iliac crest; and HC at the point of greatest gluteal protuberance.14,15 From these anthropometric measurements, we obtained indices relating to cardiometabolic health: waist-to-hip ratio (WHR), waist/height ratio (WHtR), body mass index (BMI = weightkg/heightm²) and body shape index (BSI = WC/BMI$\frac{2}{3}$ x height½).16 ## Blood test Blood samples were collected for laboratory-based biochemical measurements after the participants had fasted for 12 hours. We quantified C-reactive protein (CRP, ng/ml), total cholesterol (mg/dl), HDL cholesterol (mg/dl), LDL cholesterol (mg/dl), insulin (IU/dl) and glucose (mg/dl). Glucose values were converted from mg/dl to mmol/l using the conversion factor 0.555.13 ## IR assessment We used the homeostasis model assessments HOMA-IR and HOMA-β to indirectly determine IR, based on glucose and insulin values proposed by Matthews et al.3 IR was defined as situations with HOMA-IR ≥ 2.7,17,18,19,20,21 and dysfunction of β-cells as situations with HOMA-β > 175.4,22 ## Cardiovascular risk assessment We assessed self-reported cardiovascular risk factors in accordance with the recommendations of the American College of Sports Medicine (ACSM). The participants were asked to report any previous diagnosis of the main cardiovascular risk factors such as arterial hypertension, dyslipidemia and diabetes, along with their age, situation of physical inactivity and smoking status. We considered that the participants were physically inactive if they reported doing less than 150 minutes per week of moderate-to-vigorous physical activity.23 ## Statistical analysis We assessed correlations between anthropometric indices and HOMA-IR values, HOMA-β values and insulin concentration using Pearson correlation coefficients. Three models of stepwise multiple linear regressions were then fitted, with HOMA-IR, HOMA-β and insulin as the main outcomes. The main predictors that we chose were the anthropometric indices that significantly correlated with outcomes after univariate analysis. We checked for multicollinearity in the models by means of variation inflation factor (VIF) values < 4. The models were also adjusted for age, sex and cardiovascular risk factors. We fitted ROC curves to assess the best cutoff points for anthropometric measurements for predicting clinically high values of HOMA-IR and HOMA-β as surrogate measurements for IR. The areas under the ROC curves (AUC) and the $95\%$ confidence intervals ($95\%$ CI) were used to compare the diagnostic value of various obesity indices. We considered that values above 0.80 were excellent. The main anthropometric indices selected after multiple linear regression were used to obtain the optimal cutoff point for diagnosing IR. We calculated the sensitivity, specificity, positive and negative likelihood ratios and Youden index in relation to these values. All tests were evaluated at a two-tailed alpha level of 0.05. All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS), version 23 (SPSS Inc., Chicago, USA), and the MedCalc package, version 17 (MedCalc Software bvba, Belgium). ## Baseline characteristics of the participants The men and women involved in the present study were on average middle-aged. We found significantly higher values for weight, height, WC, WHR, NC and BSI among the men, while HC and BMI were significantly higher among the women. The participants were mostly physically inactive. We observed a greater proportion of dyslipidemia among the men and higher fasting glucose among the women (Table 1). Table 1.General characteristics of the study sample according to sex. Mean ± standard deviation (SD).HDL = high-density lipoprotein; LDL = low-density lipoprotein; HOMA-IR = homeostasis model assessment - insulin resistance; HOMA-β = homeostasis model assessment - beta-cell function. * $P \leq 0.05$ = females versus males. When stratified according to nutritional status, we found progressively impaired values for fasting insulin, HOMA-IR and HOMA-β with increasing severity of obesity, while CRP presented a significant difference only at obesity level I and total cholesterol at obesity level III (Table 2). Table 2.Description of anthropometric measurements and biochemical analysis between obesity levels. Mean ± standard deviation (SD).HDL = high-density lipoprotein; LDL = low-density lipoprotein; HOMA-IR = homeostasis model assessment - insulin resistance; HOMA- β = homeostasis model assessment - beta-cell function. a = obesity I versus obesity II, $P \leq 0.05.$ b: obesity I versus obesity III, $P \leq 0.05.$ c = obesity II versus obesity III, $P \leq 0.05.$ d = obesity I and obesity II versus obesity III, $P \leq 0.05.$ e: obesity II and obesity III versus obesity I, $P \leq 0.05.$ ## Correlation and multiple regression analysis We found strong correlations of WHtR, WC and BMI with HOMA-IR, HOMA-β and fasting insulin. On the other hand, WHR, NC and BSI showed weak correlations (Table 3). A stepwise multiple linear regression analysis was performed with HOMA-IR, HOMA-β and insulin as dependent variables. After adjustment for age, sex and obesity indices, WHtR proved to be an independent predictor of IR in this study (Table 4). Table 3.Matrix of correlations between obesity indices and values for homeostasis model assessment - insulin resistance (HOMA-IR) and for HOMA - beta-cell function (HOMA-β) in the study sampler = Pearson coefficient; WHtR = waist-to-height ratio; WC = waist circumference; BMI = body mass index; WHR = waist-to-hip ratio; NC = neck circumference; BSI = body shape index. * Significant correlations for all subjects; †Significant correlations for females versus males. Table 4.Multiple regression analysis on obesity indices that predict insulin resistanceWHtR = waist-to-height ratio; HOMA-IR = homeostasis model assessment - insulin resistance; HOMA-β = homeostasis model assessment - beta-cell function. * WHtR values are expressed in percentage. Models adjusted for age, sex, weight, waist circumference, neck circumference, body mass index, waist-hip ratio, waist-to-height ratio and body shape index. ## ROC curves The abilities of WHtR, WC and BMI to detect IR were compared using ROC curves. For HOMA-IR, we found an AUC of 0.98 for WHtR, 0.83 for WC and 0.81 for BMI, such that the AUC was significantly greater for WHtR than for WC (difference between areas = 0.150; $P \leq 0.001$) and BMI (difference between areas = 0.171; $P \leq 0.001$). We found that there was no significant difference in AUC between WC and BMI (difference between areas = 0.021; $$P \leq 0.629$$) (Figure 1). Regarding HOMA-β, the AUC of 0.83 for WHtR was significantly greater than the AUC for WC (0.75, difference between areas = 0.082; $$P \leq 0.013$$) and BMI (0.73, difference between areas = 0.099; $$P \leq 0.009$$), with no significant difference between WC and BMI (difference between areas = 0.017; $$P \leq 0.727$$) (Figure 2). The best cutoff points for HOMA-IR were 0.65, 113 cm and 38.76 kg/m2 and for HOMA-β were 0.67, 112 cm and 37.61 kg/m2, respectively for WHtR, WC and BMI (Table 5). Figure 1.Receiver operating characteristic (ROC) curve for anthropometric parameters that predict insulin resistance according to the homeostatic model assessment-insulin resistance (HOMA-IR). The areas under the ROC curves and the $95\%$ confidence intervals ($95\%$ CI) were 0.98 (0.95-0.99) for waist-to-height ratio (WHtR); 0.93 (0.76-0.89) for waist circumference (WC); and 0.81 (0.74-0.87) for body mass index (BMI). Figure 2.Receiver operating characteristic (ROC) curve for anthropometric parameters that predict insulin resistance according to the homeostatic model assessment (HOMA)-β. The areas under the ROC curves and the $95\%$ confidence intervals ($95\%$ CI) were 0.83 (0.76-0.89) for waist-to-height ratio (WHtR); 0.75 (0.67-0.82) for waist circumference (WC); and 0.73 (0.65-0.81) for body mass index (BMI). Table 5.Optimal cutoff point values and their related sensitivity, specificity, positive and negative likelihood ratios and Youden index for obesity indices, regarding discrimination of insulin resistanceHOMA-IR = homeostasis model assessment - insulin resistance; HOMA-β = homeostasis model assessment - beta-cell function; +LR = positive likelihood ratio; -LR = negative likelihood ratio. ## DISCUSSION In the present study, we observed that not all the anthropometric parameters studied were significantly associated with HOMA-IR and HOMA-β. The most promising anthropometric parameters for indicating IR in non-diabetic obese adults were WHtR, WC and BMI. Our results suggest that there are advantages to using WHtR. In our analysis, we observed that the risk of IR was raised by $0.53\%$ in HOMA-IR, $5.3\%$ in HOMA-β and $1.14\%$ in insulin for each additional $1\%$ increase in WHtR (= 0.01). Thus, WHtR was a predictor for the degree of IR and predisposition towards diabetes in our sample of obese individuals. Recently, Vikam et al.10 observed increased odds ratios for hyperinsulinemia and metabolic syndrome among individuals with WHtR > 0.5. Use of WHtR for detecting abdominal obesity and its associated risks to health was first proposed in the 1990s.24 The growing body of literature showed that this abdominal obesity indicator could predict the cardiometabolic risk even better than BMI and WC.25 A recent meta-analysis on studies evaluating different indices of adiposity showed that WHtR was a better predictor for hyperinsulinemia, diabetes, arterial hypertension, dyslipidemia, metabolic syndrome and other cardiovascular health problems than were BMI or WC, in both men and women.26 In addition, our AUC values for this anthropometric obesity indicator were higher than in previous prediction studies with WHtR,11,27,28 thus emphasizing the accuracy of AUC measurements for identifying IR in obese populations. According to Behboudi-Gandevani et al. ,11 WHtR may be proposed as a sensitive, inexpensive, noninvasive, simple-to-assess and easy-to-calculate tool for screening for IR. Taking into account that ethnicity and gender may influence body composition, studies on Brazilian and Indian overweight women also showed that the WHtR was the most important predictive measurement for IR and diabetes.27,29 However, studies on men of different ethnicity indicated that BMI was the best predictor for IR.28,30,31 It should be noted that BMI is characterized as an indicator of general adiposity because of its inability to assess the distribution of body fat, thus presenting a weaker relationship with visceral fat.27 In a recent meta-analysis, Savva et al.32 compared the association of BMI and WHtR with the cardiometabolic risk factor of diabetes in Asian and non-Asian populations. The data from cross-sectional studies indicated that WHtR is superior to BMI for detecting diabetes in both Asian and non-Asian populations. There are still few studies of this design on Brazilian populations, especially in relation to obese individuals.32 The risk of developing obesity-related comorbidities is proportional to the degree of obesity and, more specifically, the accumulation of visceral fat.33 However, the presence of metabolic disorders varies considerably among obese individuals,34 since it is known that there is one subgroup of obese individuals that seems to be protected against or is more resistant to developing cardiometabolic complications.35 Nevertheless, regarding phenotypes for metabolic status and diabetes, healthy obese and metabolically unhealthy normal-weight individuals appear to have an equivalent risk.36 In the general population, a WHtR cutoff < 0.5 is recommended, which can be presented as a simple public health message that individuals should seek to maintain their WC as less than half of their height. We showed that the higher this ratio is, the higher the risk of indirect IR is, and we proposed a cutoff > 0.65 to identify IR in non-diabetic obese individuals. This indicates that there is a need for a specific evaluation on this population, for early detection of IR that could ultimately reduce the incidence or severity of diabetes and cardiovascular diseases. In summary, we found that WHtR may be useful in clinical practice due to its advantageous simplicity. Also, it is easy to calculate, does not require any special equipment other than an inelastic tape, and only requires some rater training. The present study has limitations that should be considered. Our sample was not enough to extract the cutoff points according to sex. Since not all obese individuals have metabolic alterations, our strategy was to ascertain which anthropometric measurements were better correlated with IR, and whether non-diabetic obese individuals would present a cutoff point different from general population for predicting the onset of diabetes, thereby suggesting different reference values for a more accurate assessment in this specific group. Perhaps inclusion of a eutrophic group would have contributed towards reinforcing our important findings. Future research should aim to screen Brazilian obese populations, in order to provide support for our remarks. ## CONCLUSION We can conclude that the WHtR is a strong predictor of IR, as assessed using HOMA, among non-diabetic obese adults. 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--- title: “I am not alone”. A qualitative feasibility study of eating disorders prevention groups for young females with type 1 diabetes authors: - Trine Wiig Hage - Jan-Vegard Nilsen - Katrine M. Karlsen - Martine H. Lyslid - Anne Louise Wennersberg - Line Wisting journal: Journal of Eating Disorders year: 2023 pmcid: PMC10027265 doi: 10.1186/s40337-023-00767-2 license: CC BY 4.0 --- # “I am not alone”. A qualitative feasibility study of eating disorders prevention groups for young females with type 1 diabetes ## Abstract Young females with type 1 Diabetes constitute a high-risk group for developing eating problems and eating disorders. Interventions specifically targeted at preventing body image and body dissatisfaction issues in this group are therefore very important. The current study thus aimed to explore participants’ experiences with a Diabetes specific version of the targeted prevention program the Body Project, Diabetes Body Project. Participants were invited to attend focus groups interviews after completing the intervention. Results from the study consist of one overarching theme and four themes. Participants highly appreciated the opportunity to meet other young females with type 1 Diabetes, underlining the importance of interventions specifically targeted at this high-risk group. They also emphasized that the script would benefit from a more integrated focus on type 1 Diabetes throughout all six sessions of the intervention. ### Objective The overall aim of the current study was to qualitatively explore the feasibility of eating disorder prevention groups for people with type 1 diabetes (T1D). ### Method A generic qualitative focus group design was applied. 17 participants accepted the invitation to attend focus group interviews after completing the intervention. Five focus groups were conducted in total. ### Results The qualitative analysis generated one overarching theme, named the benefit of meeting peers with a lived experience of T1D and body image concerns, and four themes: the need for an integrated focus on diabetes, personal relevance, providing sufficient balance between structure and flexibility and enabling a different perspective. ### Conclusion Results show overall positive feedback regarding the content and structure of the intervention, and underline the importance of targeting preventive efforts to specific risk groups. ## Introduction Type 1diabetes (T1D) is autoimmune, chronic disease characterized by a lack of insulin due to an autoimmune destruction of the insulin-producing beta cells in the pancreas. Lack of insulin leads to hyperglycemia (i.e. high blood glucose levels), which is associated with increased rates of diabetes complications, including neuropathy, retinopathy, and nephropathy. In addition, hypoglycemia (i.e. low blood glucose levels) may lead to acute complications such as seizure or loss of consciousness. Treatment is largely based on self-care, and include administering insulin via an insulin pen or pump. Making decisions about insulin dosing according to blood glucose values and carbohydrate portions. The treatment requires a focus on type and amount of food consumed and carbohydrate counting. This has been suggested as one aspect of T1D management that places people at greater risk for developing an eating disorder (ED) [1]. Young females with T1D constitute a specific risk-group for developing EDs [6], with prevalence rates of diagnosable EDs being 2–3 times higher than for non-diabetic peers (Colton et al., 2015, Young, 2013). The prevalence of sub-threshold eating problems, commonly referred to as disturbed eating behaviors (DEB) is even higher [17, 28]. Thus, females who have T1D and are in their late adolescence or early adulthood appear to be at particular risk for developing DEB and EDs [28]. Both EDs and DEB are associated with significant stress and impairment of daily functioning [20]. Risk factors for developing EDs, in addition to being female and of young age, include pursuit of the thin beauty ideal, body dissatisfaction, and negative affect [21]. Additionally, potential T1D specific risk factors for developing EDs include weight loss at disease onset followed by weight gain caused by insulin treatment, dietary control as part of diabetes management, and intentional insulin omission to control weight [18]. Presence of comorbid T1D and DEB/EDs are associated with poor glycemic control and increased rates of morbidity and mortality [16]. Despite the frequency and severity of this comorbidity, no studies have previously investigated effective prevention approaches targeted at this specific high-risk group. The “Body Project” is a targeted, manualized and interactive group based prevention program, based on cognitive dissonance theory. The Body Project has, through a number of studies, been found to be the most effective intervention to reduce ED risk factors and symptoms [13, 26], and to prevent future ED onset among young females in the general population [21]. According to Festinger [7], cognitive dissonance occurs when there is a discrepancy between one’s beliefs and one’s actions. This inconsistency is theorized to create psychological discomfort, which then motivates the individual to reduce the cognitive discord by changing its beliefs. In Body Project, participants are encouraged to criticize the thin ideal through a series of verbal, written, and behavioral exercises, including homework assignments. These activities are designed to produce cognitive dissonance, thus reducing internalization of the thin body ideal, which in turn is thought to result in reductions in body dissatisfaction and ED symptoms. A qualitative study investigating experiences with the original four-session Body Project manual [19] found that participants reported an overall satisfaction with the intervention, with the group´s format being viewed as most valuable, followed by the opportunity to discuss issues with the other participants in a group setting. The program´s scripted nature was noted as less valuable by participants. Originally, Body Project was designed as a face-to face intervention. Ghaderi et al. [ 8] developed and tested a virtual version of the intervention. Their results suggest that a virtual delivery of Body Project groups may be an effective and accessible prevention strategy for individuals at risk for EDs. Given the robust evidence of the effectiveness of the Body Project for individuals without T1D, Wisting et al., [ 27] developed a virtual, T1D specific version of the Body Project, Diabetes Body Project, and tested the feasibility and acceptability of that script. Results from the quantitative part of the study suggest that the Diabetes Body Project is a feasible and acceptable ED prevention approach for females with co-occurring T1D and body image concerns. Moreover, statistically significant within-subject reductions in ED risk factors and symptoms from baseline to posttest were reported. In the current article, we report qualitative feasibility undertaken as part of the pilot study. The use of qualitative research is specifically recommended in assessing whether an intervention engages a distinct target group [5]. The overall aim of the current study was to investigate the feasibility, using qualitative methodology, of the adapted Body Project for individuals with type 1 diabetes. Specifically we aimed to (a) explore participants’ experiences with the practical organization and the way the group intervention was conducted, (b) explore participants’ experiences with the content of the intervention and (c) explore the potential impact that taking part in the program had on participants´ daily life. The results of this study, together with the quantitative results previously reported, may lead to refinements of the script and inform the development of future trials. ## Methods This paper is part of a larger project focusing on the development and piloting of Diabetes Body Project [27]. The project was supported by Dam Foundation, conducted in collaboration with the Norwegian Diabetes Association and led by the Regional Department for Eating Disorders, division of Mental Health and Addiction, Oslo University Hospital. ## Design A generic qualitative focus group design was applied, which aimed to generate a comprehensive summary of the key themes emerging from the focus group discussions [2, 11]. Focus group interviews are recognized as an effective and suitable method for exploring specific topics. In addition, the focus on discussions and interaction between group participants may generate new questions and highlight important aspects the researcher is unable to anticipate beforehand (ibid). ## Diabetes body project group intervention In the development of Diabetes Body Project, the latest version of the original four-session Body Project script [22] was translated to Norwegian. In the first four sessions, participants are asked to define, discuss and challenge current appearance ideals, as well as engage in body activism activities and discuss advantages and disadvantages of social media use. Examples of verbal and written in-group exercises and homework exercises include a variety of letter writing exercises and the “mirror exercise”, a homework assignment where participants are encouraged to find positive attributes about themselves, including at least two on appearance. The format and the content of the groups are designed to maximize cognitive dissonance. The script was adapted to include issues specifically related to T1D by adding two diabetes specific sessions following the original four sessions, delivered in the same interactive format. Specific details of the content of the six-hour Diabetes Body Project is described in the initial report [27].Two to individuals with lived experience with T1D (KMK & MHL) contributed to the development of the diabetes-specific content. Examples of T1D specific content include group discussions concerning costs of not managing the illness appropriately as well as costs of misusing insulin for weight management. Thus, the Diabetes Body Project feasibility study [27] involved six weekly delivered 1- hour sessions. Inspired by Ghaderi et al. [ 8], a virtual-delivery format was applied. ## Participants and procedure 35 female participants were included in the Diabetes Body Project feasibility study, and allocated to five Diabetes Body Project Groups. 26 participants completed all six meetings, including pre- and post-tests (delivered online). Mean age of onset of T1D was 9.34 (SD 6.03). Mean total score on the Diabetes Eating Problem survey-revised (DEPS-R) was 18.4 (SD 9.03). At baseline, $42.9\%$ of the participants scored above the DEPS-R cut-off (≥ 20), versus $26.9\%$ at posttest. Mean age of participants was 25.62 (3.92). 9 participants chose to drop out during the course of the groups. These participants did not differ significantly from participants completing all sessions in terms of baseline demographic and clinical characteristics. For a more detailed description of recruitment and sample procedures, see Wisting et al., [ 27]. All participants who completed the Diabetes Body Project group intervention were invited to participate in focus group interviews after program completion. A total of 17 out of the 26 participants accepted the invitation. Mean age of focus group participants was 26.61. Participants who declined the invitation to participate in the focus groups were either sick or otherwise engaged, e.g. with school exams. Five focus groups were conducted in total, with between 2 and 4 participants in each group. Both the Diabetes Body Project intervention and the focus group interviews were carried out during a phase of the Covid-19 pandemic characterized by lockdown. The focus group interviews were conducted on the same virtual platform as the Diabetes Body Project intervention and lasted between 53 and 71 min. Participants confirmed they were alone at the beginning of the interview. The same procedure was applied for each Body Project session. All five groups were moderated by a researcher with competence in conducting qualitative research (TWH and JVN), and a co-facilitator (ALW, KMK and MHL). Two of the facilitators (AL and TWH) had participated in running some of the intervention groups. In order to limit potential biases due to social desirability concerns, they did not conduct focus group interviews with these groups. A semi-structured interview guide was developed for the purpose of the study (available upon request). The guide was iteratively developed based on feedback from the two individuals with lived experience with T1D (KMK and MHL) and the Body Project team at the Regional Department for Eating Disorders at Oslo University Hospital. The guide was structured around two main points: overall experiences with group participation and questions concerning the content and format of the group intervention. Questions were open-ended. In order to enhance data richness, facilitators were encouraged to probe participant responses. All interviews were audio recorded and transcribed verbatim. Ethical approval for the study was obtained from the regional ethical committee (REC) north (reference 6860). All participants signed informed consent forms. ## Data analysis The audio recordings were transcribed verbatim by a professional transcription service and entered into NVivo (version 10) for initial organization and coding. Parts of the analysis was also conducted manually. Braun and Clarke’s [3] six steps of reflexive thematic analysis were used to analyze the qualitative data. The steps included [1] familiarization with the data, [2] generation of initial codes, [3] search for themes, [4] review of themes, [5] name and definition of themes connected to focus areas, and [6] report creation. The initial long list of themes and subthemes created from the data was reduced through comparison across the different focus groups and participants within the groups, guided by the research aims. The initial analysis was conducted by TWH and JVN. In line with Braune and Clarke [4], the coding approach when more than one researcher is involved was collaborative and reflexive. Embedded in this approach, is the acknowledgement that knowledge is never free of researcher influence, that our assumptions, values and choices shape the knowledge we create. An essential part of the analytic process was thus a critical reflection of our roles as researchers, designed to develop a richer and more nuanced interpretation of the data, rather than seeking a consensus on meaning. After completing the initial analysis, the two user representatives met with JVN and TWH and discussed the overall impression and tentative themes from the interviews. The two individuals with experience with T1D (KMK & MHL) were invited to share their overall impressions based on their experience with co-moderating some of the focus groups, before they were presented with the tentative themes. Although their feedback largely resonated with the preliminary thematic structure, it also brought new perspectives and insights into the data material, thus providing a broader and deeper understanding of the data. ## Results The qualitative analysis generated one overarching theme, named “the benefit of meeting peers with a lived experience of T1D and body image concerns”, and four themes; the need for an integrated focus on diabetes, personal relevance, providing sufficient balance between structure and flexibility and enabling a different perspective. The relationships between the themes are illustrated in the figure below. A more detailed presentation using examples from the data corpus follows. The extracts were chosen because they contained illustrative information of the participants’ experiences and illuminate various aspects of the total data corpus. Extracts from focus group interviews were translated by TWH and JVN from Norwegian to English, as directly as possible. All names are pseudonyms. Comments in brackets in the extracts are the authors’ own explanations / interpretations. ## The benefit of meeting peers with a lived experience of T1D and body image concerns The overarching theme, as generated by the participants in the focus groups, represents an abstraction of the main themes. Participants unanimously expressed that meeting peers with T1D overall was a highly valuable and appreciated experience. The group represented a temporary safe space that enabled a connection with a community of peers. Learning from the lived experience of peers, e.g. how fellow group members chose to solve home assignments and reflect on issues addressed throughout the groups, appeared to promote self-reflection and stimulate new perspectives regarding own attitudes and behaviors. That all participants had a lived experience of T1D seemed to strengthen the feeling of being in this together. Many participants described a limited network of other females with T1D, and thus particularly valued meeting peers. This seemed true for both participants relatively recently diagnosed with the disease and for those who had lived with T1D for most of their lives. Across all groups, most participants shared positive experiences with the virtual group format. Being able to attend the meeting from home was both described as convenient and safe, even when discussing and sharing personal issues. ## The need for an integrated focus on T1D In four of five focus groups, participants expressed that the clear division between general issues pertaining the thin beauty ideal in the first four sessions of the Diabetes Body Project intervention and the focus on T1D specific content in the last two sessions was experienced as artificial. “ I thought it was difficult to discuss body and exercise and food and everything without including the diabetes, that it was separated in the first sessions. It was like; “We are not addressing the diabetes now, now we are talking about the body”. At least for me, and I believe that others [participants] had the same impression, you can’t separate these things easily” (Kari, group 3)”. Most participants called for a greater emphasis on T1D from the outset of the intervention. Some also expressed that the program was not aligning with their expectations, as they believed they had signed up for a program emphasizing dilemmas and challenges related to living with T1D. The majority agreed that it took too long before T1D was addressed in the two last sessions of the intervention. As Siri (group 3) put it: “They [the group leaders] had a strong agenda for what should be in focus, and I found that a bit peculiar, as this [diabetes] was my reason for signing up”. Although these views were strongly highlighted in a majority of the focus groups, some participants also emphasized that they appreciated the generic focus on the thin ideal and body image issues, as well as the T1D specific content. Thus, Anne (group 2) also stated that the diabetes focus needs to be well balanced too, as “We are not just diabetics, we are normal young females living in a society as all others, with all that holds.”. Across all focus groups, participants expressed that living with T1D represented specific challenges related to health behaviors and body image, and the T1D specific content in the Diabetes Body Project intervention was highlighted as particularly helpful. Several individuals underlined that diabetes-specific challenges were experienced as highly useful to discuss with peers, like significant scar tissue from insulin injections, the insecurities of using visible equipment like glucose monitoring sensors or insulin pumps, or difficulties with having to eat when not hungry, in order to treat or prevent hypoglycemia. “I think it was a very good thing that we were able to talk about the body and the challenges from a standpoint of [being] female with diabetes, because I experience that these conversations are very different than they are with others without diabetes” (Caroline, group 5). ## Personal relevance Although most participants seemed to enjoy discussions of the thin ideal and other aspects of living in a contemporary culture promoting specific body ideals, quite a few of the participants reflected on whether the program’s focus was too narrow to allow for personal relevance. Aligning with this, several experienced that they currently were not part of a social context where the thin ideal or strict body ideals were common, leaving them with a feeling that the program’s main focus was somewhat off target. As Marie (group 1) put it:“For me and many others it is about, not necessarily to look thin, as an ideal, but it can be muscular or fit or that you look a certain way. So exactly the “thin ideal”, I do not know if the ideal always is thin. When we spoke of the “beauty ideal”, then I felt it was a more inclusive concept, that could refer to both looks, how you look, your appearance, the body, it wasn’t just about being thin”. Overall, participants across all groups called for a broader focus when addressing contemporary culture and several spoke of the importance of the program allowing sufficient flexibility to ensure personal relevance for each participant, both regarding content and homework assignments. Most participants thought that an integrated focus on aspects concerning the body or body image, different health behaviors, self-esteem, and the unique experiences of living with T1D could have increased the feeling of personal relevance. For some, the focus on social media use, both during the sessions and as part of homework assignments, was experienced as irrelevant, because they rarely posted on social media sites. Some also described that much of the content in the sessions was aimed at a younger age group, and was thus experienced as less relevant for the participants in the older age range (< 20).“Sometimes I felt that it was as if you were aiming for certain difficulties that I don’t experience” (Caroline, group 5). Importantly, most participants reflecting on the potential behavior changes that could be attributed to taking part in the sessions, emphasized that such behavioral changes usually occur in social settings not permitted during an ongoing pandemic (Covid-19 pandemic). Moreover, participants in the higher end of the age range said that several of the interventions and tasks would probably have been more beneficial/useful a few years earlier, because insecurities due to or concerning body image issues had diminished with age. ”I felt, when I joined this group, that I am a bit past having a difficult relationship with food and (my) body. I have been there, but I am not there now. So I am kind of thinking: «oh, this should have been a few years back» (Pia, group 4). ## Providing sufficient balance between structure and flexibility The structured group format seemed to enable sufficient security for participants to share personal information that they would otherwise not be likely to share in their daily life. As Veronica reflected: “…then I heard that the other participants spoke of experiences similar to mine, and then I dared to talk about my experiences [too] and reflect on these” (Veronica, group 3). Many participants in several of the focus groups reflected on the set duration of the Diabetes Body Project intervention. Many described 1 h as a good length, making it possible to attend despite a busy life schedule. “What I like with an hour, is that it is totally doable for me to set aside an hour and take part. If it was longer, it might be, like, that it would take up too much of my time” (Anne, group 2). Some participants thought that the duration of the intervention could have been more flexible, allowing for more interaction and discussion between participants. Due to the scripted nature of the manual, the sessions sometimes were experienced as rushed, and it was necessary to move on and complete all of the scheduled exercises. Much of the benefit lies in being able to respond and start a conversation based on somebody’s answer. And that I felt was very difficult to do, because you had to move on, all the time. If you started to say something after somebody had read their home assignment, it was: Ok, but now we have to move on […] they did not give us room to talk, that was not part of the framework (Linn, group 4). Participants suggested that a possible solution to this was to have longer sessions or a more flexible use of the manual, allowing for more discussions during some of the exercises, and making the manual a better fit with the dynamics in each group and the individual needs of each group member. For example, Sarah (group 3) rhetorically asked: “Did we have to listen to all homework assignments, every session? “ Others suggested that if one hour is set as the program’s time frame one could think about the possibility of inviting participants to optionally stay for another thirty minutes together with group leaders to discuss further. ## Enabling a different perspective As a whole, participating in a group format encouraging the exchange of thoughts, feelings and experiences associated with the different topics addressed in the group sessions, was valued as predominantly beneficial by participants across all groups. Several of the focus group discussions revolved around participants experiencing an enhanced self-awareness related to the different themes discussed throughout the sessions and by engaging in the homework assignments. Across all groups, participants reported, in one way or another, that group participation had stimulated their self-reflexivity concerning own body image and how the thin beauty ideal and/or different societal pressures both had and presently affected them. Throughout the focus group discussions, participants described both an enhanced awareness of their “inner voice”, i.e. how they talked to or of themselves, and a renewed awareness of how they verbally communicated with others. As described by Siri (group 3), participation in the Diabetes Body Project group enabled her to “increase [my] awareness on the difference between the thin ideal and what is health promoting [for me]”. Several of the participants had experienced, during the course of group participation, an increased attentiveness to their own language use and reported a new interest in how choice of words could come to influence both themselves and others, in potentially both positive and negative ways. This reflection had also led some to become more aware of their own responsibilities while engaging in conversations regarding the body, cultural ideals and different health behaviors with friends, family and colleagues. “I have in fact corrected a couple of friends, maybe with more confidence than before… and this led to good discussions (with my friends) that I felt was surprising” (Sophie, group 3). Being able to listen to the other participants during group discussions and witnessing how they had solved the home exercises were viewed as the most useful components of the intervention. Listening to and observing others seemed to stimulate self-reflexivity, as different perspectives on the same subjects emerged. Exercises that were highlighted as particularly beneficial for enhancing self-awareness and countering societal pressures were the “mirror exercise” (i.e. standing in front of the mirror and recording positive aspects of themselves, including physical), role-play exercises within the session where group leaders acted as people obsessed with the thin beauty ideal, and variations of letter writing. « I thought that the mirror exercise was both nice, but also a bit challenging. It really made me think. It really evoked emotions. Nice emotions too» (Kari, group 5). Moreover, an increased awareness of the influence of social media on body image, as well as personal social media use, was mentioned by participants across all groups.“(..) Things like: who you follow on social media, what you give a “like”. Things like that. How much you actually contribute to this body image pressure. If you like people who stand for (that ideal) or support businesses who do, you are part of maintaining it. So I feel that I have become a lot more conscious after we have talked (about it)” (Sophie, group 3). ## Discussion Findings from the current study yield important information about young females’ with T1D experiences with participating in Diabetes Body Project groups. Results emphasize the value of this targeted and interactive prevention program, as well as pinpointing some specific challenges and potential areas for improvement of the Diabetes Body Project script. The overarching theme underlined the benefit of meeting peers with T1D and body image concern. This is in line with previous research investigating participants experiences with the original four session Body Project intervention, which have reported that a sense of belonging and not feeling alone with their body dissatisfaction are experienced as the most important aspects of participants’ experiences [10, 19, 25]. Recognizing shared experiences, group cohesiveness, and developing hope from seeing others change are commonly reported benefits of psychotherapeutic group interventions [29]. Moreover, the Diabetes Body Project likely represents the first time several of the participants had the opportunity to voice and discuss their body image concerns with other young females with T1D. For this group, discussing and interacting with peers can be particularly important due to illness specific challenges experienced during early adulthood, in addition to normal challenges for this age group. Young individuals with T1D have previously described that other individuals with T1D are the only ones who can truly recognize how living with this disease feels and what it entails [9]. Moreover, loneliness has been reported to be more frequent among people with diabetes, and it seems that this was enhanced during the Covid 19 pandemic [14], thus underlining the need the need for opportunities to socialize and connect with peers with T1D. Participants highlighted the need for a more integrated focus on T1D throughout the Diabetes Body Project intervention. Findings from qualitative studies exploring young females experiences of living with T1D, emphasizes that integrating and accepting T1D as part of participants’ identity is central when it comes to accepting the illness as well as living a normal life [12]. Thus, although we have kept the original division of four plus two sessions in the Diabetes Body Project script, we have we have adapted the format to include T1D topics in both in-group exercises and homework throughout all 6 group sessions.. Results from the current study indicate that the term the thin beauty ideal was experienced as a bit too narrow as it did not cover all experienced issues related to being exposed to the current beauty ideal. In recent years it appears to be a shift in the body type to which women aspire from the traditional thin ideal toward being fit and toned (i.e., the fit ideal) or the thin – thick body ideal, with a flat stomach and larger thighs and butt [15]. We have incorporated this into the Diabetes Body Project script by using the terms thin ideal, beauty ideal and appearance ideal interchangeably, thus allowing for a broader definition when describing and discussing current societal expectations to appearance. The scripted nature of the Diabetes Body Project could be experienced as a bit inflexible by the participants in the current study, making the intervention feel a bit rushed and allowing for few lengthy discussions. This is similar to Shaw et al. ’s finding in their study of participants’ experiences with the original Body Project intervention, where the program’s scripted nature was noted as less valuable and in need of improvement. Moreover, Jarman et al., [ 10] found that, for some participants, the scripted nature of the intervention acted as a barrier to full participation and did not allow freedom to explore, develop, and discuss their own ideas. Lack of flexibility is a common critique against manualized treatments [24]. However, as manual adherence is important to maximize cognitive dissonance [23], and the script facilitates fidelity [10], Shaw et al. [ 19] suggest that training of group leaders should emphasize the importance of learning the script well enough to allow a natural and dynamic delivery. Findings demonstrate that a majority of the participants experienced both enhanced self-awareness and self-reflexivity during the course of the Diabetes Body Project intervention, thus underlining the importance of discussing and challenging current appearance ideals as well as supporting theorized mechanisms of change according to cognitive dissonance [10]. Of the specific exercises, letters, role-plays and the mirror exercise were described as most valuable. These findings are in concordance with previous studies on experiences with the general Body Project script [19, 25]. As described by participants, the nation-wide social restrictions at the time limited their opportunities for social encounters, thus possibly affecting potential effects of the intervention. There are some limitations to this study that deserve consideration. As the data from this qualitative research was part of a feasibility study, all findings should be interpreted with caution and not assume representation of all young females with T1D. Focus group facilitators had been involved in running some of the Diabetes Body Project groups thus potentially affecting the dynamics in the focus groups as well as the gathered data Due to dropout in the intervention groups and for the focus groups interviews, 17 of the originally enrolled participants in the Diabetes Body project intervention ($49\%$) participated in focus group interviews, possibly limiting the information gathered in the interviews. Finally, as this was a pilot study, it is not clear based on the past quantitative (ref [27] results and the results of the current paper whether the Diabetes Body Project may actually prevent ED onset. A future randomized controlled trial (RCT) is need to examine this further. ## Conclusion The current study aimed to qualitatively investigate the feasibility Diabetes Body Project groups. Results show overall positive feedback regarding the content and structure of the intervention, and underline the importance of targeting preventive efforts to specific risk groups. Refinements of the Diabetes Body Project script include allowing for a broader definition of the current beauty ideal as well as a more integrated focus on T1D throughout the intervention. ## References 1. Atkinson MA, Eisenbarth GS, Michels AW. Type 1 diabetes. The Lancet (2014.0) 383 69-82. DOI: 10.1016/S0140-6736(13)60591-7 2. Bradshaw C, Atkinson S, Doody O. Employing a qualitative description approach in health care research. Global Qual Nurs research (2017.0) 4 2333393617742282. DOI: 10.1177/2333393617742282 3. Braun V, Clarke V. Reflecting on reflexive thematic analysis. Qual Res Sport Exerc Health (2019.0) 11 589-597. DOI: 10.1080/2159676X.2019.1628806 4. 4.Braun V, Clarke V. Thematic analysis: a practical guide. SAGE Publications, Limited; 2021. https://books.google.no/books?id=25lpzgEACAAJ. 5. Campbell NC, Murray E, Darbyshire J, Emery J, Farmer A, Griffiths F, Guthrie B, Lester H, Wilson P, Kinmonth AL. Designing and evaluating complex interventions to improve health care. BMJ (2007.0) 334 455-459. DOI: 10.1136/bmj.39108.379965.BE 6. Colton PA, Olmsted MP, Daneman D, Rodin GM. Depression, disturbed eating behavior, and metabolic control in teenage girls with type 1 diabetes. Pediatr Diabetes (2013.0) 14 372-376. DOI: 10.1111/pedi.12016 7. 7.Festinger L. A theory of cognitive dissonance. Evanston, Illinois: Row, Peterson, & Co. 1957. 8. Ghaderi A, Stice E, Andersson G, EnöPersson J, Allzén E. A randomized controlled trial of the effectiveness of virtually delivered Body Project (vBP) groups to prevent eating disorders. J Consult Clin Psychol (2020.0) 88 643. DOI: 10.1037/ccp0000506 9. Habenicht AE, Gallagher S, O’Keeffe M-C, Creaven A-M. Making the leap and finding your feet: a qualitative study of disclosure and social support in university students with type 1 diabetes. J Health Psychol (2021.0) 26 260-269. DOI: 10.1177/1359105318810875 10. Jarman HK, Treneman-Evans G, Halliwell E. “I didn’t want to say something and them to go outside and tell everyone”: The acceptability of a dissonance-based body image intervention among adolescent girls in the UK. Body Image (2021.0) 38 80-84. DOI: 10.1016/j.bodyim.2021.03.011 11. Kahlke RM. Generic qualitative approaches: pitfalls and benefits of methodological mixology. Int J Qual Methods (2014.0) 13 37-52. DOI: 10.1177/160940691401300119 12. Kruger S, Deacon E, van Rensburg E, Segal DG. Young adult women’s meaning-making of living with type 1 diabetes: towards growth and optimism. Psychol Health (2021.0). DOI: 10.1080/08870446.2021.1977303 13. Le LK, Barendregt JJ, Hay P, Mihalopoulos C. Prevention of eating disorders: a systematic review and meta-analysis. Clin Psychol Rev (2017.0) 53 46-58. DOI: 10.1016/j.cpr.2017.02.001 14. Madsen KP, Willaing I, Rod NH, Varga TV, Joensen LE. Psychosocial health in people with diabetes during the first three months of the COVID-19 pandemic in Denmark. J Diabetes Complicat (2021.0) 35 107858. DOI: 10.1016/j.jdiacomp.2021.107858 15. McComb SE, Mills JS. Eating and body image characteristics of those who aspire to the slim-thick, thin, or fit ideal and their impact on state body image. Body Image (2022.0) 42 375-384. DOI: 10.1016/j.bodyim.2022.07.017 16. Nielsen S, Emborg C, Mølbak A-G. Mortality in concurrent type 1 diabetes and anorexia nervosa. Diabetes Care (2002.0) 25 309-312. DOI: 10.2337/diacare.25.2.309 17. Olmsted MP, Colton PA, Daneman D, Rydall AC, Rodin GMJDC. Prediction of the onset of disturbed eating behavior in adolescent girls with type 1 diabetes. Diabetes Care (2008.0) 31 1978-1982. DOI: 10.2337/dc08-0333 18. Rodin GM, Daneman D. Eating disorders and IDDM: a problematic association. Diabetes Care (1992.0) 15 1402-1412. DOI: 10.2337/diacare.15.10.1402 19. Shaw H, Rohde P, Stice E. Participant feedback from peer-led, clinician-led, and internet-delivered eating disorder prevention interventions. Int J Eat Disord (2016.0) 49 1087-1092. DOI: 10.1002/eat.22605 20. Sparti C, Santomauro D, Cruwys T, Burgess P, Harris M. Disordered eating among Australian adolescents: prevalence, functioning, and help received. Int J Eat Disord (2019.0) 52 246-254. DOI: 10.1002/eat.23032 21. Stice E, Marti CN, Shaw H, Rohde P. Meta-analytic review of dissonance-based eating disorder prevention programs: Intervention, participant, and facilitator features that predict larger effects. Clin Psychol Rev (2019.0) 70 91-107. DOI: 10.1016/j.cpr.2019.04.004 22. Stice E, Onipede ZA, Marti CN. A metaanalytic review of trials that tested whether eating disorder prevention programs prevent eating disorder onset. Clin Psychol Rev. (2021.0) 87 102046. DOI: 10.1016/j.cpr.2021.102046 23. Stice E, Shaw H, Marti CN. A meta-analytic review of eating disorder prevention programs: encouraging findings. Annu Rev Clin Psychol (2007.0) 3 207-231. DOI: 10.1146/annurev.clinpsy.3.022806.091447 24. Truijens F, Zühlke-van Hulzen L, Vanheule S. To manualize, or not to manualize: Is that still the question? A systematic review of empirical evidence for manual superiority in psychological treatment. J Clin Psychol (2019.0) 75 329-343. DOI: 10.1002/jclp.22712 25. Vanderkruik R, Gist D, Dimidjian S. Preventing eating disorders in young women: an RCT and mixed-methods evaluation of the peer-delivered body project. J Consult Clin Psychol (2020.0) 88 1105. DOI: 10.1037/ccp0000609 26. Watson HJ, Joyce T, French E, Willan V, Kane RT, Tanner-Smith EE, McCormack J, Dawkins H, Hoiles KJ, Egan SJ. Prevention of eating disorders: a systematic review of randomized, controlled trials. Int J Eat Disord (2016.0) 49 833-862. DOI: 10.1002/eat.22577 27. Wisting L, Haugvik S, Wennersberg AL, Hage TW, Stice E, Olmsted MP, Ghaderi A, Brunborg C, Skrivarhaug T, Dahl-Jørgensen K. Feasibility of a virtually delivered eating disorder prevention program for young females with type 1 diabetes. Int J Eat Disorders (2021.0) 54 1696-1706. DOI: 10.1002/eat.23578 28. Wisting L, Skrivarhaug T, Dahl-Jørgensen K, Rø Ø. Prevalence of disturbed eating behavior and associated symptoms of anxiety and depression among adult males and females with type 1 diabetes. J Eat Disord (2018.0) 6 1-10. DOI: 10.1186/s40337-018-0209-z 29. 29.Yalom ID, Leszcz M. The theory and practice of group psychotherapy. Basic books. 2020.
--- title: A mathematical model and numerical simulation for SARS-CoV-2 dynamics authors: - Antonino Amoddeo journal: Scientific Reports year: 2023 pmcid: PMC10027279 doi: 10.1038/s41598-023-31733-2 license: CC BY 4.0 --- # A mathematical model and numerical simulation for SARS-CoV-2 dynamics ## Abstract Since its outbreak the corona virus-19 disease has been particularly aggressive for the lower respiratory tract, and lungs in particular. The dynamics of the abnormal immune response leading to lung damage with fatal outcomes is not yet fully understood. We present a mathematical model describing the dynamics of corona virus disease-19 starting from virus seeding inside the human respiratory tract, taking into account its interaction with the components of the innate immune system as classically and alternatively activated macrophages, interleukin-6 and -10. The numerical simulations have been performed for two different parameter values related to the pro-inflammatory interleukin, searching for a correlation among components dynamics during the early stage of infection, in particular pro- and anti-inflammatory polarizations of the immune response. We found that in the initial stage of infection the immune machinery is unable to stop or weaken the virus progression. Also an abnormal anti-inflammatory interleukin response is predicted, induced by the disease progression and clinically associated to tissue damages. The numerical results well reproduce experimental results found in literature. ## Introduction The Severe Acute Respiratory Syndrome (SARS)–Corona Virus-2 (CoV-2) appeared at the end of 2019, and can be responsible for a severe inflammation of the human respiratory tract (HRT), a disease also known as Corona Virus Disease-2019 (COVID-19): it is characterized by an abnormal response of the immune system which induces the production of several inflammatory molecules going in circulation and giving rise to a so-called cytokines storm1,2, an event with outcome often lethal, and whose occurrence is shared with previous coronaviruses such as SARS-CoV and Middle East Respiratory Syndrome (MERS)-CoV2,3. In general, upon infection, the host response begins with the detection of the pathogen associated molecular patterns (PAMP) through the pattern recognition receptors (PRR), allowing the recognition of the external pathogen and leukocytes activation, then triggering the response of the innate immunity1,4. This is the first source of inflammation as response of the host to the pathogen exposure. At the same time the innate immune system, which includes monocytes, macrophages, dendritic cells, mast cells, natural killer cells, neutrophils, eosinophils and basophils, represents the first barrier of the host opposing to an external thread. Since the outbreak of the COVID-19, works have put in evidence the peculiarity of the disease as well the similarities with SARS-CoV, MERS-CoV2,5, while previous studies and mathematical models6–9 can be a foundation from which to build a mathematical modelling for SARS-CoV-2 infection. In this work we are concerned with a mathematical model trying to shed some light on the COVID-19 dynamics during the early stage of the disease progression, taking into account the virus interaction with the host innate immune response. We summarize the essential biological background needed for the model presentation and discussion, while for more details as well as review papers it is very difficult to select among a wealth of excellent ones which are present in literature, then we address the interested reader to papers cited time to time and references therein. When a pathogen intrudes a host, it is faced by monocytes, which consequently differentiate into macrophages (M) or dendritic cells (DC)10. Such cells, once captured the pathogen, interact with T lymphocytes, which in turn are grouped into four types of population, among which there is the family of the effector T lymphocytes: the latter includes cytotoxic T-lymphocytes or CD8+ T-cells, regulator T-lymphocytes or Treg cells, and helper T-lymphocytes or CD4+ T-cells11. Cytokines, a broad class of peptides that includes chemokines (CK) and interleukins (IL)1, are important mediator of the immune response and play a significant role in cell signalling and activation of the immune response12,13. Once activated as a consequence of an external threat, CD4+ T-cells start producing a variety of ILs12 that are responsible of the cell signalling, producing a broad immune response. Macrophages differentiate from monocytes in classically activated macrophages (M1), or in alternatively activated macrophages (M2)4,14–16. The former are associated to a pro-inflammatory activity since they produce pro-inflammatory ILs as, for example in tumour progression17: tumour necrosis factor (TNF), IL-1, IL-6 and IL-12. On the other hand, M2 macrophages are responsible of an anti-inflammatory activity since they produce anti-inflammatory ILs, such as IL-10, and perform a restorative and healing function for damaged cells18. ILs are molecules that can exert both pro- and anti-inflammatory functions13,19 and are produced not only by macrophages, but also by CD4+-T cells. Pro-inflammatory ILs are produced by M1 activated macrophages, and high levels of in particular IL-6 are present in severe COVID-19 patients, as well in SARS and MERS ones2. In a very recent review20 the immunoregulatory role of IL-10 in many infections has been summarized, while it has been found that IL-10 secreted in the tumour environment encourage differentiation of macrophages towards the M2 phenotype21. In Lai et al.7 a model for the dynamics of CD8+-T cells during Human Immunodeficiency Virus (HIV) -1 infection at a quasi-steady state has been presented, considering contributions from both healthy and infected CD4+-T cells, and introducing a chemotactic contribution due to infected CD4+-T cells. It is found that the steady state stability depends upon attractive or repulsive nature of the chemotactic movement22. In a recent work of Quirouette et al.8, the spread of influenza A virus (IAV) in the HRT has been modelled in terms of partial differential equations (PDEs), considering diffusion and advection terms in one spatial dimension as well the interaction of virions with both healthy and infected target cells. Models formulated in term of ordinary differential equations (ODE) have been constructed for Dengue Virus (DV) spread coupled with the immune response23, while a review for the viral dynamics of HIV, Hepatitis C Virus (HCV), IAV, Ebola Virus (EV), DV and Zika Virus (ZV) has been presented in Zitzmann & Kaderali6. Less recently, a model describing the space–time evolution of cancer cells has been formulated in terms of PDEs, taking into account their interaction with CD8+-T cells, IL-27, IL-10 and interferon-γ (IFN-γ)9, in one spatial dimension, highlighting the role of pro- and anti-inflammatory ILs. At the turn of the pandemic declaration of the World Health Organization, a couple of paper reported clinical results of studies carried out on COVID-19 patients in Wuhan, China, in which the plasma levels of IL-6 and IL-1024, and of IL-625 were measured. In this frame, the present work is aimed at: [1] investigate a possible IL-6 role on M1 polarization of macrophages while they undergo chemotaxis from SARS-CoV-2 infected T cells; hence, [2] reproduce clinical observations on SARS-CoV-2 patients, linked to the dynamics of ILs and the role they play in lung damages. To build our model we were inspired by the works of Chousterman et al.1, Rossi et al.2, Channappanavar et al.3, while the numerical simulations were compared with clinical findings contained in Huang et al.24, Zhou et al.25, taking some points of discussion and comparison also from previous experimental works on mice26,27. ## The mathematical model We consider a portion of the HRT as the domain in which an initial amount of virus (V) explicitly interacts with CD4+-T cells, from now on simply T cells (T), and in which the immune response involves classically (M1) and alternatively (M2) activated macrophages, IL-6 (L) and IL-10 (N). From the mass conservation we derive the model equations for the interacting species inside the considered biological domain, in terms of reaction–diffusion PDEs. We consider the spatial position identified by the vector x = (x,y), while t denotes the variable time. We therefore introduce the relative model equations, together with a model equation accounting for infected T cells (I). Moreover, the parameters entering in the model are detailed and quantified in the accompanying Supplementary Information online. Once SARS-CoV-2 intrudes the host, it binds to angiotensin-converting enzyme 2 (ACE2) receptors25, subsequently its presence is revealed by the toll-like receptors (TLR) from which the immune response begins with the associated inflammation2. ACE2 have been proven to be the receptors of SARS-CoV and SARS-CoV-211,28, both targeting T cells, a trait shared with HIV-17,29. Then considering only infection of such cells, the virus evolution accounts for diffusion with a coefficient DV, production by infected T cells at a rate p; the virus is cleared by immune elimination6 at a rate c:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial V}{{\partial t}} = \nabla \cdot \left({D_V\nabla V} \right) + pI - cV$$\end{document}∂V∂t=∇·DV∇V+pI-cV T cells diffuse at rate DT, undergo natural decay according to dT, and are infected by virus at rate k6,7; are activated by IL-630,31, promoted by M110, inhibited by IL-1020,32,33 and M210 according to ϕ21, ϕ22, ϕ23 and ϕ24, respectively. M1 produce, among others, IL-12 driving T cell polarization34; the presence in the airways inflammatory microenvironment of IL-4 and IL-13 induces M2 polarization of macrophages35, in turn expressing IL-10 which increases T cell deactivation. On the other hand Treg cells, which differentiation is stimulated by IL-10, inhibit T cell and DC activation35,36. In the lung, Treg are promoted by alveolar macrophages, with a predominant M2 phenotype37. Given the common features evidenced, for example, in Mannar et al.38 and Fardoos et al.39 between SARS-CoV-2 and HIV-1, along with the fact that T cells are decreased in critical COVID-19 patients40, we suppose that, similarly to what happens for HIV-17,22,41, depending on the concentration of some CKs, in presence of the SARS-CoV-2 infection, T cells undergo chemoattraction (chemotaxis) or chemorepulsion (fugetaxis). We then assume that T cells are attracted towards infected T cells, but are repulsed by virions, allowing SARS-CoV-2 immune evasion. In Eq. [ 2], χI and χV are, respectively, chemotaxis and fugetaxis coefficients for T cells; TM, instead, represents the maximum carrying capacity of T cells population. Hence2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial T}{{\partial t}} = \nabla \cdot \left\{ {D_T\nabla T - T\left[{\chi _I\left({1 - \frac{T}{T_M}} \right)\nabla I - \chi _V\left({1 - \frac{T}{T_M}} \right)\nabla V} \right]} \right\} - d_TT - kVT + \phi _{21}L + \phi _{22}M1 - \phi _{23}N - \phi _{24}M2$$\end{document}∂T∂t=∇·DT∇T-TχI1-TTM∇I-χV1-TTM∇V-dTT-kVT+ϕ21L+ϕ22M1-ϕ23N-ϕ24M2 Infected T cells are supposed to diffuse at the same rate of T cells; they are produced by the virus/T cells interaction at rate k, and decay at rate δ; it is also conceivable that the infected Tcells/IL-6 interaction can promote activation of effector T cells and deactivation of Treg cells27, contributing to a hyper-inflammation further reducing infected T cells at rate ϕ32:3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial I}{{\partial t}} = \nabla \cdot \left({D_T\nabla I} \right) + kVT - \delta I - \phi _{ 32}IL$$\end{document}∂I∂t=∇·DT∇I+kVT-δI-ϕ32IL Classically activated macrophages diffuse according to DM1. High levels of pro-inflammatory ILs, in particular IL-6, characterize the biological milieu of the SARS-CoV-2 infection1,2. From one hand it can be hypothesized that, similarly to what happens in chronic wounds, where the shift from M1 to M2 phenotype is dis-regulated with a prolonged inflammatory state14, a persistence of M1 macrophages and their predominance over M2 phenotypes can occur. On the other side, in Hadjadj et al.42 it has been found that in severe and critical COVID-19 patients an excessive inflammatory response occurs with increased levels of IL-6 which can act as a chemoattractant for macrophages and consequent tissue damage. Moreover, in COVID-19 patients an extreme increase of pro-inflammatory cytokines and other factors has been observed, among which IL-6 and IFN-γ43. Therefore, although in the literature we have not found evidence that IL-6 induces or promotes M1 directly, due to the peculiarity of the SARS-CoV-2 infection we assume that the abnormal expression of IL-6 and IFN-γ promotes M1 at a rate ϕ41. Further, the inflammation resolution involves the release of anti-inflammatory ILs18,20,21, then M1 are inhibited by IL-10 at rate ϕ42. In critical COVID-19 patients a common feature is that in lung lesions the predominant infiltrated immune cells are constituted by monocytes and macrophages28,40. The deeper penetration in tumour lesions of M1 macrophages with respect to M2 subtypes has been simulated in Leonard et al.17, Mahlbacher et al.21, accounted for introducing a chemotactic movement towards the lesion. The macrophage chemotaxis has been introduced also in Owen et al.44, studying macrophages-based therapies for drug delivery to tumour sites. M1 phenotype predominates in the early stage of inflammation and wound repair14, with a shift from M1 to M2 within 5–7 days post injury, being such behaviour dis-regulated in presence of chronic wounds. Given the pro-inflammatory nature of M1 macrophages, we then assume that they move chemotactically towards infected cells according to a chemotactic coefficient χI:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial M 1}{{\partial t}} = \nabla \cdot \left[{D_{M 1}\nabla M 1 - M1\chi _I\left({1 - \frac{M1}{{M1_M}}} \right)\nabla I} \right] + \phi _{41}L - \phi _{42}N,$$\end{document}∂M1∂t=∇·DM1∇M1-M1χI1-M1M1M∇I+ϕ41L-ϕ42N,where M1M is the maximum carrying capacity of the M1 population. Alternatively activated macrophages M2 diffuse at rate DM2, and are promoted by IL-10, which induce macrophages differentiation towards M22,21 at a rate ϕ51; the spike protein in SARS-CoV-2 is responsible of hyper production of IL-645, and in general of a complicated frame of hyper-inflammation leading to the cytokines storm, but also excess of IFN- γ has been observed43. We assume here that the level of IL-6 is representative of inflammation and therefore reflects proportionally that of IFN- γ, which in turn can repolarize M2 macrophages towards M1 phenotype46. Hence, M2 are inhibited at rate ϕ52 according to the IL-6 increase:5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial M2}{{\partial t}} = \nabla \cdot \left({D_{M2}\nabla M2} \right) + \phi _{51}{\text{N}} - \phi _{52}{\text{L}}.$$\end{document}∂M2∂t=∇·DM2∇M2+ϕ51N-ϕ52L. IL-6 diffuses at rate DL, it is produced by M1 at rate ϕ611,10,14,17; on the other hand, IL-6 is inhibited by IL-10 at rate ϕ631,20,21,47:6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial L}{{\partial t}} = \nabla \cdot \left({D_L\nabla L} \right) + \phi _{61}M1 - \phi _{63}N.$$\end{document}∂L∂t=∇·DL∇L+ϕ61M1-ϕ63N. Finally, IL-10 diffuses at rate DN, is produced by M2 macrophages at rate ϕ7114,20, while it is inhibited by IL-6 at rate ϕ731:7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\partial N}{{\partial t}} = \nabla \cdot \left({D_N\nabla N} \right) + \phi _{71}M2 - \phi _{73}L.$$\end{document}∂N∂t=∇·DN∇N+ϕ71M2-ϕ73L. ## Methods We performed numerical simulations of the above PDE system using the COMSOL Multiphysics™ package based on the finite element method (FEM)48, a technique already applied to the study of biological systems49,50 and in presence of strong anisotropies51, referring to the supplementary material for details. In Eqs. [ 2] and [4], we set TM = Tr and M1M = M1r, respectively, where Tr and M1r are reference quantities: Table 1 groups all the used parameters in dimensional and non-dimensional form, together with a short description and estimate, while once again we refer to the Supplementary Information online for details and used sources. Table 1Summary of the reference quantities and parameters used in the model. Reference quantitySymbolUnitsValueVirusVrn cm−31 × 107T cellsTrcell cm−35 × 106T cells maximum carrying capacityTMcell cm−35 × 106Infected T cellsIrcell cm−35 × 106M1 macrophagesM1rcell cm−36.9 × 106M1 macrophages maximum carrying capacityM1Mcell cm−36.9 × 106M2 macrophagesM2rcell cm−36.9 × 106IL-6Lrcell cm−32.87 × 109IL-10Nrcell cm−32.87 × 109Characteristic lengthlcm0.1Characteristic diffusion coefficientDcm s−11 × 10–6Characteristic time scaleτs1 × 104Parameter descriptionSymbolUnitsNon-Dimensional ParameterValueVirus diffusion coefficientDVcm2 s−1DVD−11 × 10–2T cells diffusion coefficientDTcm2 s−1DTD−15 × 10–3Infected T cells diffusion coefficientDIcm2 s−1DID−15 × 10–3M1 diffusion coefficientDM1cm2 s−1DM1D−15 × 10–5M2 diffusion coefficientDM2cm2 s−1DM2D−15 × 10–5IL-6 diffusion coefficientDLcm2 s−1DLD−11.45 × 10–2IL-10 diffusion coefficientDNcm2 s−1DND−11.45 × 10–2Virus production coefficientps−1pτIrVr−11.16 × 10–1Virus clearing coefficientcs−1cτ6.94 × 10–2Infected T cells chemotactic coefficientχIcm5 s−1 cell−1χIIrD−11 × 10–3Virus fugetactic coefficientχVcm5 s−1 cell−1χVVrD−15 × 10–2T cells decay ratedTs−1dTτ2 × 10–2T cells infection ratekcm3 s−1 cell−1kτVr7.4 × 10–4T cells activation rate by IL-6ϕ21s−1ϕ21τLrTr−111.5T cells production rate by M1ϕ22s−1ϕ22τM1rTr−12.3 × 107T cells inhibition rate by IL-10ϕ23s−1ϕ23τNrTr−122.96T cells inhibition rate by M2ϕ24s−1ϕ24τM2rTr−19.5 × 104Infected T cells decay rateδs−1δτ2 × 10–2Infected T cells reduction rate by hyper-inflammationϕ32cm3 s−1 cell−1ϕ32τLr10M1 production rate by IL-6ϕ41s−1ϕ41τLrM1r−11 × 10–3M1 inhibition rate by IL-10ϕ42s−1ϕ42τNrM1r−11 × 10–4M2 promotion rate by IL-10ϕ51s−1ϕ51τNrM2r−10.1M2 inhibition rate by IL-6ϕ52s−1ϕ52τLrM2r−11 × 10–32 × 10–3IL-6 production rate by M1ϕ61s−1ϕ61τM1rLr−10.5IL-6 inhibition rate by IL-10ϕ63s−1ϕ63τNrLr−11 × 10–2IL-10 production rate by M2ϕ71s−1ϕ71τM2rNr−10.1IL-10 inhibition rate by IL-6ϕ73s−1ϕ73τLrNr−19.26 × 10–5Details can be found in the Supplementary Information online. Equations [1]–[7] have been integrated in a two-dimensional square domain with 1 mm2 area, simulating a surface of HRT, in the 0–60 non-dimensional time interval. A $t = 0$ we assume the following set of initial conditions:8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{gathered} V\left({{\varvec{x}},0} \right) = {\text{exp}}\left({ - \left\| {\varvec{x}} \right\|^{{2}} \varepsilon^{{ - {1}}} } \right) \hfill \\ T\left({{\varvec{x}},0} \right) = {1} - 0.{5}V({\varvec{x}},0) \hfill \\ I\left({{\varvec{x}},0} \right) = 0.{5}V\left({{\varvec{x}},0} \right) \hfill \\ M1\left({{\varvec{x}}, 0} \right) = 0.0{5} V\left({{\varvec{x}}, 0} \right) \hfill \\ M2\left({{\varvec{x}}, 0} \right) = 0.{\text{1exp}}\left({ - \left\| {\varvec{ x}} \right\|^{{2}} \varepsilon^{{ - {1}}} } \right) \hfill \\ L\left({{\varvec{x}},0} \right) = 0.0{5}V\left({{\varvec{x}},0} \right) \hfill \\ N\left({{\varvec{x}},0} \right) = 0 \hfill \\ \end{gathered}$$\end{document}Vx,0=exp-x2ε-1Tx,0=1-0.5V(x,0)Ix,0=0.5Vx,0M1x,0=0.05Vx,0M2x,0=0.1exp-x2ε-1Lx,0=0.05Vx,0Nx,0=0 In the above equations ε = 0.02, then we admit that the initial viral load is centred on x = [0,0] with Gaussian shape. Moreover, a fraction of T cells is initially infected, while the M1 activated macrophages are a small fraction of the viral load. At $t = 0$ the M2 activation is not yet started then we assume that a small Gaussian distribution of alternatively activated macrophages is centred at x = [0,0], IL-6 is produced according to the M1 macrophages while IL-10 is not yet produced by M2. During the simulations the model has been tested for robustness by allowing each parameter to vary within ± $10\%$ of the selected value, with no significant changes observed in the virus amount in the domain at $t = 60.$ Instead, the numerical simulations have been performed as a function of the ϕ52 parameter, in order to test the biological milieu change capable to polarize macrophages towards M1 or M2 phenotype46, and for this reason we imposed ϕ52 = 1 × 10–3, 2 × 10–3. ## Results and discussion We performed the numerical simulation of Eqs. [ 1]–[7], plotting the variables spatial distribution for selected time steps in colour scale between the blue (minimum) and the red (maximum), which at $t = 0$ obey to the initial conditions imposed with Eqs. [ 8]. Moreover, each variable distribution has been normalized to its maximum value assumed during the time evolution, and was mapped in the [0,1] × [0,1] non-dimensional square domain. The variables evolution has been computed at $t = 10$ (~ 1.16 days), $t = 20$ (~ 2.3 days), $t = 30$ (~ 3.47 days), $t = 40$ (~ 4.63 days), $t = 50$ (~ 5.79 days) and $t = 60$ (~ 6.94 days): for each time step the results are shown in a single panels row, while the first and second column of panels refer to ϕ52 = 1 × 10–3 and ϕ52 = 2 × 10–3, respectively. We start showing in Fig. 1 the virus dynamics inside the simulated domain, and on going from $t = 10$ to $t = 40$ no features can apparently be observed, because of the damping due to the intensity normalization. At $t = 50$ the growing virus spatial distribution becomes visible as it has invaded almost half of the domain, while at $t = 60$ it has spread over the entire domain. No appreciable differences can be noted depending on the ϕ52 value. As expected, directly correlated to the virus dynamics is that of infected T cells, as shown in Supplementary Fig. S1 online. The dynamical evolution of the T cells is shown in Fig. 2: they progressively invade the domain as a spherical wave propagating from the origin up to $t = 40$; at $t = 50$ the primary front decreases while a secondary one starts to form, well visible at $t = 60$, insensitive to ϕ52 values. Figure 1Snapshots of the virus density. The density is linearly mapped in colour scale between the blue and red colours in the [0,1] × [0,1] square domain. Starting from the top panels row, the variable evolution has been computed at $t = 10$ (~ 1.16 days), $t = 20$ (~ 2.3 days), $t = 30$ (~ 3.47 days), $t = 40$ (~ 4.63 days), $t = 50$ (~ 5.79 days) and $t = 60$ (~ 6.94 days), while the first and second column of panels refer to ϕ52 = 1 × 10–3 and ϕ52 = 2 × 10–3, respectively. All the other parameters are as in Table 1.Figure 2Snapshots of the T cells density. The density is linearly mapped in colour scale between the blue and red colours in the [0,1] × [0,1] square domain. Starting from the top panels row, the variable evolution has been computed at $t = 10$ (~ 1.16 days), $t = 20$ (~ 2.3 days), $t = 30$ (~ 3.47 days), $t = 40$ (~ 4.63 days), $t = 50$ (~ 5.79 days) and $t = 60$ (~ 6.94 days), while the first and second column of panels refer to ϕ52 = 1 × 10–3 and ϕ52 = 2 × 10–3, respectively. All the other parameters are as in Table 1. The M1 density evolution is shown in Supplementary Fig. S2 online, damped and visible only close to the origin due to the intensity normalization, and for both ϕ52 values decreases up to $t = 40$, slightly growing from $t = 50$ on. More pronounced, instead, appears the density variation of the alternatively activated macrophages M2, which dynamical evolution is shown in Supplementary Fig. S3 online: for both ϕ52 values the M2 density grows progressively across the domain starting from the initial cluster at the origin, which close to the origin initially decreases up to $t = 40$, then from $t = 50$ increases again. The IL-6 dynamics, see Supplementary Fig. S4 online, grows without appreciable differences for both ϕ52 values until $t = 40$, while the intensity at the origin remains almost unchanged during the propagation across the domain, then slightly decreases. The IL-10 dynamical evolution shown in Supplementary Fig. S5 online, instead, appears more defined and seems a little faster for the lower ϕ52 value, and exhibits a monotonic growth. The computed densities shown in Figs. 1, 2 and in Supplementary Figs. S1–S5 online, have been integrated in order to obtain, at each time step, the total amount of each species present in the domain. The results are shown in Fig. 3, except for the infected T cells which total quantity is shown in Supplementary Fig. S6 online. In each panel we plot the total amount of the species present in the domain at the simulated time steps, for ϕ52 = 1 × 10–3 (blue dashed curve), and ϕ52 = 2 × 10–3 (red continuous curve). First of all, the plots put in evidence the small differences existing between the quantities of each species present in the domain as a function of the ϕ52 value, sometimes not even graphically resolved. The virus amount present in the domain grows progressively as a function of time, and increases dramatically above $t = 50$, while infected T cells behave very similarly. T cells increases up to $t = 50$, then start decreasing, and very similarly behave also M1 macrophages. IL-6 grows during the time evolution but reaches a maximum at $t = 40$, and then dramatically decreases up to $t = 60.$ Both M2 macrophages and IL-10 behave very similarly to the virus, and unexpectedly assume also very close values. As expected, it can be said that anti-inflammatory contributions should be promoted at low ϕ52 values, and vice versa for pro-inflammatory ones. Figure 3Calculated amount of the simulated species present in the domain at each time step. Points in each curve have been obtained by numerical integration over the spatial domain of the density maps shown in Figs. 1, 2 and Supplementary Figs. S2–S5 online. These results are consistent from a biological point of view, as M1 macrophages induce T cells activation and IL-6 production, while activated M2 macrophages induce IL-10 production. In order to gain more insights on the density spatial distribution, we extract the density profiles for each species along the diagonal line cutting the 2D density maps, hence the domain, from (x1 = 0, y1 = 0) to (x2 = 1, y2 = 1), from now on referred to as the diagonal cutline, i.e., along the line splitting each density plot into two symmetric domains with respect the diagonal line. In Fig. 4 we show the obtained cross-sectional densities for virus, T cells and infected T cells. Each row of panels refers to the simulated species labelled at the beginning of the row, while first and second column of panels refer to ϕ52 = 1 × 10–3 and ϕ52 = 2 × 10–3, respectively. Inside each panel, curves referring to different time step are diversified by colour. The variable ‘r’ on the abscissa represents the distance along the diagonal cutline. The plots referring to virus and to infected T cells are very similar, as they reflect those of Fig. 1 and Supplementary Fig. S1 online, respectively, and show a growing density from $t = 10$ to $t = 60$, quite insensitive to the ϕ52 values. The cross-sectional density for T cells, instead, shows a predominant cells redistribution: in fact, starting from a huge peak located around the origin at $t = 10$, during the time evolution such feature decreases while broadening, and a $t = 60$ a new peak arises located along the diagonal cutline at about $r = 0.9$ (0.9 mm), consistent with the secondary propagation front visible in Fig. 2.Figure 4Cross-sectional densities for virions, T cells and infected T cells. Each curve refers to the density at the pertinent time step obtained along the diagonal cutline, see text for explanation. Top row refers to virions, middle row to T cells and bottom row to infected T cells; first and second panels column refer to ϕ52 = 1 × 10–3 and ϕ52 = 2 × 10–3, respectively. We choose to group the cross-sectional densities for macrophages and interleukins in Fig. 5 and Fig. 6, respectively, where each row of panels refers to the simulated time step, while the first and second column of panels refer to ϕ52 = 1 × 10–3 and ϕ52 = 2 × 10–3, respectively. Moreover, the variable ‘r’ has the same meaning as the previous figure. In Fig. 5, curves referring to the M1 macrophages are plotted with a magenta continuous line, while curves in cyan dashed line refer to M2 phenotype. Instead, in Fig. 6, curves referring to IL-6 are plotted with red dotted line, while those referring to IL-10 are plotted with blue dash-dotted line. The peak in the density of M1 phenotype around $r = 0$ decreases up to $t = 40$, then increases again from $t = 50$, indifferently for both ϕ52 values, see Fig. 5. The M2 density peak centred at $r = 0$ decreases progressively up to $t = 40$ as it widens, while increases again for $t = 50$ and $t = 60$, superimposed on a growing background indicating the penetration of the cells into the domain. Such behaviour for M2 cells seems to be more defined at ϕ52 = 1 × 10–3, as measured by the density peak at $r = 0.$ Concerning interleukins, Fig. 6, it is evident as the IL-6 maximum intensity at $r = 0$ does not change on going from $t = 10$ to $t = 60$ along the diagonal cutline, but a background rises progressively up to $t = 40$, while at $t = 60$ it flattens towards zero. On the contrary, the IL-10 density grows progressively while infiltrating the domain and starting from $t = 40$ does it faster for ϕ52 = 1 × 10–3.Figure 5Cross-sectional densities for M1 and M2 macrophages. Each curve represents the density extracted along the diagonal cutline, see text for explanation: magenta continuous line refers to M1 macrophages, cyan dashed line to M2 macrophages; starting from the top panels row, the variables evolution have been computed at $t = 10$ (~ 1.16 days), $t = 20$ (~ 2.3 days), $t = 30$ (~ 3.47 days), $t = 40$ (~ 4.63 days), $t = 50$ (~ 5.79 days) and $t = 60$ (~ 6.94 days), while the first and second column of panels refer to ϕ52 = 1 × 10–3 and ϕ52 = 2 × 10–3, respectively. All the other parameters are as in Table 1.Figure 6Cross-sectional densities for IL-6 and IL-10. Each curve represents the density extracted along the diagonal cutline, see text for explanation: red dotted line refers to IL-6, blue dash-dotted line to IL-10; starting from the top panels row, the variables evolution have been computed at $t = 10$ (~ 1.16 days), $t = 20$ (~ 2.3 days), $t = 30$ (~ 3.47 days), $t = 40$ (~ 4.63 days), $t = 50$ (~ 5.79 days) and $t = 60$ (~ 6.94 days), while the first and second column of panels refer to ϕ52 = 1 × 10–3 and ϕ52 = 2 × 10–3, respectively. All the other parameters are as in Table 1. According to what reported in Channappanavar et al.3, in patients with fatal SARS and MERS the lower respiratory tract is involved, with experimental evidences of lung infiltration by monocytes/macrophages, and low counts of CD4 T cells. Our model describes such feature adequately. In fact, as can be seen in Fig. 4, during the time evolution the T cells cross-sectional density decreases in the seeding site redistributing in space with low proliferation and equally low infiltration of the simulated domain, except the weak secondary peak. At the same time, M2 macrophages, but not M1 phenotype, for r > 0 progressively infiltrate in the biological domain. Nicholls et al.52 and Gu et al.53 from one side, and Ng et al.54 from the other, reported lung infiltration by macrophages from autopsy samples in patients with fatal SARS and MERS, respectively. In both cases the identification of the macrophages was conducted by means of the CD68 marker, a glycoprotein highly expressed by macrophages, in particular by M2 phenotypes, which characterize an anti-inflammatory and immunosuppressive micro-environment55. In light of these considerations we deduce that, although our model incorporates a chemotaxis term for M1 macrophages, it describes a macrophages dynamics with M2 subtypes infiltrating the domain faster with respect to M1 subtypes. Also, the immunosuppressive and anti-inflammatory microenvironment as determined by the M2 predominance explains the low count of T cells as predicted by the model and reported by experimental findings in SARS and MERS patients52–54. Du et al.26 studied the regulatory effects on macrophages of cannabinoid 2 receptor (CB2R) during incised skin wound healing in mice: they measured the levels of mRNA of M1 and M2 macrophages activated markers, as well the levels of pro- and anti-inflammatory cytokines, IL-6 and IL-10 among others, in mice treated with JWH133, GP1a CB2R agonist and AM630 CB2R antagonist, which were compared to the results obtained on a vehicle group. Focusing on those latter, it can be seen as M1 macrophages increase in content up to three days from the wound, then decrease; a similar behaviour is observed for M2 subtypes, which initially increase at a low rate, and then accelerate thus demonstrating the initial predominance of the M1 activated subtypes. The M2 content peaks 5 days post injury, then slightly decreases, still remaining higher than M1 subtypes. Overall, the content of both macrophages subtypes varies in the same range. Concerning our predictions, as shown in Fig. 3, they are in part in agreement, although delayed, with the findings of Du et al.26, considering their reported experimental error. In fact, the M1 content grows up to 5.79 days ($t = 50$), and then decreases; on the contrary, the content of M2 macrophages increases monotonically, soon overwhelming the M1 one. Concerning the IL-6 and IL-10 content in Du et al.26, they reflect the behaviour of M1 and M2, respectively, and once again, their variation is contained within the same order of magnitude. Also in our simulation IL-6 and IL-10 contents reflect M1 and M2 behaviour, but IL-10 content, compared to that of IL-6, has a variation one order of magnitude larger. Huang et al.24 measured the initial plasma level of, among other, IL-6 and IL-10, in COVID-19 patients from Intensive Care Unit (ICU), non-ICU patients and healthy ones, on admission to hospitalization seven days after the onset of illness. In both ICU and non-ICU patients elevated level of both interleukins, with a predominance of IL-10 in ICU patients, were found. Further, Zhou et al.25 measured the dynamics of several laboratory markers including IL-6 in COVID-19 patients, survivors or with fatal outcome. Their findings, in the 4–7 days temporal range from the illness onset are in qualitative agreement with the predictions of our model, considering the reported experimental error. Curiously, comparing the M2 and IL-10 contents in Fig. 3, they appear to be overlapping within the graphic resolution. The plots refer to the total protein amount at each time step, so we wonder if a similar correspondence can be found in the density spatial distribution during the time evolution. The answer is in Figs. 5 and 6, for M2 and IL-10 cross-sectional densities along the diagonal cutline, respectively. M2 are initially clustered in the site of infection, and during the time evolution their density decreases and broadens while infiltrating the domain, thus indicating an initial depletion at the infection site, always predominating over M1, see Fig. 5. The IL-10 level, instead, is initially lower with respect to IL-6, but from the site of infection it grows continuously while infiltrating the domain, overwhelming the IL-6 density. Then, even if the total amount of both M2 and IL-10 species are strictly comparable at each time step, their spatial distributions are not, therefore indicating different, albeit correlated, dynamics. Globally, the simulations results confirm that the initial stage of infection is characterized by a pro-inflammatory response that during the time evolution dims in favour of the anti-inflammatory reaction. While the inflammatory response is entrusted to M1 and IL-6, the anti-inflammatory reaction instead relies on M2 and IL-10, but during the initial stage of infection it is driven mainly by M2 and confined near the infection site. Longhi et al.27 carried out experiments on influenza A infected mice assessing the role of IL-6 in limiting the activity of Treg cells. In particular, they measured the number of CD4+-T cells in lung, both primary and memory (after re-infection), in wild type and IL-6 deprived mice. It can be observed as the predicted quantity of T cell shown in Fig. 3 well reproduce qualitatively the measurements for memory CD4+-T cells in IL-6 deprived mice reported in Longhi et al.27, for the corresponding time interval. It can be deduced that SARS-CoV-2 infection impairs T cells response as in an IL-6 deprived environment. It seems that our model, accounting for SARS-CoV-2 infection, predicts some resistance to the immune machinery, as indeed the experimental results in Huang et al.24, and in Zhou et al.25 indicate. In fact, in presence of a wound healing triggered by a surgical incision26, the M1 behaviour is consistent with their inflammatory role during the first stage post injury, while M2 behave consistently to their anti-inflammatory and repairing role post inflammation. In our study, in the presence of a severe infection such as that caused by SARS-CoV-2, both M1 and IL-6 drive the inflammatory response, although with some delay with respect to the initial insult. The infection grows progressively in the simulated time interval, apparently unaffected by the immune response: we observe that the inflammatory response weakens, while the anti-inflammatory response is strengthened, precisely when the viral production increases. Moreover, what it seems wrong in the immune machinery is the anti-inflammatory reaction with an excess of M2 activation and IL-10 production, which could eventually lead to a damage of the respiratory tract interested by the infection, i.e., pulmonary fibrosis26. We are aware that the presented model is a simplification of more complex biological mechanisms, but the qualitative agreement existing between our numerical results and clinical observations in severe COVID-19 patients is convincing of a correct schematization. It therefore constitutes a baseline from which to start including models aimed at treatments and possible therapeutic strategies. After the manuscript submission a couple of articles appeared focusing on possible emerging epidemic threats, especially viruses coinfection, and new SARS-CoV-2 variants, indicating cutting edge research topics deserving attention. Haney et al.56 have conducted experiments on the coinfection of human lung cells with IAV and respiratory syncytial virus (RSV), finding the existence of hybrid virus particles (HVP) which are capable to evade anti-IAV neutralizing antibodies, thus defining in general an interaction between respiratory viruses such as SARS-CoV-2. Cao et al.57 have studied the evolution of the Omicron variant of SARS-CoV-2, demonstrating that mutations can evade neutralizing antibody drugs and convalescent plasma, suggesting that herd immunity and vaccine boosters could be inefficient to prevent infection from Omicron variants. 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--- title: Proper use of light environments for mitigating the effects of COVID-19 and other prospective public health emergency lockdowns on sleep quality and fatigue in adolescents authors: - Peijun Wen - Fuyun Tan - Meng Wu - Qijun Cai - Ruiping Xu - Xiaowen Zhang - Yongzhi Wang - Shukun Li - Menglai Lei - Huanqing Chen - Muhammad Saddique Akbar Khan - Qihong Zou - Xiaodong Hu journal: Heliyon year: 2023 pmcid: PMC10027303 doi: 10.1016/j.heliyon.2023.e14627 license: CC BY 4.0 --- # Proper use of light environments for mitigating the effects of COVID-19 and other prospective public health emergency lockdowns on sleep quality and fatigue in adolescents ## Abstract Coronavirus disease 2019 (COVID-19) remains a public health emergency of international concern, and some countries still implement strict regional lockdowns. Further, the upcoming 2023 Asian Games and World University Games will implement a closed-loop management system. Quarantine can harm mental and physical health, to which adolescents are more vulnerable compared with adults. Previous studies indicated that light can affect our psychology and physiology, and adolescents were exposed to the artificial light environment in the evening during the lockdown. Thus, this study aimed to establish and assess appropriate residential light environments to mitigate the effects of lockdowns on sleep quality and fatigue in adolescents. The participants were 66 adolescents (12.15 ± 2.45 years of age) in a closed-loop management environment, who participated in a 28-day (7-day baseline, 21-day light intervention) randomized controlled trial of a light-emitting diode (LED) light intervention. The adolescents were exposed to different correlated color temperature (CCT) LED light environments (2000 K or 8000 K) for 1 h each evening. The results for self-reported daily sleep quality indicated that the low CCT LED light environment significantly improved sleep quality ($p \leq 0.05$), and the blood test results for serum urea and hemoglobin indicated that this environment also significantly reduced fatigue ($p \leq 0.05$) and moderately increased performance, compared to the high CCT LED light environment. These findings can serve as a springboard for further research that aims to develop interventions to reduce the effects of public health emergency lockdowns on mental and physical health in adolescents, and provide a reference for participants in the upcoming Asian Games and World University Games. ## Introduction Coronavirus disease 2019 (COVID-19) remains a public health emergency of international concern, with more than 752 million confirmed cases and over 6.8 million deaths globally (last update: January 28, 2023; World Health Organization data). Some countries have implemented strict precautionary measures to prevent viral transmission and limit contact with COVID-19 patients. Some cities or communities remain locked down, and millions of people live in regions with closed-loop management systems or are required to stay at home. Although these preventative measures are critical to minimize the spread of COVID-19, lockdowns may affect people's mental and physical health, such as by increasing the risk of insomnia, depression, anxiety, fatigue, and eating disorders [[1], [2], [3]]. During COVID-19 lockdowns, people's lifestyles and interactions with others are completely altered [4]. Moreover, fear of infection aggravates anxiety [5]. These negative responses could be inextricably linked to quarantine measures [6]. A meta-analysis of nearly half a million people found that $40.5\%$ of the global population has experienced sleep problems during the COVID-19 pandemic [7]. Another study indicated that Chinese participants experienced a $29.2\%$ reduction in sleep quality while in quarantine [8]. Moreover, compared with adults, adolescents are more vulnerable to the negative effects of COVID-19 lockdowns [9]. There were $62\%$ of children with sleep disorders and $55.6\%$ of adolescents had sleep problems during the lockdowns [10,]. Sleep quality was deemed to be worse [11]. The COVID-19 pandemic and subsequent quarantine measures have had a detrimental impact on the mental and physical health of approximately $80\%$ of adolescents [12]. In addition to the current pandemic, other prospective public health emergencies, such as the Monkeypox outbreak, could also cause large-scale lockdowns. Therefore, the effects of lockdowns on health need to be considered. Exercise is an important activity in daily life and during the lockdown [13,14]; however, previous studies have indicated that COVID-19 lockdowns significantly affect fatigue and exercise performance [[15], [16], [17]], and young athletes have also been affected [18]. The 2021 Tokyo Summer Olympic Games and 2022 Beijing Winter Olympic Games were held using closed-loop management, in which all the participants trained, slept, and lived in isolation to prevent viral transmission. A closed-loop management policy will also be implemented at the upcoming 2023 Hangzhou Asian Games and 2023 Chengdu World University Games (Universidade). The impact of closed-loop management will affect tens of thousands of participants in these games, including athletes, coaches, referees, staff, journalists, and volunteers. Therefore, the purpose of this study was to simulate, establish and assess an appropriate, convenient, and replicable intervention to mitigate the effects of public health emergency lockdowns on sleep quality and fatigue in adolescents. Further, the results could be used as a reference for participants in the upcoming games. Following the discovery of intrinsically photosensitive retinal ganglion cells in the early 21st century, an increasing number of researchers have realized that light has not only visual but also non-visual effects on humans [19]. The light intervention has been demonstrated to be among the most effective and safe physical methods for improving mental and physical health [20,21]. Adolescents are particularly susceptible to the effects of evening light environments [22]. Furthermore, with the development of solid-state lighting sources, light-emitting diode (LED) technology has been widely applied for display and illumination [23]. Nowadays, LEDs are among the most popular residential light sources. Changing LED parameters, such as correlated color temperature (CCT) and wavelength, can create different light environments to achieve the objective of this study which aimed to establish and evaluate the different types of evening light environments to mitigate the effects of COVID-19 lockdown on sleep quality and fatigue in adolescents. Our findings could be expediently applied in adolescents' daily lives to improve their sleep quality and physical health, and has implications for the participants under closed-loop management. In this study, we conducted a randomized controlled trial of different CCT LED light environments with adolescents under 28-day closed-loop management, including a 7-day baseline and 21-day light intervention. In particular, we focused on the effects of light on adolescents’ sleep quality and fatigue. ## Research design This study conducted a randomized controlled trial with pre-test, mid-test and post-test design. This type of research required two independent groups which were set as two different major types of light environments in this study. They were the low CCT light environment and high CCT light environment. The light intervention lasted for 21 days, and the measurements included subjective and objective tests. Besides, in order to mitigate the effects of the COVID-19 lockdown on sleep quality and fatigue in adolescents, and provide a reference for the participants in the upcoming 2023 Asian Games and World University Games, this study recruited the participants from the Guangzhou swim team once they qualified for the requirements. ## Participants Participants in this study included 66 healthy adolescents. Inclusion criteria for the trial were as follows: [1] taking no medication for sleep, circadian rhythm, fatigue, or mental health for three months prior to the study; [2] having no sleep or light intervention experiment experience; and [3] having long-term exercise habits, defined as exercising for at least five days a week for three months prior to the study. The 66 adolescents were randomly assigned (1:1) to either the low or high CCT group. Table 1 shows participant characteristics. No significant differences were observed in gender, age, or body mass index between the two groups ($p \leq 0.05$).Table 1Participants’ demographic characteristics (Mean ± SD).Table 1Low CCT groupHigh CCT groupNumber of peoplen = 33n = 33Gender14 female, 19 male14 female, 19 maleAge (years)12.15 ± 2.3912.15 ± 2.51Tanner stages-no.2101031717466Body mass index18.9 ± 2.3918.7 ± 2.50 All participants remained in a closed-loop management environment for the duration of the study (Fig. 1). They all followed the same daily schedule during the 28-day trial (7-day baseline and 21-day light intervention). The daily schedule in this study restored their activities of daily living. They awoke at 07:00 and attended classes in the morning, participated in the same exercises (e.g., swimming or strength training under coach instruction) in the afternoon, completed their homework in the evening, and received a 1-h light intervention before bedtime (22:00). All participants also had identical diets every day during the trial, and did not drink coffee or tea. They stayed in the same quiet bedrooms, which had curtains to reduce their exposure to sunlight prior to waking, and air conditioners to maintain a room temperature of 26 °C. Aside from the light intervention, all participants were exposed to the same residential fluorescent light sources. Thus, given the participants’ consistent living conditions, variables affecting sleep quality and fatigue were controlled in this study. Fig. 1A map of the location of the experiment. All adolescent participants remained in a closed-loop management environment and followed the same daily schedule during this 28-day trial. They lived in the building indicated with a red pin on the map, which had classrooms, playrooms, bedrooms, a canteen, a fitness room, and an indoor swimming pool. Fig. 1 ## Light environments and light intervention design LEDs are among the most commonly used forms of residential lighting in this century, its advantages are the long lifespan, environmentally safe, higher energy efficiency and design flexibility when compared to traditional lighting solutions (such as the incandescent lamp and fluorescent lamp). Thus, we designed two CCT LEDs as the light sources for the intervention: the low CCT light at 2000 K and the high CCT light at 8000 K (Fig. 2). The primary design philosophy of the low CCT light was that it avoided short-wavelength light, such as blue light, and minimized circadian rhythm stimulation. The low CCT light was calculated by measuring the circadian light (CLA) and circadian stimulus (CS) [24,25]. Therefore, the low CCT light was mixed with innovative green and red LEDs, which not only had low CCT but also reached a high color reading index [80]. For the high CCT light, the blue, green, and red LEDs were mixed to form a classic white LEDs spectrum. These two light environments were considered safe and met the IEC 62471:2006 standard (Table 2).Fig. 2The spectra of the low CCT light and high CCT light. Fig. 2Table 2Parameters of the light environments. Table 2Low CCT lightHigh CCT lightAlpha-opic equivalent daylight (D65) illuminance (lx) S-cone-opic2.26254.06 M-cone-opic117.69209.00 l-cone-opic183.54208.81 Rhodopic50.49216.53 Melanopic27.81220.60 Illuminance200200Light intervention information CCT2000 K8000 K EB0.003 W/m20.221 W/m2 CLA54.74361.92 CS0.0790.353 Duration1 h/day1 h/day Period21 days21 days Two playrooms in the building (red pin on the map, Fig. 1) were refitted to serve as the experimental rooms. Low and high CCT lights were separately installed in the two rooms (Fig. 3). The illuminance of the two experimental rooms was the same, we set that at 200 lx because this illuminance was close to the actual residential light environment in the evening and also sufficient for reading. The participants underwent a 21-day light intervention and were exposed to different CCT light environments for 1 h (20:45–21:45) every night before sleep. Fig. 3A schematic diagram of the light intervention rooms. The desks were 80 cm in height. The arrows indicate where the illuminance was measured. Both the high CCT light room and low CCT light room were maintained at the same illuminance (200 lx).Fig. 3 During the light intervention, participants did their homework, but were not permitted to carry or use electronic devices. This guaranteed that no other illuminants were present in the experimental rooms. Table 2 presents information on the light environments and intervention. In this study, the participants in the different CCT groups were only exposed to their own light condition during the 1-h light intervention in the evening in the whole experiment. They went to sleep after the light intervention. The only difference between the two groups was the light environment before sleep, and the illuminance and other variables were controlled as presented in section 2.2. Therefore, the differences in the measurements were attributed to the different light environments. ## Measurements We investigated how different evening light environments affect sleep quality, fatigue, and hemoglobin in a closed-loop management environment. The experimental results and statistical analyses were based on self-report questionnaires and blood tests (Fig. 4).Fig. 4The experimental process of the 28-day study protocol. Fig. 4 Sleep quality. Participants recorded their daily sleep quality in the morning after waking using a simple self-report questionnaire on sleep quality. The questionnaire provided five options participants could select to report their perceived sleep quality from the previous night: very bad, bad, neither bad nor good, good, and very good. These five options correspond to scores ranging from one to five. Participants began recording their sleep quality seven days prior to the light intervention (baseline), and then continued to record their sleep quality daily during the light intervention (21 days). A simple daily sleep quality record is not only a convenient and accessible measurement for young adolescents (as young as 10 in the present study) but also beneficial for tracking their daily sleep quality tendencies. Serum urea. Urea in the blood is the final product of protein metabolism and among the most critical biological indicators that reflect one's degree of fatigue [26]. In this study, all participants took part in exercise training 2 h a day for three months before and during the light intervention. We hypothesized that different light environments would directly affect sleep quality and indirectly affect fatigue recovery. Participants' serum urea concentrations were measured using a urea kit (UV-GLDH method, Shanghai Kehua Bio-engineering Corporation, China) that could be used for the quantitative determination of urea in participants' serum. The serum urea concentration is proportional to fatigue. Hemoglobin. Hemoglobin is a protein that carries oxygen from the lungs to the tissues of the body, improves blood flow to working muscles, and plays a critical role in endurance sports. Hemoglobin concentrations can indicate aerobic capability and prevent sports anemia [27]. Therefore, the test of hemoglobin concentration is a classical indicator that shows one's potential performance in aerobic exercise. Participants' hemoglobin concentrations were measured using the SULFOLYSER SLS-211 A (SLS-Hb method, Sysmex Corporation, Japan). The hemoglobin concentration is positively correlated with sports performance. Over the course of the study, participants were given four blood tests, each scheduled at 08:00, to measure their serum urea and hemoglobin concentrations. The first blood test before the light intervention was recorded as the baseline measurement. The second and third blood tests were performed during the light intervention, and the fourth blood test was performed the day after the final night of the light intervention. ## Statistical analysis Statistical analyses were conducted using SPSS (IBM Corporation, USA). First, we performed a Gaussian distribution test (Shapiro-Wilk) to analyze all the data from the self-report questionnaires and blood tests. Next, a two-way (group: low CCT and high CCT; time: four blood tests) repeated measures ANOVA was used to analyze the results. Post hoc tests were corrected for multiple comparisons. We particularly focused on the group × time interaction effect and group effect. Statistical significance was set at $p \leq 0.05$, and an effect size (partial eta squared: ηp2) greater than 0.14 were considered a large effect. ## Ethical considerations The Ethics Committee of the Guangzhou Institute of Sport Science evaluated and authorized this study and its trial protocols, which adhered to the principles of the Declaration of Helsinki. The nurses from Longdong Hospital of Tianhe District, Guangzhou helped to draw the participants' blood. All the blood-test procedures qualified for the guideline of the National Health Commission of the People's Republic of China. ## Results Higher self-reported sleep quality scores were considered to indicate better sleep quality (Fig. 5 and Table S1). Neither a significant group × time interaction effect ($$p \leq 0.967$$) nor a group effect ($$p \leq 0.953$$) was observed at baseline. The average sleep quality scores were 3.84 (low CCT group) and 3.83 (high CCT group). However, we observed both a group × time interaction effect ($$p \leq 0.045$$, ηp2 = 0.075) and a group effect ($$p \leq 0.029$$, ηp2 = 0.218) during the light intervention period (21 days). The sleep quality of the low CCT group was superior to that of the high CCT group on the first day of the light intervention, and was invariably better for the low CCT group compared with the high CCT group during the entire 21-day light intervention period. The average sleep quality score of the low CCT group was 4.12, while that of the high CCT group remained at 3.80. Therefore, participants in the low CCT group reported better subjective sleep quality than did those in the high CCT group after the light intervention. Fig. 5The daily sleep quality of the two groups before and during the light intervention (Mean ± SEM). No significant between-group differences were observed for daily sleep quality prior to the light intervention ($$p \leq 0.953$$). However, the low CCT group reported significantly better sleep quality compared with the high CCT group in the post-intervention ($$p \leq 0.029$$, ηp2 = 0.218).Fig. 5 Regarding the blood test results, we observed a group × time interaction effect ($$p \leq 0.011$$, ηp2 = 0.162) for serum urea concentration (Fig. 6 and Table S2). We compared the two groups using post-hoc statistics. Prior to the light intervention, serum urea concentration in the low CCT group was 0.434 mmol/L higher than that in the high CCT group; however, this difference was not significant ($$p \leq 0.062$$). Results for the second ($$p \leq 0.314$$) and third ($$p \leq 0.895$$) blood tests also did not significantly differ between groups. In the final blood test, serum urea concentration in the low CCT group was 0.456 mmol/L lower than that in the high CCT group, and this difference was statistically significant ($$p \leq 0.049$$, ηp2 = 0.172). Participants performed the same exercises daily for three months before and during the light intervention. A lower serum urea concentration indicated better fatigue recovery. Hence, the low CCT light was found to reduce fatigue in adolescents. Fig. 6The serum urea results of the low CCT group and high CCT group (Mean ± SEM). A group × time interaction effect on serum urea concentration was observed ($$p \leq 0.011$$, ηp2 = 0.162). Post-intervention, serum urea concentration decreased in the low CCT group, but increased in the high CCT group. Therefore, the low CCT light was observed to reduce fatigue after performing the same exercise routine. Fig. 6 The hemoglobin results did not indicate a group × time interaction effect or a group effect (Fig. 7 and Table S3). Before the light intervention, hemoglobin concentration was similar between both groups (low CCT group: 133.8 g/L, high CCT group: 133.7 g/L). However, hemoglobin concentration in the low CCT group increased to 135.1 g/L and 135.3 g/L in the second and third blood tests, respectively, and decreased to 133.5 g/L in the final blood test. Hemoglobin concentration in the high CCT group declined after the light intervention and consistently remained lower than that of the low CCT group. A higher hemoglobin concentration demonstrates better aerobic capacity; however, this difference was not statistically significant ($$p \leq 0.449$$).Fig. 7The hemoglobin results of the low CCT group and high CCT group (Mean ± SEM). The low CCT group had a better tendency than the high CCT group, but this difference was not significant ($$p \leq 0.449$$).Fig. 7 ## Discussion The COVID-19 pandemic has greatly affected people's daily lives. Although quarantine can reduce viral transmission, it also affects both mental and physical health. Our study assessed the effects of different light environments on sleep and fatigue in adolescents within a long-term closed-loop management environment. The 28-day trial simulated the most severe quarantine period. Moreover, the upcoming 2023 Asian Games and World University Games will implement closed-loop management systems. Accordingly, the aim of this study was to establish and assess an appropriate residential light environment to reduce the impact of COVID-19 and other prospective public health emergency lockdowns on sleep quality and fatigue in adolescents. The results could also provide a reference for participants in the upcoming Asian Games and World University Games. The results on daily sleep quality revealed that adolescents exposed to the low CCT light environment in the evenings reported better sleep quality than those in the high CCT light environment. Although the adolescents remained in a closed-loop management environment in this study, these sleep quality results were similar to those of previous studies without closed-loop management. Studies on children [28] and adolescents [29] have shown that exposure to a high CCT light environment in the evening could affect sleep quality and reduce sleepiness, compared with a low CCT light environment. Previous studies have further indicated that high CCT light could affect circadian rhythm [[30], [31], [32]] and influence sleep quality [33]. Further, in the current study, of the positive effect of low CCT light on sleep quality in adolescents was significant and prominent from the first day of the light intervention. We observed a fast response to the light environment, and adolescents were sensitive to it in the evening. These findings on adolescents are similar to those of previous studies on adults, the light environments within the 3 h before sleep was highly associated with sleep quality [34]. For example, previous studies reported that only 15 s of high-intensity light might shift the circadian pacemaker [35], while 30 min of dim light (30 lx) exposure at home also suppressed $15\%$ melatonin among women [36], and 1 h of light exposure was shown to be enough to affect the human circadian clock [37,38]. The main difference in the spectra between the two light environments in this trial is the proportion of blue light; the low CCT light did not include blue light; however, the high CCT light did. The spectrum of the high CCT light in this study was a conventional residential white-light LED spectrum. Generally, residential white-light LEDs are created using one of two methods. The first method uses LEDs that include blue light to emit fluorescent powder. The second method combines disparate monochromatic LED lights, such as red, green, and blue light. Therefore, a high CCT light environment must have a blue-light component. Previous studies have indicated that short-wavelength-enriched light can severely suppress melatonin and delay the circadian rhythm phase in the evening [33,[39], [40], [41]]. Adolescents in the low CCT group likely had better sleep quality because the low CCT light included no blue light at all. Although this study's participants were adolescents, the effects of light on the present sample seemed to be the same as those found in adults. In addition, the type of light source appears to be unimportant, because a previous study showed that, compared with a traditional fluorescent lamp (6000 K), low CCT LEDs (2000 K) had a better influence on male adolescents' sleep quality (12.8 ± 1.7) [29]. In this study, the low CCT LEDs (2000 K) also revealed a more positive effect on the sleep quality of both male and female adolescents compared with high CCT LEDs (8000 K). Therefore, we conclude that the crucial factor affecting adolescents' sleep quality is a CCT-light environment, rather than the type of luminaires. In addition to sleep quality, we evaluated the effects of lockdowns on fatigue and performance in adolescents. During exercise, protein is decomposed into amino acids as the energy supply through deamination when glycogen and fat cannot meet the energy demand of exercise, increasing urea levels in the blood. In this study, adolescents exercised daily during the trial, and serum urea levels were evaluated. The results showed that adolescents who were exposed to the low CCT light environment had better fatigue recovery than did those exposed to the high CCT light environment. Previous studies on melatonin suppression could explain this finding. The high CCT light had a blue light component, and its peak wavelength was 455 nm, which can significantly affect circadian rhythm and sleep quality by suppressing melatonin [39,42]. Thus, the low CCT group likely had better fatigue recovery than the high CCT group because the low CCT group had good sleep quality. Further, oxygen consumption and use increase significantly during aerobic exercise, which produces free radicals that can lead to muscle fatigue and damage. Melatonin acts as an antioxidant that neutralizes harmful oxidative radicals [43,44]. In addition, melatonin plays a role in regulating the immune system, which helps improve the body's ability to resist or eliminate potential foreign substances [45]. The previous study showed that female patients who received oral melatonin significantly decreased their levels of fatigue [46]. Therefore, melatonin is not only related to sleep quality but can also affect fatigue at the same time. In this study, the low CCT light had no blue light, but instead only combined green and red LEDs. Therefore, low CCT light can prevent circadian rhythm stimulation, and a low CCT light environment may increase melatonin secretion [47]. In addition, an effective method to reduce fatigue is photobiomodulation (red or near-infrared light, from 660 nm to 950 nm), which is a low-level laser or LED therapy [[48], [49], [50]]. In this study, the peak wavelength of the low CCT light was 634 nm, and serum urea results indicated that the low CCT group had significantly decreased fatigue after the light intervention. This finding was consistent with Batoni et al. who reported that 660 nm and 850 nm LED therapy can increase performance [51]. Another study also found that 630 nm LED therapy improved muscle recovery after exercise [52]. Therefore, in this study, although the intensity of the low CCT light environment was not as strong as that of traditional photobiomodulation, it still helped adolescents experience reduced fatigue after exercise. Hemoglobin is also an important index that reflects physical condition and performance. Hemoglobin concentration significantly affects cardiopulmonary exercise performance, which is related to oxygen. A previous study conducted with adults showed that high CCT light could increase peak O2 uptake and reduce muscle fatigue [53]. In addition, hemoglobin concentration is associated with endurance performance, Zhao et al. found that high CCT light treatment improved the long-distance running performance of basketball players [54], and a similar result of improvement of physical endurance was found in an experiment on mice as well [55]. Although hemoglobin concentrations did not significantly differ between the two groups in this study, we observed that the hemoglobin concentration in the low CCT group was always higher than that in the high CCT group after the light intervention. These findings may be helpful not only for adolescents but also for professional athletes, because the upcoming 2023 Asian Games and World University Games will implement closed-loop management, and some previous studies reported that Olympians were previously negatively affected by closed-loop management [56,57]. Although this study employed a randomized controlled trial to test the effects of different CCT light environments on sleep quality and fatigue in adolescents under closed-loop management, it was not without limitations. Sleep quality was measured using a self-report questionnaire, while the ideal situation would be to monitor sleep quality using actigraphy or polysomnography in a laboratory setting. Further, although we discussed that melatonin and circadian rhythm could be responsible for the differences observed post-intervention, this is only based on the results of previous studies, and it would be preferable if we could examine the effects of melatonin and circadian rhythm in real time. Besides, fewer studies are available on adolescents compared with those on adults, and we hope more researchers will focus on adolescent health in future studies. Finally, our findings provide evidence that proper use of light environments could be a positive treatment to mitigate the effects of COVID-19 lockdown on sleep quality and fatigue in adolescents. Therefore, the organizers and participants of the 2023 Asian Games and World University Games could consider using the dynamic and intelligent lighting system to reduce the effects of closed-loop management. ## Conclusions In this study, we evaluated appropriate residential light environments to mitigate the effects of COVID-19 lockdowns on sleep quality and fatigue in adolescents. Compared with a high CCT light environment (8000 K), exposure to a low CCT light environment (2000 K) in the evening was shown to significantly improved sleep quality and reduced fatigue during 21-day closed-loop management. Our findings could be applied to quarantine and should have important implications for understanding the impact of light on adolescent health. The COVID-19 pandemic remains a public health emergency of international concern. Therefore, increasing the number of studies on specialized interventions for adolescents and other vulnerable populations in the context of a health crisis is critical. ## Author contribution statement Peijun Wen: conceived and designed the experiments; performed the experiments; analyzed and interpreted the data; contributed reagents, materials, analysis tools or data; wrote the paper. Fuyun Tan: performed the experiments; analyzed and interpreted the data. Meng Wu: contributed reagents, materials, analysis tools or data. Qijun Cai: performed the experiments. Ruiping Xu: performed the experiments. Xiaowen Zhang: performed the experiments. Yongzhi Wang: contributed reagents, materials, analysis tools or data. Shukun Li: analyzed and interpreted the data. Menglai Lei: analyzed and interpreted the data. Huanqing Chen: contributed reagents, materials, analysis tools or data. Muhammad Saddique Akbar Khan: contributed reagents, materials, analysis tools or data. Qihong Zou: contributed reagents, materials, analysis tools or data. Xiaodong Hu: conceived and designed the experiments; contributed reagents, materials, analysis tools or data. ## Funding statement This work was supported by $\frac{10.13039}{501100001809}$National Natural Science Foundation of China [12074129, 81871427]; Fundamental Research Funds for the Central Universities [2022ZYGXZR104]; Beijing United Imaging Research Institute of the Intelligent Imaging Foundation [CRIBJZD202101]. ## Data availability statement Data will be made available on request. ## Declaration of interest's statement The authors declare no conflict of interest. ## Additional information Supplementary content related to this article has been published online at [URL]. ## Supplementary data The following is the Supplementary data to this article. Multimedia component 1Multimedia component 1 ## References 1. Wu T.. 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--- title: 'Comparative Tandem Mass Tag-Based Quantitative Proteomics Analysis of Liver Against Chronic Hypoxia: Molecular Insights Into Metabolism in Rats' authors: - Jin Xu - Shenhan Gao - Mingyuan Xin - Wenjie Chen - Kaikun Wang - Wenjing Liu - Xinzong Yan - Sinan Peng - Yanming Ren journal: High Altitude Medicine & Biology year: 2023 pmcid: PMC10027340 doi: 10.1089/ham.2022.0003 license: CC BY 4.0 --- # Comparative Tandem Mass Tag-Based Quantitative Proteomics Analysis of Liver Against Chronic Hypoxia: Molecular Insights Into Metabolism in Rats ## Abstract Xu, Jin, Shenhan Gao, Mingyuan Xin, Wenjie Chen, Kaikun Wang, Wenjing Liu, Xinzong Yan, Sinan Peng, and Yanming Ren. Comparative tandem mass tag-based quantitative proteomics analysis of liver against chronic hypoxia: molecular insights into metabolism in rats. High Alt Med Biol. 24:49–58, 2023. ### Objective: Using a metabolomic approach, we uncovered key regulators in metabolism from tandem mass tag (TMT)-based proteomic analysis in animals chronically exposed to hypoxia. ### Methods: Sixteen Sprague–Dawley rats ($$n = 8$$ per group) were exposed to chronic normoxia or hypoxia (380 mmHg corresponding to a simulated altitude of 5,500 m) for 35 consecutive days. Hypoxia-induced alterations in metabolic pathways were analyzed from TMT-based proteomic analysis, complemented by western blot validation of key regulators. ### Results: We profiled biochemical parameters and serum lipids, found that serum alanine aminotransferase and blood glucose were not significantly changed due to chronic hypoxia. However, serum triglycerides, total cholesterol, high-density lipoprotein, and low-density lipoprotein (LDL) were significantly affected by chronic hypoxia. And the levels of LDL nearly doubled ($p \leq 0.05$) after hypoxia exposure for 35 days. Through Kyoto Encyclopedia of Genes and Genomes classification, we found several metabolic pathways were enriched, including lipid metabolism, cofactor and vitamin metabolism, amino acids metabolism, carbohydrate metabolism, and energy metabolism. To explore the potential functions of proteins in metabolic pathways that become a coordinated shift under chronic hypoxic conditions, Gene Ontology and pathway analysis were carried out on differentially expressed proteins. As the co-expression network shown in Figure, we identified the most significant differentially expressed proteins after chronic hypoxic changes in the livers of rats. Furthermore, we validated the gene expression profiles at the protein level using western blot. Results of western blot were in accordance with our quantitative polymerase chain reaction findings. The levels of fatty acid synthase and aquaporin 1 were significantly downregulated after 35 days and the levels of ATP citrate lyase, 2′-5′-oligoadenylate synthetase 1A, aldehyde dehydrogenase 2, and Ras-related protein Rap-1A were significantly upregulated after 35 days. ### Conclusions: Although this study cannot completely account for all the molecular mechanisms in rats, we provide a good analysis of protein expression and profiling of rats under chronic hypoxia conditions. ## Introduction Generally, hypoxia is triggered by a shortage of oxygen supply, which leads to a global transcriptional and translational change in most tissues in humans (Zhou et al, 2018). Hypoxia is coordinated shift, when oxygen delivery is disrupted or reduced, the organisms will develop numerous adaptive mechanisms to facilitate cells survived in the hypoxic condition (Chen et al, 2020). The liver is involved in most metabolic processes and acts as a key player in the maintenance of metabolic homeostasis (Conotte et al, 2018; Scha Dd E et al, 2017). Although there are many reports exploring hypoxia-related factors, similar to hypoxia-inducible factors in mammals (Chiu et al, 2019), the intracellular mechanisms involved in liver against chronic hypoxia have not been well elucidated or reported. Chronic hypoxia-related diseases usually occur in high-altitude areas. In China, chronic hypoxia sometimes even results in states of serious illness impacting different organs, such as high-altitude pulmonary edema and high-altitude cerebral edema. Hypoxia can also result in chronic intermittent hypoxia-mediated renal sympathetic nerve activation in hypertension and cardiovascular disease (Bhagwani et al, 2020). Even worse, chronic hypoxia in premature infants can result in serious lung injury (Kang et al, 2018). Thus, the diseases triggered by chronic hypoxia could result in illness in a large probability, which might bring more impairment to the patients. It is well known that the liver is a central organ that metabolizes glycogen, lipids, and supplies energy-producing substrates to peripheral tissues to maintain their function in different conditions, including hypoxia and chronic hypoxia. For example, variations in lipid metabolism caused by hypobaric chronic hypoxia has been shown to involve activation of a number of key enzymes and metabolic pathways (Varun et al, 2018). A genome-wide scan in adipocytes revealed that peroxisome proliferator activated receptor alpha and angiopoietin-like protein 4 (ANGPTL4) were significantly associated with expression of several genes encoding proteins that control fatty acid metabolism (González-Muniesa et al, 2011; Yin et al, 2009). Quantitative proteomics is a cutting-edge technique for quantifying the amount of protein present in a sample. Mass spectrometry (MS)/MS-based proteomics has become a popular technique, owing to the ability to accurately quantify >9,000 proteins across multiple samples within a single experiment (Moulder et al, 2018). Proteomics analyses are powerful tools in areas such as the investigation of novel biomarkers, unveiling biological processes, as well as identifying aberrant expression of proteins. These tools are available for researchers to use for tracking changes across thousands of proteins with simple processes. As such, it is possible for us to investigate molecular changes during chronic hypoxia challenges in rats by utilizing these techniques. To investigate the mechanisms at work within the liver under chronic hypoxia conditions, we used animal experiments and tandem mass tag (TMT)-based proteomics to identify aberrant expression of proteins in the liver of chronic hypoxia-treated rats. Finally, by taking all the results together, we generated a speculative regulation network of chronic hypoxia-induced metabolic changes in rats. ## Chronic hypoxia rat models In this study, Sprague–Dawley male rats were randomly allocated into two groups (eight animals per group) containing normal rats (Control) and rats exposed to chronic hypoxia for 35 days (Hypoxia). Rats in the chronic hypoxia group were exposed to a simulated altitude atmosphere with 5,500 m (380 mmHg), implemented by a FLYDWC50-1C low-pressure hypoxic experimental cabin (Guizhou Fenglei Air Ordnance Ltd., Guizhou, China). During breeding and experimental procedures, animals in both groups were housed in the same density per cage at a controlled ambient temperature of 25 ± 2°C and 50 ± $10\%$ relative humidity with a 12-hour light/12-hour dark cycle. Rats were given standard rodent chow and water ad libitum. After overnight fasting, rats were sacrificed under anesthesia with $10\%$ chloral hydrate (0.4 mL/100 g body weight, intraperitoneal). The right lobe of the liver was snap-frozen in liquid nitrogen and then stored at −80°C until analysis. The research protocol was approved by the Human Subject Protection Committee at the Qinghai University School of Medicine (Xining, China) (IACUC Issue No. 2019-ZJ-876). ## RNA preparation and quality control Total RNA was extracted by Trizol using the manufacture's protocol (Invitrogen Co. Ltd.). Quality control of extracted RNA was subsequently conducted using a Thermo Nanodrop 3000. Microarray analysis was only performed on quality RNA with a standard: 1.8 < A260/A280 < 2.2 by Thermo Nanodrop 3000. ## Plasma and hepatic lipid profiles Plasma lipids were measured on days 0 and 35. Plasma low-density lipoprotein (LDL), triglycerides (TG), total cholesterol (CHO), high-density lipoprotein (HDL), and very LDL were measured using the Vitros DT60 II Chemistry System (Johnson & Johnson, Minneapolis, MN). The liver samples were homogenized using a Stir-Pak® (Barrington, IL). Total CHO and TG were extracted in a chloroform-methanol mixture (2:1) and measured with the same Vitros DT60 II Chemistry System. ## Protein extraction Proteins were extracted using a protein extraction kit (Promega, USA), followed by quantification with a bicinchoninic acid (BCA) Protein Assay Kit (Bio-Rad, USA); at least 160 mg of protein was collected per liver sample. Samples were then run on a sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel electrophoresis and Coomassie bright blue staining was used to indicate the location of protein on the gel. Finally, the protein suspension was digested with trypsin (Promega) in NH4HCO3 at 37°C overnight, and the yielded proteins were filtered. Nearly 160 mg of protein was collected. ## TMT labeling of proteins and high pH reverse phase fractionation TMT reagents were used for the labeling of proteins according to the manufacturer's instructions (Thermo Fisher Scientific), samples were collected and kept on ice for liquid chromatography (LC)-MS analysis. ## LC-MS analysis For LC-MS/MS analysis, each sample was injected once for a total of 16 times. The high-performance liquid chromatography liquid phase system was used for phase separation. Proteins were analyzed by the Q Exactive-Plus (QE-Plus) software (Thermo Scientific, Waltham, MA). The resolution of the first-level MS was 65,000 at m/z 200, the first-level Maximum injection time was set to 49 ms, automatic gain control was set to 1E6, and the resolution of the second-level MS was set to 35. ## Bioinformatics analysis R packages and the statistical computing software were used to analyze the bioinformatics data. Proteins were screened with the cutoff ratio fold-change of >1.30 and p-values <0.05. Hierarchical clustering was used to visualize protein level. Gene Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO) enrichment, and protein interaction network analysis were done with R packages (Cran Inc., USA). ## Western blotting analysis Fresh liver tissue from each animal was homogenized in homogenization buffer, followed by centrifugation at 12,000 g for 15 min. The supernatant was removed and protein quantification was performed using a BCA assay. Equal amounts of protein (60 μg) were resolved on SDS-PAGE gels and transferred to a polyvinylidene fluoride membrane. The membrane was blocked using $5\%$ nonfat dry milk in Tris buffered saline with Tween-20 (TBS-T) buffer for 2 hours. The membrane was incubated with primary antibody (Abcam, USA) overnight and washed three times for 5 minutes each with TBS-T, followed by incubation with the secondary antibody (1:3,000; Santa, Inc.) for 1 hour and three additional washes for 5 minutes each with TBS-T. The blots were imaged using a Tiangen chemiluminescence system (Tiangen, China) and glyceraldehyde-3-phosphate dehydrogenase was selected as the internal reference. ## Data analysis and statistics The results were entered into a database and were analyzed using Statistical Product and Service Solutions (SPSS) 21.0 (SPSS, Inc., Chicago, IL). The mean, standard deviation (SD), standard error, and confidence interval were calculated for each parameter. Data were presented as mean ± SD. Student's t-test was applied, when appropriate, to determine the statistical significance of the differences. Pearson's correlations were also performed. The results were considered significant when the p-value was <0.05. ## General workflow and summary of this study and plasma lipid metabolism profiles To explore the influence of chronic hypoxia on lipid metabolism, we first created a chronic hypoxia animal model using Sprague–Dawley male rats according to a previous report. In brief, rats were randomly allocated to two groups (eight animals per group) containing normal rats and rats exposed to chronic hypoxia for 35 days. Our overall workflow is shown in Figure 1A. Next, we profiled biochemical parameters and serum lipids on day 35. Serum alanine aminotransferase (ALT) and blood glucose (GLU) were not significantly changed due to chronic hypoxia. However, serum TG, total CHO, HDL, and LDL were significantly affected by chronic hypoxia (Fig. 1B). Interestingly, the levels of LDL nearly doubled ($p \leq 0.05$) after hypoxia exposure for 35 days. FIG. 1.: General workflow and summary of this study and plasma lipid metabolism profiles. (A) General workflow details of each process are shown. (B) Plasma lipid metabolism profiles including LDL, CHO, HDL, GLU, TG, and ALT levels are shown. Student's t-test, paired tail, p < 0.05. ALT, alanine aminotransferase; CHO, cholesterol; GLU, glucose; GO, Gene Ontology; HDL, high-density lipoprotein; KEGG, Kyoto Encyclopedia of Genes and Genomes; LDL, low-density lipoprotein; MS, mass spectrometry; qPCR, quantitative polymerase chain reaction; TG, triglycerides; TMT, tandem mass tag.* Above all, our lipid profile analysis has demonstrated that chronic hypoxia may have a significant impact on lipid metabolism. ## KEGG pathway enrichment analyses of all differentially expressed proteins (p-value of Fisher's exact test <0.05) Protein was profiled from the experimental groups. KEGG was used to identify canonical pathways. In our survey of existing data, the protein precursors mainly involved the following pathways: metabolic pathways, steroid hormone biosynthesis, retinol metabolism, chemical carcinogenesis, PPAR signaling pathway, and glutathione metabolism (Fig. 2A). Furthermore, through KEGG classification, we found several metabolic pathways were enriched, including lipid metabolism, cofactor and vitamin metabolism, amino acids metabolism, carbohydrate metabolism, and energy metabolism (Fig. 2B). The aforementioned findings support that chronic hypoxia significantly affects metabolic pathways in the livers of rats. FIG. 2.: KEGG pathway enrichment analyses. (A) KEGG analysis of all significantly regulated proteins (p-value of Fisher's exact test <0.05). (B) KEGG classification of all significantly regulated proteins based on total gene numbers (p-value of Fisher's exact test <0.05). ## GO enrichment analysis of all differentially expressed proteins (p-value of Fisher's exact test <0.05) To explore the potential functions of proteins in metabolic pathways that become a coordinated shift under chronic hypoxic conditions, GO and pathway analysis were carried out on differentially expressed proteins. In a search of the cellular component category, we found that the extracellular region part, membrane-enclosed lumen, macromolecular complex, extracellular region, membrane, cell part, and cell junction were the most populated subcategories (Fig. 3A). For molecular function, we found that catalytic activity, binding, transporter activity, molecular function regulation, structural molecule activity, and transporter activity were most significantly enriched (Fig. 3A). FIG. 3.: GO enrichment analysis. (A) GO analysis of all differentially expressed proteins (p-value of Fisher's exact test <0.05). (B) Statistics of GO enrichment based on rich factor (p-value of Fisher's exact test <0.05). For biological processes, we found that cellular process, single-organism process, biological regulation, metabolic process, positive regulation of biological process, response to stimulus, and developmental process were the most abundant subcategories (Fig. 3A). Furthermore, through GO enrichment classification, we found several processes were mainly classified as being metabolic, including carboxylic acid metabolic process, response to other organism, response to external biotic stimulus, response to biotic stimulus, organic acid biosynthesis process, oxidoreductase activity process, and oxygen-containing compound process (Fig. 3B). The aforementioned findings support that chronic hypoxia significantly affects metabolic processes and resulted in activation of various responses in the livers of rats. ## Co-expression network of differentially expressed proteins As the co-expression network shown in Figure 4, we identified the most significant differentially expressed genes after chronic hypoxic changes in the livers of rats. Among them, fatty acid synthase (FASN), ANGPTL4, recombinant sequestosome 1 (SQSTM1), solute carrier family 24 member 1 (SLC24A1), aldehyde dehydrogenase 2 (ALDH2), ATP citrate lyase (ACLY), cytochrome P450 1A1 (CYP1A1), cytochrome P450 1A2, cytochrome P450 4A8, Ras-related protein Rap-1A (RAP1A), palmitoylated membrane protein 6, acyl-CoA synthetase medium-chain family member 2, recombinant human pyruvate kinase L/R, 2′-5′-oligoadenylate synthetase 1 (OAS1), fatty acid binding protein 2, protocadherin 18, S100 calcium binding protein A8, and solute carrier family 4 member A1 formed the core of co-expression network, which were in close relationship with fatty acid metabolism (Table 1). FIG. 4.: Co-expression network of differential expressed proteins are clearly shown; red bubble and green bubble indicated the upregulated and downregulated proteins in acute hypoxia challenged group, respectively (p-value of Fisher's exact test <0.05). TABLE_PLACEHOLDER:Table 1. ## Validation of differentially expressed proteins by real-time quantitative polymerase chain reaction After analyzing the differentially expressed genes with GO, KEGG, and co-expression network, we focused on the fatty acid metabolism, which was the most enriched pathway in response to chronic hypoxia stimulus. As shown in Figure 5A, we validated the gene expression of FASN, ANGPTL4, SLC24A1, ALDH2, CYP1A1, and SQSTM1 with real-time quantitative polymerase chain reaction (qPCR). In Figure 5B, we further validated the gene expression profiles at the protein level using western blot. Results of western blot were in accordance with our qPCR findings. The levels of FASN and aquaporin 1 (AQP1) were significantly downregulated after 35 days and the levels of ACLY, OAS1A, ALDH2, and RAP1A were significantly upregulated after 35 days. FIG. 5.: Validation differentially expressed proteins for control and hypoxia samples by real-time qPCR. (A) Real-time qPCR validation of FASN, ANGPTL4, SLC24A1, ALDH2, CYP1A1, and SQSTM1. GAPDH was used as internal control. Each experiment was conducted in triplicate. Student's t-test, paired tail, p < 0.05. (B) Western blot validation of FASN, ANGPTL4, SLC24A1, ALDH2, CYP1A1, and SQSTM1. GAPDH was used as internal control. Each experiment was conducted in triplicate; the shown figure was a typical replicate of the three experiments.* ## A schematic model of sustained reactions to chronic hypoxia conditions in the livers of rats Based on the bioinformatic analysis and validation data, we constructed a schematic model of the responses that occur in rat livers during hypoxia stimulation. In the graphic view (Fig. 6), we show the network between hypoxia stimulation, metabolic pathways, and the regulatory effects. FIG. 6.: A schematic model of acute reactions in the liver of rats by acute hypoxia. In this figure, we concluded the results and generated the schematic model. For abbreviations and explanations, please see the text. ## Discussion We used animal experiments and TMT-based proteomics analyses to identify the changes to metabolism in response to chronic hypoxia. We found that multiple chronic hypoxia responsive proteins in various biological pathways were activated. Serum ALT and aspartate transaminase have been previously described as markers for hepatocyte injury. In our study, we found that ALT was not significantly changed due to chronic hypoxia. These data were consistent with the results of Kamal et al [2017], which were that a sedentary group compared with an exercise group had no difference in ALT. Under hypoxia ($10\%$ oxygen for 4 weeks), the contents of TG, HDL, and LDL have been previously shown to increase (Sugimoto et al, 2021). Our research also found that serum TG, CHO, HDL, and LDL were higher in chronic hypoxia than in control groups. These findings support the notion that hypoxia does not result in hepatocyte injury; however, it does influence liver lipid metabolism. Another finding was the identification of protein precursors involved the following pathways: metabolic pathways, steroid hormone biosynthesis, retinol metabolism, chemical carcinogenesis, PPAR signaling pathway, glutathione metabolism, and so on. ( Fig. 2A). Furthermore, through KEGG classification, we found several metabolic pathways, including lipid metabolism, cofactor and vitamin metabolism, amino acids metabolism, carbohydrate metabolism, and energy metabolism (Fig. 2B). The aforementioned findings reveal that chronic hypoxia has significant effects on metabolic pathways in the livers of rats. In a study by Sun et al [2021] ($1\%$ oxygen for 24 hours), their results found that hypoxia resulted in differential regulation of 373 messenger RNAs (mRNAs), 334 long noncoding RNAs, 71 circular RNAs, and 33 microRNAs, which contained some of the same genes and pathways identified in our research. We carried out GO and pathway analysis based on of the differential expression of proteins. We found that the seven most populated subcategories in the search of the cellular component category were extracellular region part, membrane-enclosed lumen, macromolecular complex, extracellular region, membrane, cell part, and cell junction, that the six most significantly enriched subcategories for molecular function were catalytic activity, binding, transporter activity, molecular function regulation, structural molecule activity, and transporter activity, and that the eight most abundant subcategories for biological process were cellular process, single-organism process, biological regulation, metabolic process, positive regulation of biological process, response to stimulus, and developmental process. Furthermore, we found several processes are mainly classified as metabolic. The study of Li et al [2021] ($10\%$ oxygen for 48 hours): the unigenes of rats were individually assigned to the following GO categories, respectively: biological process, cellular component, and molecular function. In our study, ACLY, OAS1A, ALDH2, and FASN were upregulated, and AQP1 was downregulated. In the research of Lee et al [2017] ($5\%$ CO2 for 24 hours), hypoxia induced upregulation of FASN mRNA expression. Rutkovskiy et al [2013] found that hypoxia ($0.5\%$ oxygen for 4 hours) in mice reduced AQP1 mRNA expression ($p \leq 0.0001$). Hypoxia, mainly through early mitochondrial function preservation, prevents energetic failure and reactive oxygen species production, improving brain glucose utilization (Sanches et al, 2021). Based on bioinformatic analysis and validation data, we were able to construct a schematic model of the reactions in the livers of rats in response to hypoxia stimulation. In the graphic view, we show that the network overlap between hypoxia stimulation, metabolism pathways, and the regulation effects has important implications for metabolic pathways and regulation under hypoxic conditions. ## Conclusion Although this study cannot completely account for all the molecular mechanisms in rats, we provide information on protein expression and profiling of rats under chronic hypoxia conditions. The observed hypoxia-related changes in the liver proteome of the rats can help to understand hypoxia-related responses. ## Authors' Contributions Conceptualization (lead), formal analysis (lead), and writing—review and editing (equal) by J.X. Writing—original draft (lead) and writing—review and editing (equal) by S.G. Software (lead) and writing—review and editing (equal) by M.X. Methodology (lead) and writing—review and editing (equal) by W.C. Conceptualization (supporting) and writing—review and editing (equal) by K.W. Writing—original draft (supporting) and writing—review and editing (equal) by W.L. Software (supporting) and writing—review and editing (equal) by X.Y. Methodology (supporting) and writing—review and editing (equal) by S.P. Formal analysis (supporting) and writing—review and editing (equal) by Y.R. ## Author Disclosure Statement No competing financial interests exist. ## Funding Information This study was funded by a grant from the Basic Research Projects from Qinghai Science and Technology Department of China (Grant No. 2016-ZJ-924Q), China. ## References 1. Bhagwani AR, Farkas D, Harmon B. Clonally selected primitive endothelial cells promote occlusive pulmonary arteriopathy and severe pulmonary hypertension in rats exposed to chronic hypoxia. Sci Rep (2020) 10 1136. PMID: 31980720 2. Chen PS, Chiu WT, Hsu PL. Pathophysiological implications of hypoxia in human diseases. J Biomed Sci (2020) 27 63. DOI: 10.1186/s12929-020-00658-7 3. Chiu D, Tse P, Law C. Hypoxia regulates the mitochondrial activity of hepatocellular carcinoma cells through HIF/HEY1/PINK1 pathway. Cell Death Dis (2019) 10 934. PMID: 31819034 4. Conotte S, Tassin A, Conotte R. Metabonomic profiling of chronic intermittent hypoxia in a mouse model. Respir Physiol Neurobiol (2018) 256 157-173. PMID: 29522877 5. González-Muniesa P, Oliveira CD, Thompson MP. Fatty acids and hypoxia stimulate the expression and secretion of the adipokine ANGPTL4 (angiopoietin-like protein 4/fasting-induced adipose factor) by human adipocytes. J Nutrigenet Nutrigenomics (2011) 4 146-153. PMID: 21709421 6. Kang NY, Ivanovska J, Tamir-Hostovsky L. Chronic intermittent hypoxia in premature infants: The link between low fat stores, adiponectin receptor signaling and lung injury. Adv Exp Med Biol (2018) 1071 151-157. PMID: 30357746 7. Lee HJ, Jung YH, Choi GE. BNIP3 induction by hypoxia stimulates FASN-dependent free fatty acid production enhancing therapeutic potential of umbilical cord blood-derived human mesenchymal stem cells. Redox Biol (2017) 13 426-443. DOI: 10.1016/j.redox.2017.07.004 8. Li M, Tian X, Li X. Diverse energy metabolism patterns in females in. Sci Total Environ (2021) 783 147130. DOI: 10.1016/j.scitotenv.2021.147130 9. Moulder R, Bhosale SD, Goodlett DR. Analysis of the plasma proteome using iTRAQ and TMT-based Isobaric labeling. Mass Spectrom Rev (2018) 37 583-606. PMID: 29120501 10. Ranjbar K, Nazem F, Sabrinezhad R. Aerobic training and L-arginine supplement attenuates myocardial infarction-induced kidney and liver injury in rats via reduced oxidative stress. Indian Heart J (2018) 70 538-543. PMID: 30170650 11. Rutkovskiy A, Bliksoen M, Hillestad V. Aquaporin-1 in cardiac endothelial cells is downregulated in ischemia, hypoxia and cardioplegia. J Mol Cell Cardiol (2013) 56 22-33. DOI: 10.1016/j.yjmcc.2012.12.002 12. Sanches EF, Santos T, Odorcyk F. Pregnancy swimming prevents early brain mitochondrial dysfunction and causes sex-related long-term neuroprotection following neonatal hypoxia-ischemia in rats. Exp Neurol (2021) 339 113623. PMID: 33529673 13. Scha Dd EE, Tsatsaris C, Swiderska-Syn M. Hypoxia of the growing liver accelerates regeneration. Surgery (2017) 161 666-679. PMID: 27436690 14. Sugimoto K, Yokokawa T, Misaka T. High-fat diet attenuates the improvement of hypoxia-induced pulmonary artery hypertension in mice during reoxygenation. BMC Cardiovasc Disord (2021) 21 331. PMID: 34229630 15. Sun J, Song B, Ban Y. Whole transcriptome analysis of trophoblasts under hypoxia. Placenta (2021) 117 13-20. DOI: 10.1016/j.placenta.2021.10.007 16. Varun C, Singh AA, Kumar BA. Hypobaric hypoxia induced renal damage is mediated by altering redox pathway. PLoS One (2018) 13 e0195701. PMID: 30005088 17. Yin J, Gao Z, He Q. Role of hypoxia in obesity-induced disorders of glucose and lipid metabolism in adipose tissue. Am J Physiol Endocrinol Metab (2009) 296 18. Zhou X, Nian Y, Qiao Y. Hypoxia plays a key role in the pharmacokinetic changes of drugs at high altitude. Curr Drug Metab (2018) 19 960-969. PMID: 29807512
--- title: 'Patient and Provider Recommendations for Improved Telemedicine User Experience in Primary Care: A Multi-Center Qualitative Study' authors: - Saif Khairat - Prabal Chourasia - Kimberly A. Muellers - Katerina Andreadis - Jenny J. Lin - Jessica S. Ancker journal: Telemedicine Reports year: 2023 pmcid: PMC10027343 doi: 10.1089/tmr.2023.0002 license: CC BY 4.0 --- # Patient and Provider Recommendations for Improved Telemedicine User Experience in Primary Care: A Multi-Center Qualitative Study ## Abstract ### Objective: The purpose of this study was to explore telemedicine use and obtain actionable recommendations to improve telemedicine user experience from a diverse group of patients and providers. ### Methods: We interviewed adult patients and primary care providers (PCPs) across three National Patient-Centered Clinical Research Network (PCORnet) sites in New York City, North Carolina, and Florida. Both patients and providers could participate via phone or videoconferencing; patients could complete the interview in English or Spanish. Spanish interviews were conducted by a member of the research team who spoke Spanish fluently. Interviews were audio-recorded, transcribed verbatim, and when necessary, professionally translated. ### Results: We interviewed 21 PCPs and 65 patients between March and October 2021. We found that patients' and providers' perspectives on ways to improve the telemedicine experience focused on three recommendation themes: [1] expectations of care provided via telemedicine, [2] innovations to support usability, and [3] alleviation of physician burden. Key recommendations were related to expectations regarding [1] care provided, for example, adding educational content for the patients, and clarity about long-term payment models; [2] support innovation to improve telemedicine usability, for example, providing patients with remote monitoring devices, integrating in-home testing and nursing evaluation; [3] and reduce physician burden, for example, virtual rooming, reimbursement of time spent outside of the telemedicine encounter. ### Discussion: Primary care patients and providers see merit in telemedicine. However, both groups recommended novel ways to improve the quality of care and user experience. Findings from this article suggest that policymakers would be best served by addressing current gaps in patient digital literacy by creating technical support strategies, and gaps in telemedicine reimbursement to present an equitable form of payment. ## Introduction Telemedicine use has rapidly expanded, and it is now an integral part of health care delivery.1 It has proven to be instrumental in connecting physicians with patients during the COVID-19 pandemic.1 Benefits of telemedicine include increased access to care, reduced waiting and travel time, and providing more options and flexibility for patients.2 For providers, it can reduce crowding in waiting rooms and allow them to care for a wider patient population including those in remote areas. Although telemedicine has been useful in connecting physicians and patients, its sudden large-scale adoption in the face of the COVID-19 pandemic exposed challenges associated with its utilization, which can result in poor user experiences. One barrier is the lack of telemedicine training and education for physicians, health care workers, and patients. A second important barrier involves inequitable access to broadband internet and devices to use it so that telemedicine may worsen health inequities.3 Both these barriers contribute to the user (both patient and provider) experience of telemedicine. User experience is defined as “a person's perceptions and responses that result from the use or anticipated use of a product, system or service. ”4 Positive user experience is critical for the adoption, acceptability, and effectiveness of telemedicine.5,6 User experience is closely associated with patient satisfaction, an important quality-of-care indicator. Multiple studies have identified important factors associated with improved patient satisfaction with telemedicine such as ease of use, low cost, better communication, and decreased travel time.2,7–14 Another study reported that while patients found telemedicine to be less stressful, associated challenges included time lag, video freezing, uncertainty in virtual waiting room, technology problems (some needing transfer to phone), and unclear expectations leading to poor patient satisfaction.15,16 In parallel, studies have shown that provider satisfaction with telemedicine is associated with having administrative support and reliable technology, being able to provide input in its development, ease of use, and adequate reimbursement.17–21 Provider satisfaction is closely linked to provider acceptance, which has been found to be the most important factor determining success of telemedicine.22 Providers have been less satisfied with telemedicine as compared with in-person visits due to perceived reduced doctor-patient communication.23 The perceived ease of use and usefulness of telemedicine services are dominant factors affecting provider satisfaction.24 Despite prior studies analyzing provider and patient user experiences with telemedicine, there is a critical gap around actionable recommendations from end-users to improve the user experience. Improving user experience for both providers and patients in various settings (urban, suburban, rural) and age groups is critical for wider telemedicine acceptance and success. The purpose of this study was to explore telemedicine use and obtain actionable recommendations to improve telemedicine user experience from a diverse group of patients and providers. ## Participants We identified adult patients and primary care providers (PCPs) across three National Patient-Centered Clinical Research Network (PCORnet) sites in New York City (urban), North Carolina (suburban), and Florida (rural). Using a definition adapted from the Medicare specialty designation, we defined adult primary care as practices in the fields of general practice, family practice, ambulatory internal medicine, preventive, and geriatric medicine. Based on a sampling frame of 250 primary care practices and with the help of recruited clinician champions, we recruited participants (i.e., providers and patients) between March and October 2021 through several methods, including emails, patient registries, flyers, clinician referrals, and snowball referrals from participants. The clinician champions did not participate in the interviews. Eligible patients were 18 years, English- or Spanish-speaking, able to participate via telephone or videoconferencing, and had at least one chronic disease diagnosis. Eligible PCPs worked in primary care at one of the recruitment sites. Maximum variation sampling25 was used to sample participants of different ages, races, ethnicities, geographic locations, and levels of technology experience. To ensure diverse representation, the study team developed a screening checklist to ensure eligibility assessments were being conducted uniformly across sites and quotas to avoid over-sampling certain groups. The study protocol was approved by the Biomedical Research Alliance of New York institutional review board. ## Measures Semi-structured interview guides were developed in collaboration with our stakeholder board, which included patients, providers, payers, and information technology experts. Research staff conducted individual interviews asking about participants' experiences with telemedicine during the pandemic. Both patients and providers could participate via phone or videoconferencing; patients could complete the interview in English or Spanish. Spanish interviews were conducted by a member of the research team who spoke Spanish fluently. Interviews were audio-recorded, transcribed verbatim, and when necessary, professionally translated. ## Analysis Coders developed code keys for providers and patient transcripts based on a priori domains from the interview guides and emergent codes. Three researchers (K.A., K.A.M., J.J.L.) coded transcripts independently and met to compare codes and resolve discrepancies. Stakeholder board did not aid in the coding or results interpretation. Data were analyzed using interpretive description,26 an approach previously applied to health care experiences.27 Iterative analysis was conducted in parallel with recruitment, and recruitment concluded when data saturation was achieved.28 Final codes were captured using Dedoose Version 9.0.46 (Los Angeles, CA). For the current study, we retrieved all text with codes related to recommendations for improvement in telemedicine. Two domain expert researchers (S.K., P.C.) independently ranked the novelty of all codes on a 3-point scale (1 = low, 3 = high). Novelty was defined as innovative ideas, based on domain experts assessment, that can improve the telemedicine user experience. Then, the average scores were calculated and those codes with three were included in this analysis, which represented high novelty. The mean was used instead of the median since there were no outliers in the ranking since only two domain experts ranked the codes. Those codes were then categorized into themes based on patient and provider-based reported outcomes from the Benson framework.29 The Benson framework is a comprehensive taxonomy of short generic measures covering both patient-reported and provider-reported outcomes, which enables the categorization of participant responses into mutually exclusive themes. ## Results We interviewed 21 PCPs and 65 patients between March and October 2021. Of the patients, $60\%$ were female and $42\%$ self-identified as White, $25\%$ as Black, $23\%$ as Hispanic, $9\%$ as other, and $1\%$ as Asian. Half were between the ages of 41–65 years, $26\%$ were <40 years, and $22\%$ were >65 years. Two of the interviews were conducted in Spanish. Of the PCPs, $62\%$ were female and $48\%$ self- identified as White, $24\%$ as Asian, $14\%$ as Hispanic, $9\%$ as Black, and $5\%$ as other. The majority were between 41 and 60 years, with $29\%$ <40 years and $14\%$> 60 years (Table 1). Patients and PCPs were recruited uniformly from each of the three sites in New York, Florida, and North Carolina. Among the 21 PCPs, 7 were recruited from New York, 8 from Florida and 6 from North Carolina. Among patients,—were recruited from New York,—from Florida, and—from North Carolina. On average, patient interviews lasted 20–25 min, whereas provider interviews ranged from 30 to 40 min. Table 1. \| Characteristics \| Patients (n = 65) \| Providers (n = 21) \| \| --- \| --- \| --- \| \| Age group, n (%) \| Age group, n (%) \| Age group, n (%) \| \| <25 \| 2 (3) \| — \| \| 25–40 \| 17 (26) \| 6 (29) \| \| 41–60 (65) \| 32 (49) \| 12 (57) \| \| >60 (65) \| 14 (21) \| 3 (14) \| \| Female, n (%) \| 39 (60) \| 13 (62) \| \| Race/Ethnicity, n (%) \| Race/Ethnicity, n (%) \| Race/Ethnicity, n (%) \| \| Black \| 16 (25) \| 2 (10) \| \| White \| 27 (42) \| 10 (48) \| \| Asian \| 1 (1) \| 5 (24) \| \| Hispanic \| 15 (23) \| 3 (14) \| \| Location, n (%) \| Location, n (%) \| Location, n (%) \| \| Florida \| 21 (32) \| 8 (38) \| \| New York \| 24 (37) \| 7 (33) \| \| North Carolina \| 20 (31) \| 6 (29) \| \| Type of practice, n (%) \| Type of practice, n (%) \| Type of practice, n (%) \| \| Academic practice \| — \| 1 (0.5) \| \| FQHC/community \| — \| 4 (19) \| \| Teaching/training practice \| — \| 16 (76) \| We found that patients' and providers' perspectives on ways to improve the telemedicine experience focused on three recommendation themes: [1] expectations of care provided via telemedicine, [2] innovations to support usability, and [3] alleviation of physician burden (Table 2, Table 3). Four patients and five providers contributed to theme 1, six patients and five providers contributed to theme 2, and six providers contributed to theme 3. ## Theme 1: recommendations around the expectations for the care provided via telemedicine (Benson framework: care provided) Four patients and five providers contributed to this theme. One provider recommended educational videos, such as “YouTube videos” (P12) for patients to improve their understanding of the telemedicine visit, especially while waiting for the telemedicine visit to begin. In addition, patients, especially older adults, may be unable to remember all the information discussed with the provider during the telemedicine visit, and with telemedicine visits, they do not receive a printed after-visit summary. One provider offered a solution to mitigate this by reminding patients to write important instructions: “can you have pen and paper ready? Because this is my main thing that I want you to do, number one, number two, number three” (P13). The PCPs also emphasized the importance of managing patient expectations regarding insurance coverage and billable visits since telemedicine visits may not always be covered by insurance. As providers stated, the “patient gets a big bill” (P11) and “then patients are not going to be particularly happy with that” (P14). Uncertainty in telemedicine reimbursement can also hinder long-term investment to build and support a robust telemedicine infrastructure; as one provider noted, “we don't know how long we're going to be able to be reimbursed at the same rate, people are kind of unwilling to invest in that right now” (P15). Clarity about long-term payment models for telemedicine visits from the government and private insurance companies will ensure long-term sustainability and development of innovative telemedicine platforms and improve patient care and health care access. Telemedicine can be a means to assess home safety situations for patients. Providers voiced that educating PCPs of best-practices and integrating more telemedicine functions, beyond audio/video functions, can expand the unique opportunities offered by telemedicine visits compared with routine clinic visits. Patients offered several recommendations to support PCPs: “if the provider said turn your volume down; I'm going to ask you something—are you in a safe place and you can nod your head or shake your head” (P9). Another patient suggested using “chat function [to] gauge people's safety situations” (P10). Patients also expressed interest in staying connected with their PCP via “virtual telemedicine” (P6) while travelling and “group meetings” (P7) with the ability for other family members to join remotely. ## Theme 2: recommendations to support innovations to improve telemedicine usability (Benson framework: innovation) Five providers and six patients contributed to this theme. Several providers recommended that patients be provided with devices (“some sort of universal physical exam technology that everybody could get” or a “kit of easy-to-use devices” [P22]) to facilitate remote monitoring of their chronic conditions. These could be “the tools to track their chronic conditions at home” (P20). Similarly, one patient recommended an affordable vital monitoring kit that “can read the temperature off the screen [and] read your heart rate” (P8) to improve telemedicine visit care. Providing these tools to patients will also enable providers to conduct more comprehensive investigation; as a provider mentioned, with such tools, the “doctor would be more comfortable” (P22). Patients also expressed that integrating in-home testing and nursing evaluation into the telemedicine visit would improve the telemedicine experience, especially for patients with “limited mobility or disabilities” (P1). Patients also saw benefits with an in-home visit by a “nurse to check your vitals, check your lungs at home before your appointment” (P3). One patient recommended the addition of a “narrative interpretation” (P2) of test results to make them easier for patients to understand. During telemedicine visits, ancillary staff may not be available for pre-visit medication reconciliation. This causes providers to spend valuable and limited telemedicine visit time reviewing patients' home medications. One provider suggested using an automated system for virtual rooming to “get everything all set up.… they could even check their medications or put in the chief complaint” (P16). Another important recommendation from providers is integrating telemedicine interfaces with institutional electronic health records (EHRs) to make the interface more user-friendly and reduce click burden. One provider recommended, “as much as [telemedicine] can be integrated into the EHR and again one click” (P18). There is also a need for innovation in designing and creating intuitive “care pathways” (P19) built into visits that can also serve as a visual reminder of “important things without having to think of it every time, like oh, we haven't done inhaler teaching” (P19). On the other hand, patients recommended telemedicine functionality that would allow for “sharing screens more often” (P5), such as lab results (“perhaps they could put the screenshot in my lab results” [P4]) and links for information provided to patients during a visit: “So, if somebody needs a pamphlet for something, they will just have those links accessible” (P5). ## Theme 3: recommendations to alleviate physician burden with telemedicine (individual care) Six providers contributed to this theme. Technology issues can negatively impact the telemedicine user experience and make it more expensive if providers need to provide “tech support” (P17) during the visit. Similarly, inadequately trained care assistants may not be “successful in offering support for how to use the interface” (P21) to the patients. Improving technical training for care assistants for simple issues and adequate technical support for complex issues can improve telemedicine visits and care. Although physical examination is an important part of clinical evaluation, the ability to perform detailed physical examinations is limited in telemedicine. The current technologies used for physical examination are inadequate. This adds to the physician's burden by spending more time on subjective and observational assessments to compensate for physical examination limitations. One provider stated, “I don't think that there's a significant comfort amongst doctors for physical exam technology” (P25). Effective integration of state-of-the-art remote examination technologies can improve providers' trust in and satisfaction with doing telemedicine well. The PCPs do not always get credit for time spent on important patient care activities like reviewing labs, imaging, and updating patients. One provider noted that the ability to set aside time for things like “telehealth billed phone calls to talk over test results” (P26) can allow providers to see fewer patients. Building a system that “gives the provider credit for their time” (P23) spent taking care of the patient outside the clinical encounter can facilitate better focus on quality (over quantity) and can significantly reduce provider burnout. Back-to-back telemedicine visits do not allow PCPs additional time to address patients' complaints. Also, intermittent notifications about the next visit during an “emotionally charged visit” (P24) can distract providers from addressing patients' concerns. One provider suggested that having “more breaks built in between visits” (P24) can help address this issue. ## Discussion This multi-site study investigated the perceptions of patients and providers regarding ways to improve the user experience during telemedicine encounters. We report actionable recommendations from end-users based on their experiences with telemedicine visits during the COVID-19 pandemic. Recommendations were categorized into three main themes [1] expectations for the care provided via telemedicine, [2] innovations to improve telemedicine usability, and [3] alleviation of physician burden in telemedicine. To enhance the quality of care provided, patients suggested rethinking the current telemedicine visit protocol to include domestic violence situations. Recommendations for providers to always ask the patient if they are in a safe place and then, to allow patients to communicate back in verbal or non-verbal cues (head shake, type in chat, etc.) were introduced. Another recommendation was to provide at-home lab options to enable patients to send their results to their provider before their telemedicine visit. At-home kits can help providers better understand the patient's condition and hence, improve their decision-making abilities in the virtual space. The ability for patients to access provider notes and after-visit summary can improve adherence to the care plan. Patients reported that more innovation is still needed to improve telemedicine usability. Patients stated that the ability to screen share during a telemedicine visit can empower patients by allowing providers to share educational resources to help with self-management or lab results to educate patients about their health status. Providers also suggested that integrating the telemedicine platform into the EHR can improve workflow automation and reduce documentation burden. With regards to recommendations for reducing physician burden when using telemedicine, providers suggested integrating breaks in between telemedicine visits to allow providers to recalibrate especially after telemedicine visits where unpleasant news were discussed. Also, providers recommended reimagining how systems could better assess providers' time spent in patient care for reimbursement purposes. For instance, time spent on the phone for team coordination or resolving technical issues are not billable although they are clinically relevant. To ensure meaningful implementation of telemedicine, decision makers and policy makers are encouraged to reconsider how providers' time is accounted for in the reimbursement cycle. It is not enough to bill for only the time spent in the telemedicine encounter without accounting for the supplemental tasks pre- and post-visit. Another recommendation to reduce physician burden was to provide telemedicine usability support such as having ancillary staff call patients before their telemedicine visit to review medications and obtain necessary information. However, care assistants are often not trained to provide technical assistance to patients with limited digital literacy. Several providers indicated that lack of usability support for patients hinders the telemedicine experience and has cost implications due to prolonged visit duration. More ancillary support will allow physicians to utilize their valuable time for clinical evaluation, counseling, and patient support. Previous studies that offered recommendations on telemedicine best-practices were based on expert opinions and mainly focused on implementation, policy, and visit etiquette.30–32 However, they were lacking in direct recommendations from end-users on the optimization of the telemedicine user experience. We report specific recommendations to improve the telemedicine user experience by improving the quality of care provided (i.e., adding educational content for the patients, managing patients' expectations regarding insurance coverage, clarity about long-term payment models, establishment of protocols to assess domestic violence); supporting innovation to improve telemedicine usability (i.e., providing patients with remote monitoring devices, integrating in-home testing and nursing evaluation); and to reduce physician burden (i.e., virtual rooming, more ancillary as well as technical support, reimbursement of time spent outside of the telemedicine encounter). This study has several limitations. Although we worked systematically to identify and recruit diverse patient and provider participants, our patients' perspectives may reflect those of individuals more engaged with the health system. In addition, despite our efforts to recruit Spanish-speaking patient participants, we were only able to conduct two interviews in Spanish and did not include other languages as an option. Thus, we cannot describe the experiences of other patients whose communication might be even more affected in virtual settings. Though our study findings are based on qualitative interviews and might not be generalizable to a population beyond primary care, they provide meaningful insights into patients' and providers' experience and suggestions to improve telemedicine. Of the three sites, only one site recruited telemedicine champions and no information was obtained about the number of sites they represented. It is plausible that recruitment through champions may introduce bias in the sample at that site. Similarly for the other two sites, no data were obtained regarding how many practices were represented. For each provider, we recorded the practice type but not the exact practice that the providers came from as it was not responsive to the research question. ## Conclusion In summary, primary care patients and providers see merit in telemedicine. However, both groups recommended novel ways to improve the quality of care and user experience. Key recommendations were related to expectations regarding [1] care provided, for example, adding educational content for the patients, and clarity about long-term payment models; [2] support innovation to improve telemedicine usability, for example, providing patients with remote monitoring devices, integrating in-home testing and nursing evaluation; [3] and reduce physician burden, for example, virtual rooming, reimbursement of time spent outside of the telemedicine encounter. 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Patients' satisfaction with and preference for telehealth visits. J Gen Intern Med (2016) 31 269-275. DOI: 10.1007/s11606-015-3489-x 13. Levy N, Moynihan V, Nilo A. Addendum to: The mobile insulin titration intervention (MITI) for insulin glargine titration in an urban, low-income population: Randomized controlled trial protocol. JMIR Res Protoc (2015) 4 e138. DOI: 10.2196/resprot.5403 14. Müller KI, Alstadhaug KB, Bekkelund SI. Acceptability, feasibility, and cost of telemedicine for nonacute headaches: A randomized study comparing video and traditional consultations. J Med Internet Res (2016) 18 e140. DOI: 10.2196/jmir.5221 15. Gabrielsson-Järhult F, Kjellström S, Josefsson KA. Telemedicine consultations with physicians in Swedish primary care: A mixed methods study of users' experiences and care patterns. Scand J Prim Health Care (2021) 39 204-213. DOI: 10.1080/02813432.2021.1913904 16. Donaghy E, Atherton H, Hammersley V. 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Demiris G.. Examining health care providers' participation in telemedicine system design and implementation. In: AMIA Annual Symposium proceedings/AMIA Symposium AMIA Symposium 2006. (2006) 906 22. Wade VA, Eliott JA, Hiller JE. Clinician acceptance is the key factor for sustainable telehealth services. Qual Health Res (2014) 24 682-694. DOI: 10.1177/1049732314528809 23. Liu X, Sawada Y, Takizawa T. Doctor-patient communication: A comparison between telemedicine consultation and face-to-face consultation. Intern Med (2007) 46 227-232. DOI: 10.2169/internalmedicine.46.1813 24. Kissi J, Dai B, Dogbe CS. Predictive factors of physicians' satisfaction with telemedicine services acceptance. Health Informatics J (2020) 26 1866-1880. DOI: 10.1177/1460458219892162 25. Coyne IT. Sampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries? J Adv Nurs (1997) 26 623-630. DOI: 10.1046/j.1365-2648.1997.t01-25-00999.x 26. Hunt MR. Strengths and challenges in the use of interpretive description: Reflections arising from a study of the moral experience of health professionals in humanitarian work. Qual Health Res (2009) 19 1284-1292. DOI: 10.1177/1049732309344612 27. Thorne S, Con A, McGuinness L. Health care communication issues in multiple sclerosis: An interpretive description. Qual Health Res (2004) 14 5-22. DOI: 10.1177/1049732303259618 28. Guest G, Namey E, Chen M. A simple method to assess and report thematic saturation in qualitative research. PLoS One (2020) 15 e0232076. DOI: 10.1371/journal.pone.0232076 29. Benson T. Measure what we want: A taxonomy of short generic person-reported outcome and experience measures (PROMs and PREMs). BMJ Open Qual (2020) 9 e000789. DOI: 10.1136/bmjoq-2019-000789 30. Daniel H, Sulmasy LS. Policy recommendations to guide the use of telemedicine in primary care settings: An American College of Physicians position paper. Ann Intern Med (2015) 163 787-789. DOI: 10.7326/m15-0498 31. Khairat S, Pillai M, Edson B. Evaluating the telehealth experience of patients with COVID-19 symptoms: Recommendations on best practices. J Patient Exp (2020) 2374373520952975. DOI: 10.1177/2374373520952975 32. Omboni S, McManus RJ, Bosworth HB. Evidence and recommendations on the use of telemedicine for the management of arterial hypertension. Hypertension (2020) 76 1368-1383. DOI: 10.1161/HYPERTENSIONAHA.120.15873
--- title: 'Diabetes Remote Monitoring Program Implementation: A Mixed Methods Analysis of Delivery Strategies, Barriers and Facilitators' authors: - Elizabeth B. Kirkland - Emily Johnson - Chloe Bays - Justin Marsden - Rebecca Verdin - Dee Ford - Kathryn King - Katherine R. Sterba journal: Telemedicine Reports year: 2023 pmcid: PMC10027345 doi: 10.1089/tmr.2022.0038 license: CC BY 4.0 --- # Diabetes Remote Monitoring Program Implementation: A Mixed Methods Analysis of Delivery Strategies, Barriers and Facilitators ## Abstract ### Background: Remote patient monitoring (RPM) is being increasingly utilized as a type of telemedicine modality to improve access to quality health care, although there are documented challenges with this type of innovation. The goals of this study were to characterize clinic delivery strategies for an RPM program and to examine barriers and facilitators to program implementation in a variety of community clinic settings. ### Methods: Primary data were collected via individual and small group interviews and surveys of clinical staff from South Carolina primary care clinics participating in an RPM program for patients with diabetes mellitus type 2 in 2019. We used a parallel convergent mixed methods study design with six South Carolina primary care outpatient clinics currently participating in a diabetes remote monitoring program. Clinic staff participants completed surveys to define delivery strategies and experiences with the program in a variety of clinical settings. Interviews of clinic staff examined barriers and facilitators to program implementation guided by the Consolidated Framework for Implementation Research (CFIR). Quantitative survey data were summarized via descriptive statistics. Qualitative data from interviews were analyzed in a template analysis approach with primary themes identified and organized by two independent coders and guided by the CFIR. Quantitative and qualitative findings were then synthesized in a final step. ### Results: RPM program delivery strategies varied across clinic, patient population, and program domains, largely affected by staffing, leadership buy-in, resources, patient needs, and inter-site communication. Barriers and facilitators to implementation were linked to similar factors that influenced delivery strategy. ### Discussion: RPM programs were implemented in a variety of different clinic settings with program delivery tailored to fit within each clinic's workflow and meet patients' needs. By addressing the barriers identified in this study with focused training and support strategies, delivery processes can improve implementation of RPM programs and thus benefit patient outcomes in rural and community settings. ## Introduction Remote patient monitoring (RPM) is an increasingly utilized method of telemedicine in which data obtained at the point-of-care are transmitted for remote provider viewing and action.1 A growing number of health care systems currently employ RPM, with large-scale studies demonstrating effectiveness across a number of diseases.2 Diabetes mellitus has been the focus of many RPM interventions, with patients achieving sustained reductions in hemoglobin A1c (HgbA1c) during and after participation.3 The clinical benefit of RPM is maintained across diverse populations after adjusting for common social determinants of health, suggesting that RPM provides an opportunity to improve health equity.4 The American College of Physicians calls on providers and systems to utilize telemedicine to “enhance patient–physician collaborations, improve health outcomes, increase access to care and members of a patient's health care team, and reduce medical costs. ”5 Despite the promise of RPM, barriers to implementation and scalability exist.6 Barriers exist at the levels of the patient, provider, health system, digital infrastructure, and intervention design, and these barriers vary among different populations. For example, one study found that clinics serving low-income patients report greater patient-level RPM barriers, whereas clinics serving middle-income patients report greater system barriers including challenges with program scalability and reach.7 Implementation challenges impede widespread use and risk continued use of small-scale inefficient RPM programs. Community-based practices are especially vulnerable to implementation barriers and yet are uniquely poised to deliver health care to vulnerable populations. Partnerships between academic and community health centers may support RPM implementation. Such partnerships can capitalize on existing relationships between patients and their local primary care home with the resources and specialty staffing of a central RPM site. This type of arrangement has demonstrated success in statewide specialty consultation services.8 RPM intrinsically enables a patient–provider connection that transcends geographic and other barriers. To advance RPM program dissemination, a better understanding of barriers to implementation is needed. We report on implementation experiences from a diabetes RPM program that capitalizes on academic–community partnerships. The objectives of this mixed methods study were to [1] characterize clinic delivery strategies for an RPM program and [2] examine barriers and facilitators to program implementation in underserved and/or low-income community settings. ## Overview We used a parallel convergent mixed methods study design9 with six South Carolina primary care outpatient clinics currently participating in the Technology-Assisted Case Management in Low-income Adults with Type 2 Diabetes (TACM-2) program. These 6 parent clinics comprise 15 individual sites that were actively utilizing the program at time of this study. Practice managers were contacted by email with information about the study and to request participation. This study was approved by the Medical University of South Carolina Institutional Review Board, and a waiver of written informed consent was granted. The Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist guided qualitative data methods and results reporting.10 ## TACM-2 program and recruitment The TACM-2 program was established in 2016 with the goal of improving chronic disease management in South Carolina. TACM-2 is a care delivery program focused on supporting diabetes and hypertension management, although in this study we focus on diabetes only because barriers to implementation are likely to vary by clinical focus. The program is based on the success of a pilot study, which demonstrated improved diabetes control among participants randomized to TACM compared with controls.11 TACM-2 utilizes a cellularly enabled remote monitoring device that transmits home glucose readings to a web-based secure server for provider review. Program organization, oversight, and monitoring is conducted centrally at an academic hospital, whereas local sites maintain responsibility for individual patient care and HgbA1c reporting to central data repository. ## Guiding framework The Consolidated Framework for Implementation Research (CFIR) was used to guide data collection tools and assessment of a comprehensive set of implementation factors.12 This framework was developed based on existing implementation theories to guide a pragmatic approach to understanding implementation barriers, facilitators, and processes and has been widely used in chronic illness management research.13 The CFIR includes five main domains that can influence implementation, including individuals involved (i.e., characteristics and beliefs of those delivering and receiving the program), inner setting factors (i.e., influences within the clinic), outer setting factors (i.e., influences external to the clinic), intervention characteristics (i.e., features of the TACM-2 program itself), and implementation processes (i.e., planning, engaging, executing, evaluating).12 Figure 1 shows the adapted CFIR framework guiding the study. FIG. 1.: Adapted from the Consolidated Framework for Implementation Research.12,13 ## Data collection and measures Basic program enrollment characteristics, including date of program initiation, number of enrolled patients, and mean HgbA1c values, were collected as part of program participation. Additional data were collected via surveys and interviews. For parent clinics with several locations, surveys were sent to all locations and responses were averaged across the sites to maintain anonymity and address missing data. ## Site survey A clinic representative completed an online survey assessing clinic type, staffing structure, and patient characteristics. ## Champion survey Program champions were identified jointly by local clinic staff and academic medical center program staff as the main advocate for the program at the local level. Some criteria considered for champion identification included being a primary contact for the program, having hands-on experience delivering the program, and length of time with program. These champions from each clinic location completed an online survey assessing TACM-2 staffing roles and processes. This survey also assessed perceived barriers to carrying out the program (1 = not a barrier at all to 4 = major barrier) using a 12-item instrument developed for previous implementation studies.14 The percentage of clinics endorsing each barrier as moderate or major was calculated. Finally, we assessed perceptions concerning leadership and implementation culture using two 4-item validated scales (1 = strongly disagree to 5 = strongly agree).15 Due to small sample size and symmetric data, average scores (rather than median) were calculated, with higher scores indicating higher leadership and more positive culture. ## Interviews Individual and small group interviews were conducted with the identified clinic program champion and all available team members responsible for TACM-2 delivery and/or clinic leadership. Interviews were completed in-person or via telephone by two female investigators (E.J. and K.R.S.) with doctoral training in qualitative methods, who did not know participants. Using a semi-structured guide informed by the CFIR, participants described their clinic's patient care priorities, previous care practices, and perceptions related to the TACM-2 program including barriers and facilitators to implementation. Interviews were audio-taped and lasted 25–50 min. Strategies to assure theme saturation included using clinics that varied in program implementation processes to ensure coverage of diverse experiences, using probes to explore responses in depth, and monitoring field notes to track emerging themes.16 ## Data analysis Descriptive statistics were used to summarize quantitative survey data for each clinic. Transcriptions of interviews were analyzed using NVivo software (QSR International, 2020).17 A template analysis approach was used with an initial codebook guided by the CFIR also allowing new codes to be generated directly from the data.17–19 An iterative process was used with two independent coders. The codebook evolved over time by refining code definitions, collapsing a set of codes, and adding several new codes, while maintaining an audit trail of the process. Coders read and reread each transcript, organizing primary themes and resolving discrepancies in group meetings.20,21 Results from each source were summarized and compared to identify similarities and differences. The final analysis step involved synthesis of quantitative and qualitative findings. ## Clinic characteristics Six clinics, with 15 unique practice sites participating in TACM-2, were invited to participate in qualitative and quantitative study elements. Five of six parent clinics completed champion and site surveys. We conducted 10 individual and small group interviews ($$n = 20$$ participants overall; range number of participants 1–8) with each clinic represented ($$n = 6$$). Clinic characteristics and patients varied widely (Table 1). Participants highlighted consistently positive perceptions of leadership (mean score 4.15; standard deviation [SD] 0.49; range 3.5–5) and a positive implementation climate (mean score 4.4; SD 0.55; range 4–5) on champion surveys. Table 1. \| Unnamed: 0 \| Clinic 1 \| Clinic 2 \| Clinic 3 \| Clinic 4 \| Clinic 5 \| Clinic 6 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Clinic characteristics \| Clinic characteristics \| Clinic characteristics \| Clinic characteristics \| Clinic characteristics \| Clinic characteristics \| Clinic characteristics \| \| Type \| Free \| Free \| Free \| FQHC \| FQHC \| Academic medical center \| \| No. of sites \| 1 \| 1 \| 2 \| 5 \| 6 \| 1 \| \| Clinic staffing \| \| \| \| \| \| \| \| Full-time equivalent MD or DO \| <1 \| 0 \| 0 \| Missing \| 16 \| 12 \| \| Full-time equivalent PA or NP \| <1 \| 1 \| 2 \| Missing \| 10 \| 0 \| \| Full-time equivalent RN \| <1 \| 1 \| 0 \| Missing \| 23 \| 2.5 \| \| Pharmacist on-site \| Yes \| No \| Yes \| No \| Yesa \| Yes \| \| Diabetes educator on-site \| Yes \| Yes \| Yes \| No \| Yesa \| Yes \| \| Average number of patients scheduled per day, per one provider (MD, DO, PA, or NP) \| Missing \| ≤16 \| ≤16 \| Missing \| 17 to 22 \| ≤16 \| \| Annual staff turnover rates \| <10% \| <10% \| 10–25% \| —(Missing) \| 26–50%b \| 10–25% \| \| Patient demographics \| Patient demographics \| Patient demographics \| Patient demographics \| Patient demographics \| Patient demographics \| Patient demographics \| \| Total number of patients served by clinic \| 500–1000 \| 2000–3000 \| <500 \| 8000–9000 \| >30,000 \| 5000–6000 \| \| Non-English speaking \| 70% \| 60% \| <5% \| <5% \| <5% \| <5% \| \| Race of patient population \| \| \| \| \| \| \| \| White or Caucasian \| 10% \| 11% \| 15–23% \| 18% \| 61% \| 30% \| \| Black or African American \| 10% \| 12% \| 77–85% \| 74% \| 21% \| 67% \| \| Hispanic or Latinx \| 78% \| 76% \| <1% \| 5% \| 15% \| 1% \| \| Asian, Native Hawaiian, Pacific Islander, American Indian, Alaskan Native \| 2% \| 1% \| 0% \| 2% \| 1% \| 1% \| \| Other \| <1% \| <1% \| <1% \| 1% \| 2% \| 2% \| \| Most common insurance \| None/uninsured or self-pay \| None/uninsured or self-pay \| None/uninsured or self-pay \| Medicaid \| Medicaid \| Medicare, commercial \| ## Program organization and delivery strategies Table 2 provides quantitative and qualitative results associated with key delivery elements: patient enrollment, data submission, and monitoring. Interview participants described using an iterative process to identify the best delivery strategy for TACM-2, largely influenced by clinic staffing, infrastructure, and resources. Interviews and surveys highlighted heterogeneity in program organization and staffing models: some had one dedicated person in charge of all program procedures, whereas others used a team approach with all staff participating. Table 2. \| Unnamed: 0 \| Clinic 1 (n = 3) \| Clinic 2 (n = 1) \| Clinic 3 (n = 3) \| Clinic 4 (n = 2) \| Clinic 5 (n = 8) \| Clinic 6 (n = 3) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Enrollment processes \| Enrollment processes \| Enrollment processes \| Enrollment processes \| Enrollment processes \| Enrollment processes \| Enrollment processes \| \| Date of first enrolled patient \| Spring 2017 \| Summer 2017 \| Spring 2018 \| Summer 2018 \| Spring 2018 \| Fall 2017 \| \| Enrolled patients, na \| 53 \| 15 \| 69 \| 62 \| 508 \| 220 \| \| Mean baseline A1c \| 10.6% (92 mmol/mol) \| 10.7% (93 mmol/mol) \| 10.6% (92 mmol/mol) \| 10.6% (92 mmol/mol) \| 11.1% (98 mmol/mol) \| 10.1% (87 mmol/mol) \| \| Type of staff member performing the following roles \| Type of staff member performing the following roles \| Type of staff member performing the following roles \| Type of staff member performing the following roles \| Type of staff member performing the following roles \| Type of staff member performing the following roles \| Type of staff member performing the following roles \| \| Screening or identifying eligible patients \| RN \| Physician \| CMA or LPN \| Missing \| APP \| Physician \| \| Enrolling patients \| RN \| Support staff \| CMA or LPN \| Missing \| Support staff \| RN \| \| Training patients on device \| RN \| Support staff \| CMA or LPN \| Missing \| Support staff \| RN \| \| Location for enrollment \| Examination room \| Examination room \| Nurses station \| Missing \| Conference room, examination room, nursing supervisor's or CDE's office \| Conference room \| \| \| “Once the patient is diagnosed with diabetes, which has been pretty often, … we explain to them how—what diabetes is. And then we provide, if they qualify, we give them the meter, we show them how to use it. But we also give them general information about healthy eating, the importance of general checkups, the importance of taking medications, lifestyle changes. So, we give them information in their language.” (clinic 2) \| “Once the patient is diagnosed with diabetes, which has been pretty often, … we explain to them how—what diabetes is. And then we provide, if they qualify, we give them the meter, we show them how to use it. But we also give them general information about healthy eating, the importance of general checkups, the importance of taking medications, lifestyle changes. So, we give them information in their language.” (clinic 2) \| “Once the patient is diagnosed with diabetes, which has been pretty often, … we explain to them how—what diabetes is. And then we provide, if they qualify, we give them the meter, we show them how to use it. But we also give them general information about healthy eating, the importance of general checkups, the importance of taking medications, lifestyle changes. So, we give them information in their language.” (clinic 2) \| “Once the patient is diagnosed with diabetes, which has been pretty often, … we explain to them how—what diabetes is. And then we provide, if they qualify, we give them the meter, we show them how to use it. But we also give them general information about healthy eating, the importance of general checkups, the importance of taking medications, lifestyle changes. So, we give them information in their language.” (clinic 2) \| “Once the patient is diagnosed with diabetes, which has been pretty often, … we explain to them how—what diabetes is. And then we provide, if they qualify, we give them the meter, we show them how to use it. But we also give them general information about healthy eating, the importance of general checkups, the importance of taking medications, lifestyle changes. So, we give them information in their language.” (clinic 2) \| “Once the patient is diagnosed with diabetes, which has been pretty often, … we explain to them how—what diabetes is. And then we provide, if they qualify, we give them the meter, we show them how to use it. But we also give them general information about healthy eating, the importance of general checkups, the importance of taking medications, lifestyle changes. So, we give them information in their language.” (clinic 2) \| \| \| “What I've seen here at the office is whenever the patient comes in for their three month or six month diabetic visit, the provider will check to see if they're already checking their blood sugar, if they have insurance that covers that or even if they do or they don't, just the simple fact that we're able to monitor their blood sugar through your system gets some of the providers into the habit of having them go ahead and setting up with a TACM machine before they leave our office.” (clinic 5) \| “What I've seen here at the office is whenever the patient comes in for their three month or six month diabetic visit, the provider will check to see if they're already checking their blood sugar, if they have insurance that covers that or even if they do or they don't, just the simple fact that we're able to monitor their blood sugar through your system gets some of the providers into the habit of having them go ahead and setting up with a TACM machine before they leave our office.” (clinic 5) \| “What I've seen here at the office is whenever the patient comes in for their three month or six month diabetic visit, the provider will check to see if they're already checking their blood sugar, if they have insurance that covers that or even if they do or they don't, just the simple fact that we're able to monitor their blood sugar through your system gets some of the providers into the habit of having them go ahead and setting up with a TACM machine before they leave our office.” (clinic 5) \| “What I've seen here at the office is whenever the patient comes in for their three month or six month diabetic visit, the provider will check to see if they're already checking their blood sugar, if they have insurance that covers that or even if they do or they don't, just the simple fact that we're able to monitor their blood sugar through your system gets some of the providers into the habit of having them go ahead and setting up with a TACM machine before they leave our office.” (clinic 5) \| “What I've seen here at the office is whenever the patient comes in for their three month or six month diabetic visit, the provider will check to see if they're already checking their blood sugar, if they have insurance that covers that or even if they do or they don't, just the simple fact that we're able to monitor their blood sugar through your system gets some of the providers into the habit of having them go ahead and setting up with a TACM machine before they leave our office.” (clinic 5) \| “What I've seen here at the office is whenever the patient comes in for their three month or six month diabetic visit, the provider will check to see if they're already checking their blood sugar, if they have insurance that covers that or even if they do or they don't, just the simple fact that we're able to monitor their blood sugar through your system gets some of the providers into the habit of having them go ahead and setting up with a TACM machine before they leave our office.” (clinic 5) \| \| \| “When we're signing people up we should have a contract that says this is what is expected of you: A1cs [drawn] at 6 months and 12 months, you to be checking your sugar. It's a contract. We're giving you this, these are all the fun things. This is what is expected of you to do it. And to have people to agree to that up front.” (clinic 5) \| “When we're signing people up we should have a contract that says this is what is expected of you: A1cs [drawn] at 6 months and 12 months, you to be checking your sugar. It's a contract. We're giving you this, these are all the fun things. This is what is expected of you to do it. And to have people to agree to that up front.” (clinic 5) \| “When we're signing people up we should have a contract that says this is what is expected of you: A1cs [drawn] at 6 months and 12 months, you to be checking your sugar. It's a contract. We're giving you this, these are all the fun things. This is what is expected of you to do it. And to have people to agree to that up front.” (clinic 5) \| “When we're signing people up we should have a contract that says this is what is expected of you: A1cs [drawn] at 6 months and 12 months, you to be checking your sugar. It's a contract. We're giving you this, these are all the fun things. This is what is expected of you to do it. And to have people to agree to that up front.” (clinic 5) \| “When we're signing people up we should have a contract that says this is what is expected of you: A1cs [drawn] at 6 months and 12 months, you to be checking your sugar. It's a contract. We're giving you this, these are all the fun things. This is what is expected of you to do it. And to have people to agree to that up front.” (clinic 5) \| “When we're signing people up we should have a contract that says this is what is expected of you: A1cs [drawn] at 6 months and 12 months, you to be checking your sugar. It's a contract. We're giving you this, these are all the fun things. This is what is expected of you to do it. And to have people to agree to that up front.” (clinic 5) \| \| \| “We have a person in our clinic that does all the data sorting, he will create the list of patients that have had an A1C in our clinic over eight, and that list will be sent to the resident. They will identify who needs to be called because they will view the patient's chart for any exclusions.” (clinic 6) \| “We have a person in our clinic that does all the data sorting, he will create the list of patients that have had an A1C in our clinic over eight, and that list will be sent to the resident. They will identify who needs to be called because they will view the patient's chart for any exclusions.” (clinic 6) \| “We have a person in our clinic that does all the data sorting, he will create the list of patients that have had an A1C in our clinic over eight, and that list will be sent to the resident. They will identify who needs to be called because they will view the patient's chart for any exclusions.” (clinic 6) \| “We have a person in our clinic that does all the data sorting, he will create the list of patients that have had an A1C in our clinic over eight, and that list will be sent to the resident. They will identify who needs to be called because they will view the patient's chart for any exclusions.” (clinic 6) \| “We have a person in our clinic that does all the data sorting, he will create the list of patients that have had an A1C in our clinic over eight, and that list will be sent to the resident. They will identify who needs to be called because they will view the patient's chart for any exclusions.” (clinic 6) \| “We have a person in our clinic that does all the data sorting, he will create the list of patients that have had an A1C in our clinic over eight, and that list will be sent to the resident. They will identify who needs to be called because they will view the patient's chart for any exclusions.” (clinic 6) \| \| \| “I came up with a system, where we identified all of our diabetic patients through the EMR and then flagged them and reached out to them and then once I saw them, made sure that they had consistent follow up and those sorts of things.” (clinic 1) \| “I came up with a system, where we identified all of our diabetic patients through the EMR and then flagged them and reached out to them and then once I saw them, made sure that they had consistent follow up and those sorts of things.” (clinic 1) \| “I came up with a system, where we identified all of our diabetic patients through the EMR and then flagged them and reached out to them and then once I saw them, made sure that they had consistent follow up and those sorts of things.” (clinic 1) \| “I came up with a system, where we identified all of our diabetic patients through the EMR and then flagged them and reached out to them and then once I saw them, made sure that they had consistent follow up and those sorts of things.” (clinic 1) \| “I came up with a system, where we identified all of our diabetic patients through the EMR and then flagged them and reached out to them and then once I saw them, made sure that they had consistent follow up and those sorts of things.” (clinic 1) \| “I came up with a system, where we identified all of our diabetic patients through the EMR and then flagged them and reached out to them and then once I saw them, made sure that they had consistent follow up and those sorts of things.” (clinic 1) \| \| Data submission processes \| Data submission processes \| Data submission processes \| Data submission processes \| Data submission processes \| Data submission processes \| Data submission processes \| \| On-site laboratory presence \| No \| No \| No \| Yes \| Yes \| Yes \| \| Location for follow-up visits \| Examination room \| Examination room \| Nurses station \| Missing \| Conference room, examination room, nursing supervisor's or CDE's office \| Examination room \| \| 6-Month data submission rates \| 65% \| 50% \| 75% \| 65% \| 72% \| 97% \| \| 12-Month data submission rates \| 55% \| 25% \| 40% \| 23% \| 60% \| 94% \| \| Enrolled patient outcomes \| Enrolled patient outcomes \| Enrolled patient outcomes \| Enrolled patient outcomes \| Enrolled patient outcomes \| Enrolled patient outcomes \| Enrolled patient outcomes \| \| Mean A1c reduction at 6 months \| 1.7% (18 mmol/mol) \| 1.5% (16 mmol/mol) \| 1.8% (19 mmol/mol) \| 1.3% (14 mmol/mol) \| 2.2% (24 mmol/mol) \| 1.5% (16 mmol/mol) \| \| Mean A1c reduction at 6 months \| 1.1% (12 mmol/mol) \| 0.4% (4 mmol/mol) \| 1.5% (16 mmol/mol) \| 0.8% (8 mmol/mol) \| 2.1% (23 mmol/mol) \| 1.3% (15 mmol/mol) \| \| \| “Our nurses … were used to doing the paper. So when they got the internet, they submitted it and then something would be missing in that patient, blood pressure, that patient account number. So I think the paper worked better than the internet.” (clinic 4) \| “Our nurses … were used to doing the paper. So when they got the internet, they submitted it and then something would be missing in that patient, blood pressure, that patient account number. So I think the paper worked better than the internet.” (clinic 4) \| “Our nurses … were used to doing the paper. So when they got the internet, they submitted it and then something would be missing in that patient, blood pressure, that patient account number. So I think the paper worked better than the internet.” (clinic 4) \| “Our nurses … were used to doing the paper. So when they got the internet, they submitted it and then something would be missing in that patient, blood pressure, that patient account number. So I think the paper worked better than the internet.” (clinic 4) \| “Our nurses … were used to doing the paper. So when they got the internet, they submitted it and then something would be missing in that patient, blood pressure, that patient account number. So I think the paper worked better than the internet.” (clinic 4) \| “Our nurses … were used to doing the paper. So when they got the internet, they submitted it and then something would be missing in that patient, blood pressure, that patient account number. So I think the paper worked better than the internet.” (clinic 4) \| \| \| “I don't have any concerns as far as the portal. It's easy to go in there and update any kind of information. It's user friendly. The way we're submitting the data now is so much better than having to fax it. Like I said, the e-mails, the monthly [reminder] e-mails on what patients are needing is very, very helpful.” (clinic 5) \| “I don't have any concerns as far as the portal. It's easy to go in there and update any kind of information. It's user friendly. The way we're submitting the data now is so much better than having to fax it. Like I said, the e-mails, the monthly [reminder] e-mails on what patients are needing is very, very helpful.” (clinic 5) \| “I don't have any concerns as far as the portal. It's easy to go in there and update any kind of information. It's user friendly. The way we're submitting the data now is so much better than having to fax it. Like I said, the e-mails, the monthly [reminder] e-mails on what patients are needing is very, very helpful.” (clinic 5) \| “I don't have any concerns as far as the portal. It's easy to go in there and update any kind of information. It's user friendly. The way we're submitting the data now is so much better than having to fax it. Like I said, the e-mails, the monthly [reminder] e-mails on what patients are needing is very, very helpful.” (clinic 5) \| “I don't have any concerns as far as the portal. It's easy to go in there and update any kind of information. It's user friendly. The way we're submitting the data now is so much better than having to fax it. Like I said, the e-mails, the monthly [reminder] e-mails on what patients are needing is very, very helpful.” (clinic 5) \| “I don't have any concerns as far as the portal. It's easy to go in there and update any kind of information. It's user friendly. The way we're submitting the data now is so much better than having to fax it. Like I said, the e-mails, the monthly [reminder] e-mails on what patients are needing is very, very helpful.” (clinic 5) \| \| \| “As far as following up with patients, when I first started, I think that the TACM program would send over the monthly reports … of who had essentially been lost to follow up, you know, who we didn't have a 6 month or 12 month A1C on, and there was a huge, huge gap, there. And that's [our] clinic's issue. So, we cleaned that up significantly and made sure we had patients following up regularly and getting their labs. So, that was clinic side stuff.” (clinic 1) \| “As far as following up with patients, when I first started, I think that the TACM program would send over the monthly reports … of who had essentially been lost to follow up, you know, who we didn't have a 6 month or 12 month A1C on, and there was a huge, huge gap, there. And that's [our] clinic's issue. So, we cleaned that up significantly and made sure we had patients following up regularly and getting their labs. So, that was clinic side stuff.” (clinic 1) \| “As far as following up with patients, when I first started, I think that the TACM program would send over the monthly reports … of who had essentially been lost to follow up, you know, who we didn't have a 6 month or 12 month A1C on, and there was a huge, huge gap, there. And that's [our] clinic's issue. So, we cleaned that up significantly and made sure we had patients following up regularly and getting their labs. So, that was clinic side stuff.” (clinic 1) \| “As far as following up with patients, when I first started, I think that the TACM program would send over the monthly reports … of who had essentially been lost to follow up, you know, who we didn't have a 6 month or 12 month A1C on, and there was a huge, huge gap, there. And that's [our] clinic's issue. So, we cleaned that up significantly and made sure we had patients following up regularly and getting their labs. So, that was clinic side stuff.” (clinic 1) \| “As far as following up with patients, when I first started, I think that the TACM program would send over the monthly reports … of who had essentially been lost to follow up, you know, who we didn't have a 6 month or 12 month A1C on, and there was a huge, huge gap, there. And that's [our] clinic's issue. So, we cleaned that up significantly and made sure we had patients following up regularly and getting their labs. So, that was clinic side stuff.” (clinic 1) \| “As far as following up with patients, when I first started, I think that the TACM program would send over the monthly reports … of who had essentially been lost to follow up, you know, who we didn't have a 6 month or 12 month A1C on, and there was a huge, huge gap, there. And that's [our] clinic's issue. So, we cleaned that up significantly and made sure we had patients following up regularly and getting their labs. So, that was clinic side stuff.” (clinic 1) \| \| \| “The transportation, the lack of ability to reach [patients]. If they don't have a lot of family or friend support, they tend to be less compliant than most [with follow-up]. And some of them, not a whole lot, don't quite understand. Maybe they're young or they just don't grasp the concept of how well this is going to help them, so there's that barrier as well.” (clinic 5) \| “The transportation, the lack of ability to reach [patients]. If they don't have a lot of family or friend support, they tend to be less compliant than most [with follow-up]. And some of them, not a whole lot, don't quite understand. Maybe they're young or they just don't grasp the concept of how well this is going to help them, so there's that barrier as well.” (clinic 5) \| “The transportation, the lack of ability to reach [patients]. If they don't have a lot of family or friend support, they tend to be less compliant than most [with follow-up]. And some of them, not a whole lot, don't quite understand. Maybe they're young or they just don't grasp the concept of how well this is going to help them, so there's that barrier as well.” (clinic 5) \| “The transportation, the lack of ability to reach [patients]. If they don't have a lot of family or friend support, they tend to be less compliant than most [with follow-up]. And some of them, not a whole lot, don't quite understand. Maybe they're young or they just don't grasp the concept of how well this is going to help them, so there's that barrier as well.” (clinic 5) \| “The transportation, the lack of ability to reach [patients]. If they don't have a lot of family or friend support, they tend to be less compliant than most [with follow-up]. And some of them, not a whole lot, don't quite understand. Maybe they're young or they just don't grasp the concept of how well this is going to help them, so there's that barrier as well.” (clinic 5) \| “The transportation, the lack of ability to reach [patients]. If they don't have a lot of family or friend support, they tend to be less compliant than most [with follow-up]. And some of them, not a whole lot, don't quite understand. Maybe they're young or they just don't grasp the concept of how well this is going to help them, so there's that barrier as well.” (clinic 5) \| \| Patient monitoring processes \| Patient monitoring processes \| Patient monitoring processes \| Patient monitoring processes \| Patient monitoring processes \| Patient monitoring processes \| Patient monitoring processes \| \| Type of staff member performing the following roles \| Type of staff member performing the following roles \| Type of staff member performing the following roles \| Type of staff member performing the following roles \| Type of staff member performing the following roles \| Type of staff member performing the following roles \| Type of staff member performing the following roles \| \| Monitoring blood glucose data \| RN \| Support staff \| APP \| Missing \| APP \| Physician \| \| Calling patients to make adjustments \| CMA or LPN \| Support staff \| APP \| Missing \| Support staff \| Physician \| \| \| “And so my role after receiving the list of patients to go through is to review their postings of their glucose log and their blood pressure log and then review what medications they're on, come up with my own clinical plan, so what I'd like to do in terms of medication adjustments.” (clinic 6) \| “And so my role after receiving the list of patients to go through is to review their postings of their glucose log and their blood pressure log and then review what medications they're on, come up with my own clinical plan, so what I'd like to do in terms of medication adjustments.” (clinic 6) \| “And so my role after receiving the list of patients to go through is to review their postings of their glucose log and their blood pressure log and then review what medications they're on, come up with my own clinical plan, so what I'd like to do in terms of medication adjustments.” (clinic 6) \| “And so my role after receiving the list of patients to go through is to review their postings of their glucose log and their blood pressure log and then review what medications they're on, come up with my own clinical plan, so what I'd like to do in terms of medication adjustments.” (clinic 6) \| “And so my role after receiving the list of patients to go through is to review their postings of their glucose log and their blood pressure log and then review what medications they're on, come up with my own clinical plan, so what I'd like to do in terms of medication adjustments.” (clinic 6) \| “And so my role after receiving the list of patients to go through is to review their postings of their glucose log and their blood pressure log and then review what medications they're on, come up with my own clinical plan, so what I'd like to do in terms of medication adjustments.” (clinic 6) \| \| \| “… once they're in the system, the provider will review their uploads and all their glucose testing that they do throughout the week and then they, once they're coming for their follow up, then they'll look at their readings and see if they've had any spikes or are they getting stable or consistent and educate them accordingly on medication or diet, exercise, and so on.” (clinic 1) \| “… once they're in the system, the provider will review their uploads and all their glucose testing that they do throughout the week and then they, once they're coming for their follow up, then they'll look at their readings and see if they've had any spikes or are they getting stable or consistent and educate them accordingly on medication or diet, exercise, and so on.” (clinic 1) \| “… once they're in the system, the provider will review their uploads and all their glucose testing that they do throughout the week and then they, once they're coming for their follow up, then they'll look at their readings and see if they've had any spikes or are they getting stable or consistent and educate them accordingly on medication or diet, exercise, and so on.” (clinic 1) \| “… once they're in the system, the provider will review their uploads and all their glucose testing that they do throughout the week and then they, once they're coming for their follow up, then they'll look at their readings and see if they've had any spikes or are they getting stable or consistent and educate them accordingly on medication or diet, exercise, and so on.” (clinic 1) \| “… once they're in the system, the provider will review their uploads and all their glucose testing that they do throughout the week and then they, once they're coming for their follow up, then they'll look at their readings and see if they've had any spikes or are they getting stable or consistent and educate them accordingly on medication or diet, exercise, and so on.” (clinic 1) \| “… once they're in the system, the provider will review their uploads and all their glucose testing that they do throughout the week and then they, once they're coming for their follow up, then they'll look at their readings and see if they've had any spikes or are they getting stable or consistent and educate them accordingly on medication or diet, exercise, and so on.” (clinic 1) \| \| \| “At first, it's like, ‘Oh man.’ It is a lot of responsibility and you've got to do it and find a way to manage the patients every two weeks because we would get the reports from MUSC, but the supervisors were finding that … pulling the reports ourselves, we could get it faster and quicker.” (clinic 5) \| “At first, it's like, ‘Oh man.’ It is a lot of responsibility and you've got to do it and find a way to manage the patients every two weeks because we would get the reports from MUSC, but the supervisors were finding that … pulling the reports ourselves, we could get it faster and quicker.” (clinic 5) \| “At first, it's like, ‘Oh man.’ It is a lot of responsibility and you've got to do it and find a way to manage the patients every two weeks because we would get the reports from MUSC, but the supervisors were finding that … pulling the reports ourselves, we could get it faster and quicker.” (clinic 5) \| “At first, it's like, ‘Oh man.’ It is a lot of responsibility and you've got to do it and find a way to manage the patients every two weeks because we would get the reports from MUSC, but the supervisors were finding that … pulling the reports ourselves, we could get it faster and quicker.” (clinic 5) \| “At first, it's like, ‘Oh man.’ It is a lot of responsibility and you've got to do it and find a way to manage the patients every two weeks because we would get the reports from MUSC, but the supervisors were finding that … pulling the reports ourselves, we could get it faster and quicker.” (clinic 5) \| “At first, it's like, ‘Oh man.’ It is a lot of responsibility and you've got to do it and find a way to manage the patients every two weeks because we would get the reports from MUSC, but the supervisors were finding that … pulling the reports ourselves, we could get it faster and quicker.” (clinic 5) \| \| \| “But I do know that we had some major issues. Like, I mean, month delay getting test strips and lancing devices and stuff like that.” (clinic 1) \| “But I do know that we had some major issues. Like, I mean, month delay getting test strips and lancing devices and stuff like that.” (clinic 1) \| “But I do know that we had some major issues. Like, I mean, month delay getting test strips and lancing devices and stuff like that.” (clinic 1) \| “But I do know that we had some major issues. Like, I mean, month delay getting test strips and lancing devices and stuff like that.” (clinic 1) \| “But I do know that we had some major issues. Like, I mean, month delay getting test strips and lancing devices and stuff like that.” (clinic 1) \| “But I do know that we had some major issues. Like, I mean, month delay getting test strips and lancing devices and stuff like that.” (clinic 1) \| \| \| “I mean whatever the patients weren't uploading, we would call them. We would have them bring their machines. If there was any troubleshooting that needed to be done.” (clinic 5) \| “I mean whatever the patients weren't uploading, we would call them. We would have them bring their machines. If there was any troubleshooting that needed to be done.” (clinic 5) \| “I mean whatever the patients weren't uploading, we would call them. We would have them bring their machines. If there was any troubleshooting that needed to be done.” (clinic 5) \| “I mean whatever the patients weren't uploading, we would call them. We would have them bring their machines. If there was any troubleshooting that needed to be done.” (clinic 5) \| “I mean whatever the patients weren't uploading, we would call them. We would have them bring their machines. If there was any troubleshooting that needed to be done.” (clinic 5) \| “I mean whatever the patients weren't uploading, we would call them. We would have them bring their machines. If there was any troubleshooting that needed to be done.” (clinic 5) \| \| \| “Often because these patients are complex, we end up pulling in the in-clinic pharmacist as well to assist with some of this medication change decisions.” (clinic 6) \| “Often because these patients are complex, we end up pulling in the in-clinic pharmacist as well to assist with some of this medication change decisions.” (clinic 6) \| “Often because these patients are complex, we end up pulling in the in-clinic pharmacist as well to assist with some of this medication change decisions.” (clinic 6) \| “Often because these patients are complex, we end up pulling in the in-clinic pharmacist as well to assist with some of this medication change decisions.” (clinic 6) \| “Often because these patients are complex, we end up pulling in the in-clinic pharmacist as well to assist with some of this medication change decisions.” (clinic 6) \| “Often because these patients are complex, we end up pulling in the in-clinic pharmacist as well to assist with some of this medication change decisions.” (clinic 6) \| \| \| “So having the machine where they didn't have to pay for it, but it also uploaded to the cloud where we could monitor them really was a saving grace. It allowed us to keep up with our patients in between visits, see how their blood sugars were running, do any medication changes. It was very helpful for the patients.” (clinic 5) \| “So having the machine where they didn't have to pay for it, but it also uploaded to the cloud where we could monitor them really was a saving grace. It allowed us to keep up with our patients in between visits, see how their blood sugars were running, do any medication changes. It was very helpful for the patients.” (clinic 5) \| “So having the machine where they didn't have to pay for it, but it also uploaded to the cloud where we could monitor them really was a saving grace. It allowed us to keep up with our patients in between visits, see how their blood sugars were running, do any medication changes. It was very helpful for the patients.” (clinic 5) \| “So having the machine where they didn't have to pay for it, but it also uploaded to the cloud where we could monitor them really was a saving grace. It allowed us to keep up with our patients in between visits, see how their blood sugars were running, do any medication changes. It was very helpful for the patients.” (clinic 5) \| “So having the machine where they didn't have to pay for it, but it also uploaded to the cloud where we could monitor them really was a saving grace. It allowed us to keep up with our patients in between visits, see how their blood sugars were running, do any medication changes. It was very helpful for the patients.” (clinic 5) \| “So having the machine where they didn't have to pay for it, but it also uploaded to the cloud where we could monitor them really was a saving grace. It allowed us to keep up with our patients in between visits, see how their blood sugars were running, do any medication changes. It was very helpful for the patients.” (clinic 5) \| \| \| “I think we've seen such a dramatic improvement in the patient care and the quality of the care that we can provide … So, we absolutely are appreciative of this program.” (clinic 1) \| “I think we've seen such a dramatic improvement in the patient care and the quality of the care that we can provide … So, we absolutely are appreciative of this program.” (clinic 1) \| “I think we've seen such a dramatic improvement in the patient care and the quality of the care that we can provide … So, we absolutely are appreciative of this program.” (clinic 1) \| “I think we've seen such a dramatic improvement in the patient care and the quality of the care that we can provide … So, we absolutely are appreciative of this program.” (clinic 1) \| “I think we've seen such a dramatic improvement in the patient care and the quality of the care that we can provide … So, we absolutely are appreciative of this program.” (clinic 1) \| “I think we've seen such a dramatic improvement in the patient care and the quality of the care that we can provide … So, we absolutely are appreciative of this program.” (clinic 1) \| ## Enrollment Procedures included identification of patients with a qualifying HgbA1c via embedded electronic medical record screening or in-office laboratory and/or chart review. Following identification, consenting patients were typically enrolled and provided education on the TACM-2 device the same day. Enrollment paperwork and device training took ∼30 min with variability based on patient need (e.g., clinical, translation, or education needs; Table 2). A variety of clinic staff members participated in enrollment processes, with nursing and support staff most commonly enrolling and training patients. Staff turnover varied among clinics and impacted training needs for enrollment. ## Data submission TACM-2 data submission processes included the submission of enrolled patient HgbA1c data at baseline, 6 and 12 months through a web-based portal. Some staff voiced the convenience of this system, whereas others preferred a fax system. Several mentioned the benefits of receiving reminders from the program. Data submission was challenged by lack of on-site laboratory services and patient failure to follow-up. ## Patient monitoring Clinic TACM-2 patient monitoring processes included managing supply needs, review of program-generated reports flagging patients with high blood sugars, direct blood sugar review in the portal, calling patients not regularly monitoring, and using data to guide medication changes. As with other TACM-2 processes, clinics appeared to have varied staffing models and resources (e.g., certified diabetes educators and/or pharmacists) to support patient monitoring. Some clinics only retrieved data to use as ancillary data during office appointments, whereas other clinics used data in real time or between visits to support additional follow-up. While some clinics were highly satisfied with program communication and receipt of supplies and data to monitor patients, others complained about delays. The majority of clinics reported that the program improved quality of care by providing increased communication and monitoring between the clinics and patients. ## Implementation barriers and facilitators Surveys and interviews identified barriers and facilitators to the implementation of the TACM-2 program. Surveys highlighted that few barriers were perceived to carrying out the program. Specifically, financial resources, staff commitment, evidence about the program's value, leadership, space, communication within the clinic and with central program staff, and staff training were unanimously considered only a minor barrier or not a barrier at all ($$n = 5$$). The following factors were considered a major or moderate barrier in only one clinic each: having designated staff to coordinate, other issues being higher priority and information technology. The top barrier was time, endorsed by three of five ($60\%$) of clinics. Interviews identified unique themes representing barriers and facilitators to implementation (Table 3). Table 3. \| Theme \| Definition \| Exemplary Quotations \| \| --- \| --- \| --- \| \| Intervention characteristics \| Intervention characteristics \| Intervention characteristics \| \| Relative advantage \| Stakeholders' perceptions of the advantage of implementing the program vs. an alternate solution or usual care. \| “I can say very generally that meter supplies are expensive. So, that's certainly a barrier for patients as far as glucose monitoring and frequency … certainly, the program helped with that, keeping on top of that.” (clinic 1)“It creates an atmosphere where we don't have to worry about patients bringing in a log of blood sugars, or trying to troubleshoot a glucometer. We can just go online and download the data that's available.” (clinic 3) \| \| Complexity \| Perceived difficulty/ease of implementation, reflected by duration, scope, disruptiveness, and number of steps. \| “It was very simple. We loved the use of technology. Like I said, it made our lives much easier.” (clinic 1)“You know troubleshooting those machines is difficult. So having somebody else reach out to them when there was technical problems, that was very helpful.” (clinic 5) \| \| Outer setting \| Outer setting \| Outer setting \| \| Patient demand/needs \| The extent to which patient demand and service needs exist; clinic awareness and prioritization of patient needs and barriers to meeting those needs. \| “We, as you know, as an FQHC, we are under water all the time, trying to provide services for our patients and resources are limited, so when we do have opportunities, we tend to only go for the opportunities that will lead to the biggest impacts for our patients, which is why we're doing this program. So that is one of our priorities … our staff, they wear so many hats as it is and so this is just another thing, but the need is greater than the pain of it.” (clinic 5)“So, we have a fairly complex group of patients that are medically complex that in turn are also fairly socially complex. I think we have a pretty high percentage of Medicaid population and a population that is a fairly high use of medical resources due to their medical and social complexity.” (clinic 6)“But patients come here because they cannot afford sometimes to even pay for medications, so they couldn't afford to buy a glucometer. So, a lot of them will stay uncontrolled and they will just come here and find out how bad their blood sugar was. Even, you know, only when they would come here.” (clinic 2)“Overall, when the patient comes in, especially because they come from low poverty as well as their educational background, they receive it very happily. They are very grateful in getting the supplies that they need free of cost, because it's such a burden on them. And if they have to choose whether I buy a monitor or I supply something else for the family, then they're not going to purchase the monitor or their medications or go see a doctor, for that matter.” (clinic 1) \| \| External policy/guidelines and incentives \| External strategies to spread and sustain programs, including policy and regulations, external mandates, and guidelines. \| “The quality improvement initiatives often come from any sort of grant funds. It is a health clinic, mainly run on donations and any sort of grants. They're going on all the time. So, the South Carolina Free Health Clinic Association … they would essentially put forth quality measures.” (clinic 1)“Because the fact that we are with the South Carolina Free Clinic Association. And we do have to monitor blood pressures and A1Cs, like consistently reporting about it. So, this device has been so helpful for us to pull those numbers all together. So now that we can work with your portal, it is so much more helpful.” (clinic 3) \| \| Program–clinic partnerships \| Telemedicine program operational style, partnership-building strategies, and interactions around training and support provision for program implementation. \| “I think they really did all the hard work, the footwork, they had the paperwork, any questions we had, they got back to us. We did at one time have a lot of hard time with the equipment. They were able to troubleshoot and send us information to help us troubleshoot to get the meters running and connecting the way they should have. They were just very supportive. I don't know that I would have asked that they did more.” (clinic 5)“No challenges. The only thing I did see is that it seemed like we—once we got the program, everything fell on us. We had to monitor the patients. We need to contact the patient. And that became a barrier because I thought for me learning that they would come out and help more, but they didn't.” (clinic 4)“But it would be nice if locally if there's somebody else around here using the [program] like us, maybe to help staff stay in compliance themselves, maybe if they had another person in this area they can talk to, and go sit down with, or they can come over here, and they can network more and see how successful their clinic is, versus ours. And that would be wonderful to have that kind of support, as well.” (clinic 3)“So, again, that stuff might have been communicated, but it just feels like the support drops off for patients who are out of that 12 month window. Which is fair … but yeah, we were trying to make arrangements to continue access for our patients who have gotten used to something. I think overall if we could have in person meetings …, quarterly meetings, that would be great.” (clinic 1) \| \| Inner setting \| Inner setting \| Inner setting \| \| Networks and communication \| The nature and quality of webs of social networks and formal and informal communications between leaders, nurses, physicians, and staff within an organization. \| “It's every nurse, every nurse, all hands on deck, running the reports, giving them to the doctors, calling the patients. So it's a lengthy process, but we had staff wanting to help the patients, for sure. Nobody was like, ‘Oh no.’ It is tedious and it's time consuming for the staff, but we do what we have to do to make it work.” (clinic 5)“You just have to take on a little bit more. But once you get it in and you get it structured, it's pretty much easy. But just to get a staff to buy in sometimes has been very difficult.” (clinic 3)“[The Coordinator and Nurse] work closely together to make sure I'm available when the patient's going to come …. we also are very closely connected—he has to know how many devices I have in stock before he can call people. So we have to kind of go back and forth.” (clinic 6) \| \| Compatibility \| The degree of tangible fit between the intervention and staff norms, values, and perceived risks and needs, and how the intervention fits with existing workflows and systems. \| “So initially, three years ago, when [program staff] came in to explain the process to us, initially, we were really not confident that our patients would be able to do it because of the low literacy level, and explaining to them the Internet—because most of our patients, they don't have a phone line or they don't have the computer, they're not very computer savvy.” (clinic 1)“Well, the program itself, TACM is not the only thing we are doing. And when we have to work in somebody else's system it's like getting out of our system and working in someone else's system. Which tends to be, a lot more work.” (clinic 5)“It—well, when I first started TACM, I felt like it would have fit into our busy clinic, because it helped with the patients who were greater than … A1Cs greater than eight. But from being in the program, I realized that it's kind of put like a barrier on our program because those patients are not able to get the measures over their internet because they don't have Wi-Fi.” (clinic 4)“I think it fits well. The vision of our clinic; the vision and mission is to improve the life of individuals in [our county]. And being that we serve patients who are uninsured, a program that provides the means for them to take control and monitor their diabetes status at no cost to them aligns perfectly with the vision, and values, and the work flow of the clinic.” (clinic 3) \| \| Leadership \| Commitment, involvement, and accountability of leaders and managers with implementation; presence of site champion. \| “As far as clinic level leaders, I was the only primary care provider, so that was kind of it. And then [the administrator] is the head of the clinic, so she was pretty removed for the process, but certainly supportive.” (clinic 1)“We've had the CEO supporting the program from the very beginning, including our CMO, they've always been supportive … Our CEO has been involved with it. Our pharmacy's been involved with it. It's just kind of embedded in us now all the way around at every site in the state.” (clinic 5)“Our [Center Director] is our interim director. So he's our direct boss and then the other people working with us on this are [two clinician leaders]. And those three people equally, they are on all of our calls, our weekly staff meetings. They've all gone with us on site visits. They've been fantastic.” (clinic 6) \| \| Resources \| The level of resources dedicated for implementation and ongoing operations, including money, training, education, physical space, staffing, and time. \| “Yes. The biggest, biggest, biggest challenge is the diabetes educator, you know? So, we tried several different options. First, we had a volunteer diabetes educator, and she was great—but again, because of her limited communication ability [in Spanish], she got a little frustrated because, again, the patients would not understand. And she did classes and she tried to do all those things, but I think it was just a little overwhelming for her, too.” (clinic 1)“In brief, I know from the clinician side, we are very fortunate to have a direct resource in an in-clinic pharmacist on a daily basis who is also heavily involved in the TACM programs and will alternate with the providers seeing these patients or calling these patients. And so they're an in-house resource that kind of is within our work flows for making changes or running things by—from a clinician side, that's extremely helpful.” (clinic 6)“I feel like we have the staff and our board who were willing to make it work and make it happen regardless of what we had to do. As far as space goes, I feel like each supervisor of the nursing staff, they all had space in their offices to keep the supplies put up and the machines and those sorts of things. Our biggest challenge was our manpower, but it didn't mean that we didn't want to help.” (clinic 5)“And just getting one [staff] person to get excited about it and do it, and then something happens. That person has to retrain, retrain. And I want to say we retrained three—this will probably be the fourth—this will be the third person on the device that has to be trained. That's been our biggest challenge. Just trying to get somebody to stay, just to get that program running.” (clinic 3) \| ## Relative advantage (intervention characteristics) The degree to which participants perceived that the TACM-2 program was an advantage over previous care practices and how these beliefs impacted implementation was mixed. Two free clinics specifically described that the program offered an advantage, as it provided glucose monitoring resources and supplies at no cost, while also offering convenience of tracking patient data from home. However, one clinic perceived that the program was not advantageous to patients based on a misunderstanding of the program's required communication platform (internet vs. cellular access). ## Complexity (intervention characteristics) Limited feedback was provided regarding the complexity of the TACM-2 program and how this impacted implementation. One free clinic and one Federally Qualified Health Center (FQHC) described the TACM-2 program as simple, user-friendly, and easy to implement; yet, staff from a different clinic reported difficulties in troubleshooting the devices. Another FQHC site commented on the value of patient-directed technical support. ## Patient demand/needs (outer setting) Patient demand and clinic commitment to address uncontrolled diabetes represented key drivers of implementation. All clinics reported high demand for a program such as TACM-2. Due to high rates of diabetes, lack of insurance, and poverty in the rural and underserved communities, patients experience barriers to blood glucose monitoring that TACM-2 addresses, including expense of testing supplies and medications. One FQHC clinic considered the pros and cons of investing time and resources into programs, ultimately committing to TACM-2 due to high patient demand and promising benefits. In parallel to high patient demand, five clinics perceived that patients were grateful for TACM-2 as it supports diabetes management and provides supplies not otherwise accessible. Participants from these five clinics observed that through the program, patients learned to take ownership of their diabetes and how to identify abnormal glucose levels. Over time, patients valued being able to see their HgbA1c levels decrease. Although TACM-2 was perceived to meet patient demand and improve patient satisfaction, participants within all clinics still noted implementation barriers as some patients lacked transportation for enrollment and follow-up visits. Low educational attainment, low literacy levels, and language barriers were described as challenges to device training. In addition, some clinics perceived that access to technology (cell phones, internet access, comfort with technology) represented communication barriers that impacted program feasibility, representing a true digital divide. ## External policy/guidelines (outer setting) Several external factors, including diabetes management and reporting guidelines, were considered influential to TACM-2 delivery. Various guidelines were followed for quality initiatives and care goals, and two free clinics directly described how the program facilitated adherence to quality initiatives (e.g., HgbA1c levels). The TACM-2 portal for data monitoring and reporting allowed clinics to efficiently aggregate data for reporting. ## Clinic–program partnerships (outer setting) There were mixed perspectives about partnerships between clinics and the academic medical center and their influence on program implementation. Five clinics reported timely and effective communication with the program during early implementation and start-up processes. These clinics appreciated the detailed easy-to-follow setup steps as well as ongoing support for troubleshooting, technical assistance, and reporting. Challenges establishing partnerships were defined by one of the clinics, typically during program setup and abated by identification of a clinic champion and clear delineation of clinic responsibilities. Suggestions to improve program–clinic partnerships included quarterly in-person training and monitoring, connections to other clinics implementing TACM-2 for networking, and more support and resources as patients' transition off the 12-month program. ## Networks and communications (inner setting) Teamwork and communication within the clinic were viewed as important to program delivery, but clinics varied in the ways they assembled teams around the program. Respondents commonly reported that it took time to establish implementation practices after trialing varied roles and strategies. Regardless of the approach taken by teams, all clinics highlighted the importance of communication for timely completion of program steps. Some successful clinic strategies described for improving communication and teamwork included consideration of varied models to identify best practices and holding routine team meetings to discuss program issues. ## Compatibility (inner setting) Perceptions about program compatibility were important to implementation. Reflections included staff perceptions about the suitability of the program to address patient needs as well as consideration of the program's fit within clinic workflows. Generally, respondents emphasized that the program was a good match as it focused on meeting high-risk patients' needs, although a concern was raised about technological access and literacy. Two clinics appreciated that the program streamlined current workflows (facilitating review of data at appointments) and offered additional support (program oversight of patient data). In contrast, a different site highlighted inconsistency between program and clinic practices (patient identification, treatment algorithms), which added work and was disruptive. ## Leadership (inner setting) While clear leadership for the program was not explicitly described as a driver of successful program implementation, all sites did identify a program leader and described their varied roles. Leadership structure and involvement varied among sites with most sites having one primary leader and a few sites having a more collaborative leadership team. The majority of leaders were on-site, with some holding administrative roles and others with clinical roles. ## Resources (inner setting) Resources such as staffing, space, and supplies were considered important to the program and facilitated successful delivery. As described above, clinics had varied models of staffing to support and reinforce the program. Clinics frequently described the burden of program-related tasks in addition to other job responsibilities. Having a small number of staff and high rates of turnover were reported as common challenges to TACM-2 delivery in all free clinics and FQHCs, and retraining efforts drained clinic resources. A few clinics mentioned the need for materials and staff to communicate with Spanish-speaking patients. Suggestions to improve patient engagement from individual clinics included direct technical support, educational materials and support in Spanish, and patients' ability to log on and monitor blood sugar numbers themselves. ## Data Synthesis Quantitative data showed that clinic, patient, and program delivery characteristics varied in TACM-2 clinics. Qualitative data offered additional insight into implementation barriers and facilitators. Survey data showed variability in resources available to support program delivery, and interview data highlighted that appropriate staffing models as well as infrastructure were supportive of the program. Both survey and interview data highlighted time as a key barrier to program implementation. Interviews also highlighted high staff turnover rates as a challenge, causing staff shortages, a need for increased training, and further increasing time demands. Qualitative data also described patient resource barriers including transportation as well as language and knowledge barriers hindering program understanding and the ability to carry out program demands at home. Surveys demonstrated positive perceptions of leadership in clinics, and interviews revealed that while all sites did identify a leader, there were different leadership styles among clinics that may have influenced implementation. Participants appeared to perceive the presence of a collaborative leadership team as facilitating implementation processes. Surveys revealed positive perceptions of the implementation climate for delivery of the program, but interviews highlighted variability in this area with only some clinics reporting positive communication and teamwork practices. Interviews additionally revealed that positive collaborations between the clinics and the academic center contributed to productive implementation climates. High patient demand, or high rates of diabetes in clinic communities, and clinic commitment to help patients were the main facilitators to implementation. ## Discussion RPM has been increasingly utilized in clinical practice for patients with diabetes and has demonstrated improved patient outcomes.3,4,22 However, there have been challenges to widespread dissemination of this multifaceted complex innovation.6,22 This CFIR-guided mixed methods study addressed this research–practice gap and utilized implementation science methodologies12 to examine program delivery strategies and common implementation barriers and facilitators to delivery of a diabetes RPM program in underserved community settings. Delivery strategies varied widely across clinics as they had diverse workflows, staffing and leadership models, and practical support to maintain the program. This variability highlights the need for RPM programs to be flexible and the feasibility of delivering programs in varied settings. Results also identified barriers and facilitators at the clinic, patient, and program levels. At the clinic level, leadership buy-in emerged as important to program implementation. However, despite strong endorsement of leadership for the program, practical barriers had to be overcome to facilitate progress. Specifically, staffing was influential to program delivery, as has been documented in RPM studies of other chronic diseases.23 Free clinics generally relied on more volunteer staff, whereas academic clinics relied more heavily on trainees and resident physicians. All sites struggled with the impact of staff turnover, which demanded additional time for training and programmatic inefficiencies. This could lead to lower program enrollment, less consistent monitoring, less fidelity in process measurement, and diversion of resources to accommodate repeated trainings. Importantly, staffing time increased to meet patient needs, including lower health literacy, lack of diabetes awareness, and non-English-speaking language preference. This may have stretched staffing, increased program-specific burnout, and/or limited available resources to engage other patients. Several clinics described multidisciplinary resources embedded within the clinics, including on-site laboratories, pharmacists, and clinical diabetic educators (CDEs) who provided ancillary support and education. By sharing program responsibilities with other staff members, multidisciplinary clinics may have faced less strain on staffing and time. Additionally, the added educational support and convenience of completing blood draws on-site may influence a patient's likelihood to remain engaged in the program. Further investigation into the impact of multidisciplinary resources on program engagement is important. Patient factors influenced program delivery as clinics tailored implementation to meet their needs. The free devices and monitoring were seen as a benefit for low-income and uninsured patients. As such, clinics serving low-income populations may be more likely to invest in the program, and patients may be more likely to engage. Contrarily, a large non-English-speaking population may be a barrier to implementation owing to greater staffing demands, as described above. Patients lacking stable housing, transportation, and insurance face barriers to program retention and follow-up data collection, a metric by which clinics were measured. A tailored implementation approach accounting for needs of the target population is important, as demonstrated by our work and that of others.24 Program factors, including local-central communication patterns and comfort with the data reporting system, impacted program delivery and were represented in reported barriers and facilitators to implementation. Program-level barriers to RPM implementation are well documented and include cost, poor integration with current workflows, data overload, and increased workload for providers.23–26 In our study, most clinics communicated frequently with the central site and received timely supply shipments, thereby facilitating program implementation. For clinics well versed in online monitoring and data reporting, the program's online reporting system was viewed as beneficial, increasing clinics' abilities to achieve reliable convenient data exchange between patient and provider. Alternatively, some clinics found the program to be taxing on staff and disruptive to usual workflow. Collaboration between academic medical centers and community partners has been accomplished in other studies, as in ours, and has been emphasized as an important tool for disseminated care.8,27 Our qualitative data demonstrate the importance of clear expectations and roles, with ambiguities threatening program success. Results highlight the promise of RPM programs in community settings and potential support strategies needed at the clinic, patient, and program levels for implementing RPM programs. Similar to findings in a recent qualitative study of mobile health technologies, we found complex relationships of clinical staff with telehealth programs, specifically regarding inter-site collaboration, care delivery efficiency and flexibility, financial and technological barriers, training requirements, and patient needs and skills.28 A rigorous planning phase could support the assessment of clinic readiness and patient needs to guide tailored practices for staffing and workflows to set clinics up for success. Building clinic teams, assigning roles and responsibilities, establishing communication preferences, and providing training will also help teams prepare for implementation. Clear messaging in the planning phase can help define expectations to prepare to meet patient needs. More work is needed to guide development of protocols for ongoing training, technical assistance, and troubleshooting challenges to support programs over time. Despite the strengths including use of the CFIR to guide examination of a comprehensive set of implementation factors and our mixed methods approach, there are several limitations of this work. First, findings are subject to nonresponse bias as not all participants responded to all portions of the study. A strength, however, is that all sites were represented in interviews to allow comprehensive qualitative perspectives. Second, the perspective of patients is not captured and should be a focus of future studies, although work in the cardiovascular field suggests that patients appreciate the benefits of RPM.23 Third, the program under study is implemented within a single state, and thus, generalizability is limited. There are differences in state regulations related to RPM across states, including insurance payor variations, which were not assessed in this study. ## Conclusions Clinic, target population, and program elements impact RPM program delivery, but such programs can be adapted for community clinics with varied structures. Clinical staff perceive a number of barriers to program implementation that can be mitigated with training and support strategies, such as a structured planning to accommodate unique clinic staffing and clinic workflows, support for team-building within clinics, and development of tailored protocols for training, program delivery, and support. Despite implementation challenges, RPM programs can capitalize on clinical staff's motivation and commitment to help patients28 to improve patient outcomes. Future research is needed to expand the current study's findings to other geographic areas, which will allow descriptions of differences across state populations and with regard to differences in state regulations and payor variations. Future studies should also evaluate planning, training, team building, and implementation strategies to support program sustainability. ## Authorship Contribution Statement All authors contributed to this article, and all contributors agree to submit this article for publication. E.B.K.: conceptualization, formal analysis, data curation, writing, visualization, supervision, and funding acquisition. E.J. and K.R.S.: conceptualization, investigation, formal analysis, data curation, writing, visualization, supervision, and funding acquisition. C.B. and J.M.: software, formal analysis, and data curation. R.V.: project administration, and writing—review and editing. D.F. and K.K.: conceptualization, supervision, project administration, and funding acquisition. ## Disclaimer The content is solely the responsibility of the authors and does not necessarily represent the official views nor an endorsement by the National Institutes of Health, the Health Resources and Services Administration, the U.S. Department of Health and Human Services, or the U.S. Government. ## Author Disclosure Statement No competing financial interests exist. ## Funding Information This publication was supported, in part, by the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH) under Grant Number UL1 TR001450 and by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) as part of the National Telehealth Center of Excellence Award (U66 RH31458). 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--- title: Parental Resilience and Physical Health in Parents of Children With Type 1 Diabetes in Northern Greece journal: Cureus year: 2023 pmcid: PMC10027411 doi: 10.7759/cureus.35149 license: CC BY 3.0 --- # Parental Resilience and Physical Health in Parents of Children With Type 1 Diabetes in Northern Greece ## Abstract Background: Type 1 diabetes mellitus (T1DM) is the most common endocrine and metabolic disorder in children. On the other hand, little is known regarding the health of parents whose children suffer from T1DM. Aim: The study aims to investigate the mental resilience and physical health of parents of children with type 1 diabetes. Methods: The sample consisted of 80 parents of children and adolescents with T1DM.The study was conducted with the contribution of associations of parents of children with type 1 diabetes in a large hospital in Northern Greece between April 2021 and September 2021. A demographic and clinical questionnaire, the Wagnild and Young Resilience Scale-14 (RS-14), and the General Health 28 Physical Health Measurement Questionnaire (GHQ-28) were used to collect the research data. Results: Of the parents, $18.8\%$ were male while $65\%$ were female. The mean age of the parents was 44.02±6.71 years while the age of their children with diabetes was 13.13±6.05 years. Almost half of the children followed intensive insulin treatment ($47.5\%$) whereas 22,$5\%$ reported that their children received insulin via a pump. A higher percentage of parents reported measuring their children's blood sugar more than six times a day (46,$3\%$) and having their glycated hemoglobin (HbA1c) levels checked four times a year ($51.2\%$). Finally, statistically significant effects on the physical symptoms and severe depression of parents of children with type 1 diabetes were observed. Conclusions: Additional research is needed to assess the Greek parent population’s resilience and physical health. This study will help healthcare providers to expand their knowledge and meet parents’ needs. ## Introduction Type 1 diabetes mellitus (T1DM) is one of the most common chronic diseases among children. Globally, it is estimated that 1.2 million children and adolescents suffered from T1DM in 2021 [1]. In Greece, the incidence of T1DM is estimated at 9.7 per 100,000 per year [2]. Resilience is defined as the ability to maintain physical and psychological well-being during an individual’s encounter with significant adversity or stressful situations [3]. Specifically, resilient individuals tend to exhibit adaptive behaviors, especially in areas such as social functioning and mental and physical health, and to experience positive emotions even amid stressful situations [4]. T1DM affects not only the children who suffer from it but also the entire family. Parents tend to have more stress and worry when caring for a child with T1DM, resulting in more family conflict and less social interaction [5]. Parents of children with T1DM are responsible for the majority of their child's T1DM management, a complex and time-consuming task that requires adherence to a T1DM care regimen involving frequent blood glucose monitoring, insulin administration, regulation of diet, and physical activity [6]. From this, we understand that the daily life of parents with children with T1DM can become demanding, leading to stress and anxiety. Two recent studies have examined the impact of resilience and stress on the lives of parents with children suffering from T1DM. The results of the first survey showed that parents who had high resilience faced fewer depressive symptoms and their children had better glycemic control [7]. Additionally, the results of the second study showed that parents who reported higher resilience had a better quality of life and better mental health [5]. Recent studies also showed that parents’ emotional and physical adjustment can affect their children’s adherence and self-regulation. Furthermore, parental emotional burdens, including feelings of social isolation, fear of diabetes complications, and misunderstandings between children, can contribute to increased anxiety and depression among parents of children with T1DM [8-9]. Additionally, a systematic mixed-studies review, combining quantitative and qualitative research, examined the psychological experience of parents of children with T1DM. The study again showed the psychological distress of the parents [10]. Undoubtedly, there is a growing interest generally in the mental resilience and physical health of parents with children who have T1DM. Our research showed that few studies have been conducted internationally using different types of questionnaires concerning the resilience of these parents. To the best of our knowledge, no research using the Resilience Scale (RS-14) of Wanglid and Young and the General Health Questionnaire (GHQ-28) has been conducted in Greece. The purpose of this study was to assess the resilience and physical health of parents with children with T1DM in Greece using these two different instruments. Specifically, we aimed to assess the following research questions: (i) Is there a difference in parental demographics regarding parental resilience? ( ii) Is there a difference in physical health depending on the parental demographics? ( iii) Is there a difference in the mental resilience of the parents depending on the child's clinical characteristics? ( iv) Is there any difference depending on the clinical characteristics of the child and the physical health of the parents? ( v) What are the predicted factors that influence the mental resilience of parents? ( vi) What are the predictive factors that affect the physical health of parents? ## Materials and methods Study design and sample This study was a cross-sectional study conducted in a convenience sample of 80 parents of children with T1DM in AHEPA University General Hospital, Thessaloniki, in Northern Greece between April 2021 and September 2021. The inclusion criteria for participants were that they were adult parents of children and adolescents with T1DM and had the ability to speak, read and write in Greek. All eligible participants provided written, informed consent before completing a structured questionnaire. Patients and treatment characteristics were collected from patients’ records. The study followed the declaration of Helsinki. The study was approved by the International Hellenic University Ethics Committee (approval number: $\frac{7}{17}$-3-2021). Instruments The questionnaire consisted of three parts. The first part included the demographic characteristics of parents and clinical characteristics of children with T1DM. The second part measured the mental resilience of parents using the RS-14 by Wanglid and Young, and the third part concerned the physical health of parents using the GHQ-28. The RS-14 of Wanglid and Young The RS-14 is a seven-point Likert-type scale, with scores ranging from 1 (strongly disagree) to 7 (strongly agree). The sum of the score ranges from 14 to 98, with higher scores indicating stronger resilience. Scores above 90 indicate high resilience, while scores below 56 indicate very low resilience [4,11]. The RS-14 was translated into Greek by the Resilience Center (Montana, United States). The reliability of the questionnaire has been established in previous studies [11-14]. GHQ-28 The GHQ-28 was developed by Goldberg in 1978 and has since been translated into 38 languages. Developed as a screening tool to identify those likely to have or at risk of developing psychiatric disorders, the GHQ-28 is a 28-item measure of emotional distress in medical settings. Through factor analysis, the GHQ-28 has been divided into four subscales: physical symptoms, anxiety/insomnia, social dysfunction, and major depression. There are different methods to score the GHQ-28. It can be scored from 0 to 3 for each response with a total possible score ranging from 0 to 84. Using this method, a total score of $\frac{23}{24}$ is the threshold for the presence of distress. The bigger the score, above 24, the higher the distress. Alternatively, the GHQ-28 can be scored with a binary method where Not at all, and No more than usual score 0, and Rather more than usual and Much more than usual score 1 [15]. The number of symptoms is calculated across the GHQ-28 [16]. The validity of the GHQ-28 has been tested in various clinical settings and in a large number of cultures and languages. The GHQ-28 was translated into the Greek language by a group consisting of three psychiatrists and a clinical psychologist [17]. The reliability of the questionnaire has been established in previous studies [17-20]. Data analysis Qualitative variables are described as n (%) whereas continuous variables are presented as mean ± standard deviation or as median (interquartile range). The normality of distribution was assessed using the Lillifors test. The association between the continuous variables was assessed by estimating the Spearman correlation coefficients. The effects of the demographic and clinical characteristics on the RS-14 score (normally distributed) were assessed using the independent t-test (for two groups) or the one-way ANOVA (for more than two groups). To allow analysis, quantitative variables (child’s age, age of diabetes diagnosis, and age of parent) were divided into two separate groups based on the median. The effects of the demographic and clinical characteristics on the General Health Questionnaire were assessed using the Mann-Whitney U test (for two groups) or the Kruskal Wallis H test (for three groups or more). On the Kruskal-Wallis test, significant main effects were followed by posthoc multiple comparisons with Dunn-Bonferonni Correction. For the statistically significant differences, Cohen’s d was computed using the platform Psychometrica™NPC (George, South Africa) [21]. Finally, a series of multiple regression analyses were conducted for each GHQ-28 subscale score to assess the predictors of compromised mental health. The first model (main analysis) included the RS-14 score as a predictor to assess whether resilience predicted scores on GHQ-28 whereas the second model (exploratory) added the demographic characteristics as predictors to assess whether there were additional variables explaining the total variance of the GHQ-28 scores. Data were analyzed using IBM SPSS Statistics for Windows, Version 25.0 (Released 2017; IBM Corp., Armonk, New York, United States). The significance level was set to $p \leq 0.05.$ ## Results Demographic, clinical, and psychometric characteristics The mean age of the parents was 44.02±6.71 years, while the mean age of their children with T1DM was 13.13 ± 6.05 years old. The mean age of T1DM onset was 8.19±4.60 years. The children’s mean blood sugar level was 139.35 ± 55.52 mg/dL whereas mean glycated hemoglobin (HbA1c) level was 7.23 ± $1.70\%$. Almost half of the children followed intensive insulin treatment ($47.5\%$) whereas 22,$5\%$ reported that their children received insulin via a pump. A higher percentage of parents reported measuring their children's blood sugar more than six times a day (46,$3\%$) and having their HbA1c levels checked four times a year ($51.2\%$). The demographic characteristics of parents and their children with diabetes are shown in Table 1. Table 1 \| Characteristics \| Characteristics.1 \| n \| % \| \| --- \| --- \| --- \| --- \| \| Gender \| Male \| 15 \| 18.8 \| \| Gender \| Female \| 65 \| 81.3 \| \| Age of participanta \| <44 years old \| 38 \| 48.7 \| \| Age of participanta \| ≥44 years old \| 40 \| 51.3 \| \| Number of children \| One child \| 18 \| 22.5 \| \| Number of children \| Two children \| 38 \| 47.5 \| \| Number of children \| Three children \| 20 \| 25.0 \| \| Number of children \| Four children \| 2 \| 2.5 \| \| Number of children \| > four children \| 2 \| 2.5 \| \| Living Status \| Lives with spouse and child/ren \| 68 \| 85.0 \| \| Living Status \| Lives only with their child/ren \| 12 \| 15.0 \| \| Residence \| Urban \| 51 \| 63.7 \| \| Residence \| Semi-urban \| 21 \| 26.3 \| \| Residence \| Rural \| 8 \| 10.0 \| \| Mother’s employment status \| unemployed \| 10 \| 12.5 \| \| Mother’s employment status \| homemaker \| 11 \| 13.8 \| \| Mother’s employment status \| Public servant \| 30 \| 37.5 \| \| Mother’s employment status \| Private sector worker \| 15 \| 18.8 \| \| Mother’s employment status \| farmer \| 1 \| 1.3 \| \| Mother’s employment status \| freelancer \| 10 \| 12.5 \| \| Mother’s employment status \| retired \| 3 \| 3.8 \| \| Father’s employment status \| unemployed \| 2 \| 2.5 \| \| Father’s employment status \| Public servant \| 25 \| 31.3 \| \| Father’s employment status \| Private sector worker \| 19 \| 23.8 \| \| Father’s employment status \| farmer \| 5 \| 6.3 \| \| Father’s employment status \| freelancer \| 25 \| 31.3 \| \| Father’s employment status \| retired \| 4 \| 5.0 \| \| Mother’s educational status \| Primary School \| 2 \| 2.5 \| \| Mother’s educational status \| Middle School \| 2 \| 2.5 \| \| Mother’s educational status \| High school diploma \| 26 \| 32.5 \| \| Mother’s educational status \| Bachelor’s degree \| 34 \| 42.5 \| \| Mother’s educational status \| Postgraduate degree \| 14 \| 17.5 \| \| Mother’s educational status \| PhD \| 2 \| 2.5 \| \| Father’s educational status \| Primary School \| 3 \| 3.8 \| \| Father’s educational status \| Middle School \| 2 \| 2.5 \| \| Father’s educational status \| High school diploma \| 33 \| 41.3 \| \| Father’s educational status \| Bachelor’s degree \| 28 \| 35.0 \| \| Father’s educational status \| Postgraduate degree \| 7 \| 8.8 \| \| Father’s educational status \| PhD \| 7 \| 8.8 \| \| Children with diabetes gender \| male \| 34 \| 42.5 \| \| Children with diabetes gender \| female \| 46 \| 57.5 \| \| Child’s with diabetes age \| ≤12 years old \| 42 \| 52.5 \| \| Child’s with diabetes age \| >12 years old \| 38 \| 47.5 \| \| Age of diabetes diagnosis \| < 8 years old \| 40 \| 50.0 \| \| Age of diabetes diagnosis \| ≥ 8 years old \| 40 \| 50.0 \| \| Insulin Therapy type \| Conventional \| 24 \| 30.0 \| \| Insulin Therapy type \| Intensive \| 38 \| 47.5 \| \| Insulin Therapy type \| Pump \| 18 \| 22.5 \| \| Blood sugar measurement frequency \| 1-2 times a day \| 10 \| 12.5 \| \| Blood sugar measurement frequency \| 2-4 times a day \| 8 \| 10.0 \| \| Blood sugar measurement frequency \| 4-6 times a day \| 25 \| 31.3 \| \| Blood sugar measurement frequency \| >6 times a day \| 37 \| 46.3 \| \| HbA1c measurement frequency \| 1 time a year \| 3 \| 3.8 \| \| HbA1c measurement frequency \| 2 times a year \| 9 \| 11.3 \| \| HbA1c measurement frequency \| 3 times a year \| 27 \| 33.8 \| \| HbA1c measurement frequency \| 4 times a year \| 41 \| 51.2 \| \| Blood sugar levelsa \| ≤100 mg/dL \| 20 \| 25.3 \| \| Blood sugar levelsa \| ≥100 mg/dL \| 59 \| 74.7 \| \| HbA1c levelsa \| <7 \| 47 \| 60.3 \| \| HbA1c levelsa \| > 7 \| 31 \| 39.7 \| In Table 2, the descriptive characteristics of the GHQ-28 and the RS-14 are shown. Regarding internal consistency, all Cronbach’s alpha are satisfactory for both questionnaires. Table 2 \| Unnamed: 0 \| Minimum \| Maximum \| Mean \| SD \| Cronbach’s alpha \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| RS total \| 26.0 \| 98.0 \| 79.23 \| 14.84 \| 0.95 \| \| GHQ somatic \| 0.0 \| 20.0 \| 6.8 \| 4.55 \| 0.88 \| \| GHQ anxiety & insomnia \| 0.0 \| 21.0 \| 8.32 \| 5.0 \| 0.88 \| \| GHQ Social dysfunction \| 1.0 \| 21.0 \| 6.64 \| 3.38 \| 0.81 \| \| GHQ Severe depression \| 0.0 \| 21.0 \| 2.81 \| 4.24 \| 0.93 \| \| GHQ total scale \| 4.0 \| 81.0 \| 24.44 \| 14.73 \| 0.95 \| \| GHQ number of symptoms \| 0.0 \| 28.0 \| 5.94 \| 6.55 \| - \| Table 3 shows that almost half of the participants had more than four symptoms on the GHQ-28 ($45.6\%$) while the $15.2\%$ of parents reported having a low or very low resilience. Table 3 \| GHQ number of symptoms categoriesa \| n \| % \| \| --- \| --- \| --- \| \| <5 under cut-off (subclinical) \| 43 \| 54.4 \| \| ≥5 over cut-off (possibly clinical) \| 36 \| 45.6 \| \| RS categoriesa \| n \| % \| \| Very low (14-56) \| 6 \| 7.6 \| \| Low (57-64) \| 6 \| 7.6 \| \| On the low end (67-73) \| 8 \| 10.1 \| \| Moderate (74-81) \| 24 \| 30.4 \| \| Moderately High (82-90) \| 13 \| 16.5 \| \| High (91-98) \| 22 \| 27.8 \| Effects of demographic and clinical characteristics on resilience and mental health scores Regarding resilience, parents that reported measuring blood glucose levels more than four times a day had a significantly lower RS-14 score (77.20±14.21) than those reporting measuring up to four times a day (86.1±15.26, t[77] = -2,30, $p \leq 0.05$, $d = 0.62$). No other effects on RS-14 scores were reported. Regarding mental health, no significant effects of participants’ gender, family status, living status, HbA1c measurement frequency, children’s blood sugar levels, and children’s insulin therapy type were noticed (p = ns). Table 4 shows the means and standard deviations of GHQ-28 scores across the demographic and clinical characteristics in which significant effects on the scores were noticed. Significant main effects of the number of children were noticed on the total score ($$p \leq 0.02$$), on the GHQ-28 somatic score ($p \leq 0.001$), and on the GHQ-28 severe depression score ($$p \leq 0.04$$). Participants with two children had a higher median total score on the GHQ-28 ($$p \leq 0.013$$) and on the GHQ-28 somatic score ($$p \leq 0.03$$) than those with three children. In regards to the place of residence, participants had a higher median score on the total GHQ-28 scale ($$p \leq 0.03$$, $d = 0.50$) and in the number of symptoms ($$p \leq 0.03$$, $d = 0.50$). Regarding age, participants at least 44 years old had significantly lower score in the GHQ anxiety and insomnia subscale than the younger participants ($$p \leq 0.01$$, $d = 0.63$). Moreover, participants that had a child aged less than 12 years old had a significantly higher score in the GHQ-28 anxiety and insomnia subscale than those with older children ($$p \leq 0.01$$, $d = 0.63$). Moreover, participants that reported that their child with T1DM had HbA1c levels of more than $7\%$ at the last measurement had a significantly lower median number of symptoms ($$p \leq 0.03$$, $d = 0.52$) and significantly lower score on anxiety and insomnia ($$p \leq 0.03$$, $d = 0.51$). Finally, participants that reported checking their blood sugar levels more than four times a day had a higher GHQ-28 social dysfunction score than those reporting measuring blood sugar levels up to four times a day ($$p \leq 0.02$$, $d = 0.55$). Table 4 \| Variable \| Variable.1 \| Median (IQR) \| U or H (df) \| p-value \| Cohen’s d \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| GHQ total scale \| GHQ total scale \| GHQ total scale \| GHQ total scale \| GHQ total scale \| \| \| Number of children \| a. One child \| 25 (17.50) \| 8.22 (2) \| 0.02 \| 0.6 \| \| Number of children \| b. Two children \| 26 (20.25)c \| 8.22 (2) \| 0.02 \| 0.6 \| \| Number of children \| c. Three children \| 17 (11.50)b \| 8.22 (2) \| 0.02 \| 0.6 \| \| Place of residence \| rural \| 25 (21) \| 514.5 \| 0.03 \| 0.5 \| \| Place of residence \| Semi-urban or rural \| 17 (14) \| 514.5 \| 0.03 \| 0.5 \| \| GHQ number of symptoms \| GHQ number of symptoms \| GHQ number of symptoms \| GHQ number of symptoms \| GHQ number of symptoms \| \| \| HbA1c levels \| <7% \| 4 (9.50) \| 501 \| 0.03 \| 0.52 \| \| HbA1c levels \| >7% \| 2 (7) \| 501 \| 0.03 \| 0.52 \| \| Place of residence \| rural \| 5 (10.25) \| 515 \| 0.03 \| 0.5 \| \| Place of residence \| Semi-urban or rural \| 2 (7) \| 515 \| 0.03 \| 0.5 \| \| GHQ somatic \| GHQ somatic \| GHQ somatic \| GHQ somatic \| GHQ somatic \| \| \| Number of Children \| a. One child \| 7 (5) \| 10.83 \| <0.001 \| 0.73 \| \| Number of Children \| b. Two children \| 8 (7.50)c \| 10.83 \| <0.001 \| 0.73 \| \| Number of Children \| c. Three children \| 3.50 (4.75)b \| 10.83 \| <0.001 \| 0.73 \| \| GHQ anxiety & insomnia \| GHQ anxiety & insomnia \| GHQ anxiety & insomnia \| GHQ anxiety & insomnia \| GHQ anxiety & insomnia \| \| \| Age of the parent \| <44 years old \| 11 (8) \| 483 \| 0.01 \| 0.63 \| \| Age of the parent \| ≥44 years old \| 6.50 (5.75) \| 483 \| 0.01 \| 0.63 \| \| Age of the child \| ≤12 years old \| 8 (8) \| 544.00 \| 0.02 \| 0.54 \| \| Age of the child \| >12 years old \| 7 (6.50) \| 544.00 \| 0.02 \| 0.54 \| \| HbA1c levels \| <7% \| 8 (8) \| 506 \| 0.03 \| 0.51 \| \| HbA1c levels \| >7% \| 6 (8) \| 506 \| 0.03 \| 0.51 \| \| GHQ Social dysfunction \| GHQ Social dysfunction \| GHQ Social dysfunction \| GHQ Social dysfunction \| GHQ Social dysfunction \| \| \| Blood sugar measurement frequency \| 1-4 times a day \| 4 (4.25) \| 342.50 \| 0.02 \| 0.55 \| \| Blood sugar measurement frequency \| More than 4 times a day \| 7 (3) \| 342.50 \| 0.02 \| 0.55 \| \| GHQ Severe depression \| GHQ Severe depression \| GHQ Severe depression \| GHQ Severe depression \| GHQ Severe depression \| \| \| Number of children \| a. One child \| 1 (2.50) \| 6.29 (2) \| 0.04 \| 0.49 \| \| Number of children \| b. Two children \| 2 (3.25) \| 6.29 (2) \| 0.04 \| 0.49 \| \| Number of children \| c. Three children \| 9 (3) \| 6.29 (2) \| 0.04 \| 0.49 \| Predictors of resilience of parents with children with diabetes A regression model with demographic and clinical characteristics as predictors and RS-14 score as outcome was conducted. The model did not significantly explain the variance of the RS-14 score, R2 = 0.07, F[17, 75] = 1.33, $$p \leq 0.21.$$ No predictors predicted the resilience score. Predictors of the mental health of the parents with children with T1DM For each subscale and total score of the GHQ-28, two separate regression models were conducted. The first model included the score on the RS-14 as a predictor which was followed by a second model (Enter Method) which included the demographic and clinical characteristics of the participants as predictors. The frequency of checking the HbA1c levels and age of the child were excluded from the model due to having high VIF levels. Table 5 shows the predictors of the GHQ-28 total score and GHQ-28 number of symptoms. The first model on the total GHQ-28 score accounted for $26.4\%$ of the total variance (F(1¸73) = 25.80, $p \leq 0.001$). The RS-14 score was a significant negative predictor of the GHQ-28 total score (b = -0.51). After including the demographic and clinical characteristics (second model), the model accounted for $51.1\%$ of the total variance (F[16, 73] = 3.72, $p \leq 0.001$). The RS-14 score continued to be a significant negative predictor (b = -0.46). Living in an urban place ($b = 0.28$) and having two children ($b = 0.39$) were positive predictors of the total score. Reporting that their children receive intensive insulin treatment was a negative predictor of the total score (b= - 0.26). Table 5 \| Unnamed: 0 \| Unnamed: 1 \| GHQ total score \| GHQ total score.1 \| GHQ total score.2 \| GHQ total score.3 \| GHQ total score.4 \| GHQ symptom \| GHQ symptom.1 \| GHQ symptom.2 \| GHQ symptom.3 \| GHQ symptom.4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Model \| Predictors \| B \| SE B \| β \| 95% CI \| 95% CI \| B \| SE B \| β \| 95% CI \| 95% CI \| \| Model \| Predictors \| B \| SE B \| β \| LL \| UL \| B \| SE B \| β \| LL \| UL \| \| 1 \| (Constant) \| 63.89 \| 7.96 \| \| 12.24 \| 27.97 \| 20.10 \| 3.94 \| \| 12.24 \| 27.97 \| \| 1 \| RS total score \| -0.50 \| 0.10 \| -0.51* \| -0.28 \| -0.08 \| -0.18 \| 0.05 \| -0.40* \| -0.28 \| -0.08 \| \| 2 \| (Constant) \| 72.01 \| 17.16 \| \| 6.10 \| 39.23 \| 22.66 \| 8.27 \| \| 6.10 \| 39.23 \| \| 2 \| RS total score \| -0.45 \| 0.10 \| -0.46* \| -0.25 \| -0.06 \| -0.16 \| 0.05 \| -0.34 \| -0.25 \| -0.06 \| \| 2 \| Age of participant \| -0.31 \| 0.22 \| -0.15 \| -0.34 \| 0.08 \| -0.13 \| 0.11 \| -0.14 \| -0.34 \| 0.08 \| \| 2 \| Age of child’s diabetes diagnosis \| -0.03 \| 0.31 \| -0.01 \| -0.34 \| 0.26 \| -0.04 \| 0.15 \| -0.03 \| -0.34 \| 0.26 \| \| 2 \| Urban Semi-urbana \| 7.98 \| 3.08 \| 0.28* \| 1.47 \| 7.41 \| 4.44 \| 1.48 \| 0.34 \| 1.47 \| 7.41 \| \| 2 \| Blood sugar measurement frequency more than 4 times a day Blood sugar measurement frequency less than 4 times a daya \| -0.28 \| 3.83 \| -0.01 \| -4.00 \| 3.39 \| -0.30 \| 1.85 \| -0.02 \| -4.00 \| 3.39 \| \| 2 \| Blood sugar levels ≥100 mg/dL Blood sugar levels <100 mg/dLa \| 0.04 \| 3.15 \| 0.00 \| -2.29 \| 3.80 \| 0.76 \| 1.52 \| 0.05 \| -2.29 \| 3.80 \| \| 2 \| hba1c levels > 7 hba1c levels < 7a \| -2.39 \| 2.93 \| -0.09 \| -4.49 \| 1.17 \| -1.66 \| 1.41 \| -0.13 \| -4.49 \| 1.17 \| \| 2 \| Male femalea \| -2.13 \| 3.60 \| -0.06 \| -4.89 \| 2.05 \| -1.42 \| 1.73 \| -0.09 \| -4.89 \| 2.05 \| \| 2 \| Lives with spousea Lives only with child \| -0.80 \| 4.15 \| -0.02 \| -4.75 \| 3.27 \| -0.74 \| 2.00 \| -0.04 \| -4.75 \| 3.27 \| \| 2 \| Child male Child femalea \| -0.76 \| 3.02 \| -0.03 \| -2.29 \| 3.54 \| 0.62 \| 1.46 \| 0.05 \| -2.29 \| 3.54 \| \| 2 \| One child \| 6.35 \| 3.97 \| 0.20 \| -1.82 \| 5.85 \| 2.01 \| 1.91 \| 0.14 \| -1.82 \| 5.85 \| \| 2 \| Two children Three or more childrena \| 10.36 \| 3.39 \| 0.39 \| 1.38 \| 7.92 \| 4.65 \| 1.63 \| 0.38** \| 1.38 \| 7.92 \| \| 2 \| Intensive insulin treatment \| -6.92 \| 3.34 \| -0.26* \| -6.98 \| -0.54 \| -3.76 \| 1.61 \| -0.30* \| -6.98 \| -0.54 \| \| 2 \| Pump Conventional therapya \| -5.31 \| 4.12 \| -0.17 \| -6.78 \| 1.16 \| -2.81 \| 1.98 \| -0.19 \| -6.78 \| 1.16 \| \| 2 \| Mother employed Not employeda \| -4.85 \| 2.89 \| -0.17 \| -5.50 \| 0.07 \| -2.71 \| 1.39 \| -0.20 \| -5.50 \| 0.07 \| \| 2 \| Father employed Not employeda \| 0.74 \| 6.03 \| 0.01 \| -4.63 \| 7.01 \| 1.19 \| 2.91 \| 0.05 \| -4.63 \| 7.01 \| Regarding the number of symptoms, the first model on the total GHQ-28 score accounted for $15.9\%$ of the total variance (F(1¸73) = 13.62, $p \leq 0.001$). The RS-14 score was a significant negative predictor of the GHQ-28 total score (b = -0.40). After including the demographic and clinical characteristics (second model), the model accounted for $32.3\%$ of the total variance (F[16, 73] = 3.18, $$p \leq 0.001$$). The RS-14 score continued to be a significant negative predictor (b = -0.34). Living in an urban place ($b = 0.34$) and having two children ($b = 0.38$) were positive predictors of the total score. Reporting that their children receive intensive insulin treatment was a negative predictor of the total score (b= - 0.30). Table 6 shows the predictors of the scores in the GHQ-28 somatic and anxiety and insomnia subscales. The first model on the somatic GHQ-28 score accounted for $13.6\%$ of the total variance (F(1¸73) = 11.35, $$p \leq 0.001$$). The RS-14 score was a significant negative predictor of the GHQ-28 somatic score (b = -0.37). After including the demographic and clinical characteristics (second model), the model accounted for $45.5\%$ of the total variance (F[16, 73] = 2.97, $$p \leq 0.001$$). The RS-14 score continued to be a significant negative predictor (b = -0.30). Having one child ($b = 0.32$) and having two children ($b = 0.51$) were positive predictors of the total score. Regarding the anxiety and insomnia subscale, the first model accounted for $16.3\%$ of the total variance (F(1¸73) = 13.98, $p \leq 0.001$). The RS-14 score was a significant negative predictor of the GHQ-28 anxiety and insomnia score (b = -0.40). After including the demographic and clinical characteristics (second model), the model accounted for $43.9\%$ of the total variance (F[16, 73] = 2.79, $$p \leq 0.002$$). The RS-14 score continued to be a significant negative predictor (b = -0.39). The age of the participant ((b = -0.31) was a negative predictor of the score whereas having one ($b = 0.36$) or two children ($b = 0.40$) were positive predictors. Table 6 \| Unnamed: 0 \| Unnamed: 1 \| GHQ somatic \| GHQ somatic.1 \| GHQ somatic.2 \| GHQ somatic.3 \| GHQ somatic.4 \| GHQ anxiety & insomnia \| GHQ anxiety & insomnia.1 \| GHQ anxiety & insomnia.2 \| GHQ anxiety & insomnia.3 \| GHQ anxiety & insomnia.4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Model \| \| B \| SE B \| β \| 95% CI \| 95% CI \| B \| SE B \| β \| 95% CI \| 95% CI \| \| Model \| \| B \| SE B \| β \| LL \| UL \| B \| SE B \| β \| LL \| UL \| \| 1 \| (Constant) \| 16.18 \| 2.87 \| \| 10.47 \| 21.89 \| 19.35 \| 3.03 \| \| 13.31 \| 25.40 \| \| 1 \| RS total score \| -0.12 \| 0.04 \| -0.37 \| -0.19 \| -0.05 \| -0.14 \| 0.04 \| -0.40* \| -0.22 \| -0.07 \| \| 2 \| (Constant) \| 16.01 \| 6.03 \| \| 3.95 \| 28.08 \| 32.85 \| 6.57 \| \| 19.70 \| 45.99 \| \| 2 \| RS total score \| -0.10 \| 0.03 \| -0.30 \| -0.17 \| -0.03 \| -0.14 \| 0.04 \| -0.39* \| -0.21 \| -0.06 \| \| 2 \| Age of participant \| -0.09 \| 0.08 \| -0.13 \| -0.24 \| 0.07 \| -0.23 \| 0.08 \| -0.32** \| -0.40 \| -0.06 \| \| 2 \| Age of child’s diabetes diagnosis \| 0.05 \| 0.11 \| 0.05 \| -0.17 \| 0.26 \| 0.01 \| 0.12 \| 0.01 \| -0.22 \| 0.25 \| \| 2 \| Urban Semi-urbana \| 2.16 \| 1.08 \| 0.23* \| 0.00 \| 4.33 \| 1.75 \| 1.18 \| 0.17 \| -0.61 \| 4.11 \| \| 2 \| Blood sugar measurement frequency more than 4 times a day Blood sugar measurement frequency less than 4 times a daya \| 0.64 \| 1.35 \| 0.06 \| -2.05 \| 3.34 \| -1.52 \| 1.47 \| -0.13 \| -4.45 \| 1.42 \| \| 2 \| Blood sugar levels ≥100 mg/dL Blood sugar levels <100 mg/dLa \| -0.33 \| 1.11 \| -0.03 \| -2.55 \| 1.88 \| 0.25 \| 1.21 \| 0.02 \| -2.17 \| 2.66 \| \| 2 \| hba1c levels > 7 hba1c levels < 7a \| -0.77 \| 1.03 \| -0.08 \| -2.83 \| 1.29 \| -1.67 \| 1.12 \| -0.17 \| -3.92 \| 0.57 \| \| 2 \| Male femalea \| -0.31 \| 1.26 \| -0.03 \| -2.84 \| 2.22 \| -0.91 \| 1.38 \| -0.07 \| -3.66 \| 1.85 \| \| 2 \| Lives with spousea Lives only with child \| -0.11 \| 1.46 \| -0.01 \| -3.03 \| 2.81 \| 0.01 \| 1.59 \| 0.00 \| -3.17 \| 3.19 \| \| 2 \| Child male Child femalea \| -0.92 \| 1.06 \| -0.10 \| -3.04 \| 1.21 \| -1.89 \| 1.16 \| -0.20 \| -4.20 \| 0.43 \| \| 2 \| One child \| 3.38 \| 1.39 \| 0.32* \| 0.59 \| 6.17 \| 4.08 \| 1.52 \| 0.36* \| 1.04 \| 7.12 \| \| 2 \| Two children Three or more childrena \| 4.51 \| 1.19 \| 0.51** \| 2.13 \| 6.89 \| 3.78 \| 1.30 \| 0.40* \| 1.19 \| 6.38 \| \| 2 \| Intensive insulin treatment \| -2.29 \| 1.17 \| -0.26 \| -4.63 \| 0.06 \| -2.21 \| 1.28 \| -0.23 \| -4.77 \| 0.34 \| \| 2 \| Pump Conventional therapya \| -1.25 \| 1.44 \| -0.12 \| -4.14 \| 1.64 \| -2.17 \| 1.57 \| -0.20 \| -5.32 \| 0.98 \| \| 2 \| Mother employed Not employeda \| -1.96 \| 1.01 \| -0.20 \| -3.99 \| 0.07 \| -1.07 \| 1.11 \| -0.10 \| -3.28 \| 1.14 \| \| 2 \| Father employed Not employeda \| 0.81 \| 2.12 \| 0.05 \| -3.43 \| 5.04 \| -2.99 \| 2.31 \| -0.16 \| -7.61 \| 1.63 \| Table 7 shows the predictors of the scores in the GHQ-28 social dysfunction and Severe depression subscales. The first model on the social dysfunction GHQ-28 score accounted for $33.7\%$ of the total variance (F(1¸73) = 36.64, $p \leq 0.001$). The RS-14 score was a significant negative predictor of the GHQ-28 social dysfunction score (b = -0.58). After including the demographic and clinical characteristics (second model), the model accounted for $44.4\%$ of the total variance (F[16, 73] = 2.85, $$p \leq 0.002$$). The RS-14 score continued to be a significant negative predictor (b = -0.54). Living in an urban place was a positive predictor of the score ($b = 0.24$). Regarding the severe depression subscale, the first model accounted for $182\%$ of the total variance (F(1¸73) = 15.99, $p \leq 0.001$). The RS-14 score was a significant negative predictor of the GHQ-28 severe depression score (b = -0.43). After including the demographic and clinical characteristics (second model), the model accounted for $23.8\%$ of the total variance (F[16, 73] = 2.42, $$p \leq 0.007$$). The RS-14 score continued to be a significant negative predictor (b = -0.37). Living in an urban place was a positive predictor of the score ($b = 0.33$) Table 7 \| Unnamed: 0 \| Unnamed: 1 \| GHQ social dysfunction \| GHQ social dysfunction.1 \| GHQ social dysfunction.2 \| GHQ social dysfunction.3 \| GHQ social dysfunction.4 \| GHQ severe depression \| GHQ severe depression.1 \| GHQ severe depression.2 \| GHQ severe depression.3 \| GHQ severe depression.4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Model \| \| B \| SE B \| β \| 95% CI \| 95% CI \| B \| SE B \| β \| 95% CI \| 95% CI \| \| Model \| \| B \| SE B \| β \| LL \| UL \| B \| SE B \| β \| LL \| UL \| \| 1 \| (Constant) \| 16.46 \| 1.66 \| \| 13.15 \| 19.77 \| 11.90 \| 2.35 \| \| 7.22 \| 16.58 \| \| 1 \| RS total score \| -0.12 \| 0.02 \| -0.58* \| -0.17 \| -0.08 \| -0.12 \| 0.03 \| -0.43* \| -0.17 \| -0.06 \| \| 2 \| (Constant) \| 13.95 \| 4.02 \| \| 5.90 \| 21.99 \| 9.21 \| 5.30 \| \| -1.40 \| 19.82 \| \| 2 \| RS total score \| -0.12 \| 0.02 \| -0.54* \| -0.16 \| -0.07 \| -0.10 \| 0.03 \| -0.37 \| -0.16 \| -0.04 \| \| 2 \| Age of participant \| 0.01 \| 0.05 \| 0.02 \| -0.09 \| 0.11 \| -0.01 \| 0.07 \| -0.01 \| -0.14 \| 0.13 \| \| 2 \| Age of child’s diabetes diagnosis \| -0.03 \| 0.07 \| -0.05 \| -0.18 \| 0.11 \| -0.06 \| 0.10 \| -0.07 \| -0.25 \| 0.13 \| \| 2 \| Urban Semi-urbana \| 1.46 \| 0.72 \| 0.24* \| 0.02 \| 2.90 \| 2.61 \| 0.95 \| 0.33* \| 0.71 \| 4.51 \| \| 2 \| Blood sugar measurement frequency more than 4 times a day Blood sugar measurement frequency less than 4 times a daya \| 0.26 \| 0.90 \| 0.04 \| -1.54 \| 2.06 \| 0.33 \| 1.18 \| 0.04 \| -2.03 \| 2.70 \| \| 2 \| Blood sugar levels ≥100 mg/dL Blood sugar levels <100 mg/dLa \| -0.27 \| 0.74 \| -0.04 \| -1.75 \| 1.21 \| 0.40 \| 0.97 \| 0.05 \| -1.55 \| 2.34 \| \| 2 \| Hba1c levels > 7 hba1c levels < 7a \| -0.43 \| 0.69 \| -0.07 \| -1.80 \| 0.95 \| 0.48 \| 0.90 \| 0.06 \| -1.33 \| 2.29 \| \| 2 \| Male femalea \| -0.11 \| 0.84 \| -0.01 \| -1.80 \| 1.57 \| -0.81 \| 1.11 \| -0.08 \| -3.03 \| 1.42 \| \| 2 \| Lives with spousea Lives only with child \| 0.70 \| 0.97 \| 0.09 \| -1.24 \| 2.65 \| -1.40 \| 1.28 \| -0.13 \| -3.97 \| 1.17 \| \| 2 \| Child male Child femalea \| 0.46 \| 0.71 \| 0.08 \| -0.95 \| 1.88 \| 1.58 \| 0.93 \| 0.21 \| -0.28 \| 3.45 \| \| 2 \| One child \| -0.46 \| 0.93 \| -0.07 \| -2.32 \| 1.40 \| -0.64 \| 1.23 \| -0.07 \| -3.10 \| 1.81 \| \| 2 \| Two children Three or more childrena \| 0.30 \| 0.79 \| 0.05 \| -1.29 \| 1.88 \| 1.78 \| 1.05 \| 0.24 \| -0.31 \| 3.87 \| \| 2 \| Intensive insulin treatment \| -0.60 \| 0.78 \| -0.10 \| -2.16 \| 0.97 \| -1.83 \| 1.03 \| -0.24 \| -3.89 \| 0.23 \| \| 2 \| Pump Conventional therapya \| -0.82 \| 0.96 \| -0.12 \| -2.75 \| 1.11 \| -1.07 \| 1.27 \| -0.12 \| -3.61 \| 1.48 \| \| 2 \| Mother employed Not employeda \| -0.41 \| 0.68 \| -0.07 \| -1.77 \| 0.94 \| -1.41 \| 0.89 \| -0.17 \| -3.20 \| 0.37 \| \| 2 \| Father employed Not employeda \| 0.89 \| 1.41 \| 0.08 \| -1.94 \| 3.71 \| 2.04 \| 1.86 \| 0.14 \| -1.69 \| 5.77 \| ## Discussion The objective of the study was to assess the mental resilience and physical health of parents with children who have T1DM. The study contributes to the growing body of evidence regarding the emotional and physical well-being of parents who have children suffering from T1DM and provides important information for parents, diabetes-specialized nurses, and school nurses as well. In this study, we found significant findings regarding resilience and physical health and how these contribute to social dysfunction, anxiety, and depression in parents of children with type 1 diabetes. We are the first to use RS-14 and GHQ-28 scales to assess the resilience and physical health of parents with children who have T1DM in the Greek population. Other studies have mainly measured resilience using the Conor-Davidson Resilience Scale. In this study, we found that parents who reported measuring blood glucose levels more than four times a day had significantly lower resilience and greater social dysfunction than those reporting measuring up to four times a day. This finding is, to a certain extent, consistent with other studies, which showed that parents who tended to measure their children’s blood glucose four to six times per day demonstrated higher parental fear and concern for complications such as hypoglycaemic episodes [22,23]. A significant result of our study was the effect the number of children had on parents’ emotional and physical health. Particularly, parents with two children reported more physical symptoms and severe depression compared to parents with one or three children. To the best of our knowledge, this is an original finding. Further research is needed to draw safe conclusions. Additionally, participants aged 44 or over had significantly lower scores in the GHQ-28 anxiety and insomnia subscale compared to younger participants in the survey. This finding is the first of its kind internationally and we assume that parents aged 44 or over have more experience and, as a result, lower stress levels because they know how to handle difficult situations that can arise from their child's T1DM, they can manage T1DM, and they can face the complications of the disease. Therefore, this finding of the research is justified due to the better control that experience has given to parents aged 44 or over, resulting in lower anxiety and better sleeping habits/lack of sleeping disorders. Moreover, participants who had a child aged less than 12 years old had a significantly higher score in the GHQ-28 anxiety and insomnia subscale than those with older children. These results are consistent with previous research, which has demonstrated that parents of younger children with T1DM experience sleep disruption and anxiety [6,24,25]. Moreover, participants who reported that their child with T1DM had HbA1c levels of more than $7\%$ at the last measurement had a significantly lower median number of symptoms and significantly lower scores on anxiety and insomnia. We assume that parents know how to manage T1DM, checking their children’s blood glucose frequently on a daily basis, and, as long as the blood measurements are within the permissible limits, there is no reason to be stressed. Additionally, the HbA1c examination takes place three to four times per year, so maybe parents believe that it can be fixed in the next HbA1c examination. However, this finding is inconsistent with the results of a recent study that showed that parents of children with higher HbA1c tend to have more anxiety and concern about the complications that can occur from T1DM [26]. Limitations This study has some limitations. It was conducted in one hospital located in a major Greek city, with a relatively small sample of parents and simultaneously via an online form filled by associations of parents of children with T1DM, so the results cannot be generalized to the entire Greek population. Another limitation was the short timeline of the study, which was conducted over six months. However, the results provide valuable information on the issue at hand and illustrate the great need for further research to draw reliable conclusions. Despite these limitations, our study has one significant strength: to the best of our knowledge, this is the first population-based study to use the combination of RS-14 and GHQ-28 scales to investigate the mental resilience and physical health of parents of children with T1DM in Greece. ## Conclusions Our study confirms that resilience and physical health play a significant role among parents of children with T1DM. Glucose monitoring, measurements of HbA1c, depression, anxiety, insomnia and age, according to our study, are associated with parents’ social well-being and their everyday life. This study adds to the growing interest in the psychosocial and physical well-being of parents of children with T1DM. Nevertheless, further research is needed to evaluate the mental resilience and physical health of Greek parents of children with T1DM. The results of this study will help parents to cope with difficult mental, emotional, and social dysfunctions, and healthcare professionals to expand their knowledge and meet parents’ needs. ## References 1. International Diabetes Federation, IDF Diabetes Atlas*. IDF Diabetes Atlas, 10th Edition* (2021) 2. Diabetes Research and Clinical Practice (Book in Greek). Diabetes Research and Clinical Practice (Book in Greek) (2023) 107842 2022 3. Richardson GE. The metatheory of resilience and resiliency. J Clin Psychol (2002) 58 307-321. PMID: 11836712 4. 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--- title: Delayed seropositivity is associated with lower levels of SARS-CoV-2 antibody levels in patients with mild to moderate COVID-19 authors: - Marwa M. Fekry - Hanan Soliman - Mona H. Hashish - Heba S. Selim - Nermin A. Osman - Eman A. Omran journal: Journal of the Egyptian Public Health Association year: 2023 pmcid: PMC10027427 doi: 10.1186/s42506-023-00131-x license: CC BY 4.0 --- # Delayed seropositivity is associated with lower levels of SARS-CoV-2 antibody levels in patients with mild to moderate COVID-19 ## Abstract ### Background Patients with COVID-19 can develop a range of immune responses, including variations in the onset and magnitude of antibody formation. The aim of this study was to investigate whether SARS-CoV-2 antibody levels vary in patients with mild to moderate COVID-19 in relation to the onset (days) of their post-symptom seropositivity and to explore host factors that may affect antibody production ### Methods This was a prospective, multiple measurements study involving 92 PCR-confirmed patients with mild to moderate COVID-19. Antibody testing for anti-nucleocapsid (anti-NP) and spike proteins (anti-S) was performed using ELISA tests. Serum samples were collected over a period of 55 days from symptom onset of COVID-19 infection, and repeated as necessary until they turned positive. ### Results No significant differences were found between the positivity rates of anti-S or anti-NP regarding any clinical symptom ($p \leq 0.05$). The majority of patients who tested positive for anti-NP and anti-S showed early seropositivity (within 15 days of symptom onset) ($75.9\%$ for anti-NP and $82.6\%$ for anti-S). Younger patients, those without chronic diseases, and non-healthcare workers had the highest percentage of seroconversion after day 35 post-symptom onset ($$p \leq 0.002$$, 0.028, and 0.036, respectively), while older patients and those with chronic diseases had earlier seropositivity and higher anti-NP levels ($$p \leq 0.003$$ and 0.06, respectively). Significantly higher anti-S ratios were found among older ($$p \leq 0.004$$), male ($$p \leq 0.015$$), and anemic patients ($$p \leq 0.02$$). A significant correlation was found between both antibodies ($$p \leq 0.001$$). At the end of the study, the cumulative seroconversion rate for both antibodies was almost $99\%$. ### Conclusions Some COVID-19 patients may exhibit delayed and weak immune responses, while elderly, anemic patients and those with chronic diseases may show earlier and higher antibody responses. ### Supplementary Information The online version contains supplementary material available at 10.1186/s42506-023-00131-x. ## Introduction In December 2019, an outbreak of coronavirus infectious disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), became a pandemic with a significant public health impact [1]. Patients with mild COVID-19 typically experience symptoms such as fever, fatigue, sore throat, cough, and others, but no lower respiratory symptoms. In contrast, patients with moderate COVID-19 exhibit additional evidence of pneumonia but their oxygen saturation is still ≥ $94\%$ on ambient air [1, 2]. Due to limited medical resources, a large number of suspected or confirmed mild-to-moderate COVID-19 patients are not hospitalized, and are instead isolated and treated at home [3]. SARS-CoV-2 contains several immunogens, importantly, the spike (S), and nucleocapsid protein (NP). Antibodies against NP indicate previous exposure to the virus and are highly abundant, sensitive, and immunogenic. Anti-S immunoglobulins (IgGs) reflect the immune status and correlate well with neutralizing antibodies [4, 5]. Two opposing theories have been proposed regarding the role of antibodies in viral infections. The first theory suggests that antibodies have a protective role in clearing the viral infection, while the second theory suggests they could lead to antibody-dependent enhancement and deterioration in clinical severity [6]. A systematic review of 150 studies reported that the IgG levels peak between weeks 3 and 7 post-symptom onset [7]. Reviews of the published literature indicate that more than $90\%$ of patients develop IgG seropositivity following primary infection, with rates ranging between $91\%$ and $99\%$ in large studies [7, 8]. Monitoring anti-SARS-CoV-2 immune response can help predict the chances of re-infection. It is still not clear why some patients experience delayed or absent seropositivity following confirmed COVID-19 infection, and whether specific host factors contribute to this [9]. This study aimed to investigate the levels of SARS-CoV-2 antibodies in relation to the onset of seropositivity among mild-to-moderate COVID-19 patients. Additionally, factors associated with weaker and delayed seroconversion were studied. ## Study design and sample size calculation This prospective study involved repeated sample measurements within a 55-day period, through the period from June 2020 to December 2020 which coincided with the first and second waves of COVID-19 in Egypt. During this period, the predominant Pango lineages in Egypt were B, B.1, B.1.1, B.1.1.1, B.1.1.7 (alpha variant), B.1.170 and C.36 [10]. The sample size was calculated by assuming a seroconversion rate of $85\%$ with a $10\%$ error, an alpha of 0.05, and $80\%$ power, resulting in a minimum required sample size of 75 patients [11]. To account for anticipated dropouts, the sample size was increased to 92. The sample size was calculated using G* power 3.1.9.6. Patients were consecutively contacted and recruited through web-based invitations following their PCR-confirmed results until the required sample size was reached. A structured interview questionnaire sheet was designed and completed for each patient, including socio-demographic data, clinical data such as symptoms at the time of the first sample collection, and history of contact with COVID-19-positive patients. The results of chest computed tomography (CT) were recorded at the initial presentation. Serum samples were collected at pre-defined intervals according to serostatus since the onset of COVID-19 symptoms: 15 days, 25 days, 35 days, 45 days, and 55 days. No samples were taken before “day 15” of symptom onset, as IgG antibodies require time to appear in serum. These time points were chosen to monitor the onset of seroconversion and its association with antibody titer. Samples showing positive results (seroconversion) for either antibody were not repeated, i.e., samples were repeated only when both antibodies showed negative results. Results of antibody levels and onset of post-symptom seroconversion were interpreted in relation to patient-associated risk factors such as age, gender, comorbidities, and others. ## Ethical considerations Informed consent was obtained from each patient. The study was conducted in compliance with the Helsinki Declaration and was approved by the “Ethics Committee” of the High Institute of Public Health, Alexandria University. Participants were informed of their right to discontinue their participation in the study at any time without consequences. Anonymity and confidentiality were assured. ## Patient selection The 92 enrolled patients were all adults who were home-isolated and confirmed by PCR to have had COVID-19 diagnosis, 15 days before our sample collection. All patients had mild-to-moderate COVID-19 infection and were treated and monitored by their physicians. All symptomatic patients reported that this was their first COVID-19 attack. Asymptomatic home-isolated close contacts were also included in our study. Asymptomatic individuals were defined as PCR-confirmed SARS-CoV-2 cases who did not exhibit any clinical symptoms including fever, upper respiratory symptoms, pneumonia, fatigue, and gastrointestinal symptoms at the time of testing and remained asymptomatic until the end of the study. ## Sample collection and processing Blood samples of 3 ml were collected from each patient and divided into two portions. Two ml of blood were taken in EDTA tubes for measuring hemoglobin, white blood cell counts (WBCs), and lymphocytes. One milliliter of blood was kept in a plain tube for antibody detection. Blood samples collected for antibody detection were centrifuged at 3000 rpm, and the sera were separated and stored at − 20 °C until testing. Antibodies against the viral nucleocapsid were detected using a commercially available electrochemiluminescence immunoassay kit “Elecsys anti-SARS-COV-2 kit (Roche, Basel, Switzerland) run on the Cobas® e411 automated platform (Roche Diagnostics). Serum samples were also tested for the detection of immunoglobulin class IgG against the S1 domain of the viral spike protein using the anti-SARS-CoV-2 sandwich enzyme-linked immunoassay technique (ELISA) (EuroImmun, Lübeck, Germany) According to the manufacturer’s instructions, the results of the anti-NP test were interpreted as follows: values of the cut-off index (COI; signal sample/cut-off) < 1.0 were considered negative, while results ≥ 1.0 were considered positive. COI values were also recorded numerically (as recommended by several authors) [11, 12]. According to the manufacturer, the overall agreement of this anti-NP kit with a pseudo-viral neutralization test was found to be $87.0\%$, with a specificity of $99.80\%$, and a sensitivity of $99.5\%$ (calculated at ≥ 14 days post-PCR confirmation). According to the manufacturer’s instructions, the results of the anti-spike IgG ELISA test were evaluated semi-quantitatively by calculating the ratio of sample extinction to that of the calibrator. Results were interpreted as follows: ratios < 0.8 were negative, ratios ≥ 0.8–< 1.1 were borderline, and those ≥ 1.1 were considered positive. According to the manufacturer, the sensitivity of this test was $94.4\%$ after 10 days from symptom onset, and its specificity was $99.6\%$ [13]. Males were categorized as “anemic” if their hemoglobin level was < 13 g/dl, while females were considered anemic if their hemoglobin level < 12 g/dl. The normal lymphocytic count was considered to be 1000–4800 while lymphopenia was indicated at lower levels and lymphocytosis at levels > 4800 cells/cmm3. Normal WBC was considered to be 4000–11,000 cells/cmm3 [14]. In case of negative results for either antibody, serum samples were followed by repeated collection and measurement at 10-day intervals until seroconversion occurred or until the end of the 55 days decided for the end of the study. Laboratory and radiological results were only performed for the initial samples and were not repeated for subsequent samples. ## Statistical analysis Statistical analyses were performed using IBM (SPSS) Statistics Version 24.0* software. Qualitative data were presented using frequency and percentage while quantitative data were described, and tests of significance were determined after checking normality using the Kolmogorov–Smirnov (K-S test) and Shapiro tests. The Mann-Whitney test and Kruskal-Wallis test were used to compare the variables between the two independent groups and more, respectively. The Friedmann test was used to compare the ratio/ COI among consecutive samples. A proportional Z test was conducted to compare single categories between two independent groups. The Spearman correlation test was used to test the association between quantitative parameters. The Kappa test was used to test the positive agreement between the studied antibodies. The McNemar-Bowker test was conducted to check the significant risk categories in determining the positivity of samples. A significance level was set below $5\%$ [15]. Since laboratory and radiological test results were only done at initial presentation (first sample only), all statistical associations and correlations between laboratory results/clinical symptoms with antibody levels were only analyzed for the antibody results of the first samples. ## Results Of the 92 COVID-19 patients who were home-isolated, the majority ($70.7\%$) were between the ages of 30 and 59 years, while $16.3\%$ were over 60 years old. Females were predominant ($59.8\%$), including two pregnant women. The majority of participants ($38\%$) did not work, while $34.8\%$ were healthcare workers and the remaining $27.2\%$ had other occupations. Almost half of the patients ($51.1\%$) did not have any chronic disease, while $16.3\%$ had hypertension and $23.9\%$ had more than one chronic disease. A history of contact with a confirmed COVID-19 case was reported in $60.6\%$ of patients, while $23.9\%$ reported having a confirmed COVID-19 case in their households. The majority of patients ($73.9\%$) had pneumonia as evidenced by chest CT. Fever was the most common symptom among patients ($85.9\%$), followed by respiratory symptoms ($69.6\%$), myalgia, and arthralgia ($46.7\%$ %). The rates of loss of taste and smell and the presence of gastrointestinal manifestations (nausea, vomiting, and diarrhea) were comparable ($40.2\%$ and $34.8\%$, respectively). Sore throat, sneezing, rhinorrhoea had a similar prevalence among these patients ($27.2\%$, $3.3\%$, and $3.3\%$, respectively). The median (IQR) of WBCs was 4150 [1787] cells/cmm3, and for hemoglobin was 12.85 (1.60) g/dl, while the median lymphocytic count was 1320 [580] cells/cmm3. Three individuals remained asymptomatic until the end of the 55-day study period despite having close contact with symptomatic cases. One of them was a 53-year-old female nurse with diabetes and a normal chest CT, who tested positive for both anti-NP and anti-S antibodies after 13–15 days. The second asymptomatic patient was a 54-year-old, male smoker, with pneumonia on CT, who tested positive for anti-NP at day 35 and for anti-S at day 25. The third asymptomatic patient was a 29-year-old male smoker, who had a positive anti-NP result at day 35 and a positive anti-S result at day 45. All patients, except for two, achieved seroconversion within 55 days. One patient was seronegative for anti-NP and another one was seronegative for anti-S antibodies. Both patients refused further follow-up for their antibodies. An 18-year-old female with systemic lupus erythematosus and on immunosuppressive therapy showed seroconversion for both antibodies only at day 55. This patient had. Both pregnant women in our study showed seroconversion for both antibodies in their initial samples (day 15), and thus did not require further sample collection. There was no significant difference in the positivity of either anti-S or anti-NP with any of the clinical symptoms ($p \leq 0.05$ using the Z test, data not shown). Overall, the anti-S was more seroprevalent in patients with any the symptoms (except sneezing and rhinorrhea) compared to the anti-NP. Fever, respiratory symptoms, and sore throat had the highest seropositivity of anti-S (ranging between $84\%$ and $84.8\%$) (Fig. 1). Fig. 1Clinical symptoms at initial presentation and antibody positivity (anti-NP and anti-S) of 92 patients with mild-moderate COVID-19 Pairwise comparison showed a significant difference between the titers of the samples showing seroconversion on days 15, 25, and 35. There was a significant difference ($$p \leq 0.03$$) in the median (min–max) COI values of anti-NP corresponding to the onset of seropositivity, which were as follows: 4.3 (0.08–140) on day 15, 2.23 (0.11–17.6) on day 25, and 1.54 (0.10–8.15) on day 35. There was a borderline significant difference ($$p \leq 0.05$$) between the median (min–max) ratios of anti-S, which seroconverted at different onsets: 3.39 (0.24–122) on day 15, 1.9 (0.5–4.5) on day 25, and 0.89 (0.45–1.11) on day 35. There was a significant difference ($$p \leq 0.01$$) between the results of the anti-NP and anti-S only in samples that seroconverted early (day 15), while this difference was not observed between both antibodies in samples that seroconverted on days 25 or 35 (probably due to their small sample size) (Table 1). The two samples showing seroconversion at days 45 and 55 were excluded from this table due to their statistical insignificance. Table 1Mean and median of SARS-CoV-2 anti-NP (COI) and anti-S (ratio) after 15, 25, and 35 days after onset of symptoms of COVID-19 among 92 home-isolated patients, Alexandria, EgyptAntibody levelp valueDays after onset of COVID-19 symptomsCOI of anti-NPRatio of anti-S15 days Mean ± SD11.73 ± 20.018.8 ± 18.9 Median (min–max)4.3 (0.08–140)3.39 (0.24–122)0.01* b25 days Mean ± SD4.1 ± 4.91.9 ± 1.150.23 b Median (min–max)2.23 (0.11–17.6)1.9 (0.5–4.5)35 days Mean ± SD2.61 ± 2.940.83 ±.32 Median (min–max)1.54 (0.10–8.15)0.89 (0.45–1.11)0.31 bP value0.03* a0.05 aa$P \leq 0.05$ (significant) using Friedman testb$P \leq 0.05$ (significant) using Wilcoxon signed rank test Table 2 shows that there was no statistically significant difference between the seroconversion rates for both antibodies. Table 2Seroconversion of anti-NP and anti-S antibodies in relation to the days elapsed after the onset of symptoms of COVID-19 among 91 home-isolated patients, Alexandria, EgyptaDays after onset of symptomsAnti-NPAnti-SN%N%p$156975.9\%$$7682.6\%$$0.283251617.6\%$$1112.0\%$$0.4133555.4\%$$22.2\%$$0.4594500.0\%$$11.1\%$$0.9995511.1\%$$11.1\%$0.477$p \leq 0.05$ (significant)aOne patient remained negative for anti-NP and another patient remained negative for anti-spike antibodies beyond 55 days after onset of symptoms and therefore they were not included in this table There was substantial agreement between the results of anti-NP and anti-S antibodies (Kappa = 0.64 with p value = 0.0001). The agreement was calculated for the first samples only, as they constituted the majority of samples ($\frac{69}{92}$ samples were positive for anti-NP and $\frac{76}{92}$ were positive for anti-spike). A significant correlation was found between both antibodies ($$p \leq 0.001$$). At the end of the study, the cumulative seroconversion rate for anti-NP and anti-S was $98.9\%$ and $99\%$, respectively. Using the Spearman correlation test, a significant correlation was found between age and both anti-NP ($$p \leq 0.0001$$, $r = 0.428$) and anti-S ($$p \leq 0.003$$, $r = 0.303$). No statistical correlations were found between either of the antibodies or white blood cell count (WBCS), lymphocytic count, or hemoglobin (data not shown). The majority of patients showed anti-NP and anti-S seroconversion in their first sample (15 days post-symptom onset). This was statistically significant for anti-NP regarding age, occupation, and the presence of chronic diseases ($$p \leq 0.02$$, 0.036, and 0.028 respectively). It was found that $73.4\%$ of patients who were 30–59 years old, and all patients ≥ 60 years, showed anti-NP seroconversion after 15 days, compared to only $58.3\%$ of patients < 30 years old. One-third of patients < 30 years old seroconverted for anti-NP after 35–55 days. Regarding occupation, healthcare workers (HCWs) showed anti-NP seroconversion in $80.6\%$ of their samples after 15 days. This was in contrast to non-healthcare workers, who showed relatively later seroconversion ($$p \leq 0.036$$). It was also noticed that, among patients with chronic diseases, $86\%$ of them showed anti-NP seroconversion on day 15, compared to $66.7\%$ of patients without chronic diseases (Table 3).Table 3Demographic data and risk factors of 91 COVID-19 home-isolated patients according to the onset of positivity of SARS-CoV-2 anti-NPDemographic data and risk factorsDays from symptom-onset till anti-NP positivityp value15 days25 days≥ 35 daysTotalNo$.\%$No$.\%$No%Age categories(years) 30758.318.3433.3120.002* 30–594773.41523.523.164 60+1510000.000.015Sex Male3183.838.138.1370.158 Female3870.31324.135.654Occupation Not working2674.3822.912.9350.036* Healthcare workers2580.6619.400.031 Non-healthcare workers18722852025Symptoms Asymptomatic133.300.0266.731 Symptomatic6877.31618.264.588Chronic diseases No3266.71020.8612.5480.028* Yes37866140043Smoking Non-smoker5173.91420.345.8690.46 Smoker1881.829.129.122Contact with confirmed case No2877.8616.725.5361 Yes4174.51018.247.355Confirmed household No5578.61115.745.7700.41 Yes1466.7523.829.521Chest scan Not done763.6218.2218.2110.813 Normal583.3116.7006 Pneumonia5176.11217.94667 Bronchitis685.7114.3007Hemoglobin level Anemic2586.2413.800290.19 Non-anemic44711219.469.662WBC count Less than 40003075717.537.5400.86 Normal (4000–11,000)38769183650 More than 11,000110000001Lymphocytic level Lymphopenia00000001 Normal6575.61517.46786 Lymphocytosis480120005$p \leq 0.05$ (significant) using McNemar-Bowker test There was a borderline significant association between age and seroconversion of anti-S ($$p \leq 0.051$$ (Table 4).Table 4Demographic data and risk factors of 91 COVID-19 home-isolated patients according to the onset of positivity of SARS-CoV-2 anti-SDemographic data and risk factors Days from symptom-onset till anti-S positivityp value15 days25 days≥ 35 daysTotalNo$.\%$No$.\%$No$.\%$NAge categories < 301083.300216.7120.051 30–595179.71117.223.164 60+15100000015Sex Male3491.925.412.7370.215 Female4277.8916.635.654Occupation Not working2982.9514.312.8350.34 Healthcare workers2787.1412.90031 Non-healthcare workers20802831225Symptoms Asymptomatic133.3133.3133.331 Symptomatic7583.41112.244.490Chronic diseases No4081.6510.248.2490.19 Yes3685.7614.30042Smoking Non-smoker5782.61014.522.9690.23 Smoker1986.414.529.122Contact with confirmed case No3391.7383.300360.16 Yes4378.2814.647.255Confirmed household member No6188.468.722.9690.08 Yes1568.2522.729.122Chest scan No763.619.1327.3110.06 Normal583.3116.7006 Pneumonia5885.3913.211.568 Bronchitis610000006WBC count Less than 40003382.541037.5400.53 Normal (4000–11,000)42847141250 More than 11,000110000001Hemoglobin level Anemic279026.713.3300.82 Non-anemic4980.3914.834.961Lymphocytic level Lymphopenia00000000.6 Normal7283.71011.644.786 Lymphocytosis480120005$p \leq 0.05$ (significant) using McNemar-Bowker test Older age was associated with higher values of anti-NP COI, with patients aged ≥ 60 years having the highest COI (median = 10.4), followed by those aged 30–59 years (median = 4.3), and then the youngest patients (< 30 years) (median = 2.5), $$p \leq 0.003$$). Significantly higher antibody ratios were also found among patients with chronic diseases (median (IQR) = 8.99 (22.9) compared to those without chronic diseases (median (IQR) = 3.40 (6.59), $$p \leq 0.006$$ (Supplement, Table S1). Older age was associated with a higher anti-spike ratio, with patients aged 30–59 years, followed by those aged ≥ 60 years, having a significantly higher median (IQR) antibody ratio compared to younger patients (< 30 years) (ratios = 2.91 (4.6), 2.84 (7.9), and 2.28 [5], respectively, $$p \leq 0.004$$). Significantly higher antibody ratios were also found among males than females (ratio = 5.4 versus 2.4, respectively, $$p \leq 0.015$$). Anemia was also associated with higher anti-spike ratios compared to normal hemoglobin levels (ratio = 4.80 (9.98) versus 2.80 (4.76), $$p \leq 0.02$$) (Supplement, Table S2). ## Discussion There are several uncertainties regarding the timing, quantity, kinetics, and persistence of SARS-CoV-2 antibody production. Although most individuals produce antibodies following infection, some patients either experience delayed seroconversion or do not generate an immune response at all [13, 16]. In our study, fever was the most common symptom among patients ($85.9\%$), followed by respiratory symptoms ($69.6\%$) and myalgia and arthralgia ($46.7\%$ %). Zhang et al. [ 17] reported that fever was the most common symptom ($75.8\%$), while Guan et al. [ 18] reported a lower percentage of fever among similar patients ($48.7\%$). No statistical differences were observed between either of the two antibodies examined and any of the symptoms studied. In a similar study, fever and body ache were found to be correlated with higher antibody levels [19]. Differences between studies could be attributed to variations in disease severity, antibody kits used, and differences in sample collection. The importance of asymptomatic infections lies in their ability to spread the infection in the community without taking precautionary measures, as they are not aware of their infectious state [4, 6]. A meta-analysis revealed that $17\%$ of the total PCR-confirmed COVID-19 patients were asymptomatic [20]. In our study, only $3.3\%$ of home-isolated patients were asymptomatic, which is comparable to another study reporting $2.1\%$ of home-isolated patients without symptoms [13]. However, an Egyptian study among HCWs reported a much higher rate of asymptomatic cases, where anti-S seropositivity was found in $39.1\%$ of unvaccinated participants. This might be due to their higher exposure to COVID-19 infection [21]. It is worth noting that one of our asymptomatic patients had pneumonia detected by CT, highlighting the importance of careful screening and follow-up of such cases. A meta-analysis reported that the asymptomatic rates were significantly lower among the elderly compared with children [13]. Another study found that asymptomatic COVID-19 persons were significantly more likely to be females and younger than symptomatic patients (38 versus 52 years) [16]. However, associated factors for the asymptomatic state could not be studied in our work due to the small numbers of asymptomatic participants. We observed that our three asymptomatic patients were seropositive at different time points. Only one asymptomatic patient in our study seroconverted early while the other two seroconverted more than 25 days after PCR positivity. The significance of delayed seroconversion in some patients is still unclear [22]. Unfortunately, in our study we could not assess the effect of viral load on symptomatic/asymptomatic states as we could not obtain data on viral load for all patients. We found that the rate of seroconversion decreased with time, and late seroconverters also had a significantly lower antibody response compared to early-sero-converters. This finding is consistent with the results of Lucas et al., who also reported that antibody responses that develop within 14 days of symptom onset correlated with recovery, whereas those induced at later time points appear to lose this protective effect [23]. The time to seroconversion of anti-NP was significantly associated with age ($$p \leq 0.002$$), chronic diseases ($$p \leq 0.028$$), and occupation ($$p \leq 0.036$$) but not with sex. Additionally, the median anti-NP COI value, anti-S seropositivity, and ratio were all higher in the older age group indicating a stronger humoral response among older patients. This finding is consistent with the study by Amjadi et al. [ 19]. In contrast, other studies have reported an impaired antibody response with aging, attributed to B cell contraction and impaired antibody production following infections and vaccination [23]. Liu et al. did not find any associations between any demographic, clinical, and laboratory data with serostatus, although they reported a trend for increasing antibody positivity with increasing symptom severity [24]. In our study, we found significantly higher median anti-S ratios among males compared to females ($$p \leq 0.015$$). This finding is consistent with results reported by Amjadi et al. [ 19] who found that males had higher antibody levels and were also correlated with severe disease among hospitalized patients. This suggests that, males who are not hospitalized may be at greater risk of developing the severe disease compared to females. In our work, it was observed that, older participant and those with anemia or chronic diseases had a higher and/or earlier immune response. Consistent with this finding, another study found that patients who tested positive for total antibodies were more likely to be diabetic or have an underlying malignancy than those who tested negative [18]. Amjadi et al. also reported in their study that greater disease severity, older age, male sex, and higher Charlson Comorbidity Index scores consistently correlated with higher antibody titers [19]. However, other reports suggest that older age and obesity may impair antibody responses [23, 25]. The contradictory results regarding differences in antibody production in relation to comorbidities warrant further investigations into their possible mechanisms. To better understand the determinants of immune response in COVID-19 patients and improve the early management of high-risk patients, more studies need to investigate the differences in risk factors. Long et al. [ 8] and Egger et al. [ 9] found a correlation between levels of COIs of anti-NP-positive patients and viral loads in their throat samples by PCR. They concluded that numeric values of this assay could be used to indicate the magnitude of antibody response. In our study, the mean value of COI of anti-NP was 11.73 ± 20.01, and the mean ratio of anti-S was 8.8 ± 18.9. The lower antibody levels in our study compared to those in other studies might be due to the difference in disease severity. Our study included only mild to moderate cases while other studies reported higher antibody levels in more severe COVID-19 patients [14]. In this study, we found that within 15 days after symptoms appeared a higher percentage of participants had positive anti-S compared to anti-NP ($82.6\%$ versus $75\%$). These results are consistent with the findings of Orth-Höller et al., who reported positive IgG titers in most mild and moderate patients after 2 to 3 weeks [26]. Similar to our findings, a study found that 1–3 months after symptoms, $98.3\%$ of mild-moderate patients tested positive for anti-S compared to $85.6\%$ for anti-N [27]. However, some researchers observed that anti-NP tend to appear earlier and are more sensitive than anti-S antibodies [5]. Our current study has shown a very high cumulative seropositivity rate ($99\%$) by the end of the study period (55 days after symptom onset) which is consistent with previous studies [7, 8]. However, this is in contrast to the results of a study from Japan that reported anti-S seropositivity of only $47.8\%$ in similar patients [6]. As our study has shown delayed or absent antibody production in some patients we recommend repeating negative antibody testing for clinically suspected patients who show seronegativity, until an average period of one month has elapsed. It is important to note that antibody testing results reflect a history of exposure rather than a recent infection diagnosis. In our study, we found that two patients failed to seroconvert at the end of the 55 days. One patient remained anti-NP seronegative, while the other remained seronegative for anti-S. Other researchers have reported similar findings of serostatus suggesting that a small proportion of patients may have difficulty in rapidly developing immunity against SARS-CoV-2 and that their management needs to be more cautious as they may be prone to recurrence or re-infection [3]. It is also likely that seroconverters and non-seroconverters will probably also respond differently to vaccination. Recent studies revealed that seropositive persons have a heightened antibody response even after the first dose of vaccine, than those with weaker antibody responses. Additionally, COVID-19 patients who have been confirmed by PCR may be less inclined to get vaccinated, believing they are no longer at risk of infection. However, observations on the serostatus of infected persons in our study contradict this assumption [24]. Our results on seroconversion following infection by SARS-CoV-2 should be interpreted in the context of the prevailing viral variants in the country at the time of the study. Different viral variants can produce variable immune responses in terms of magnitude and neutralizing abilities. For instance, a study on B.1.1.298 variant showed a 10-fold lower antibody titer 24 h after inoculation compared to other SARS-CoV-2 strains [28]. Similar findings have been reported in studies on Omicron sub-lineages. The differences in antibody production can be attributed to antigenic differences between viral variants [29]. Moreover, it is expected that antibody responses will vary between different groups based on their characteristics and co-morbidities. ## Study strengths and limitations Identification of factors associated with delayed seroconversion following infection can help in identifying individuals who are likely to show similar seronegativity after vaccination. In our study, clinical symptoms, and laboratory and radiological results were only recorded at the time of the first sample. So, they were studied in relation to the values of first tests for anti-S and anti-NP. A limitation of the study was that further samples were not studied with respect to the clinical condition of the patients. This should be considered in future studies. Association with PCR viral load could be also of value for correlation with antibody levels. Further studies with larger sample sizes are recommended to confirm of the reported risk factors, allowing the regression analysis model to identify predictors of weak and delayed seropositivity. ## Conclusions The majority of mild-moderate COVID-19 patients showed seropositivity within 25 days following symptom onset. However, a minority of patients showed delayed seroconversion or did not convert at all. Early seroconverters were found to have a higher magnitude of antibody response when compared to delayed seroconverters, who had lower antibody levels. Host factors such as old age, chronic diseases, anemia, and male sex were found to be associated with early converters and those with heightened immune responses. ## Supplementary Information Additional file 1: Table S1. SARS-CoV-2 anti-NP (COI) in relation to demographic data and risk factors of 92 COVID-19 home-isolated patients. Table S2. 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--- title: Functional characteristics of hub and wave-initiator cells in β cell networks authors: - Marko Šterk - Jurij Dolenšek - Maša Skelin Klemen - Lidija Križančić Bombek - Eva Paradiž Leitgeb - Jasmina Kerčmar - Matjaž Perc - Marjan Slak Rupnik - Andraž Stožer - Marko Gosak journal: Biophysical Journal year: 2023 pmcid: PMC10027448 doi: 10.1016/j.bpj.2023.01.039 license: CC BY 4.0 --- # Functional characteristics of hub and wave-initiator cells in β cell networks ## Abstract Islets of Langerhans operate as multicellular networks in which several hundred β cells work in synchrony to produce secretory pulses of insulin, a hormone crucial for controlling metabolic homeostasis. Their collective rhythmic activity is facilitated by gap junctional coupling and affected by their functional heterogeneity, but the details of this robust and coordinated behavior are still not fully understood. Recent advances in multicellular imaging and optogenetic and photopharmacological strategies, as well as in network science, have led to the discovery of specialized β cell subpopulations that were suggested to critically determine the collective dynamics in the islets. In particular hubs, i.e., β cells with many functional connections, are believed to significantly enhance communication capacities of the intercellular network and facilitate an efficient spreading of intercellular Ca2+ waves, whereas wave-initiator cells trigger intercellular signals in their cohorts. Here, we determined Ca2+ signaling characteristics of these two β cell subpopulations and the relationship between them by means of functional multicellular Ca2+ imaging in mouse pancreatic tissue slices in combination with methods of complex network theory. We constructed network layers based on individual Ca2+ waves to identify wave initiators, and functional correlation-based networks to detect hubs. We found that both cell types exhibit a higher-than-average active time under both physiological and supraphysiological glucose concentrations, but also that they differ significantly in many other functional characteristics. Specifically, Ca2+ oscillations in hubs are more regular, and their role appears to be much more stable over time than for initiator cells. Moreover, in contrast to wave initiators, hubs transmit intercellular signals faster than other cells, which implies a stronger intercellular coupling. Our research indicates that hubs and wave-initiator cell subpopulations are both natural features of healthy pancreatic islets, but their functional roles in principle do not overlap and should thus not be considered equal. ## Significance Healthy pancreatic islets contain hundreds of β cells that operate in synchrony to secrete pulses of insulin and thereby ensure metabolic homeostasis. The collective activity within these functional syncytia is influenced by two subpopulations of β cells, namely hubs and wave-initiator cells. The latter operate as triggers of intercellular Ca2+ signals that synchronize the cells, while the former facilitate their spreading due to their exceptional role in the multicellular network. Here, we determine signaling characteristics of β cell populations while they are still embedded in pancreatic tissue, assess their potential overlap, and their persistency over time. Understanding how the collective rhythmicity is established within heterogeneous cellular subpopulations is of paramount importance also to assess the changes associated with the pathogenesis of diabetes. ## Introduction Decoding cellular responses to changes in the environment is of fundamental importance to our understanding of living systems [1]. While in the past most of the studies focused on isolated cells or population averages, the scope is nowadays shifting toward networked cell populations. This approach is also being applied to the insulin-secreting β cells from pancreatic islets of Langerhans. These microorgans orchestrate oscillations in the circulating insulin with a period of about 3–15 min, which is crucial for maintaining normal homeostasis of glucose and other nutrients [2,3]. β Cells are the most prevalent cell type within islets [4,5,6], and are nutrient-sensing units that respond to glucose stimulation with two distinctive phases [7,8,9,10,11,12]. Following stimulation and after a time needed for β cells to metabolize glucose, β cells respond with an initial transient increase in intracellular Ca2+ concentration ([Ca2+]IC). This phase is typically referred to as the first phase of response. Afterward, β cells exhibit repetitive [Ca2+]IC oscillations that persist during the course of stimulation, and this phase is named the second or the sustained phase of response. Both phases were found to be strongly glucose dependent [11,13,14]. Most importantly, β cells do not interact only with their environment but also among themselves. They are strongly electrically coupled and form a functional syncytium. Cell-to-cell interactions encompass direct electrical coupling through gap junctions composed of connexin36, as well as by paracrine, autocrine, and juxtacrine signaling [15,16,17,18,19]. Intercellular coupling is essential for the coordination of cellular responses through which insulin is released in proportion to stimulation and metabolic demands [17,20,21,22,23]. If, however, cell-to-cell communication is impaired, the coherent patterns of cellular activity are abolished, leading to dysregulated plasma insulin oscillations and to glucose intolerance [21,24], as observed in numerous models of obesity and diabetes mellitus [20,25,26,27,28]. Early studies assumed that β cell populations are rather homogeneous, but the subsequent functional analyses have discovered a remarkable degree of heterogeneity that manifested itself on the transcriptomic [29,30,31,32,33], metabolic [34], electrophysiological [31,32,35,36,37,38], calcium [11,35,39], and secretory [40,41] levels. This heterogeneity has important functional implications, as the presence of specialized subpopulations of β cells probably has a significant impact on how the cells respond to changes in the biochemical composition of their environment, on their collective activity, and consequently on insulin release [7,42]. In recent years, advances in multicellular imaging of [Ca2+]IC accompanied by network analyses have become an indispensable tool for investigating how cellular heterogeneity within islets affects their collective activity and function [27,43,44,45,46,47,48,49,50]. Historically, network analysis focused mainly on the response during the sustained glucose stimulation. It is now generally accepted that, during this phase a subpopulation of cells (termed hub cells) shows a disproportionally high number of functional connections with other cells [35,43,44,46,49,51,52,53,54]. A relatively high fraction of hub cells, short internodal distances, and highly clustered organization, all characteristics of broad-scale small-world networks, are believed to enhance communication capacity and robustness to perturbations within islets [7,44,55]. The role of hub cells and their impact on islet function has been extensively debated, offering opposing views on the matter. In principle, electrophysiologists are in general skeptic about the concept of exceptional cells, whereas imaging and molecular biology experts are vigorously defending these ideas [46,53,56,57,58,59,60]. Currently, the presence of hub cells in β cell functional networks is well acknowledged, but their exact functional roles have yet to be determined. It is important to note that broad-scale small-world functional networks with hub cells can arise due to heterogeneous nearest-neighbor coupling of heterogeneous β cells that are synchronized by propagating intercellular waves, without the need for physical long-range connections or small-world networks [61]. Furthermore, [Ca2+]IC wave analyses along with photopharmacological interventions identified subpopulations of cells that rank first during a particular intercellular wave, i.e., wave-initiator cells. In the literature, these cells have often been termed pacemaker cells [43,44]. Since some authors suggest that the term pacemaker should be reserved for cells that display an intrinsic oscillatory behavior largely independent of the prevailing conditions and are necessary for oscillations to occur in other cells (which β cells do not), in this work, we refer to these cells as wave initiators [42]. In addition, since there are different types of oscillations in the islets that are possibly all synchronized by intercellular waves, we wish to underline that, in this work, we focused on the so-called fast [Ca2+]IC activity and their corresponding intercellular waves [6,62]. This subpopulation of cells elevate their [Ca2+]IC earlier than the rest of the cells during the course of wave spreading and have been reported to be characterized by elevated excitability levels, increased glucokinase activity, and higher-than-average natural frequencies [13,63,64,65]. Importantly, the wave-initiator cells should not be confused with another subpopulation, i.e., first responder cells. These cells were identified in the initial transient phase when the cells respond to stimulatory glucose levels and differ in principle from the cells that trigger the repetitive intercellular waves during sustained activity. The first responder cells have been shown to display high excitability [7,44,59] and large heterogeneity that is glucose dependent [11,66]. Thus, the existence and importance of β cell subpopulations are now generally acknowledged, and we are starting to unveil the relative contributions of these subpopulations to collective β cell activity in different phases. However, the studies described above focused on particular subpopulations and, to the best of our knowledge, none characterized the subpopulations simultaneously, thus hampering our understanding of their complex interactions. In an attempt to unify seemingly opposing and partial views on β cell heterogeneity, in this study we applied complex network-based analyses in combination with high-resolution multicellular confocal [Ca2+]IC imaging in acute mouse pancreas tissue slices. We meticulously describe the Ca2+ signaling characteristics of different β cell subpopulations and evaluate their overlap as well as their temporal stability, with a special emphasis on the recently debated relationship or overlap between hub and wave-initiator cells. ## Experimental protocol The study was conducted in strict accordance with all national and European legislation (Directive $\frac{63}{2010}$/EU) and recommendations on care and work with laboratory animals and approved by the Administration for Food Safety, Veterinary Sector and Plant Protection of the Republic of Slovenia (approval nos. U34401-$\frac{12}{2015}$/3 and U34401-$\frac{35}{2018}$-2). Acute pancreas tissue slices were prepared from nine male NMRI mice aged 2–5 months, as described previously [39,67,68]. In brief, after sacrificing the animals by cervical dislocation, the abdominal cavity was accessed via laparotomy. The common bile duct was clamped distally at the major duodenal papilla. The pancreas was injected at the proximal end with $1.9\%$ low-melting-point agarose (Lonza Rockland, ME), which was dissolved in extracellular solution (ECS) (consisting of 125 mM NaCl, 26 mM NaHCO3, 6 mM glucose, 6 mM lactic acid, 3 mM myo-inositol, 2.5 mM KCl, 2 mM Na-pyruvate, 2 mM CaCl2, 1.25 mM NaH2PO4, 1 mM MgCl2, 0.5 mM ascorbic acid), and maintained at 40°C. Following injection, the pancreas was cooled with the ice-cold ECS, extracted from the animal, placed into a petri dish containing ice-cold ECS, and cut into approximately 100 mm3 pieces, which were afterward embedded into agarose and cut with a vibratome (VT 1000 S, Leica Biosystems, Deer Park, IL) into 140 μm thick slices. Throughout the entire procedure, the ECS was continuously bubbled with a gas mixture of $95\%$ O2 and $5\%$ CO2 at barometric pressure to ensure a pH of 7.4 and proper oxygenation. During cutting slices were gently collected and transferred into a 100 mm petri dish containing 40 mL of HEPES-buffered saline (HBS) (consisting of 150 mM NaCl, 10 mM HEPES, 6 mM glucose, 5 mM KCl, 2 mM CaCl2, 1 mM MgCl2; titrated to pH 7.4 with 1 M NaOH) with 6 mM glucose at room temperature until being dyed. For dye loading, the slices were transferred into a 5 mL petri dish containing 6 μM Calbryte 520 AM (AAT Bioquest, Sunnyvale, CA), $0.03\%$ Pluronic F-127 (w/v), and $0.12\%$ dimethyl sulfoxide (v/v) dissolved in HBS for 50 min at room temperature on an orbital shaker. Unless stated otherwise, all chemicals were obtained from Sigma-Aldrich (St. Louis, MO). Individual stained slices were placed into the recording chamber for microscopy, continuously perfused with carbogenated ECS containing 6 mM glucose at 37°C. Perfusate was changed manually to ECS containing 8 or 12 mM glucose concentration at 37°C for 40 or 20 min, respectively, to stimulate β cells. The slice was reintroduced to the perfusate containing 6 mM glucose in ECS for at least 15 min after stimulation. For confocal functional multicellular Ca2+ imaging, we used a Leica TCS SP5 AOBS Tandem II upright confocal microscope system with a Leica HCX APO L water immersion objective (20×, NA = 1.0). Fluorophore was excited two to three cell layers deep in the tissue [39] with a 488 nm argon laser to avoid damaged cells at the slice surface. The fluorescence was detected with a Leica HyD hybrid detector in the range of 500–700 nm (all from Leica Microsystems, Wetzlar, Germany). Before and after the time series, high-resolution (1024 × 1024 pixels) images were acquired to assess possible sample motions. The resolution of the image series was 512 × 512 pixels at 10 Hz allowing discrimination of individual [Ca2+]IC oscillation at single-cell resolution. Individual cells in islets were manually selected for each time series without motion artifacts as regions of interest (ROIs) [46] and exported using custom software (ImageFiltering, copyright Denis Špelič). Further analysis with in-house MATLAB scripts included a combination of linear and exponential fitting to eliminate photobleaching effects [39]. ## Ca2+ signal processing and analysis of β cell activity Ca2+ traces (fluorescence signals of Calbryte 520 AM) for manually selected ROIs were exported as the F/F0 ratio employing custom software (ImageFiltering, copyright Denis Špelič). Signals with extensive artifacts or a too low signal-to-noise ratio and those with evident non-β cell-like features were excluded from further analysis. The activation delay of the first phase response, i.e., the delay for initial increase in activity following glucose stimulation [39], was assessed manually for each ROI from unprocessed time series. For the plateau phase activity, i.e., the phase of sustained oscillatory activity following the first phase, all Ca2+ traces were processed with a zero-lag band-pass filter (cutoff frequencies 0.05 and 1.0 Hz) and additionally smoothed with an adjacency averaging procedure. After smoothing, traces were binarized [48], and binarized time series were used to calculate the relative active time and the interoscillation interval variability. The former reflects the average fraction of time that cells spend in an active state with increased [Ca2+]i and was simply determined as a fraction of 1 (i.e., “on” states), whereas the latter indicates the regularity of oscillations and is defined as the ratio between the SD of interoscillation interval lengths and the corresponding mean interval length [69]. Binarized traces were also used to extract individual intercellular Ca2+ waves and to determine the activation sequence of β cells within individual waves, as explained in continuation. The methodology of Ca2+ signal processing is illustrated in Fig. 1 A.Figure 1Methodology to analyze Ca2+ signals and construct functional correlation-based β cell networks. ( A) Raw Ca2+ signal in a representative β cell (upper panel, dark gray line) during glucose stimulation with 8 mM glucose. Depicted are the activation delay after switching to stimulatory conditions, and the plateau phase of sustained oscillatory activity. The light gray line below represents the corresponding processed β cell signal. The lower panel shows a zoom-in on the plateau phase of the processed Ca2+ signal. The blue line represents the corresponding binarized activity used for further analysis. The hatched area denoted by tAT signifies the active time of one selected Ca2+ oscillation. ( B) Correlation matrix computed based on pairwise comparisons of Ca2+ signals. ( C) A representative correlation-based functional β cell network. Nodes represent the physical locations of β cells, and the connections signify cells with highly correlated cellular activity. Red dots indicate the cells with the most connections (i.e., $17\%$ hub cells with the highest node degree values). ( D) Swarm plot presenting the relative node degree distribution. Red dots indicate the $17\%$ of most connected cells (as in (C)). Individual node degrees were normalized with the islet size, i.e., the number of all cells in the islet. To see this figure in color, go online. ## Assessing hub and wave-initiator cells with network analysis We first briefly describe the general principles of network analyses that we used to determine hub and wave-initiator cells along with their signaling characteristics. Both subpopulations were in principle determined on the basis of the whole plateau phase of sustained activity (20–60 min intervals). Hub cell populations were determined from functional correlation-based networks from which we extracted $\frac{1}{6}$ of the cells with the most functional connections. To determine wave-initiator cells, we constructed network layers for each Ca2+ wave and determined the cells that were among the wave initiators—the $\frac{1}{6}$ of cells that most often activated among the first within individual waves were deemed as the wave-initiating subpopulation. Typically, a series of 80–120 wave-based network layers was used for this analysis. For the extracted subpopulations we then separately investigated whether they exhibit exceptional Ca2+ signaling characteristics and assessed their potential overlap. Specifically, we analyzed how the relative active time, interoscillation interval variability, and activation delay depend on the number of functional connections and on the initiation parameter value. It should be noted that, with the analyses of activation delays, i.e., the time lag of individual cells responding to stimulation, we studied the possible relation to the so-called first responding cell subpopulation. Moreover, we investigated the relationship between functional and wave-based network characteristics with emphasis on the relation between hub and wave-initiator cells. Finally, we also determined the temporal persistency of hub and wave-initiator cells. The latter was assessed by evaluating the role of cells between consecutive wave-based network layers. For the former, we additionally generated evolving functional correlation-based networks. Specifically, we have used rolling correlation analysis to generate a temporal series of functional networks and determined how the number of functional connections and the role of hub cells evolved throughout the whole interval of sustained activity. All methodological details are further explained in the following subsections. ## Construction of correlation-based functional networks We computed the pairwise Pearson’s correlation coefficients to quantify the collective β cell activity during the whole plateau phase of sustained activity. By this means, correlation matrices (Fig. 1 B) were determined for each islet. These were then used to construct functional networks (Fig. 1 C) in which nodes represent individual β cells, and their locations correspond to the physical positions of cells in tissue slices [54,55]. Connections were established based on the correlation matrix. To avoid an arbitrary threshold selection, we used variable thresholds to extract the networks so that the average node degree (number of connections per cell) in all islets was kavg≈8. This seemingly arbitrary number was used to mimic realistic β cell connectivity in the tissue [70]. However, it should be noted that within reasonable limits the results are qualitatively independent of the exact value [62], as we also demonstrate in our supporting material. Heterogeneity in network connectivity was assessed through the computation of node degrees. Fig. 1 D shows the distribution of node degrees for a representative islet with indicated $\frac{1}{6}$ ($17\%$) of the most connected cells, i.e., hub cells. ## Characterizing intercellular signals with Ca2+ wave-based network layers Individual intercellular Ca2+ waves were extracted from binarized cellular signals using a space-time clustering algorithm described before [48]. In short, cells i and j were considered part of the same wave, i.e., wave-network layer α, if their onsets of oscillations (Ti and Tj) were close enough in space and time. Specifically, the spatial threshold Rs was determined on the basis of the average intercellular distance and the corresponding SD: Rs=<dij>−xSD(dij), where dij is the distance between cells i and j in a given islet. For the temporal threshold we took Rt = 0.7 s, which roughly corresponds to $\frac{1}{3}$ of the total time for a wave to travel across the whole islet. Most importantly, within reasonable and physiologically meaningful values of the threshold, our analyses are qualitatively independent on the choice of their values, as we also demonstrate in the supporting material. Moreover, all cells belonging to the given Ca2+ wave were ranked by their order of activation (Fig. 2 A). Typically, between 80 and 120 waves were detected during the plateau phase per islet, and the wave front encompassed cells spanning over several tens of micrometers, implying that relatively small errors in binarization due to inherent experimental noise in the Ca2+ signal could result in inconsistent coordinates of wave initiators. In an attempt to overcome this issue, each β cell was assigned an initiation parameter. The initiation parameter was defined as the fraction of waves in which a cell was within the smallest $10\%$ of activation ranks. The distribution of the initiation parameter per islet was used to define cells with the highest tendency to initiate waves (i.e., wave initiators) using an arbitrary cutoff value of $17\%$ ($\frac{1}{6}$). Furthermore, weighted and directed connections between all cell pairs i and j within each wave-based layer α were established, and the direction was i − > j if Ti < Tj and vice versa. If the two cells activated simultaneously, they were connected with an undirected connection (Fig. 2 C). Weights of the connections were determined based on the time delays between the activations of cells i and j within the wave (\|Ti−Tj\|). Therefore, the weights reflect how fast the excitation signal traveled between different cell pairs. The average weights of individual cells determined from all temporal layers were used as a proxy for intercellular coupling, as they encode the information on how fast on average a given cell transmits intercellular waves. As demonstrated previously, the efficiency of intercellular coupling between direct neighbors depends on the gap junctional conductivity and the so-called input conductance, which is essentially the nonjunctional plasma membrane conductance, with the KATP and Ca2+-dependent potassium conductance playing major roles. To complicate things further, both junctional and membrane conductance change or may change repeatedly during the plateau phase of fast oscillations. We elaborate this into more detail in the discussion. By this means, each wave-based network layer contains a set of connections encompassing the course of the wave and the transmission delays along its path. Our analysis considered only intercellular Ca2+ waves in which more than $45\%$ of all cells in the islet participated. To assess the interwave similarity and the persistency of wave initiators, we constructed multilayer networks by stacking all waves on top of each other in chronological order for each recording (Fig. 2 D) [48], as explained in more detail in continuation. Since in this work we, for the first time, use two different kinds of networks, it is worth explicitly pointing out that they are fundamentally different. The correlation-based network (Fig. 1) is the now standard approach to constructing and analyzing functional network properties, such as the number of links, based on similarities between long Ca2+ traces that each contain a large number of fast oscillations, whereas the Ca2+ wave-based network (Fig. 2) is a new approach to quantify intercellular wave propagation by means of directed weighted graphs and each such graph is essentially based on a single intercellular wave that synchronizes a single fast oscillation, and tens of such graphs are then pooled to obtain wave network parameters, such as the initiation parameter. Figure 2Intercellular Ca2+ wave analysis. ( A) Average Ca2+ signal of a representative islet (upper panel) and corresponding raster plot with indicated oscillation onsets in individual intercellular Ca2+ waves (lower panel). The colors of dots represent the activation rank of individual cells within each wave, with red and blue colors denoting the first and last activated cell, respectively (see also the color bar in (C). Stars indicate the time point of the onsets of different Ca2+ waves. ( B) Raster plots for four subsequent Ca2+ waves (see the color of the stars to link with the data on (A)). ( C) Directed Ca2+ wave network extracted from the first Ca2+ wave in (B) in which the colors of the cells reflect the sequence of activation. The activation ranks are color coded as indicated by the color bar. ( D) The sequence of the four successive Ca2+ waves is presented as a multilayer temporal network. The connections and the colors of cells have the same meaning as in (C). To see this figure in color, go online. ## Quantifying the overlap of β cell subpopulations We quantified the extent of overlap between the four subpopulations of cells: 1) first responders (during the first phase), 2) initiators of waves (i.e., wave-initiator cells), 3) most active cells (during the plateau phase), and hub cells (cells with a high number of functional connections during the plateau phase). The subpopulations of hub and wave-initiator cells were defined above. First responder cells were defined as the top $17\%$ ($\frac{1}{6}$) of the cells that responded first to stimulatory glucose concentrations (8 or 12 mM). The most active cells were defined as the top $17\%$ ($\frac{1}{6}$) of the cells with the highest relative active time values. We computed the pairwise overlap of cellular subpopulations (Oi,j) as the ratio of the actual overlap probability (Poverlapi,j) and the probability that cells belong to both subpopulation i and j by chance (Prand):[1]Oi,j=Poverlapi,jPrandi,j∈[Hubs,Initiators,Firstresponders,Mostactive]i≠j,where Poverlapi,j=m(i∩j)N; m(i∩j) is the cardinality of the overlap of subpopulations i and j and N is the number of cells in the recording, and Prand=m(i)m(j)N2 (m(i) and m(j) are the cardinalities of sets i and j). ## Temporal persistency of the initiator and hub cells For each detected Ca2+ wave α, a set of initiator cells (Iα) was constructed based on the aforementioned $10\%$ of the first activated cells within individual Ca2+ waves. Temporal initiator cell stability IS was calculated as the relative overlap of the sets of initiator cells in waves α and α′ as the so-called Jaccard similarity:[2]ISα,α′=Isharedα,α′Iuniqueα,α′,where Isharedα,α′ is the cardinality of the intersection of the sets of initiator cells and Iuniqueα,α′ is the cardinality of the union of the two sets of initiator cells. Equation [2] yields a value between 0 and 1, where 0 means there are no cells in the intersection and 1 means a perfect intersection. The latter reflects the scenario where all initiator cells in layers α and α′ were the same. With Eq. [ 2], we constructed initiator similarity matrices for all Ca2+ wave pairs α and α′ in individual recordings and calculated the average initiator similarity IS¯(n) as a function of the temporal distance for all wave pairs that were m steps apart as:[3]IS¯$m = 1$M∑α=1MISα,α+m,where $m = 1$,2,… and M is the number of Ca2+ waves that are m steps apart. To assess the temporal persistency of functional connectivity patterns and hub cells, we computed the temporal evolution of the correlation-based network using a sliding window of Tsw=300 s. By this means we generated a temporal series of functional networks rolling over the whole plateau phase, so that networks generated in each step represent a temporal layer β with kavg≈8. To ensure comparison with the analysis of the initiator cell persistency, we used a temporal step size equal to the average interoscillation interval in each recording. In this case, the temporal distance between two layers β and βʹ roughly corresponds to the interval between two subsequent Ca2+ waves α and α′, which are, on average, also separated by the same temporal distance. The average node degree in all temporal layers and the corresponding SDs were calculated for each cell in the islet. Moreover, in each layer β, $17\%$ ($\frac{1}{6}$) of the cells with highest node degrees were designated hub cells and formed a set Hβ. Temporal hub similarity [71] was then assessed via the Jaccard similarity index as the relative overlap of the sets of hub cells in temporal layers β and β′ as:[4]HSβ,β′=Hsharedβ,β′Huniqueβ,β′,where Hsharedβ,β′ is the cardinality of the intersection of the sets of hub cells, and Huniqueβ,β′ is the cardinality of the union of the two sets of hub cells. This yields a value of HS for layers β and β′ between 0 (empty intersection) and 1 (perfect intersection), with the latter representing the scenario where hub cells in both layers β and β′ were the same. With Eq. [ 4], we constructed hub similarity matrices for all layer pairs β and β′ in individual recordings and calculated the average hub similarity HS¯(n) as a function of temporal distance for all pairs that were n layers apart as:[5]HS¯$$n = 1$$N∑β=1NHSβ,β+n,where $$n = 1$$,2,… and N is the number of all network layer pairs that are n layers apart. ## Data pooling, normalization, and statistical analysis To compare different signaling parameters from all cells in different islets, we first normalized all values with the average value of the parameter in the islet, yielding values distributed around unity. We pooled the data separately for 8 and 12 mM glucose stimulation. Next, pooled parameter values were separated into the lowest $\frac{1}{6}$, intermediate $\frac{2}{3}$, and highest $\frac{1}{6}$ tier. Statistical significance of differences (p values) between individual tiers for each parameter was calculated with the Kruskal-Wallis one-way analysis of variance on ranks with a post hoc pairwise multiple comparison procedure (Dunn’s method). In addition to p values, we also calculated the effect size δ (Cliff’s Delta) of differences between pairs of parameter tiers Qi and Qj (i,j ∈ [lowest $\frac{1}{6}$, intermediate $\frac{2}{3}$, and highest $\frac{1}{6}$], i≠j) as follows [72]:[6]δ=m(Qi>Qj)−m(Qi<Qj)m(Qi)m(Qj),where m(Qj>Qj) and m(Qi<Qj) are the cardinalities of the subsets, where Qi is larger than Qj and vice versa, and m(Qi) and m(Qj) are the cardinalities of the two tiers of the parameter. The effect size was then categorized as negligible – N (δ < 0.147), small – S (0.33 > δ ≥ 0.147), medium – M (0.474 > δ ≥ 0.33), or large – L (δ ≥ 0.474) depending on the value of δ [73]. ## Results We applied functional multicellular calcium imaging and in-depth network analysis to assess β cell roles and functional parameters within the pancreatic islets. We created functional correlation-based networks to identify highly interconnected cells that act as signal transduction hubs (i.e., hub cells). We also created intercellular Ca2+ wave-based networks to identify Ca2+ wave-initiator cells and describe the course of the intercellular signals. Recordings of β cell activity in acute tissue slices were performed either under physiological (8 mM) or supraphysiological (12 mM) glucose concentration. ## Analyzing the Ca2+ signaling characteristics of hub and wave-initiator cells First, we investigated how cellular signaling parameters relate to their corresponding node degree, i.e., the number of functional connections in the correlation-based network. Results are shown in Fig. 3 for all recordings performed with 8 mM (upper panels, cyan) and 12 mM (lower panels, blue) glucose stimulation. Data points for individual cells (gray dots) are shown in each panel, along with the collective data for cells with the $\frac{1}{6}$ lowest, $\frac{2}{3}$ intermediate, and $\frac{1}{6}$ highest correlation network node degrees (boxplots). Note that the signaling parameter values of each cell were normalized with the average value of their corresponding islet to allow the comparison between different islets. Fig. 3 A shows the relative active time of cells as a function of the node degree. A clear correlation between the number of functional connections and the relative active time was observed in both glucose concentrations, indicating that the hub cells tend to be among the most active cells. Figure 3Relationship between various Ca2+ signaling parameters and the number of functional connections in correlation-based networks. Dependence of the relative active time (A), interoscillation interval variability (B), activation delay (C), initiation parameter (D), wave-network node degree (E), and the average node weight in the wave network, i.e., average transmission delay (F) on the correlation network degree for stimulation protocols with 8 mM glucose (first and third row, cyan) and 12 mM glucose (second and fourth row, blue). Gray dots represent normalized values of individual cells, and boxplots show the lowest $\frac{1}{6}$, the middle $\frac{2}{3}$, and the highest $\frac{1}{6}$ connected cells in the functional network. All panels show the pooled data from all recordings whereby individual values were normalized by the average value of the specific parameter in the given islet. Boxes determine the interval between the 25th and the 75th percentile, whiskers denote the 10th and the 90th percentile, and lines within the boxes indicate the median. Data were pooled from the following number of islets/cells: $\frac{8}{865}$ (8 mM glc); $\frac{8}{1103}$ (12 mM glc). Statistical significance: ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$; n.s., not significant. Effect sizes: negligible – N (δ < 0.147), small – S (0.33 > δ ≥ 0.147), medium – M (0.474 > δ ≥ 0.33), or large – L (δ ≥ 0.474). To see this figure in color, go online. In contrast, a stark anticorrelation was found between the interoscillation interval variability and the node degrees (Fig. 3 B). The cells with the most functional connections exhibited the most regular calcium oscillations, and the trend was prominent under both stimulatory glucose levels. In Fig. 3 C, we show how the delay in the onset of Ca2+ oscillations after switching from substimulatory to stimulatory glucose depends on the node degree. There appears to be no correlation between activation delays and node degrees under 8 mM glucose, while under 12 mM glucose the most connected cells tended to activate first. Next, we examined the interrelations between cells' roles in the correlation-based functional networks and in the Ca2+ wave-derived network layers. Fig. 3 D shows the relation between the initiation parameter values of individual cells and the number of functional connections. It turned out that there was no discernible correlation under physiological stimulation, whereas under supraphysiological stimulation, a slight anticorrelation was inferred. These results indicate that the most connected cells in the correlation-based functional networks, i.e., hub cells, do not principally act as wave initiators in the wave-based networks of the same islets and that, under supraphysiological conditions, the waves are actually more likely to be initiated from less connected cells. Moreover, there is a powerful relation between the number of connections each cell has in the functional and Ca2+ wave-based network (Fig. 3 E), which substantiates their central role in transmitting intercellular waves. Most importantly, results in Fig. 3 F reveal a compelling correlation between the node degree in the correlation network and the average node weights in the wave-based network. The latter reflects the delays in intercellular signal transmission, which implies that hub cells exhibit a stronger intercellular coupling when compared with cells with less functional connections. It should be noted that the average node degree in the wave-based network (Fig. 3 E) portrays only the number of connections with direct neighbors in the observed focal plane, without taking into account their weights, whereas the average node weight (Fig. 3 F) is computed as the sum of all in- and out-weights of connections divided by the number of connections of this cell. In other words, hub cells, as defined by the correlation-based network, have both a higher number of direct neighbors in the wave-based network and the average phase-shift between their own oscillations and oscillations in their neighbors are significantly shorter. Finally, since theory predicts that the velocity of waves should not change with increasing glucose (see [86] and discussion for more details) we quantified the glucose dependence of node weights in the wave-based network (Fig. S1). The range of average node weights was 0.1–0.35 s and thus consistent with a wave velocity in the range of 30–100 μm/s. In hub cells, the average node weight was approximately $20\%$ lower in both glucose concentrations. Most importantly the node weight, corresponding to the temporal delay between neighboring cells, did not importantly differ between 8 and 12 mM glucose. We then examined how different cellular signaling parameters depend on the Ca2+ wave initiation parameter to characterize wave-initiator cells. Results are shown in Fig. 4, where panels in Fig. 4 A clearly demonstrate that the wave-initiating cells are the most active under physiological as well as under supraphysiological stimulation levels. Interestingly, in contrast to hub cells (Fig. 3 B), these wave-initiator cells are not exceptional in terms of the regularity of oscillations (Fig. 4 B). Fig. 4 C presents the activation delays of cells after the onset of stimulation depending on the initiation parameter. Upon stimulation with 8 mM glucose, there was a tendency of the wave initiators responding first during the first phase, whereas under 12 mM glucose, no correlation between these parameters was inferred. Figure 4Relationship between various Ca2+ signaling parameters and the wave initiation parameter. Dependence of the relative active time (A), interoscillation interval variability (B), activation delay (C), correlation-based network node degree (D), wave-network node degree (E), and average node weight in the wave network (F) on the initiation parameter for stimulation protocols with 8 mM glucose (first and third row, cyan) and 12 mM glucose (second and fourth row, blue). Gray dots represent values of individual cells, and boxplots show the lowest $\frac{1}{6}$, the intermediate $\frac{2}{3}$, and the highest $\frac{1}{6}$ connected cells in the correlation network. All panels show the pooled data from all recordings whereby individual values were normalized by the average value of the specific parameter in the given islet. Box charts are defined as in Fig. 3. Boxes determine the interval between the 25th and the 75th percentile, whiskers denote the 10th and the 90th percentile, and lines within the boxes indicate the median. Data were pooled from the following number of islets/cells: $\frac{8}{865}$ (8 mM glc); $\frac{8}{1103}$ (12 mM glc). Statistical significance: ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$; n.s., not significant. Effect sizes: negligible – N (δ < 0.147), small – S (0.33 > δ ≥ 0.147), medium – M (0.474 > δ ≥ 0.33), or large – L (δ ≥ 0.474). To see this figure in color, go online. Furthermore, our results indicate that the wave initiation parameter relates very weakly to the cells role in the β cell networks. In 8 mM glucose, there was no correlation, whereas, in 12 mM glucose, wave-initiator cells tended to have a lower-than-average number of connections in the correlation-based network (Fig. 4 D). Interestingly, in 8 mM and especially in 12 mM glucose, the wave initiators tended to have a lower-than-average number of links with their neighbors in the wave-based network. Moreover, in neither of the two glucose concentrations, a correlation between the initiation parameter and the average intercellular signal transmission delay between neighboring cells was observed, i.e., average weighted wave-based network node degree (Fig. 4 F). The above indicates that the cells that often serve as wave initiators are not distinguished by their strength of intercellular coupling with respect to individual neighbors, however, they tend to have fewer neighbors. To design functional and wave-based networks, to determine hubs and wave initiators, and to categorize cells to groups, we have used a specific set of parameters, which were determined based on the nature of the data as well as physiological relevance (see materials and methods). However, for the sake of scientific rigor, we additionally investigated whether our findings are sensitive to the choice of these parameters. In the supplementary figures we therefore present the calculations obtained for a broad range of different values of these parameters. We first tested whether the arrangement of cells affects the conclusions and performed two additional data splits: 1) lowest $\frac{1}{10}$, intermediate $\frac{4}{5}$, highest $\frac{1}{10}$ cells (Figs. S2 and S3), and 2) lowest $\frac{1}{4}$, intermediate $\frac{1}{2}$, highest $\frac{1}{4}$ cells (Figs. S4 and S5) for the correlation network node degree and for the initiation parameter. Results clearly show that all investigated signaling parameters follow the same trend regardless of the selected data split. Next, we investigated the impact of the selected average correlation network node degree on the identified hub cells and the most notable signaling parameters (relative active time, interoscillation interval variability, wave network node weight, and initiation parameter). We performed the analysis on a representative islet for average correlation network node degrees kavg = 6.0, kavg = 8.0, and, kavg = 10.0 (Fig. S6). The identified hub cells ($\frac{1}{6}$ of most connected cells) are largely independent of the selected average node degree (Fig. S6 A) and the selected signaling parameters also appear to follow the same trend for each value (Fig. S6 B). Finally, we analyzed whether the wave-network layer extraction is sensitive to the distance and delay threshold parameters for wave detection, i.e., Rs and Rt (see materials and methods). To that end, we performed the same wave detection process for multiple combinations of those parameters on a representative islets plateau phase and present the findings in Fig. S7. There is only a minor difference in the number of detected waves, which indicates that within reasonable limits our results are not sensitive to the specific values of these parameters. In the following, we investigated whether there is an overlap between specific β cell subpopulations. The results in Fig. 5 A feature a typical islet with marked top $\frac{1}{6}$ of cells with the most functional connections (hubs), cells with the highest initiator parameter values (wave initiators), cells that responded first to stimulation (first responders), and that had the highest relative active time (most active cells). A visual inspection of the plot indicates some overlap between specific cell subpopulations. To investigate this further, we quantified the overlap of cells in all recordings in 8 mM (cyan bars) and in 12 mM (blue bars) glucose (Fig. 5 B). The relative overlaps of specific cell subpopulations are expressed concerning the overlap by chance, such that 0 corresponds to overlap purely by chance (see the materials and methods section for further details). The results reveal that the intersections between the designated cell subpopulations are not large. In both glucose concentrations, the overlap is rather well pronounced only between the wave-initiating and the most active cells, where approximately $42\%$ of specific cell types were present in both groups. There also appears to be a certain overlap between the initiators and the first responders and between the first responders and the most active cells, but only under physiological stimulatory conditions. The relation between the hub and the first responding cells seems to be very glucose dependent, as in 8 mM a negative overlap was detected. In contrast, in 12 mM glucose, the opposite was observed. Finally, practically no overlap between the hub and the wave-initiator cells was detected in either glucose concentration, indicating that, in principle, they represent different and independent β cell subpopulations. Figure 5Characterizing the overlap of specific β cell subpopulations. ( A) Correlation-based functional β cell network extracted from a typical islet. Node size reflects the number of functional connections per cell. The red-colored cells indicate $\frac{1}{6}$ of the 1) most connected cells, i.e., hub cells (upper left), 2) wave initiators, i.e., cells with the highest initiation parameter values (upper right), 3) first responding cells to stimulatory glucose concentrations, i.e., cells with the lowest activation delays (lower left), and 4) most active cells, i.e., cells with the highest active times (lower right). ( B) Bar plots show the relative overlap of specific cell subpopulations compared with the overlap by chance that two cells belong to the same sets of subpopulations (zero means an approximately $2.8\%$, i.e., ($\frac{1}{6}$)2, overlap). To see this figure in color, go online. ## Assessing the temporal persistency of hub and wave-initiator cells Finally, we analyzed the temporal persistence of hub and wave-initiator cells over the course of the plateau phase of a single square pulse stimulation by glucose. In Fig. 6 A, we present color-coded temporal evolution of node degrees for each cell for two representative islets stimulated with 8 mM (left) and 12 mM (right) glucose. In each time step, node degrees were computed from the correlation-based networks within a sliding window of Tsw = 300 s, and the values were normalized relative to the highest node degree. It appears that there are some variations in node degrees during 8 and 12 mM glucose, but in general, the roles of the hub and nonhub cells remained preserved even after prolonged exposure to glucose. To assess the temporal stability of wave-initiator cells, we visualized the cellular activation sequences within individual Ca2+ waves as raster plots with indicated Ca2+ oscillations (Fig. 6 B). The colors of stripes denote cellular activation ranks, with the red color denoting the cells that activated first during a given wave, as indicated by the color bar. The plots indicate that, in 8 mM glucose, the activation patterns are rather changeable, whereas, in 12 mM, the activation patterns seem to be relatively stable even over prolonged periods. To obtain a general insight and to quantify how stable hubs, wave-initiator cells, and the paths of Ca2+ waves are across time, we computed the interlayer similarity between correlation- and wave-based temporal network layers (see materials and methods for details) for all recordings in 8 and 12 mM glucose. In Fig. 6 C, we show the average absolute values of the hub interlayer similarity (right panel) and the normalized interlayer hub similarity as a function of temporal distance between network layers (left panel). Similarly, in Fig. 6 D, we show the average absolute values of the interlayer wave initiator similarity (left panel) and the normalized interlayer initiator similarity as a function of temporal distance between network layers (right panel). It turned out that, on average, the hub similarity between network layers is much higher than the wave initiator similarity, which indicates that the role of hub cells is significantly more persistent than the role of wave-initiator cells. Figure 6Assessing the temporal persistency of hub and wave-initiator cells. ( A) The evolution of relative node degrees in the functional β cell network within a sliding temporal window (width 300 s, step 20 [8 mM] and 5 [12 mM] s) in two typical islets with 8 mM (left) and 12 mM (right) glucose stimulation. The relative number of functional connections is color coded as indicated by the color bar. ( B) Upper panels show the mean-field Ca2+ signal of the same two representative islets as in (A), and lower panels show raster plots indicating individual Ca2+ oscillations with color-coded activation sequence within individual Ca2+ waves. The ranks for oscillation onsets are color coded as indicated by the color bar, such that the red dots reflect the initiating cells within a given wave. ( C) Average interlayer hub similarity as a function of interlayer distance n (left) and the average values of interlayer hub similarity (right). ( D) The average values of wave-network interlayer initiator similarities (left) and the average wave-network interlayer initiator similarity as a function of the interlayer distance m (right). ( E) Temporal variability of node degrees in correlation-based networks dependent on the node degree. Gray dots denote the values of individual cells, and the boxplots the $\frac{1}{6}$ lowest, $\frac{2}{3}$ intermediate, and $\frac{1}{6}$ highest connected cells. ( F) Temporal cellular activation rank variability dependent on the initiation parameter. Gray dots represent the values of individual cells, and the boxplots the $\frac{1}{6}$ lowest, $\frac{2}{3}$ intermediate, and $\frac{1}{6}$ highest connected cells. Boxes determine the interval between the 25th and the 75th percentile, whiskers denote the 10th and the 90th percentile, and lines within the boxes indicate the median. ( C–F) The pooled data from the following number of islets/cells: $\frac{8}{865}$ (8 mM glc); $\frac{8}{1103}$ (12 mM glc). Statistical significance: ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$; n.s., not significant. Effect sizes: negligible – N (δ < 0.147), small – S (0.33 > δ ≥ 0.147), medium – M (0.474 > δ ≥ 0.33), or large – L (δ ≥ 0.474). To see this figure in color, go online. Moreover, the temporal stability of hub cells did not depend on glucose concentration, whereas the role of wave-initiator cells was found to be more stable in 12 mM compared with 8 mM glucose. The results showing how the normalized values of interlayer similarities decay with the temporal distance further corroborate this notion, as the interlayer Ca2+ wave similarity declines much slower in 12 mM than in 8 mM glucose. To further evaluate the characteristics of β cells concerning their roles in correlation- and Ca2+ wave-based networks in detail, we quantified the dispersion of temporal node degrees and initiation parameter values. The relationship between the relative correlation-based network node degrees of cells and their corresponding SD shown in Fig. 6 E indicates that the number of functional connections fluctuates in time much less in cells with the most functional connections in both glucose concentrations. In contrast, we found no clear relation between the SD of average activation ranks inferred from onsets of Ca2+ oscillations within individual waves and the initiation parameter (Fig. 6 F). These results suggest that the roles of wave-initiating cells are more variable and less determined than those of the hub cells. ## Discussion Pancreatic islets are highly interconnected structures and display a fascinating coordination of their rhythmic activity, which plays a key role in the regulation of metabolic homeostasis and becomes progressively impaired in diabetes mellitus. The increasing awareness that the intercellular coupling and its modulation are vital to the normal islet function has stimulated immense interest in how different subpopulations of heterogeneous cells are functionally arranged throughout the islets and how they mediate intercellular signals. Recent advances in optogenetics, photopharmacology, and computational tools have allowed the assessment of multicellular β cell behavior revealing that the mediating Ca2+ waves are initiated from specific subregions of the islet with specific local excitability and metabolic profiles [7,25,35,48,59,64,65,74,75,76,77,78]. Moreover, network analyses have emerged as promising tools to elucidate the collective activity of β cell populations. It turned out that the functional β cell connectivity patterns are much more heterogeneous than one would expect from a gap junction coupled syncytium, displaying small-worldness and a hub-like connectivity architecture [7,11,42,43,46,49,54,59,61,79]. The complexity of these functional interactions results from the intricate islet-wide Ca2+ dynamics that is influenced by a multilayered cellular heterogeneity along with heterogeneous intercellular interactions and by the extracellular environment. However, how specific specialized subpopulations of β cells contribute to synchronized dynamics, network activity, persistency, and initiation of intercellular signals and what are their functional characteristics, is not clear and is a matter of ongoing research. Particularly the term pacemaker has in the recent literature been loosely applied to refer to hub cells, wave-initiator cells, or to the first responding cells that control Ca2+ elevations in response to stimulatory glucose, as has been clarified in a very recent review by Benninger and Kravets [7]. Therefore, classifying specific β cell types and subpopulations, assessing their functional characteristics, and elucidating how the multicellular consortium coordinates the intercellular Ca2+ activity and insulin secretion from an islet, have attracted a lot of attention by the islet community and represent very vibrant topics. In this study, we systematically addressed the aforementioned issues by combining multicellular Ca2+ imaging in mouse pancreas tissue slices with network science approaches. We constructed correlation-based functional multicellular networks based on the temporal similarity of the measured cellular dynamics, such that nodes represented individual β cells and connections between them were established based on thresholded pairwise correlations of Ca2+ imaging signals [55]. By these means, we detected a fraction of very well functionally connected cells, i.e., hub cells. This subpopulation has already been identified before in various experimental [44,46,49,79] and theoretical [37,56,63,80] studies and its existence was suggested to be important for normal islet functioning [7,42,46]. Moreover, utilization of advanced optogenetic and photopharmacological strategies has indicated that the hub cells exhibit hyperpolarized mitochondria, a lower insulin content resembling a transcriptionally immature phenotype due to the low expression levels of signature β cell transcription factors, and that they are metabolically highly active [35,46,81]. Increased metabolic activity could at least in part be associated with our findings that hub cells exhibit a higher-than-average activity (Fig. 3 A), although a clear overlap between hub cells and the most active cells was observed only under 12 mM glucose stimulation and not under 8 mM (Fig. 5 B). Moreover, we noticed a tendency of hub cells to exhibit the most regular oscillations (Fig. 3 B). This observation indicates that hub cells are typically almost always included in whole-islet Ca2+ waves, which implies that they play an important role in the mediation of intercellular signals. Since hub cells tend to have a larger number of direct neighbors and shorter phase lags in the wave-based network, their Ca2+ signals are more likely to be highly correlated to a larger number of other cells. Moreover, the above regularity is in line with our previous finding that hub cells tend to dissipate more energy [79]. Interestingly, we also noted a tendency of hub cells being activated first when stimulated with 12 mM glucose, while under 8 mM glucose no such trend was observed (Fig. 3 C), which corroborates our previous findings, but on a larger data set [11]. Nevertheless, the discrepancy observed between both stimulation levels indicates that the guidance of Ca2+ elevations and the mediation of intercellular signals are very complex processes that are influenced by the interplay of a wide variety of factors, such as the metabolic activity, local connectivity, and variations in gap junctional conductances, as well as by the variability of excitability profiles [7,42]. To make matters even more complicated, many of these factors are known to be glucose dependent and change with time. These reasons might account for the apparent inconsistencies reported in the recent literature [35,44,54,59,63,65]. It has been shown that specific subpopulations of β cells, whose metabolic fingerprints overlap with the characteristics of hub cells, can recruit a disproportionally high number of their neighbors [35,65], and that there exists an overlap between the cells that drive glucose-stimulated Ca2+ elevations and hub cells [59]. In contrast, it has also been reported that the first responding cells that guide the first responses to stimulation are defined principally by their excitability profiles and that they do not spatially or functionally overlap with hub cells [44], and similar conclusions have been drawn from comprehensive in silico studies [63]. While the connection between hub and first responder cells does not seem to be entirely clear, it is becoming apparent that the hub cells are not the cells that impose the cellular rhythm in the phase of sustained activity. Our results show that hubs are definitely not specialized initiators of intercellular waves (or pacemaker cells as also sometimes called in the literature). Specifically, there seems to be no correlation between the number of functional connections and the wave initiation parameter in 8 mM glucose, while in 12 mM glucose there was even a slight anticorrelation (Fig. 3 D), and there was only a random overlap between these two subpopulations (Fig. 5 B). These findings are also in agreement with a recent report on human islets [43] and with results predicted by a multicellular computational β cell model [63]. However, even though the hub cells do not initiate Ca2+ waves, they could play an important role in the coordination of intercellular signals. In our analyses we compared their roles in the functional correlation-based network and in wave-derived networks and detected a strong relation between the number of connections in both types of networks, thereby highlighting their exceptional role in mediating the collective activity. Most importantly, a strong trend of hub cells exhibiting the shortest delays in transmitting the intercellular signal to their neighbors was detected in both glucose concentrations. This implies a higher level of intercellular coupling, which might be one of the crucial aspects of their decisive role in coordinating Ca2+ oscillations across the islets. What we call intercellular coupling essentially reflects the local efficiency of spreading depolarization and Ca2+ waves and has been termed this way in classical electrophysiological studies [82,83]. This local spreading not only depends on the intercellular conductance determined by gap junctions but also on the capacitance and nonjunctional membrane conductance of the neighboring cells that are being depolarized by junctional currents [70]. While the cell capacitance was estimated to contribute only marginally to the time lag between neighboring cells [70], the junctional and nonjunctional conductance are not only important but also change periodically between bursts and silent phases depending on a cell’s state of activation [82,83]. We chose the term intercellular coupling since it is impossible in our current experiments to separately quantify the contributions by the junctional and nonjunctional conductance. Previous modeling suggested that KATP conductance of neighboring cells may determine the wave velocity more importantly than gap junctional conductance at the beginning of β cell bursting immediately following activation by glucose [84]. Later during the sustained plateau phase the degree of synchronicity is higher and all cells are close to their thresholds for firing a burst when being activated by the incoming depolarization front, and thus the speed of the intercellular wave may be predominantly proportional to the square root of the harmonic mean of gap junctional conductivity [85]. This theory also predicts that the wave velocities should be largely independent of the stimulatory glucose concentration, and we confirmed this (Fig. S1). Furthermore, the range of average node weights in this study was consistent with a wave velocity in the range of 30–100 μm/s, which is comparable with previous experimental studies [39,64,75,84]. Notably, the absolute values of wave velocities tend to be higher in modeling studies, unless a heterogeneity in intrinsic cell parameters or gap junctional coupling or both are introduced [85,86]. In hub cells, the delays were approximately $20\%$ shorter and, together with a larger number of neighbors, this could importantly contribute to the higher correlation of their Ca2+ signals with signals in other cells. In addition to the above circumstantial arguments about the importance of gap junctional coupling, to account for systematically higher local wave velocities between a hub cell and its neighbors across a large number of waves and wave directions going through it over tens of minutes, a larger gap junctional conductance of the observed hub cell could be a more plausible explanation than a significantly higher input resistance or state of activity in all of its neighbors from a probabilistic point of view, in the spirit of the Occam’s razor. However, this remains to be clarified in future studies. To identify Ca2+ wave initiators and to assess intercellular interactions more precisely, we made use of the multilayer network formalism and regarded each calcium wave as an individual network layer with weighted directed connections portraying the intercellular signal propagation. We identified β cell subpopulations that act as wave initiators, and these cells represented the most active cells in the islet (Figs. 4 A and 5 B). This observation goes well in hand with previous studies in which the cells that lead Ca2+ oscillations were indirectly shown to have a faster intrinsic oscillation frequency, consistent with a rhythmic pacemaker concept [65,74]. As such, the wave initiator population has been argued to be important for proper regulation of pulsatile insulin release during the second phase [59,65,74]. In addition, this cell population was also found to have elevated excitability levels [13,74,75] and lower metabolic rates as they seem to exhibit lower NAD(P)H responses [65] and therefore differ in this respect from the hub cell population, in which an enhanced metabolic activity was noticed [35,46,81]. Furthermore, we observed a rather small overlap between the subpopulation of cells that act as wave initiators and the cells responding first to initial exposure to stimulatory glucose concentration (Fig. 5 B). In addition, a clear tendency between the activation delay and the wave initiation parameter was only observed in 8 mM and not in 12 mM glucose (Fig. 4 C). On one hand this might be due to the abrupt cellular activations provoked by supraphysiological stimulation levels, but on the other hand this result once again indicates that the response to stimulation encompasses a series of complex factors, as already argued above. Furthermore, the cells that recurrently initiate Ca2+ waves have an average number of connections in the correlation-based networks and are thus not hubs. ( Fig. 4 D). Our observation that wave initiators seem to have a lower-than-average number of connections may intuitively be ascribed to the fact that the waves start in these cells and thus they necessarily have fewer links in the wave-based network (with no cells preceding them). However, it is also consistent with previous suggestions that the cells with less neighbors may first be able to escape the hyperpolarizing or clamping effect of nonactive cells [74,87]. Judging by the node weights, once the waves are established, compared with other cells in the wave-based network, the local intercellular coupling between wave initiators and their direct neighbors does not seem to be higher or lower than the average. Two important questions in islet biology that remain open are whether heterogeneous nearest-neighbor coupling is sufficient to explain the patterns of observed intercellular waves and small-world functional networks properties and whether the behavior observed in a single focal plane is representative of the behavior in all three dimensions. Cappon and Pedersen demonstrated that structural long-range connections are not necessary for the observed intercellular waves and small-world functional network properties and that the latter can arise from heterogeneous nearest neighbor coupling of heterogeneous cells that are synchronized by heterogeneous propagating waves [61]. Indeed, possible structural long-range connections typically prevent propagating waves [88] and the combination of experimentally observed intercellular waves and small-world functional network properties speaks against a structural small-world substrate. Notably, further research also suggested that the small-world characteristics can in part be attributed to the multimodal nature of the oscillatory β cell activity, whereby the slow oscillatory component contributes more directly to long-range connectivity [54] and that the slow-activity-derived networks are less dependent on the structural gap junctional network [62,91]. Furthermore, both modeling and experimental studies suggest that the intercellular waves and functional networks in a single focal plane are largely representative of the behavior in the islet as a whole [59,61,64]. Clearly, further studies are needed to address these two questions into more detail. Furthermore, our results display a rather high dispersion of data. Individual data points are profoundly scattered across the main trends, which not only indicates that β cells are characterized by a large functional heterogeneity but also that the division to specific subpopulations cannot be done definitively as there is certainly some overlap. In other words, there are, for example, also some hub or wave-initiator cells with a lower-than-average activity, although the main trend clearly indicates that the cells with many functional connections or the cells that frequently initiate waves have on average higher active times. Most importantly, due to the heterogeneous nature of data, we gave particular emphasis to the statistical measures and interpretation of our results. Typically, the correlations between parameters have been studied and interpreted based solely on the statistical significance (p value) that shows only whether an effect exists and does not reveal the size of the effect (substantive significance). A major disadvantage of the p value is the dependency on the sample size. With a sufficiently large sample, as in our study, the p value will almost always show a significant difference, which can bring confusion to the interpretation of the results. As in our analyses we dealt with a high number of rather scattered data points; we additionally evaluated the results in terms of the effect size (δ value), which is independent of sample size and helps us to better understand the magnitude of the p value difference found [89]. By these means we identified which correlations between parameters are physiologically meaningful despite the high degree of variability. To gain further insight into the data dispersion, we additionally investigated how the central Ca2+ signaling parameters depend on the node degrees and initiation parameters in individual islets. The results presented in Figs. S8 and S9 indicate that in most islets the same trends are noticed as in the main results with a few minor exceptions. Apparently, the dispersion of data points that characterize the results is more due to the intercellular variability than due to interislet variability. Finally, representing each Ca2+ wave as a network layer with weighted directed connections not only enabled us to portray the intercellular signal propagation but also allowed us to assess the spatiotemporal stability of calcium waves [48]. It turned out that subregions exist in the islet that serve as initiators in a large portion of events, but the course of the Ca2+ waves were found to change with time, whereby the changes were more frequent at a physiological glucose concentration (Fig. 6 D). We argue that this reflects the fact that, under high glucose conditions, i.e., 12 mM, the supply of metabolic energy is high, all cells are on average more excited, and the relative cell-to-cell variability becomes smaller, which facilitates a more stable course of intercellular signals. On the other hand, if the supply of metabolic energy is moderate, i.e., in 8 mM glucose, the cells remain less excited, more heterogeneous, and are therefore more prone to stochastic effects and influences from neighboring cells, resulting in less coherent spatiotemporal activity patterns. These ideas are also in agreement with recent theoretical studies that indicate that presence of wave initiators in networks of excitable cells as an emergent and dynamic population behavior [8,13,90]. Notably, applying an equivalent analysis for the persistency of hub cells revealed that their role is much more stable in time, as particularly the number of functional connections of the most connected cells was not found to change significantly during the recordings (Fig. 6, C and E). Moreover, in contrast to wave-initiator cells, the persistency of hub cells did not depend on glucose concentration. These results suggest that the role of hub cells might be more predetermined by their intrinsic functional characteristics (such as their metabolic activity and the degree of intercellular coupling) and less on the level of stimulation compared with wave-initiator cells. In future studies, it would be worthwhile to assess the stability of the hub role over longer periods of time, e.g., several hours or even days, and to reassess if this stability is related to the cell maturation stage and other functional properties by analyzing expression of different transcription factors. In sum, in the present work we assess multicellular activity in pancreatic islets with emphasis on specialized subpopulations of β cells, which substantially affect the collective dynamics. Our findings indicate that both hub and wave-initiator cells are genuine features of islets, but they differ in several aspects of Ca2+ signaling and their roles do not seem to overlap. Moreover, while on the molecular and single-cell level clear discrepancies have been identified, suggesting differences in function [7,31,52], it appears that the roles of cells, when operating in a multicellular environment, are not completely obvious and predetermined. Particularly the initiation of intercellular waves was found to be rather dynamic and is most probably affected by a wide range of factors. It seems that a higher fraction of cells exhibits the potential to become a wave initiator, but the underlying socio-cellular context then principally defines the true roles. This idea is also in agreement with recent theoretical studies highlighting that pacemakers (in the context of wave initiators) can emerge naturally in cellular networks [90]. Finally, we did not perform silencing or deletion of wave-initiating cells but, given the rather large number of physically separate cell clusters capable of initiating waves found in our study, it seems reasonable to speculate that silencing or deleting some of them would probably not abolish intercellular waves. The role of hub cells seems to be more determined, whereby a higher degree of intercellular coupling along with the specific metabolic characteristics are the main determinants that ensure their more stable roles. Importantly, our results do not indicate that the roles of hub cells are reserved exclusively to a very small fraction, i.e., a few extraordinary and irreplaceable cells. Rather, we argue that, in the heterogeneous β cell population, a certain fraction of cells possesses more exceptional values of certain electrophysiological and metabolic characteristics that are otherwise distributed continuously among cells. Therefore, they participate in the majority of Ca2+ waves, have the shortest delays to Ca2+ signals in other cells, and their oscillations are less variable. As a consequence, their signals are correlated with a large number of other cells and they emerge as hubs when viewed through the prism of network analysis. A similar concept has recently been proposed theoretically [51], unifying thereby the seemingly opposing views on β cell hubs [53,58]. Furthermore, based on the rather weak and inconsistent overlap between different cell subpopulations reported here, as well as in the recent literature, we can presume that the functional heterogeneity in the β cell population exists for sure, but it is probably not as clear-cut as to divide cells into clearly predefined subgroups and that to some extent the influential cells can manifest themselves endogenously within the β cell collectives. We share the belief that such a design represents a functionally more robust and evolutionary advantageous architecture. ## Author contributions M.Š., A.S., J.D., and M.G. conceived and designed the research. M.S.K., L.K.B., J.D., E.P.L., and J.K. performed the experiments. M.Š. and M.G. developed software and analyzed data. A.S., J.D., M.P., M.S.R., and M.G. interpreted results of the experiments. M.Š. prepared the figures. J.K. performed statistical analyses. M.G. and A.S. supervised the study. M.Š., J.D., and M.G. drafted the manuscript. 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--- title: Dietary Antioxidant Capacity Indices are Negatively Correlated to LDL-Oxidation in Adults authors: - Mehran Nouri - Mitra Soltani - Milad Rajabzadeh-Dehkordi - Nastaran Rafieipour - Moein Askarpour - Maryam Najafi - Shiva Faghih journal: International Journal of Clinical Practice year: 2023 pmcid: PMC10027462 doi: 10.1155/2023/5446163 license: CC BY 4.0 --- # Dietary Antioxidant Capacity Indices are Negatively Correlated to LDL-Oxidation in Adults ## Abstract ### Introduction Former research studies have demonstrated controversial associations between dietary indices and oxidative stress biomarkers including oxidized low-density lipoprotein (ox-LDL) and malondialdehyde (MDA). So, in this cross-sectional study, we aimed to assess the association of dietary total antioxidant capacity (DTAC), oxidative balance score, and phytochemical index (PI) with ox-LDL/MDA in a healthy adult population of Shiraz, Iran. ### Methods 236 individuals participated in this cross-sectional study. DTAC, OBS, and PI were calculated using a 168-item food frequency questionnaire (FFQ), which was previously validated in Iran. We measured ox-LDL and MDA in blood samples of the participants using commercially existing kits. Crude and adjusted models of linear regression were used to evaluate the relation of dietary indices with ox-LDL and MDA. ### Results There was a significant association between ox-LDL and DTAC in both crude (β = −1.55; $95\%$ CI: −2.53, −0.58; P-trend = 0.002) and adjusted (β = −1.65 $95\%$ CI: −2.66, −0.64; P-trend = 0.001) models. Also, a negative association was observed between ox-LDL and PI in the crude (β = −1.26 $95\%$ CI: −2.33, −0.29; P-trend = 0.01) and adjusted (β = −1.36 $95\%$ CI: −2.38, −0.34; P-trend = 0.01) models. ### Conclusion Results of this study showed that DTAC and PI were inversely associated with ox-LDL as markers of lipid peroxidation. But no correlations were seen between MDA and dietary antioxidant indices. ## 1. Introduction It is approved that oxidative stress play a key role in the pathogenesis of age-related chronic diseases such as diabetes mellitus, neurodegenerative diseases, cardiovascular diseases, cancers, chronic kidney disease, and chronic obstructive pulmonary disease [1]. The main carrier of cholesterol in the body is low-density lipoprotein (LDL). The protein and lipid content of LDL can go through oxidation, and as a result, accumulation of cholesterol occurs [2–4]. Moreover, this oxidized LDL (ox-DL) lead to platelet activating factor (PAF) and oxidized phospholipids (oxPLs) production by bringing changes in the activity of key enzymes [5, 6]. Similar to oxidized LDL (ox-LDL) another biomarker that can be widely assessed as a measurement of oxidative stress is malondialdehyde (MDA). MDA is an end product of lipid peroxidation of polyunsaturated fatty acids with cytotoxic and carcinogenic features [7, 8]. Endogenous and exogenous antioxidants protect body against free radicals and oxidative process [1]. Exclusively, dietary antioxidants which are known as exogenous antioxidants can activate antioxidant signaling, improve endogenous antioxidant status, and suppress oxidative stress [9]. Dietary total antioxidant capacity (DTAC) is an integrated measurement of dietary components that reflects the accumulative and synergistic interactions of foods [10]. It can be considered as a predictor of the total antioxidant status and the linkage between dietary antioxidants and oxidative stress in disease development [11]. Also, the oxidative balance score [12], which is referred as a balance between dietary and lifestyle pro- and anti-oxidants, contributes negatively with oxidative stress and related chronic diseases [13]. It is proved that the antioxidant components of OBS are involved in free radical scavenging specially those producing through lipid peroxidation, blocking pro-oxidant enzymes activities or expression, and eventually promoting activation of antioxidant enzymes [13, 14]. Phytochemicals are other natural compounds that bring about miscellaneous health benefits. Thus, dietary phytochemical index (PI) which is calculated by the percentage of calories derived from phytochemical-rich foods out of the total energy intake was introduced [15, 16]. It was previously reported that oxidative stress which is involved in the development of various chronic diseases can be influenced by dietary and lifestyle modifications. In addition, studies have illustrated that a combination of nutrients is more efficient than a single dietary factor in the risk of oxidative stress-related diseases due to their correlations [17, 18]. While some studies found significant correlation between dietary indices and oxidative biomarkers, some others have demonstrated controversial associations between dietary parameters and oxidative measurements including ox-LDL and MDA [19–22]. Also, an inverse relation was seen between DTAC and food pattern rich in antioxidants with inflammation markers [23, 24]. So, in this cross-sectional study, we aimed to assess the association of DTAC, OBS, and PI with ox-LDL/MDA in a healthy adult population of Shiraz, Iran. ## 2.1. Study Population 236 adults participated in this cross-sectional study. Study sample size was calculated based on α = 0.01, β = 0.10, and r = ±0.25 by the following formula:[1]$C = 0.5$×ln1+r1−r,$$n = 3$$+Z1−α/2+Z1−β÷ C2. The age of the participants was between 20 and 50 years. We selected the participants from medical centers in Shiraz, Iran, by using cluster random sampling. We divided Shiraz into four sections and selected a community health center in each one. The participants did not have any chronic disease or did not follow any special diet (the details of this study have been previously published [25, 26]). This study was confirmed by the Shiraz University of Medical Sciences (IR.SUMS.REC.1394.S146). ## 2.2. Dietary Assessments Participants' food intakes were evaluated using a 168-item food frequency questionnaire (FFQ), which was previously validated in Iran [27]. After extracting the food intakes in grams, NUTRITIONIST IV (version 7.0; N-Squared Computing, Salem, OR, USA) was used to calculate the intake of energy and nutrients. Dietary TAC was acquired according to ferric reducing-antioxidant power (FRAP) [12]. This method measures the power of antioxidants of foods to convert ferric to ferrous ions [28]. The total mean of TAC was considered for similar foods (for example, some types of breads). At the end, the frequency of each food item intake was multiplied by its corresponding FRAP value, then summed up to estimate participant's DTAC [29, 30]. According to the method suggested by Goodman et al. [ 31], the OBS was calculated using the following items: intake of dietary pro-oxidants such as iron, polyunsaturated fatty acids (PUFAs), and saturated fatty acids (SFAs); nondietary pro-oxidants including smoking and obesity; dietary antioxidants such as fibers, folate, vitamin C, vitamin E, beta cryptoxanthin, lycopene, lutein/zeaxanthin, alpha-carotene, beta-carotene, selenium, and zinc; and nondietary antioxidants such as physical activity [32–35]. The scores of abovementioned 12 components were summed up and the OBS ranged between 0.0 and 24.0 (Table 1) [36, 37]. Dietary PI was computed using the method developed by McCarty [PI = (phytochemical-rich foods (g/d)/total food intake (g/d)) × 100] [16]. Food items included whole grains, nuts, legumes, olives, olive oil, soy products, also spices, coffee, and tea. Potatoes were often consumed as a starchy food and were not considered as vegetables. Natural juices of vegetables and fruits were included in the vegetable and fruit groups, because these components are also considered as high sources of phytochemicals [29, 30]. ## 2.3. Other Assessments 5 cc blood sample was taken from each participant and stored at −70°C for ox-LDL and MDA assessment. Ox-LDL and MDA was measured using commercially existing kits (Pars Azmoon, Tehran, Iran). The participants' sex, age, alcohol use, and smoking habit were recorded through a face to face interview. Weight (with 100 g precision), height (with 0.5 cm precision), and waist circumference were measured and then BMI was calculated. Also, physical activity was evaluated by the International Physical Activity Questionnaire (IPAQ) [38]. ## 2.4. Statistical Analysis SPSS software (version 20.0, SPSS Inc., Chicago IL, USA) and STATA 16.0 were used for data analysis. The level of significance was P value <0.05. Crude and adjusted models of linear regression were used to evaluate the relation of dietary indices with ox-LDL and MDA. In adjusted models, the effects of age, energy intake, physical activity, BMI, sex, and smoking history were controlled. ## 3. Results Table 2 illustrates the baseline characteristics of the participants. The mean of age, body mass index and waist circumference of the participants were 45.97 years, 28.28 kg/m2, 94.21 cm, respectively. There was a significant association between ox-LDL and DTAC in both crude (β = −1.55; $95\%$ CI: −2.53, −0.58; P-trend = 0.002) and adjusted models (β = −1.65 $95\%$ CI: −2.66, −0.64; P-trend = 0.001). Furthermore, a negative association was observed between ox-LDL and PI in the crude (β = −1.26 $95\%$ CI: −2.33, −0.29; P-trend = 0.01) and adjusted models (β = −1.36 $95\%$ CI: −2.38, −0.34; P-trend = 0.01). We observed no significant association between MDA and any of the dietary indices (DTAC, OBS, and PI) (Table 3). ## 4. Discussion Based on the results of the present study, ox-LDL was inversely associated with DTAC and PI; however, no significant relationship was seen between OBS and ox-LDL. In addition, MDA was not related to the DTAC, OBS, or PI. In line with our results, Hermsdorff et al. found a negative relationship between ox-LDL and DTAC in healthy young adults [19]. The key of similarity between these two studies findings was the study population. Indeed, both studies were conducted on healthy adults. Nevertheless, a 9-month observational study which included 35 postmenopausal women failed to find any significant correlation between DTAC and oxidative stress markers such as MDA, ox-LDL, and antioxidant enzymes [20]. In contrast, in a cross-sectional study conducted on 175 postmenopausal women, serum MDA levels were negatively associated with DTAC, while no relationship was seen between DTAC and ox-LDL [22]. The higher concentration of MDA in this study could be a potential reason for the inconsistency [22]. The results of the present study showed no correlation between OBS, MDA, and ox-LDL. We found no studies on the relationship between OBS, MDA, or ox-LDL. But in a study by Lakkur et al., F2-isoprostanes (FIP), which is an end product of MDA in lipid peroxidation, were negatively related to OBS [39]. Similarly, a case-control study on patients with colorectal cancer and the healthy control group showed the same negative association between OBS and FIP [40]. It should be mentioned that FIP is the gold standard biomarker of oxidative stress [41]. Considering that oxidative cascade includes different pathways with varied markers which are somehow linked to each other, the results of the other oxidative factors such as FIP can be suggestive of the probable correlations between OBS and MDA or ox-LDL. In a cross-sectional study of 206 participants, increased vitamin C and E consumptions were correlated with lower ox-LDL. Also, a similar relationship was reported between the higher fruits/vegetables intakes and reduced ox-LDL, which was faded after adjustment for saturated fatty acids, physical activity, and smoking (components of OBS) [42]. Regarding PI, in agreement with the result of the present study, adherence to Mediterranean diet resulted in lower ox-LDL levels in a cohort study. Moreover, fruits, vegetables, and olive oil intake were inversely associated with ox-LDL. As the Mediterranean diet includes a large proportion of whole grains, fruits, vegetables, and olive oils, it provides higher amounts of beta-carotene, vitamin B group, polyphenols, vitamin C, and vitamin E [43]. Apart from the observational studies, several intervention trials have shown that consumption of foodstuffs which exert antioxidant activities also resulted in plasma resistance to oxidation [44]. On the other hand, in a cross-sectional study on premenopausal women, greater PI was related to lower MDA levels [21]. Gender differences are particularly due to antioxidant features of estrogen; thus, less susceptibility of premenopausal women to oxidative stress might play a crucial role in the controversial results [45]. Antioxidants derived from diet neutralize exogenous or metabolic free radicals, prevent reactive species formations through inhibition of reactions with iron and copper, and prevent or repair susceptible structures including carbon-carbon bonds of PUFA, proteins, and DNAs [46, 47]. Furthermore, it has been illustrated that lipophilic antioxidants which are carried by LDL-C in the circulation system protect LDL-C from oxidation and as a result, suppressed ox-LDL concentrations [48]. Some limitations should be considered for this study. For instance, cross-sectional studies cannot clarify the exact effect of dietary intakes on oxidative biomarkers and vice versa, thus, clinical trials might be more efficient. In addition, while FFQ is a validated tool, its dependency on participants' memory might lead to under- or over-estimation of nutrients intakes. Moreover, since the present study was performed in Shiraz city, we should be cautious about extending the results to other regions. In addition, lower serum concentrations of oxidative markers in healthy populations might attenuate their associations with dietary components. It is worth noting that to the best of our knowledge, the current study is the first research evaluating the correlation between DTAC, OBS, and PI and MDA/ox-LDL. Results of the present study expanded the findings of the modulatory effects of dietary anti- and pro-oxidants on the body's antioxidant defense system. In this cross-sectional study, while DTAC and PI were inversely associated with ox-LDL as a marker of lipid peroxidation, no correlations were seen between MDA and dietary antioxidant indices. Future studies are required to approve the positive influence of diets high in antioxidant on oxidation. ## Data Availability The data used to support the findings of this study are available on request from the authors. ## Ethical Approval The present study was approved by the Research Ethics Committee of Shiraz University of Medical Sciences, Shiraz, Iran (IR.SUMS.REC.1394.S146). ## Conflicts of Interest All the authors declare that they have no conflicts of interest. ## Authors' Contributions M. N, M. S, M. R, and M. N contributed to data collection and were involved in writing the first draft. M. N and M. A contributed to all the data and statistical analysis and the interpretation of data. S. F. contributed to the research concept, supervised the work, and revised the manuscript. All authors read and approved the final manuscript. ## References 1. Liguori I., Russo G., Curcio F.. Oxidative stress, aging, and diseases. (2018) 13 757-772. DOI: 10.2147/cia.s158513 2. 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--- title: 'The efficacy of flexor tenotomy to prevent recurrent diabetic foot ulcers (DIAFLEX trial): Study protocol for a randomized controlled trial' authors: - M.A. Mens - T.E. Busch-Westbroek - S.A. Bus - J.J. van Netten - R.H.H. Wellenberg - G.J. Streekstra - M. Maas - M. Nieuwdorp - G.M.M.J. Kerkhoffs - S.A.S. Stufkens journal: Contemporary Clinical Trials Communications year: 2023 pmcid: PMC10027496 doi: 10.1016/j.conctc.2023.101107 license: CC BY 4.0 --- # The efficacy of flexor tenotomy to prevent recurrent diabetic foot ulcers (DIAFLEX trial): Study protocol for a randomized controlled trial ## Abstract ## Unknown Foot ulcers are a frequent and costly problem in people with diabetes mellitus and can lead to amputations. Prevention of these ulcers is therefore of paramount importance. Claw/hammer toe deformities are commonly seen in people with diabetes. These deformities increase the risk of ulcer development specifically at the (tip of) the toe. Percutaneous needle tenotomy of the tendon of the m. flexor digitorum longus (tendon tenotomy) can be used to reduce the severity of claw/hammer toe deformity with the goal to prevent ulcer recurrence. The main objective of this randomized controlled trial is to assess the efficacy of flexor tenotomy to prevent recurrence of toe ulcers in people with diabetes and a history of toe (pre-)ulcers. Additionally, we aim to assess interphalangeal joints (IPJ) and metatarsophalangeal joint (MTPJ) angles in a weight-bearing and non-weight-bearing position, barefoot plantar pressure during walking, cost-effectiveness and quality of life before and after the intervention and compare intervention and control study groups. Sixty-six subjects with diabetes and claw/hammer toe deformity and a recent history of (pre-)ulceration on the tip of the toe will be included and randomized between flexor tenotomy of claw/hammer toes (intervention) versus standard of care including orthosis and shoe offloading (controls) in a mono-center randomized controlled trial. ### Clinicaltrials.gov registration NCT05228340. ## Introduction Diabetic foot ulcers are a common problem, with a global prevalence of $6.3\%$ in people with diabetes, and are one of the biggest risk factors for lower extremity amputation [[1], [2], [3]]. The formation of these ulcers is multifactorial and causes include peripheral neuropathy, vascular deficiency and mechanical stress [4]. In peripheral neuropathy the protective sensation in the extremities, mainly in the feet, deteriorates [5]. Detection of foot trauma is diminished causing people with diabetes to neglect taking measures when risk for foot ulcers increases. Hyperglycemia impairs leukocyte and complement function, thereby increasing chances for invasive infections [6]. Micro- and macrovascular disease is a co-morbidity often seen in people with diabetes mellitus debilitating ulcer healing [7]. Diabetic foot ulcers require off-loading treatment (e.g. total contact casts) often accompanied by (long-term) antibiotics [8]. These treatments can burden people in their daily activities. When healed, these ulcers have a high recurrence rate of $40\%$ per person per year in Europe [9,10]. Prevention of diabetic ulcers is therefore of great importance. In people with diabetic peripheral polyneuropathy, deformities of the feet are more prevalent than in people without diabetes [11]. Common deformities are claw toe and hammer toe deformity. The exact mechanism behind these deformities in this patient group is not fully understood, but seems related to a mismatch in extensor and flexor function due to intrinsic muscle atrophy [12]. In claw/hammer toe deformity there is additional pressure either underneath the metatarsal heads or on the tip of the toe [13]. This excess pressure increases the chance of ulcer development on these places on the foot. Claw/hammer toe deformity can be treated conventionally with off-loading techniques such as orthopedic shoes or a toe orthosis [11]. This is not always sufficient since pressure points can still occur when the shoes do not fit properly or when the patient is not adherent to wearing them [14]. A surgical option for treating claw/hammer toe deformity is flexor tenotomy [15]. In this procedure, which has been practiced for many years worldwide, the long flexor tendon of the affected toe is severed. This is a minimally invasive procedure that can take place in the out-patient clinic [16]. A surgeon uses a needle to sever the tendon (duration 1–2 min) and due to the sensory loss caused by peripheral polyneuropathy anesthesia is often not necessary [17]. The procedure causes the toes to straighten, reducing the angles in the distal and proximal interphalangeal joint (DIPJ, PIPJ) and the metatarsal phalangeal joint (MTPJ), and reducing the plantar pressure [18]. The beneficial effects of flexor tenotomy in people with diabetes and claw/hammer toe deformity have been investigated in retrospective and prospective case series [[16], [17], [18], [19], [20], [21], [22]]. Currently, one randomized controlled trial is being conducted and one was published in September 2022 [23,24]. However, there is still need for more evidence to further substantiate the benefits of flexor tenotomy as well as the need for evaluation in a controlled study design of changes in the biomechanical and musculoskeletal structure of the foot due to the flexor tenotomy. This randomized controlled trial aims to assess the efficacy of flexor tenotomy to prevent recurrent diabetic foot ulcers, the biomechanical and musculoskeletal changes due to the procedure, the changes in quality of life and the cost-effectiveness of the procedure. ## Objectives The primary objective is to assess the efficacy of flexor tenotomy (intervention) versus standard of care (including orthosis and shoe offloading, control) on the incidence of ulcer recurrence on the toes indicated for flexor tenotomy, on the adjacent toes and on the metatarsal heads. Secondary objectives are musculoskeletal changes expressed in MTPJ, PIPJ and DIPJ angles, biomechanical changes expressed in barefoot plantar pressure during walking, quality of life, the cost-effectiveness of flexor tenotomy and adverse events of the surgery. ## Trial design This study concerns a randomized controlled trial (RCT) with a 24-month follow-up period at the out-patient clinic (Table 1). Sixty-six participants will be included. Inclusion started in March 2022. After informed consent is signed, participants are randomized into two groups: usual care (control) or usual care plus flexor tenotomy (intervention). The researchers analyzing the effect of the data will be blinded for group allocation. The treating physician, orthopedic surgeon and participant will not be blinded to group allocation. The flexor tenotomy will be an addition to usual care and is scheduled after randomization. This means that the intervention group will receive the same standard care as the control group including orthopedic shoes. Table 1Standard protocol items. Table 1Time pointStudy periodEnrolmentAllocationPost-allocationClose-out−2 weeks−1 week01 week6 months12 months24 monthsEnrolmentInitial eligibility screenXStudy information to participantXInitial willingness to participateXCrosscheck inclusion/exclusion criteriaXInformed consentXFinal eligibility screenXAllocationXInterventionsUsual care (both groups)XXXXXFlexor tenotomy (intervention group only)XAssessmentsDemographic and disease-related characteristicsXBarefoot pressureXXXWeight-bearing CTXXXSF-36XXXXEQ-5D-5LXXXXUlcer formationXXXXXNotes of received foot careXXXProcess evaluationXXX ## Eligibility criteria In order to be eligible to participate in this study, a participant must meet all of the following criteria.•A minimum age of 18 years•Sufficient understanding of Dutch/English language•Capable of providing informed consent•Loss of protective sensation as a result of peripheral polyneuropathy•Diabetes mellitus type 1 or 2•A minimum of 1 claw/hammer toe on either foot•A documented history of diabetic (pre-)ulcers underneath the tip of the toe in the past 5 years. Pre-ulcers include abnormalities of or damage to the nail, callus formation and hematomas. A potential participant who meets any of the following criteria will be excluded from participation in this study.•Open ulcer(s) on the toes•Previous participation in the study•Pregnant women•Concomitant participation in a study in which the participant is exposed to X-rays (due to the use of weight-bearing CT in this study)•Critical ischemia (i.e. ankle-brachial index <0.5 or toe pressure <30 mmHg) ## Intervention: Percutaneous needle flexor tenotomy Percutaneous flexor tenotomy is a minimally invasive procedure used to treat claw/hammer toe deformity [15,18,22] The foot is sanitized using chlorhexidine/alcohol and proper measures to ensure sterility are taken. In most cases, local anesthesia is not necessary due to the sensory loss in the feet of this patient group. The protective sensibility of a patient is tested before the procedure. If anesthesia is needed, local infiltration with lidocaine is used. The ankle and toe are placed in dorsiflexion to put the long digital flexor tendon under pressure. A needle is inserted at the level of the middle phalanx, making a puncture wound (Fig. 1). The tendon can be felt with the tip of the needle. Using micro movements the tendon is carefully severed (duration 1–2 min) and the needle is removed. Pressure is applied until there is no more bleeding. A bandage is placed on the wound and the subjects are advised to minimalize loading of the operated foot for 24 h. A week after the procedure the wound is checked by the treating physician. During this check, adverse events such as infection, hematomas and pain at the puncture location will be recorded. Fig. 1Percutaneous needle flexor tenotomy. The tendon of the long flexor muscle is severed. Fig. 1 Both the intervention group and the control group receive standard care. This includes proper wound sanitation, removing of excess callus and debridement of the ulcers by a podiatrist, evaluation of barefoot pressure measurements and in-shoe measurements of the current footwear. If necessary based on high in-shoe pressure, current footwear is adapted or new footwear is fitted by an orthopedic shoemaker and orthoses or felt are used for further off-loading. ## Outcomes All data and outcomes will be registered in a Castor EDC database [25]. Using this database assessors will be blinded for the outcomes. ## Ulcer recurrence The main study outcome is ulcer recurrence on the treated toe within 2 years of follow-up. Transfer ulcers on the adjacent toes, or metatarsal heads within 2 years of follow-up will also be recorded. The formation of a claw or hammer toe in the adjacent toes will be recorded as a complication. Ulcers are defined according to the IWGDF-guideline [26]. Participants are regularly checked for ulceration by the treating physicians of the out-patient clinic or by their podiatrist. ## Toe joint angles DIPJ, PIPJ and MTPJ angles will be measured before, and 6 and 12 months after flexor tenotomy during weight-bearing and non-weight conditions using weight-bearing CT and non-weight-bearing CT. Weight-bearing and non-weight-bearing images of both forefeet will be acquired on the Planmed Verity® CT system. This system uses cone-beam CT technology to provide 3D-images of the extremities. Subjects stand on one leg in the small bore of the weight-bearing CT scanner, with a field-of-view of approximately 13 × 16 cm. All images will be acquired subsequent to out-patient scheduled visits, therefore no additional hospital visits will be needed. In-house developed software is utilized to segment bones using region growing and manual editing where necessary [27,28]. The center of the articular surface will be computed on either side of the relevant bones. The line between these centers is used to measure the joint angles. The segmentations can be used on multiple scans of the same foot in the same patient even before and after the flexor tenotomy, using a registration technique. ## Barefoot pressure Dynamic barefoot pressure measurements are performed using an EMED-X pressure platform (Novel GmbH, Munich, Germany). A two-step protocol with four trials per foot and a self-selected walking speed will be used. This is a reliable method to acquire pressure data without unnecessary barefoot steps [29]. The pressure distribution at the sole of the foot will be divided into 9 regions: hallux, second toe, third toe, fourth/fifth toe, metatarsal head 1, metatarsal head 2, lateral metatarsal heads, midfoot, heel. Mean peak pressure over the four steps will be calculated for each region as outcome. ## Quality of life Quality of life is measured using the EQ-5D-5L and SF-36 questionnaires at baseline, 6 months, 12 months and 24 months. The EQ-5D-5L is a validated and extensively used tool to measure Quality of Life [30]. This questionnaire is divided in 5 dimensions: mobility, self-care, usual activities, pain/discomfort and anxiety/depression. Additionally, general quality of life is visually assed with a visual analog scale. The SF-36 is a set of questions relying on patient self-reporting [[31], [32], [33]].This tool comprises of questions relating to physical functioning, social functioning, mental health, energy, pain and perception of health. ## Cost-effectiveness With the economic evaluation the total costs related to diabetic foot disease for all participants will be determined. These costs will be related to the effects of the treatments in the groups. The outcomes of the EQ-5D-5L and SF-36 will be used in the economic evaluation to determine cost-utility and the ulcer recurrence is used to determine cost-effectiveness. All relevant costs related to treatment will be recorded. These include.•Cost of the flexor tenotomy•Other costs related to prevention of recurrent ulcers prescribed at the diabetic foot rehabilitation out-patient clinic. These include: orthopedic footwear and adaptations to the footwear, felted foam, casts and orthoses•Costs of treatment of recurrent ulcers or newly formed ulcers: wound dressing, antibiotics and treatment by podiatrist at the diabetic feet rehabilitation out-patient clinic, costs of hospitalization, interventions related to ulceration (amputation) and homecare hired due to foot ulceration•Costs related to additional visits to the podiatrist, general practitioner, emergency department or the diabetic feet rehabilitation out-patient clinic due to diabetic foot ulcers The costs will be valued using the guidelines published in the updated handbook for economic evaluation in the Netherlands [34]. The costs of medication will be estimated on the basis of prices charged by the Royal Society of Pharmacy. ## Sample size calculation For our primary outcome of ulcer recurrence, we estimate $8\%$ recurrence in 12 months in the intervention group. This is a slightly conservative estimate, given the $11\%$ ulcer recurrence in 53 subjects during 93 weeks of follow-up following needle tenotomy as reported by Hedegaard Andersen and colleagues [20]. In the control group, we estimate $36\%$ ulcer recurrence in 12 months. This is slightly more positive compared to the $40\%$ ulcer recurrence in 12 months seen in a recent review [10], and similar to the ulcer recurrence in the control group of a recently completed RCT by these authors [35]. With $8\%$ ulcer recurrence in the intervention group and $36\%$ in the control group, power 0.8, alpha 0.05, 1:1 randomization and intention-to-treat analysis, a total of 66 participants (33 per group) are required. For our predefined secondary outcome of barefoot peak pressure, we estimate average barefoot peak pressure at the target toe of 400 kPa (standard deviation (SD): 250) in the control group and 180 kPa (SD: 100) in the intervention group following tenotomy. These estimates are based on clinical pilot data from our gait lab, with slightly more conservative estimates than found in our pilot study. With power 0.8, alpha 0.05, 1:1 randomization and intention-to-treat analysis, a total 40 participants (20 per group) are required for this predefined secondary outcome. With this number smaller than required for our primary outcome, the RCT can also be considered adequately powered for this outcome. The calculations were performed using clincalc.com [36]. ## Statistical analysis Statistical analysis will be performed using SPSS. The Shapiro-Wilk W test will be used to determine the distribution. Continuous variables will be expressed as mean ± SD for normally distributed data and as median and interquartile range for not normally distributed and ordinal data. Shapiro-Wilk test and visual inspection will be performed to check for normality. In case of small sample sizes and when data is not normally distributed, Wilcoxon signed rank test will be used. Differences between groups will be compared using Fisher's Exact test or Kruskal-Wallis. A level of $p \leq 0.05$ is considered statistically significant. ## Conclusion This protocol describes a randomized controlled trial exploring the efficacy of percutaneous needle flexor tenotomy to prevent recurrent ulceration. The study will assess clinical outcomes as well as biomechanical and anatomical changes of the toes and cost-effectiveness. This will provide a comprehensive analysis of the effects of this operation. The results of the DIAFLEX trial is expected support the implementation of needle flexor tenotomy in diabetic foot care. ## Funding M.A.M. is supported by a personal AMC-PhD scholarship 2019. M.N. is supported by a personal ZonMw VICI grant 2020 [09150182010020]. ## Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ## Data availability No data was used for the research described in the article. ## References 1. Zhang P., Lu J., Jing Y., Tang S., Zhu D., Bi Y.. Global epidemiology of diabetic foot ulceration: a systematic review and meta-analysis (†). Ann. Med. (2017.0) 49 106-116. DOI: 10.1080/07853890.2016.1231932 2. Goldman M.P., Clark C.J., Craven T.E., Davis R.P., Williams T.K., Velazquez-Ramirez G., Hurie J.B., Edwards M.S.. Effect of intensive glycemic control on risk of lower extremity amputation. J. Am. Coll. Surg. (2018.0) 227 596-604. DOI: 10.1016/j.jamcollsurg.2018.09.021 3. Fortington L.V., Rommers G.M., Postema K., van Netten J.J., Geertzen J.H., Dijkstra P.U.. 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--- title: Comprehensive mass spectrometry lipidomics of human biofluids and ocular tissues authors: - Glenda Vasku - Caroline Peltier - Zhiguo He - Gilles Thuret - Philippe Gain - Pierre-Henry Gabrielle - Niyazi Acar - Olivier Berdeaux journal: Journal of Lipid Research year: 2023 pmcid: PMC10027555 doi: 10.1016/j.jlr.2023.100343 license: CC BY 4.0 --- # Comprehensive mass spectrometry lipidomics of human biofluids and ocular tissues ## Body Lipids are essential components of cell membranes and have important biological roles, particularly in energy storage, cell signalling, and structure [1, 2]. They account for $30\%$ of the human body weight and are found in high quantities in the adipose tissue, where they consist of triglycerides (TAG), $98\%$, cholesterol (Chol), $0.26\%$, and glycerophospholipids (GPh), $0.29\%$ [3]. Also, the nervous system is rich in lipid content, represented by the brain and peripheral neurons, which conversely are mainly composed of sphingolipids, GPh and Chol in almost equal ratios [4]. Within the nervous system, lipids have a different distribution that is structure- and/or cell-specific [5]. The retina, which is an extension of the nervous system, is a thin sensory membrane which covers the back of the eye, being responsible for vision [6, 7]. The lipid composition of the retina is mainly represented by GPh, particularly glycerophosphoethanolamine and glycerophosphocholine [8]. Other lipid classes, such as cholesteryl esters (CE) [9], Chol, and PUFAs, particularly docosahexaenoic acid [10], are also abundant in the retina. Indeed, PUFAs are extensively incorporated into GPh units and play physical roles having an impact on visual transduction [11]. Lipids are not only fundamental building blocks for cell membranes but also crucial for cell signalling. Hence sphingolipids and glycosphingolipids are involved in visual signaling and neuronal growth, while protecting against retinal damage [12]. Retinal lipid homeostasis can be disturbed due to genetic, age-related, and environmental factors [13, 14, 15, 16, 17, 18]. Recent studies have shown that individuals with high total plasma Chol and TAGs, elevated HDL-Chol, LDL-Chol, and VLDL-Chol, have a significantly higher risk of developing retinal degenerative diseases [19, 20]. In the retina, the transport of these lipids between the blood compartment and the photoreceptors (PRs) is mediated by retinal pigment epithelium (RPE) cells. In situations where the lipid transit is disturbed, conspicuous lipid-containing deposits, called “drusen”, accumulate at the basement of RPE cells. This process triggers an inflammatory cascade [21] leading to RPE and PR degeneration as observed in age-related macular degeneration (AMD), the leading cause of visual loss in western countries [22]. Lipid metabolism is severely impaired in AMD, resulting in metabolites that appear to be hallmarks of the disease. It was shown that lipid composition of the retina is strongly related to food intake. For instance, linoleic acid, an essential FA, was found to be the most prominent FA in RPE cells [9]. The retinal lipid composition is also partly distinguishable due to the presence of very long chain polyunsaturated fatty acids (VLC-PUFAs), which are mainly incorporated into ethanolamine and choline GPh units [23]. Their role is not yet fully understood but a retinal deficiency in VLC-PUFAs is associated with the accumulation of drusen material and further PR cell death. On the other hand, studies have shown that genetic mutation in the VLC-PUFA elongase (ElovL4) is associated with Stargardt disease type-3, a juvenile autosomal dominant form of macular degeneration [24]. Other studies have indicated that certain lipid metabolites, such as sphingosine 1-phosphate (S1P), ceramide 1-phosphate (C1P) and ceramides (Cer), have an important impact on retinal and RPE cells physiology. Thus, C1P stimulates photoreceptor survival and induces PR differentiation in the retina, while Cer emerged as a key mediator of cell death and impairment of the autophagic flux [25]. According to a study, it was shown that low concentrations of Cer prevented photoreceptor degeneration, while high concentrations resulted in photoreceptor loss [26]. In addition, in a recent metabolomic study on human plasma, Lains and collaborators found that 28 lipid metabolites from the GPh, sphingolipid, and lyso GPh pathways, differed significantly between AMD subjects and controls [27]. In the neural retina and RPE, lipids act through different signalling pathways in which different lipid classes and mechanisms are implicated [28]. To better understand these mechanisms which are implicated in AMD pathophysiology, it is important to study the entire lipidome by considering all lipid classes/subclasses. Therefore, this study aims to provide a comprehensive lipidomic approach through the complementarity of utilizing two chromatographic methods [hydrophilic interaction liquid chromatography (HILIC) and reversed-phase chromatography (RPC] coupled to high-resolution mass spectrometry (HRMS), focusing on their specific benefits and drawbacks for screening different classes/subclasses of lipids. In addition, this dual approach can be advantageous for annotating and identifying various lipid classes/subclasses, resulting in a more comprehensive lipidomic profile of these biological samples. The comparison of these analytical methods and the lipidomic data generated on samples from healthy human subjects may be utilized in future research aimed at elucidating the different distribution of these lipid molecules, which appear to have a significant role in the pathogenesis of AMD. ## Abstract Evaluating lipid profiles in human tissues and biofluids is critical in identifying lipid metabolites in dysregulated metabolic pathways. Due to various chemical characteristics, single-run lipid analysis has not yet been documented. Such approach is essential for analyzing pathology-related lipid metabolites. Age-related macular degeneration, the leading cause of vision loss in western countries, is emblematic of this limitation. Several studies have identified alterations in individual lipids but the majority are based on targeted approaches. In this study, we analyzed and identified approximately 500 lipid species in human biofluids (plasma and erythrocytes) and ocular tissues (retina and retinal pigment epithelium) using the complementarity of hydrophilic interaction liquid chromatography (HILIC) and reversed-phase chromatography (RPC), coupled to high-resolution mass spectrometry. For that, lipids were extracted from human eye globes and blood from 10 subjects and lipidomic analysis was carried out through analysis in HILIC and RPC, alternately. Furthermore, we illustrate the advantages and disadvantages of both techniques for lipid characterization. RPC showed greater sensitivity in hydrophobicity-based lipid separation, detecting diglycerides, triglycerides, cholesterol, and cholesteryl esters, whereas no signal of these molecules was obtained in HILIC. However, due to coelution, RPC was less effective in separating polar lipids like phospholipids, which were separated effectively in HILIC in both ionization modes. The complementary nature of these analytical approaches was essential for the detection and identification of lipid classes/subclasses, which can then provide distinct insights into lipid metabolism, a determinant of the pathophysiology of several diseases involving lipids, notably age-related macular degeneration. ## Ethics statement Collection of the samples from human subjects was conducted in accordance with the guidelines of the Declaration of Helsinki, as previously described [10]. A written consent was obtained and the protocol was approved by the local ethics committee (CPP Sud Est I, CHU Saint Etienne, Saint Etienne, France). ## Reagents Optima LC-MS grade water, methanol, chloroform, acetonitrile, isopropanol, formic acid, ammonium acetate were purchased from Fisher Chemical (Thermo Fisher Scientific, Illkirch, FR). All internal standards (IS) used in this study were from Avanti Polar Lipids, Inc (Alabaster, AL). ## Human tissues Human biological tissues (retina and RPE/Choroid) and biofluids (erythrocytes and plasma) were obtained from ten human donors for this study. Following death, the bodies were initially stored at 4°C, and the postmortem delay between death and tissue isolation/freezing was less than 24 h. The blood sample was collected in heparinized tubes via venipuncture. Erythrocytes were separated from plasma by centrifuging at 3000 rpm for 10 min at 4°C. The posterior pole of the eye globe was placed on a table with backlighting and the retina was examined under an operating microscope to help determine healthy subjects included in the study. There were no signs of large drusen, severe pigment epithelial alterations, macular haemorrhage, or other chorioretinal pathologies. The vitreous body was carefully removed and the entire neural retina ($$n = 10$$) was carefully separated from the RPE/choroid ($$n = 10$$). All samples were kept at −80°C until lipid extraction. ## Lipid extraction Samples from ten human donors (erythrocytes, plasma, retina, and RPE/Choroid) were spiked with 10 μl of a mix of ISs at different concentrations. The lipid species concentrations in the mix of IS were as follows: phosphatidylglycerol (PG) (14:$\frac{0}{14}$:0) at 250 μg/ml, phosphatidylethanolamine (PE) (14:$\frac{0}{14}$:0) at 100 μg/ml, phosphatidylcholine (PC) (14:$\frac{0}{14}$:0) at 100 μg/ml, PS (14:$\frac{0}{14}$:0) at 250 μg/ml, lysophosphatidylethanolamine (LPE) (14:0) at 100 μg/ml, lysophosphatidylcholine (LPC) (14:0) at 100 μg/ml, DG (12:$\frac{0}{12}$:0) at 250 μg/ml, TAG d5 (19:$\frac{0}{12}$:$\frac{0}{19}$:0) at 250 μg/ml, SM (d18:$\frac{1}{12}$:0) at 250 μg/ml, LacCer (d18:$\frac{1}{12}$:0) at 250 μg/ml, GlcCer (d18:$\frac{1}{12}$:0) at 250 μg/ml, Cer (d18:$\frac{1}{12}$:0) at 250 μg/ml, FA (17:0) at 250 μg/ml, and CE (17:0) at 250 μg/ml. Lipids were isolated from tissues (retina, RPE/Choroid) using the Folch's procedure, whereas plasma and erythrocytes were extracted using the Moilanen and Nikkari method [29, 30]. The lipid-containing phase was placed in vials and dried under a nitrogen stream. Samples were reconstituted in 200 μl of chloroform/methanol (1:1), vortexed before injection into the LC-MS, and then stored at −30°C after analysis. Quality controls (QCs) were prepared by pooling 10 μl of each sample and injecting them every ten samples throughout the sequence. A system blank composed of methanol and an extraction blank were prepared. The injection order of blanks was every ten samples before the QCs. Lipid analysis was carried out on an LC-MS high-resolution mass spectrometer in both normal and reversed-phase separation conditions, injected alternately. ## RPC Samples were injected in a reversed-phase ACQUITY ultra performance liquid chromatography (UPLC) ethylene-bridged C18 column, 1.7 μm, 2.1 mm × 100 mm (Waters). Mobile phase A consisted of acetonitrile/water 50:50 (v/v) and phase B consisted of acetonitrile/isopropanol/water 10:88:2 (v/v) with 10 mM ammonium formate and $0.1\%$ formic acid for both phases. The solvent-gradient system comprised as follows: 0 min A/B (%) $\frac{60}{40}$, 10–12 min A/B (%) $\frac{0}{100}$, and 12.1–17 min A/B (%) $\frac{60}{40.}$ The flow rate was 400 μl.min−1 and the column temperature were set at 50°C. ## HILIC Separation of samples was performed on an ACQUITY UPLC ethylene-bridged HILIC column, 1.7 μm, 2.1 mm × 100 mm (Waters). Mobile phase A consisted of acetonitrile/water 96:4 (v/v) and phase B consisted of acetonitrile/water 50:50 (v/v). For both phases, ammonium acetate (10 mM) was added. The solvent-gradient system comprised as follows: 0–0.33 min A/B (%) $\frac{95}{5}$, 0.33–5.33 min A/B (%) $\frac{80}{20}$, 5.33–6.67 min A/B (%) $\frac{60}{40}$, and 6.67–10 min A/B (%) $\frac{100}{0.}$ The flow rate was chosen at 1200 μl.min−1 and the column temperature were set at 50°C. ## MS Acquisition of mass spectra was performed on a high-resolution mass spectrometer Orbitrap Fusion™ Tribrid™ (Thermo Fisher Scientific, San Jose, CA). Lipid extracts were separated by LC and the column effluent was directly introduced into the EASY-Max Next Generation Atmospheric Pressure Ionization source (Heated-ESI mode), (Thermo Fisher Scientific, San Jose, CA). The acquisition was performed in positive and negative runs, separately, in MS and MS2. The method summary for the full MS scan experiment was as follows: the infusion mode was LC with an expected LC peak width at 30 (s), at a charge state of one with an internal mass calibration EASY-IC™. The ion source type used was H-ESI with a static spray voltage at 3500 V and 2800 V in positive and negative ionization mode, respectively. Sheath, auxiliary, and sweep gas were maintained at 60, 20, and 1 respectively, in arbitrary units. The ion transfer tube temperature was 285°C and the vaporizer temperature was set at 370°C. The detector was an Orbitrap with a resolution of 120.000 on a m/zrange of 200–1600, using quadrupole isolation. The RF lens (%) was 50, while the normalized automatic gain control target (%) was maintained at 112.5. The maximum injection time was 100 (ms) and the data type was acquired in centroid. The method for the MS2 negative ion mode experiment was performed using filters such as dynamic exclusion with a duration of 15 (s) and mass tolerance of 5 ppm. For the MS2 a data-dependent mode was chosen with a scan in ddMS2 orbitrap high collision energy. The isolation mode was performed on a quadrupole with an isolation window m/z at 1.2. The collision energy was fixed at 27 eV and the data type was acquired in centroid. ## Data analysis and processing We optimized the previous laboratory chromatographic separation method from HPLC to ultra performance liquid chromatography, and therefore the total run time for HILIC [31] and RPC [32] was reduced from 45 to 10 and 17 min, respectively. QCs were analyzed in triplicate, initially in MS2 scan in negative and positive ionization mode, to allow for lipid species annotation, and then in MS full scan, while samples were only analyzed in MS full scan in positive and negative ionization mode, separately. Raw files were converted to a readable format, mzXML, and then processed in R, including peak extraction and normalization, resulting in the generation of specific files containing data on m/z, ppm, and peak intensities. Raw files were visualized with Xcalibur 4.2 (https://www.thermofisher.com/order/catalog/product/OPTON-30965) and Freestyle 1.8 (https://assets.thermofisher.com/TFS-Assets/CMD/manuals/man-xcali-97962-freestyle-14-user-manxcali97962-en.pdf), whereas LipidSearch 4.1 (https://www.thermofisher.com/fr/fr/home/industrial/mass-spectrometry/liquid-chromatography-mass-spectrometry-lc-ms/lc-ms-software/multi-omics-data-analysis/lipid-search-software.html) was used for lipid annotation. Ions were identified based on respective adducts (H−, OAc−, H+, NH4+). Results were compared to the manual annotation of all parent ions from MS2 spectrum elucidation. Finally, R Studio was used to acquire and process all data. Raw MS files were converted to mzXML format using MSConvertGUI 64 bit (https://proteowizard.sourceforge.io/download.html) and then processed using a preestablished database of parent molecular ions based on their exact m/z mass for each lipid species. Prior annotation via LipidSearch and MS2 spectrum elucidation of all lipid species in the QC samples was used to establish the database of lipid classes and species. Minimum and maximum retention time windows were set manually for each lipid class, while the m/z tolerance was chosen at 10 ppm. ## Human donors’ characteristics Ten subjects participated in the study, three females and seven males, all caucasians. The median age of donors was 87.50 years (mean SD: 87.4 ±6.55 years). Erythrocytes, plasma, retina, and RPE/choroid were collected from each subject. The median postmortem delay was 12.5 h (mean SD: 14.2±7.02 h), with a minimum and maximum of 3 h and 24 h, respectively (Table 1).Table 1Human’s donor characteristicsHuman donor's CharacteristicsSubjectGenderAgePostmortem Delay(h)Collected Tissues1M873erythrocytes, plasma, retina, RPE/Choroid2F938erythrocytes, plasma, retina, RPE/Choroid3M8613erythrocytes, plasma, retina, RPE/Choroid4M9324erythrocytes, plasma, retina, RPE/Choroid5M9710erythrocytes, plasma, retina, RPE/Choroid6M9212erythrocytes, plasma, retina, RPE/Choroid7M7919erythrocytes, plasma, retina, RPE/Choroid8M8210erythrocytes, plasma, retina, RPE/Choroid9F7719erythrocytes, plasma, retina, RPE/Choroid10F8824erythrocytes, plasma, retina, RPE/ChoroidMean87.414.2SD6.5527.021Median87.512.5Interquartile range9.7509Minimum773Maximum9724Range2021 ## General workflow The overall design of the study was depicted in Fig. 1. Erythrocytes, plasma, retina, and RPE/Choroid lipid samples were extracted and the lipid extract was separated through two alternating chromatographic techniques (HILIC and RPC) in positive and negative ionization mode. After separation, lipid extracts were introduced to the ESI ionization source connected to Orbitrap Fusion HRMS for MS and MS2 scan. After data collection, lipid annotation was accomplished using LipidSearch and manual spectral elucidation of MS2 scan files, whereas relative lipid quantification was attained using MS full scan files. R 4.0.2 was used to process data, resulting in the generation of files containing information on ppm, peak intensity, and m/z. The annotation of each lipid species within each sample, as well as the relative distribution, exact mass, and ppm features of each lipid species were reported. Fig. 1General workflow of the study. Human retina, retinal pigment epithelium (RPE/Choroid), erythrocytes and plasma were analyzed as lipid extracts through Hydrophilic Interaction Liquid Chromatography (HILIC) and reversed-phase chromatography (RPC), coupled to high-resolution mass spectrometry. Data analysis was performed through R and ion’s peak intensities were extracted from conversed files in mzXML. Files were matched with the integrated lipid database that was previously prepared and m/z masses were extracted with their respective intensities. Created with: BioRender. ## METHOD VALIDATION Lipid annotation was achieved from MS2 scans in positive and negative ionization mode in HILIC and RPC. Manual annotation on precursor ions confirmed the lipid composition of species after using LipidSearch as a database for lipid identification. Such approach resulted in the annotation of 15 lipid classes and approximately 500 lipid species. Each precursor ion’s relative intensity was extracted automatically via R and the results were computed in a centroided mode. Each mzXML file was compared to the database of precursor ions, which was prior established, and then three separate files (.csv) were generated with data on m/z, ppm, and peak intensities. The lipid database comprising of m/z and all the lipid species annotated through HILIC (supplemental Table S1) and RPC (supplemental Table S2), is available in the supplementary section. To validate this methodology, results of the automatic processing of peak extraction from R, were compared with those obtained by manually extracting peak intensities from all QC samples through Freestyle software. We demonstrate a positive correlation between automatic peak extraction through R and manual extraction directly from Freestyle software for the two techniques (RPC and HILIC), in positive and negative ionization mode. ( supplemental Fig. S3). ## Relative distribution of lipid classes The lipid profile of erythrocytes, plasma, retina, and RPE/Choroid was determined through RPC (Fig. 2). and HILIC (Fig. 3). Cer, dihexosylceramide (Hex2Cer), hexosylceramide (HexCer), PE, PC, phosphatidylserine (PS), LPC, LPE, sphingosine bases, and sphingomyelin (SM) were detected as protonated adducts [M+H]+. Lipid classes such as PE, LPE, phosphatidylglycerol (PG), phosphatidylinositol (PI), and free nonesterified fatty acyls (FFA) were detected as deprotonated [M-H]− adducts in negative mode. CE, TAG, followed by Chol and diglycerides (DAG), were detected only in RPC, as ammonium adducts [M+NH4]+ in positive ionization mode (Fig. 2), as we report no signal from these molecules in HILIC (Fig. 3A). Such response might be explained due to the column properties and the chemical characteristics of the molecules which contribute to the overall molecular hydrophobicity. The hydrophobicity of a molecule is determined by the length of the carbon chain and the presence of unsaturations (double bonds in this case); thus, longer carbon chains with multiple unsaturations have higher hydrophobic properties. These molecules are therefore more retained in the RPC column than polar compounds, which are less retained and consecutively elute faster. It should be noted that depending on the type of samples being analyzed and the lipids of interest, this can be significant. Chol, CE, and TAG, for example, are abundant in plasma/serum, so RPC can be the method of choice for their analysis. HILIC was chosen as a secondary method to evaluate the lipid profile of erythrocytes, plasma, retina, and RPE/Choroid. In contrast to RPC, lipids in HILIC were primarily separated based on their polarity. Furthermore, PC species containing VLC-PUFA (VLC-PC) were detected via HILIC and RPC, only in the retina, as this subclass is only found in retinal tissues (Fig. 3B) [23]. Furthermore, VLC-PC eluted in similar RT with another class, such as LPE, that is also detected in positive ionization mode. Additionally, in Fig. 3C we show that the extracted chromatographic peak of plasma, along with the extracted ions appear to belong to the LPE and not VLC-PC. Indeed, no molecular ions corresponding to VLC-PC appear in the selected mass range spectra. Identification of these molecular ions was done through spectral elucidation through MS2 fragmentation. Figure 4A, B depicts the relative intensity of all common lipid classes detected in both HILC and RPC. Based on their relative intensity, we can conclude that for the same lipid subclass, chromatographic separation produces a different response, which is then translated into a varying intensity for that specific lipid class. Moreover, depending on the chromatographic affinity and conditions used to separate these classes, each lipid class or species has a different behavior. The general distribution of lipid’s intensity in both modes in erythrocytes, plasma, retina, and RPE/Choroid is given for each class, as a (%) relative intensity distribution. In RPC and HILIC, the intensity of PC and PE in positive was presented as their total sum, as well as that of LPE and LPC. This was done to present a general and comparable intensity in RPC and HILIC, between these classes only in positive, while considering the presence of some isobaric species between PE and PC and between LPE and LPC. The separation of these isobaric species cannot be achieved in RPC in positive ionization mode, because these species have very similar RT and exact mass. For instance, due to their exact masses, PE 36:1, m/z 746.5694 and PC 33:1, m/z 746.5694 cannot be distinguished through RPC. Furthermore, differentiation between these classes in RPC is difficult with our method because coelution occurs within the same retention time (RT: 4.5–9 min). However, due to their different exact masses, we can easily distinguish these species in negative ionization mode. Furthermore, in HILIC, these species can be distinguished in positive mode based on different RT windows. In HILIC, the PE subclass eluted at (RT: 2–2.7 min), whereas the PC subclass eluted at (RT: 3–4 min). Other isobaric lipid species from the PE and PC subclasses were evidenced (supplemental Table S4). In positive, the same result was observed for LPE and LPC, where their relative distribution was presented as a sum of both species compared to the overall intensity, in order to be easily compared between RPC and HILIC, considering the presence of isobaric species between these subclasses. ( Fig. 4). For example, LPE (22:1), at m/z 536.3711 and LPC (19:1) at m/z 536.3711 cannot be distinguished through RPC in positive in ionization mode due to exact mass and similar RT time window. However, these species can be distinguished in negative ionization mode, based on different masses. Furthermore, we can distinct them in HILIC mode, as in this case the RT time of these species will differentiate them. For these subclasses the RT in HILIC were as follows: LPE the (RT: 2.9–3.5 min) and for LPC the (RT: 4.6–5 min) (supplemental Table S5). PS, Cer, HexCer, Hex2Cer, and SM were also found to be common lipid subclasses between HILIC and RPC. Overall, the distribution of lipids between these classes is comparable but there is a significant difference in intensity. The relative distribution of PS, Cer, HexCer, and SM in HILIC and RPC is comparable and this tendency is better visualized after zooming in the graphic bars. However, Hex2Cers’ intensity was shown to be overestimated in RPC compared to HILIC.Fig. 2A: Total ion chromatogram (TIC) of QC samples of retina (red) and (B) TIC of plasma (blue), in positive and negative ionization mode in RPC. RPC, reversed-phase chromatography; QC, quality control. Fig. 3A: (a), Total ion chromatogram (TIC) of QC samples of retina (red) and (b) TIC of plasma (blue), in positive and negative ionization mode in HILIC. B: (a): Extracted total ion chromatogram (TIC) (RT:3-3.5 minutes), showing the elution window for VLC-PC in the retina, in HILIC in positive ionization mode. ( b): Mass spectra extracted from the RT:3–3.5 minutes, at m/z 1000-1100, representing a few of the VLC-PC species that are present in the retina. C: (a) Extracted total ion chromatogram (TIC) (RT:3–-3.5 minutes), showing the elution window for LPE in plasma, in HILIC in positive ionization mode. ( b): Mass spectra extracted from the RT:3–-3.5 minutes, at m/z 450–-570, representing a few of the LPE species. HILIC, hydrophilic interaction liquid chromatography; LPE, lysophosphatidylethanolamine; VLC-PC, PC species containing VLC-PUFA; QC, quality control. Fig. 4A: Relative lipid distribution of lipid classes in (%), PE+PC (mean SD: 37.24± 6.59), LPE+LPC (mean SD: 26.42± 10.34), PS (mean SD: 0.12± 0.06), Cer (mean SD: 12.53± 5.38), HexCer (mean SD: 6.75±7.95), Hex2Cer (mean SD: 1.78± 1.23), and SM (mean SD: 15.17± 3.91) in RPC in positive ionization mode, evaluated in erythrocytes, plasma, retina, and RPE/Choroid. B: Relative lipid distribution of lipid classes PE+PC (mean SD: 46.48± 14.51), LPE+LPC (mean SD: 14.78± 3.36), PS (mean SD: 0.79± 0.34), Cer (mean SD: 0.92± 0.56), HexCer (mean SD: 0.29±0.27), Hex2Cer (mean SD: 2.32± 1.44), and SM (mean SD: 9.77± 3.44) in HILIC in positive ionization mode, evaluated in erythrocytes, plasma, retina, and RPE/Choroid. Lipid classes such as PS, Cer, HexCer, Hex2Cer were evidenced (zoom 50x) in HILIC and RPC. Cer, ceramide; HexCer, hexosylceramide; Hex2Cer, dihexosylceramide; HILIC, hydrophilic interaction liquid chromatography; LPE, lysophosphatidylethanolamine; LPC, lysophosphatidylcholine; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PS, phosphatidylserine; RPC, reversed-phase chromatography; RPE, retinal pigment epithelium; SM, sphingomyelin. Furthermore, we investigated the relative intensity of lipid classes for each chromatographic method separately which is evidenced in Fig. 5A, B in positive ionization mode and in Fig. 5C, D, in negative ionization mode, for RPE/Choroid and retina. The intensity of each lipid class is compared to the overall intensity of all lipid classes analyzed separately through RPC and HILIC method. The relative distribution of lipid classes in RPC and HILIC in retina and RPE/Choroid in positive mode was presented from ten subjects (Fig. 5A, B), while the relative distribution of PE, PI, PG, LPE and FFA was evaluated in negative mode (Fig. 5C, D). We highlight that in RPC graphic representation, the intensity of PC might be overestimated due to the presence of coeluting isomeric peaks between PC and PE. A difference in relative lipid’s distribution was visualized for subject 10, which presented a higher value of HexCer class in the retina (Fig. 5B), compared to other subjects. Additionally, in the RPE/Choroid subject 6 presented a higher relative intensity of LPC class in positive ionization mode (Fig. 5A) and a higher intensity for the FFA class in negative ionization mode in HILIC (Fig. 5C).Fig. 5A: Relative lipid distribution of ten subjects’ lipid profile in positive ionization mode in RPC and HILIC in RPE/Choroid. B: Relative lipid distribution of ten subjects’ lipid profile in positive ionization mode in RPC and HILIC in retina. C: Relative lipid distribution of ten subjects’ lipid profile in negative ionization mode in RPC and HILIC in RPE/Choroid. D: Relative lipid distribution of ten subjects’ lipid profile in negative ionization mode in RPC and HILIC in retina. HILIC, hydrophilic interaction liquid chromatography; RPC, reversed-phase chromatography; RPE, retinal pigment epithelium. ## Comparative assessment of RPC and HILIC RPC and HILIC conditions were chosen for the analysis of lipid classes in lipid extracts of erythrocytes, plasma, retina and RPE/Choroid, as complementary techniques. Both techniques were tested on different lipid matrices, that differ at some extent on their lipid composition. Most of the classes were visible in both RPC and HILIC (PE, PC, PI, PG, PS, LPE, LPC, FA, Cer, HexCer, Hex2Cer, SM), and other classes were only visible through RPC (Chol, CE, DAG, and TAG). ## Neutral lipids (CE and TAG), Chol, and DAG are efficiently detectable through RPC Neutral lipids such as CE and TAG which lack charged groups in order to be detected need to be ionized. In the UPLC-ESI-MS2 and MS full scan analysis of Chol, CE, DAG, and TAG, they were mainly detected as ammonium adducts [M+NH4]+. TAG and CE are highly hydrophobic structures and they are more retained by the RPC column. Consequently, in this method they coeluted at a RT range of 9–11 min, in positive ionization mode (Fig. 2A, B). Meanwhile, DAG coeluted before TAG. Such behavior might be explained by its overall less hydrophobic structure compared to TAG, due to the presence of two fatty acyl chains instead of three in TAG. Indeed, DAG coeluted with other lipid classes such as GPh, at a (RT: 5–9 min) (Fig. 2A, B). CE were detectable in RPC in positive mode, as ammonium adducts, but there was a noticeable loss of cholesterol moiety from the CE structure, giving a specific fragment at m/z 369.35 corresponding to a dehydrated cholesterol [M+H-H2O]+ [33]. Due to suppressive ionization processes cholesterol was fragmented from the CE structure as a dehydrated moiety at an (RT: 9–11.5 min). In the text and figures, the fragmented Chol from CE was denominated as “derived cholesterol”. ## PC, PE, PS, and lyso GPhs (LPE, LPC) are efficiently separated through HILIC PC and PE were both detectable in RPC, with PC being detected specifically in positive mode and PE in both ionization modes. It must be noted that PC and PE species might be confounded due to isomeric species sharing exact m/z, which cannot be differentiated through our method in positive mode in RPC. In contrast, PE and PC are easily distinguished in HILIC positive ionization mode due to different retention times. Indeed, the extracted chromatogram peak of two standards, PC (14:$\frac{0}{14}$:0) and PE (14:$\frac{0}{14}$:0), which was evaluated in RPC and HILIC, illustrates this behavior (Fig. 6). Due to distinct retention times and nonoverlapping peaks, it was observed that the separation between these classes was more efficient in HILIC. Furthermore, when both chromatograms were compared, PC (14:$\frac{0}{14}$:0) eluted before PE (14:$\frac{0}{14}$:0), in RPC, whereas PC (14:$\frac{0}{14}$:0) eluted after PE (14:$\frac{0}{14}$:0), in HILIC, indicating that PC might be more polar than PE (Fig. 6). However, this assumption is made for two lipid species that differ only by their polar head. Other factors, such as the number of double bonds and carbon number interfere with this separation and thus the specific retention time of each species varies significantly within class. The same behavior was observed for other endogenous PE and PC species in the analyzed samples. Other lipid subclasses such as LPE could be detected in either positive or negative mode due to their ionization efficiency in both ionizations, whereas LPC were only detected in positive mode. We also demonstrated the LPC and LPE tend to coelute in RPC, however these classes are efficiently separated in HILIC, as we have shown through the behavior of the two IS (Fig. 6). Furthermore, we show that PS were also detected in positive and negative ionization modes. This lipid class presented isobaric species along with PC in negative mode. PS is detected as a deprotonated adduct in negative ionization mode, whereas PC was detected as an acetate adduct [M+OAc]- in negative ionization mode. For instance, PS (36:0) and PC (32:1) are two isobaric species with an exact mass at m/z 790.5604 in negative mode. Their separation cannot be performed in RPC as these classes coelute (RT: 4.5–9 min). For the quantification of these species this can be an issue as these lipids cannot be separated through our method, therefore we chose to relatively quantify these species only in positive mode, in which they share distinct m/z. The negative ionization mode is however very beneficial for the annotation process. For instance, the analysis of the PC in the negative mode, as an [M+OAc]- adduct, was the basis for the annotation process of these molecular species to confirm the nature of the FFA and lysoforms. Overall, the separation of these classes, particularly GPh, lyso-GPh, Cer, and derivatives such as HexCer, Hex2Cer, can be performed more efficiently through HILIC mode, where intraclass differentiation is far more sensitive as the separation is primarily based on their polarity. Nonetheless, RPC remains an effective method for within class separation. For example, within PE class, lipids can be distinguished based on the hydrophobicity which is favored by the presence of fatty acyl chains, which contributes to the molecule’s hydrophobicity and thus separates more efficiently these lipids. Classes such as Cer, HexCer, Hex2Cer, sphingosine bases, and SM also presented a better sensitivity in HILIC, implying that this method might be a better option when analyzing these species. Also, PE, LPE, PG, PI, and FA lipids were detected in negative in RPC and HILIC. Moreover, FA responded better in negative ionization mode but represented a far better response in HILIC in negative than in RPC.Fig. 6Lipid analysis of a mixture of internal standards of 13 lipid classes, separately analyzed in both ionization modes, in RPC and HILIC. HILIC, hydrophilic interaction liquid chromatography; RPC, reversed-phase chromatography. ## RPC and HILIC are complementary methods for global lipid analysis Accurate lipidomic studies in biological tissues/biofluids provide the basis for understanding molecular pathways of diseases associated with lipid metabolism dysregulations, such as AMD [28, 34, 35]. Most of lipidomic research is focused on targeted techniques. Understanding how metabolic pathways may be involved in disease onset and progression requires a comprehensive representation of nearly all lipid classes. On the other hand, absolute quantification through untargeted techniques is challenging due to a lack of IS for many endogenous species. It is now acceptable to use one IS per class, although it may result in errors because molecules from the same class have distinct properties [36, 37]. Likewise, lipids are very complex structures ranging from highly polar to hydrophobic. These characteristics make it difficult to design a global analytical procedure through a single analytical run and such studies have not yet been reported to our knowledge. We attempt to present the benefits of a dual analytical method of using RPC and HILIC, alternatively, to identify and quantify the relative amounts of about 15 lipid classes and approximately 500 lipid species, in positive and negative ionization modes, from four different biological tissues/biofluids (erythrocytes, retina, RPE/Choroid, plasma). Lipid classes/subclasses were annotated through the combination of the LipidSearch software and additional manual spectra elucidation, which warrants a robust and reliable annotation process. We highlight the benefits and drawbacks of the RPC and HILIC method, for analyzing lipid classes, as well as pointing out their sensitivity based on lipid separation and response in both ionization modes. Based on the biological matrices that were analyzed, such information can be relevant, as some lipids exhibit better response through RPC rather than HILIC. Although both chromatographic methods can separate a wide range of lipid classes, it is understandable that using one or the other alone has some drawbacks, as some lipid species have a better signal with one instead of the other. We suggest that the complementarity of these chromatographic methods is highly recommended in untargeted lipidomic approaches which can be adapted to analyze lipids in a global approach. The current developed combination technique has several advantages, including (i) short time analysis (10 min for HILIC and 17 min for RPC), which can be used for long series analysis, (ii) coupling of these chromatographic methods with HRMS which guarantees high resolution in terms of mass exactitude, and (iii) the applicability of these methods on almost all biological tissues and biofluids. Such an approach can be highly beneficial when analyzing lipids of a certain category in specific tissues. For instance, in ocular tissues, such as the human retina, GPh are major classes while other classes are less abundant. In this case, the HILIC method is preferable for lipid analysis. In contrast, for the analysis of human plasma, where Chol, CE, and TAG are abundant, the RPC may be the preferred method. We show that RPC approach in untargeted lipidomics tends to separate better species such as Chol, CE, DAG, and TAG. Other conditions in addition to chromatographic separations might interfere with this behavior, such as ionization sources, although they were not studied in this work. When an ESI source is used, for example, CEs tend to fragment in source. This fragmentation results in the formation of dehydrated cholesterol, reducing the detection and relative quantification of CE. Furthermore, it has been suggested that an atmospheric-pressure chemical ionization source may also be an appropriate method to analyze CE [33]. The advantages of HILIC are primarily focused on the separation of GPh. Such separation is also achieved in RPC but it is more challenging to achieve intraclass separation for GPh because these classes tend to coelute. Also, the presence of isobaric molecular species that are either in positive ionization (between PE and PC or LPE and LPC) or negative ionization (PS and PC) were not differentiated in RPC. However, due to different RT, which can distinguish these species, it was possible to separate them in HILIC. Lipid annotation is the most challenging process in lipidomics. This is due in part to the large number of lipid species and the inability to detect and represent all lipid classes in a single analytical run. Furthermore, the annotation process has not yet been fully automated, even though several software, such as LipidSearch, MZmine (http://mzmine.github.io/), MS-Dial (http://prime.psc.riken.jp/compms/msdial/main.html) and others provide a good approach. In comparison to metabolomics software, lipidomics, and data processing software, particularly the annotation process, is still in development. Based on those developments, we performed lipid annotation using a combination of LipidSearch and manual annotation. We were able to investigate the spectra of all ions obtained in HILIC and RPC in positive and negative ionization modes in MS2. Chol, CE, DAG, and TAG, for example, were not detected in HILIC. As a result, in order to annotate these species, we investigated the parent ion of this species as ammonium adducts [M+NH4]+ using the RPC method. These methods' complementarity was critical for the overall identification and annotation of lipid species. We evaluated the overall lipid distribution for RPE/Choroid and retina in RPC and HILIC in positive and negative ionization modes. We demonstrated that lipid distribution of certain lipid classes differed between subjects. For instance, in the retina, subject 10 presented a higher HexCer than other subjects. No such result was observed for RPE/Choroid. Dysregulated metabolic process might explain these results. Prior studies have suggested that high retinal glucosylceramide content is present in individuals suffering from diabetic retinopathy. The overproduction might be due to the augmented uridine diphosphate glucose production through the pentose pathway, suggesting that hyperglycemia in diabetes induces glucosylceramide production [38]. However, we hypothesize that potential dysregulated sphingolipid metabolism in retinal tissues could explain the abnormal lipid degradation that results in higher HexCer in the retina, pointing out to potential directions for further research into sphingolipids as potential targets in retinal degenerations. We do not exclude the necessity of targeted approaches directly analyzing specific lipids, in order to better understand these differences in lipid distributions. Furthermore, based on the comparison of two methods the relative distribution of lipid classes differs. That was mostly evidenced for the Hex2Cer, which presented a higher intensity in the RPC than HILIC. The separation of this class can be performed more efficiently through HILIC than RPC. In RPC, Hex2Cer coelutes with other species, including mostly phospholipid subclasses. In contrast, in HILIC, Hex2Cer elutes at a retention time that is easily distinguishable from that of phospholipids and other classes. Typically, we rely on the RT time for identification. We hypothesize that the coelution effect and matrix ionization processes may induce competition between ions, particularly between phospholipids (which occupy nearly 50 % of the retina) and Hex2Cer. This behaviour may hinder the detection of these species, as well as their relative intensity, which may contribute for the “overestimation” of this class in RPC. For the detection and relative quantification of Hex2Cer and other sphingolipid subclasses, such as (Cer and HexCer, we recommend the HILIC method, which provides a more accurate separation based on retention times that do not overlap, than RPC. Higher lipid distribution was observed in RPE/Choroid for LPC in positive mode and FFA in negative mode, implying GPh lipid degradation due to prior sample manipulation or storage. Although all necessary precautions were taken to ensure proper sample manipulation, we hypothesize that previous sample withdrawal in hospital facilities should be considered in order to avoid degradation resulting from improper manipulation such as leaving samples on the bench for prolonged periods of time, repetitively performing analysis, and thawing. Such variations, while seemingly insignificant, can promote enzymatic activities that lead to lipid degradation. These variables might induce errors when performing lipid analysis in biological tissues and we acknowledge, therefore we recommend considering these factors during analysis to minimize them if possible. However, more in-depth investigation and targeted quantification is required, especially by targeting specific compounds to determine if disrupted metabolism or simply poor sampling manipulation interferes with lipid distribution, which might be performed between control and diseased subjects. These suggestions highlight the importance of evaluating lipids that share same metabolic pathways to illustrate more concretely these lipid disturbances. Overall, this methodological approach provides a comprehensive technique as well as an explanatory analysis of how different lipids respond to different analytical separation techniques, which needs to be considered for detailed and accurate lipid identification and quantification. In addition, these findings could be beneficial to the lipidomic community, particularly those focusing on lipid metabolism dysregulations especially those related to retinal degenerations such as AMD. ## Data availability Data that support the plots within this publication and other findings of this study are available from the corresponding authors upon reasonable request. ## Supplemental data This article contains supplementary data. Supplemental Table S1 Supplemental Table S2 Supplemental Table S3 Supplemental Table S4 Supplemental Table S5 ## Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article. ## Author contributions G. V., N. A., and O. B. conceptualization; G. V., N. A., and O. B. methodology; G. V. and C. P. software; G. V. data curation; G. V. writing-original draft preparation; C. P., N. A., and O. B. visualization; G. V., C. P., N. A., and O. B. investigation; N. A. and O. B. supervision; G. V. and O. 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--- title: Fitm2 is required for ER homeostasis and normal function of murine liver authors: - Laura M. Bond - Ayon Ibrahim - Zon W. Lai - Rosemary L. Walzem - Roderick T. Bronson - Olga R. Ilkayeva - Tobias C. Walther - Robert V. Farese journal: The Journal of Biological Chemistry year: 2023 pmcid: PMC10027564 doi: 10.1016/j.jbc.2023.103022 license: CC BY 4.0 --- # Fitm2 is required for ER homeostasis and normal function of murine liver ## Body The endoplasmic reticulum (ER) is the major cellular site of lipid synthesis and production of cell surface and secreted proteins. The protein fat storage–inducing transmembrane protein 2 (FIT2) has emerged as an important determinant of ER homeostasis in cells. FIT1 and FIT2 genes were identified as targets of the transcription factor PPARα in the murine liver [1]. Both FIT1 and FIT2 are ER-resident proteins with six putative transmembrane segments and ∼$50\%$ amino acid sequence similarity [2]. Murine FIT1 and FIT2 have different tissue expression patterns: FIT1 is expressed mainly in skeletal and cardiac muscle, and FIT2 is ubiquitously expressed, with the highest levels in adipose tissue [1]. The molecular functions of FIT proteins have been somewhat enigmatic. FIT2 was initially implicated in lipid metabolism and, in particular, lipid droplet (LD) biogenesis [1, 3]. Overexpression of FIT2 in murine liver results in increased lipid storage in hepatocytes, and this finding was replicated in a variety of cell types [1]. In cells, FIT2 was localized to the ER and found at sites of LD biogenesis [4]. FIT2 binds neutral lipids and was hypothesized to partition neutral lipids into a storage pool [5]. The FIT2 gene is essential in worms and mice [6, 7], highlighting the importance of FIT2 function. Tissue-specific deletions of Fitm2 in mice revealed crucial functions in adipocyte differentiation, enterocyte function, and pancreatic β-cells [7, 8, 9]. In humans, homozygous FITM2 deficiency causes deafness–dystonia syndrome [10, 11], and human FIT2 is required for cancer cell fitness during exposure to interferon γ (IFNγ) [12]. The diverse deletion phenotypes show that FIT2 is crucial for life and highlight that FIT2 deficiency manifests differently in different biological systems. FIT2 is required for normal cellular ER homeostasis. It was identified as a putative lipid phosphate phosphatase enzyme by homology searches [13], and it has acyl-CoA diphosphatase activity in vitro, utilizing a variety of acyl-CoA substrates to generate acyl 4′-phosphopantetheine and adenosine-3′,5′-bisphosphate products [14]. This enzymatic activity is critical to preserving cellular ER homeostasis, as FIT2 deficiency in mammary carcinoma cells and yeast results in ER dilation and whorls, ER stress, and reduced LD biogenesis capacity [6, 14]. Consistent with these data, FIT2 orthologs in yeast, SCS3 and YFT2, were implicated in ER homeostasis [15, 16]. In the current study, we sought to determine whether FIT2 acts as an acyl-CoA–cleaving enzyme and functions in ER homeostasis in vivo by deleting Fitm2 in murine hepatocytes. We studied the effects of FIT2 deficiency on hepatic ER and lipid homeostasis by analyzing the phenotypes of mice fed chow or high-fat diets (HFD). Our results demonstrate the necessity of FIT2 for ER homeostasis in vivo and provide insights into its physiological functions in this tissue. ## Abstract The endoplasmic reticulum (ER)–resident protein fat storage–inducing transmembrane protein 2 (FIT2) catalyzes acyl-CoA cleavage in vitro and is required for ER homeostasis and normal lipid storage in cells. The gene encoding FIT2 is essential for the viability of mice and worms. Whether FIT2 acts as an acyl-CoA diphosphatase in vivo and how this activity affects the liver, where the protein was discovered, are unknown. Here, we report that hepatocyte-specific Fitm2 knockout (FIT2-LKO) mice fed a chow diet exhibited elevated acyl-CoA levels, ER stress, and signs of liver injury. These mice also had more triglycerides in their livers than control littermates due, in part, to impaired secretion of triglyceride-rich lipoproteins and reduced capacity for fatty acid oxidation. We found that challenging FIT2-LKO mice with a high-fat diet worsened hepatic ER stress and liver injury but unexpectedly reversed the steatosis phenotype, similar to what is observed in FIT2-deficient cells loaded with fatty acids. Our findings support the model that FIT2 acts as an acyl-CoA diphosphatase in vivo and is crucial for normal hepatocyte function and ER homeostasis in the murine liver. ## Generation of hepatocyte-specific FIT2 knockout mice To investigate the function of FIT2 in murine liver, we generated hepatocyte-specific FIT2 knockout mice (FIT2-LKO) using Cre-loxP technology. We crossed Fitm2loxP/loxP (floxed) mice with mice expressing Cre recombinase under the control of the albumin promoter, leading to the deletion of exon 1 of Fitm2 (Fig. S1A). Hepatic Fitm2 transcript and protein levels, as measured by immunoblot analysis, were reduced by $95\%$ (Fig. 1, A and B). Mass spectrometry of liver lysates also demonstrated the loss of FIT2 protein in FIT2-LKO livers (Table S1). We confirmed that Cre recombinase expression was restricted to liver and not found in muscle or adipose tissue of FIT2-LKO mice (data not shown). Consistently, Fitm2 mRNA levels were normal in skeletal muscle, brown adipose tissue, gonadal, and inguinal white adipose tissue (Fig. 1A). To determine whether hepatocyte FIT2 deletion resulted in compensation by FIT1, we measured FIT1 protein levels by mass spectrometry. FIT1 was present in skeletal muscle but was not detected in livers of either floxed control or FIT2-LKO mice, consistent with reports that FIT1 protein is not expressed in murine liver (Table S1) [1].Figure 1Hepatocyte-specific FIT2-LKO mice have altered hepatic lipid composition. A, hepatocyte-specific FIT2 knockout results in the near loss of Fitm2 transcript in liver ($$n = 9$$–11/genotype) but does not alter Fitm2 transcript levels in adipose tissue and muscle ($$n = 6$$/genotype) as measured by qRT-PCR. B, immunoblotting indicates loss of FIT2 protein in liver tissue. C–G, MS-based measurements of CoA and CoA derivatives in livers of Flox and FIT2-LKO mice ($$n = 6$$/genotype). C, FIT2-LKO livers contain elevated levels of total long-chain fatty acids (≥14 carbon length). D, Amounts of individual long-chain acyl-CoA species in livers of Flox and FIT2-LKO mice. Levels of hepatic free CoA (E), acetyl-CoA (F), and total CoA-containing species (G) are not significantly altered between Flox and FIT2-LKO mice. Hepatic triglyceride levels (H) and cholesterol levels (I) are elevated in FIT2-deficient livers ($$n = 9$$–11/genotype). Lipidomic analyses of levels of neutral lipids (J) and phospholipids (K) in male FIT2-LKO mice ($$n = 6$$/genotype). Representative images (L) and steatosis scoring (M) of Oil Red O staining indicates more neutral lipid content in FIT2-LKO livers than in Flox livers. ( $$n = 6$$/genotype). Scale bar = 50 μm. Data represents mean ± SD. Statistical significance for was evaluated with unpaired Student two-tailed t test for (A–K) and a Mann–Whitney U test for nonparametric data in (M). ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$, ∗∗∗∗$p \leq 0.0001.$ Cer, ceramide; CerG1, glucosylceramide; ChE, cholesterol ester; CL, cardiolipin, DAG, diacylglycerol; FIT2, fat storage–inducing transmembrane protein 2; FIT-LKO, FIT2 knockout mice; L, lyso; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol, PI, phosphatidylinositol; PS, phosphatidylserine; SM, sphingomyelin; TG, triglyceride. ## FIT2 deficiency alters acyl-CoAs and other hepatic lipids To determine whether FIT2 functions as an acyl-CoA diphosphatase in hepatocytes, we tested whether FIT2 deficiency leads to the accumulation of acyl-CoA substrates. We measured acyl-CoA and CoA levels in control and FIT2-LKO livers (Table S2) and found that long-chain fatty acyl-CoA levels were elevated by ∼$60\%$ (Fig. 1C). Specifically, we found marked increases in the levels of several species of acyl-CoAs, including the monounsaturated fatty acyl-CoAs 18:1 and 20:1 and lesser increases in several minor species (Fig. 1D). In contrast, levels of free CoA, acetyl-CoA, and total CoA-containing species were normal (Fig. 1, E–G and Table S2). These data are consistent with previous findings that FIT2 hydrolyzes long-chain unsaturated fatty acyl-CoAs but not short-chain acyl-CoA species or free CoA [14]. Because FIT2 functions in fatty acid metabolism, we also investigated changes in hepatic lipids. In contrast to reduced triglyceride (TG) storage in cells with FIT2 deficiency [1, 3, 14], hepatic triglyceride content was unexpectedly elevated ∼2.5-fold, and cholesterol content was increased ∼$20\%$ in both male and female mice (Figs. 1, H and I and S1B). Mass spectrometry–based lipidomic analyses of the livers of control and FIT2-LKO mice corroborated the elevated triglyceride levels and revealed a twofold increase in diacylglycerol levels (Fig. 1J). Levels of the major phospholipids, phosphatidylcholine (PC) and phosphatidylethanolamine (PE), were not altered, but levels of several other phospholipids (e.g., lysophosphatidylcholine, lysophosphatidylethanolamine, and phosphatidylglycerol) were slightly elevated, and phosphatidylserine levels were modestly reduced (Fig. 1K). Levels of ceramide and sphingomyelin were modestly elevated (Fig. 1K). We analyzed lipid deposition in histological sections with Oil Red O and H&E staining (Figs. 1L and S1C). Neutral lipid deposition was low in both genotypes and agreed with minimal lipid accumulation noted under chow feeding. However, steatosis scores were greater in FIT2-LKO mice than in floxed control mice (Fig. 1M). The increase in hepatic triglycerides was accompanied by elevated liver weights in both males and females (Fig. S1D). Body weights were not altered between the genotypes after chow feeding (Fig. S1E). Collectively, these results show that liver-specific FIT2 deficiency increases neutral lipid (i.e., TG) storage under conditions of chow feeding. ## Loss of FIT2 disrupts ER homeostasis and causes liver injury Since FIT2 is crucial for maintaining ER homeostasis in cultured mammalian and yeast cells, we examined ER morphology and function in FIT2-LKO mice. FIT2-LKO mice exhibited modest ER stress, reflected in increased mRNA levels of transcription factors (e.g., Xbp1, Atf3, Chop) and chaperones (e.g., BiP) associated with the unfolded protein response in FIT2-LKO livers (Fig. 2A). Phosphorylation of eIF2α was ∼threefold greater in the livers of FIT2-LKO mice than in floxed control littermates (Fig. 2B). Analysis of hepatocyte ER structure by EM showed that hepatic FIT2 deficiency did not cause detectable ER dilation (Fig. S2, A and B). Total ER content was also apparently unaltered as quantified by EM (Fig. S2C) and proteomic analysis of ER markers (Fig. S2D). Consistent with these measurements, the levels of PC and PE, major phospholipids constituents of the ER, were not altered (Fig. 1K).Figure 2FIT2-LKO mice exhibit increased ER stress and liver injury. A, gene expression analyses demonstrate that FIT2-LKO mice have elevated transcript levels of chaperones and transcription factors associated with ER stress compared to chow-fed Flox mice ($$n = 9$$–11). B, immunoblotting indicates more phosphorylation of eIF2α in FIT2-LKO livers than in Flox littermates ($$n = 3$$–4). C, FIT2-LKO livers do not show much evidence for inflammation, as indicated by transcript levels of macrophage markers and cytokines ($$n = 9$$–11/genotype). D, FIT2-LKO livers do not show evidence for substantial fibrosis ($$n = 9$$–11/genotype). E, FIT2-LKO mice exhibit liver injury. Levels of circulating alanine transaminase (ALT) and aspartate transaminase (AST) are higher in FIT2-LKO mice than in Flox controls ($$n = 9$$–11). F, plasma albumin, total protein, alkaline phosphatase (ALP) and bilirubin are unaltered in FIT2-LKO mice ($$n = 6$$). Data represent mean ± SD. Statistical significance for (A, C and D) was evaluated with unpaired Student two-tailed t test for parametric data and a Mann–Whitney U test for nonparametric data. Statistical significance for (B) was evaluated with unpaired Student’s two-tailed t test. For (E and F), two-way analysis of variance with Šidák correction was used. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$, ∗∗∗∗$p \leq 0.0001.$ ER, endoplasmic reticulum; FIT2, fat storage–inducing transmembrane protein 2; FIT-LKO, FIT2 knockout mice. Because inflammation often accompanies ER stress, we assessed hepatic immune cell infiltration and cytokine production. Consistent with chow feeding eliciting minimal inflammation and negligible fibrosis, transcript levels of these markers were low in both genotypes (data not shown). However, transcript levels of macrophage markers and cytokines trended higher in FIT2-deficient livers (Fig. 2C). Also, some markers of fibrosis and transcript levels of pro- and anti-apoptotic genes were elevated (Figs. 2D and S2, E and F). Despite minimal evidence for inflammation or apoptosis, FIT2-deficient livers displayed evidence of injury. Plasma levels of transaminases alanine transaminase and aspartate transaminase were elevated seven- and twofold, respectively (Fig. 2C). Plasma markers of synthetic liver function (total protein, albumin) and cholestasis (bilirubin, alkaline phosphatase) were normal (Fig. 2D). ## Impaired TG secretion and reduced fatty acid oxidation capacity contribute to TG accumulation in chow-fed FIT2-LKO mice To elucidate the causes of hepatic TG accumulation found in chow-fed FIT2-LKO mice, we analyzed pathways that influence TG levels. We evaluated very low-density lipoprotein (VLDL) secretion since this pathway depends on ER phospholipid and protein composition and occurs in the ER lumen [17, 18], the proposed location of the catalytic residues of FIT2 [2, 14]. Steady state plasma levels of TG were unaltered, and levels of the primary protein component of VLDL, apolipoprotein (apo) B, were increased in FIT2-LKO animals (Fig. 3, A and B). LDL-cholesterol levels were also similar in both genotypes (Fig. 3C). However, we found that TG secretion by the liver was reduced by ∼$30\%$ in the FIT2-LKO mice (Fig. 3D). In contrast, secretion of apoB, quantified by immunoblotting, was similar between genotypes (Figs. 3D and S3A). Proteomic analyses of the livers indicated that protein levels of apoB and the microsomal TG transfer protein, required for lipidation of apoB, were similar among genotypes (Fig. S3B). Since circulating apoB was consistently elevated and TG secretion was reduced in FIT2-LKO mice, we hypothesized that FIT2-LKO hepatocytes secrete smaller VLDL particles. To test this, we determined the size distribution of particles recovered from the d < 1.063 g/ml fraction of plasma. We found an increase in the total percentage of particles ≤51 nm in diameter ($88\%$ v. $72\%$) and a decrease in particles ≥72 nm in diameter ($12\%$ v. $27\%$) in FIT2-LKO plasma, consistent with this hypothesis. Among factors required for optimal lipoprotein secretion, lysophosphatidylcholine acyltransferase 3 (LPCAT3, also known as MBOAT5) catalyzes the formation of PC with arachidonyl-CoA on the ER lumen, which promotes lipoprotein secretion [17, 19]. However, levels of 20:4-PC were not reduced in FIT2-LKO livers (Fig. S3C), indicating that changes in PC acyl-chain composition are unlikely to be the cause of the secretion phenotype of FIT2-LKO livers. Figure 3Chow-fed FIT2-LKO mice exhibit alterations in TG metabolism that contribute to steatosis. A, steady-state plasma TG levels are unaltered in Flox and FIT2-LKO mice ($$n = 9$$–11/genotype). B, steady-state levels of plasma apoB-100 and apoB-48 are higher in plasma of FIT2-LKO mice than in Flox controls. Mice were fasted 2 h before sacrifice in (A) and (B). C, plasma cholesterol levels were decreased in the FIT2-LKO; specifically, HDL-C was reduced, but LDL-C remained the same. D, FIT2-LKO mice exhibit reduced rates of hepatic TG secretion but unaltered rates of hepatic apoB secretion. Plasma was collected before ($t = 0$) and 1, 2, and 4 h after intravenous administration of polaxomer-407, a lipoprotein lipase inhibitor. Plasma TG was assayed biochemically. Relative plasma apoB-100 protein levels were determined by quantification of apoB-100 band intensity on immunoblots (Fig. S3A). E, dynamic light scattering measurements indicate that lipoprotein (density < 1.063) particle diameter (number distribution) is reduced in FIT2-LKO mice compared to Flox mice ($$n = 7$$–9/genotype; mice were fasted 4-h before sacrifice). F, FAO capacity is reduced in FIT2-LKO liver lysates ($$n = 5$$–8/genotype). G, hepatic glycogen levels are reduced in male FIT2-LKO mice compared to Flox mice ($$n = 9$$–11/genotype). Data represent mean ± SD. Statistical significance for (A, C, F, and G) was evaluated with unpaired Student two-tailed t test. For (D, left), repeated-measures analysis of variance (ANOVA) was used and for (D, right), unpaired Student two-tailed t test was used. For (E), two-way ANOVA with Šidák correction was used. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001.$ ER, endoplasmic reticulum; FIT2, fat storage–inducing transmembrane protein 2; FIT-LKO, FIT2 knockout mice; TG, triglyceride. We also investigated whether impaired fatty acid oxidation contributes to the increased steatosis of chow-fed FIT2-LKO. Testing mitochondrial fatty acid oxidation was compelling as the deafness–dystonia syndrome reported in humans with FITM2 mutations is reminiscent of a similar disorder, Mohr-Tranebjaerg syndrome, that is caused by defects in mitochondrial function [20, 21]. Liver lysates from FIT2-LKO mice had a reduced capacity to produce acid-soluble metabolites and CO2 by oxidizing fatty acids (Fig. 3F). This was not due to a reduction in transcript or protein levels of fatty acid oxidation enzymes or mitochondrial content as assessed by oxidative phosphorylation gene expression, protein levels, and mitochondrial DNA content (Fig. S3, D–I). Impaired fatty acid oxidation appeared to alter fuel utilization and increased reliance on glucose oxidation in FIT2-LKO. Electron microscopy revealed a marked decrease in glycogen in hepatocytes of FIT2-LKO mice (Fig. S2A), which was corroborated by a biochemical assay for hepatic glycogen (Fig. 2G). ## HFD worsens liver injury in FIT2-LKO mice To further test the role of FIT2 in liver lipid and ER homeostasis, we challenged mice with an HFD ($42\%$ kcal from fat). We hypothesized that this diet would result in sustained acyl-CoA overexposure and exacerbate the phenotypes found with standard chow feeding. With respect to general parameters, FIT2-LKO mice unexpectedly gained less weight than control littermates during the 11-week feeding study (Fig. S4A). The reduced weight gain was due to reduced body fat, with both gonadal and inguinal white adipose tissues showing a reduced mass in FIT2-LKO mice (Fig. S4B). Weekly food consumption was similar among genotypes suggesting that increased energy expenditure led to the lower body weight phenotype (Fig. S4C). We did not investigate this aspect of the phenotype further, but hepatic injury may have resulted in more energy expenditure. In contrast to the results with a chow feeding, hepatic TG content was ∼$50\%$ less in the FIT2-LKO mice fed the HFD than in controls (Fig. 4A). This was accompanied by reductions in plasma TGs and cholesterol (Fig. 4, B and C). The decreased hepatic lipid content was visible in H&E-stained liver tissue sections and scoring of Oil Red O staining (Figs. 4, D and E and S4D). Examination of the lipid deposition revealed that Flox control mice exhibited extensive centrilobular microsteatosis; in contrast, the FIT2-LKO livers exhibited predominately macrosteatosis and fat accumulation localized to the periportal zone. Consistent with the findings of reduced lipid levels, FIT2-LKO livers also showed a decrease in the expression of genes of de novo lipogenesis (Fig. S4E). FIT2-LKO also exhibited reduced liver glycogen levels (Fig. S4F), consistent with the hypothesis that they utilize carbohydrates for fuel. HFD-fed FIT2-LKO mice exhibited elevated plasma ketone bodies (Fig. S4G), although they showed little to no differences in the expression of fatty acid oxidation or oxidative phosphorylation-related genes (Fig. S4H). These metabolic changes were accompanied by a ∼$20\%$ reduction in total long-chain acyl-CoA levels in FIT2-LKO compared with controls, driven largely by decreases in 16:1 acyl-CoA and 18:1 acyl-CoA (Fig. S4, I–J and Table S3). Free CoA levels were increased and acetyl-CoA levels were decreased in HFD-fed FIT2-LKO mice (Fig. S4, K–L and Table S3). An explanation for reduced long-chain acyl-CoA levels in HFD-fed FIT2-LKO mice (versus the analogous chow feeding studies) is not immediately apparent but may reflect the complexity of CoA metabolism under HFD conditions, as reflected in the change in the other CoA pools. Figure 4High-fat diet (HFD) feeding exacerbates ER stress and liver injury in FIT2 liver-specific KO mice. Levels of hepatic TG (A), plasma TG (B), and plasma cholesterol (C) are decreased in FIT2-LKO mice after 11 weeks of HFD feeding. D, representative images of H&E staining of livers from Flox and FIT2-LKO mice after HFD feeding. Scale bar = 50 μm. E, steatosis scoring of Oil Red O staining of Flox and FIT2-LKO mice given HFD challenge ($$n = 5$$–9/genotype). F, RT-qPCR studies show that under HFD, FIT2-LKO mice have increased expression of ER stress genes. G, this is supported by increased phosphorylation of the UPR protein eIF2α, as shown with western blotting ($$n = 4$$/genotype). H, after HFD challenge, FIT2-LKO mice exhibit exacerbated liver injury, as shown by measurement of plasma alanine transaminase and aspartate transaminase (ALT and AST). I, this phenotype presents with relatively minor changes in the expression of gene markers for apoptosis, inflammation, or fibrosis. J, FIT2-LKO mice had increased levels of hepatic cholesterol. Data represent mean ± SD. $$n = 9$$ to 12/genotype, unless otherwise noted above for specific experiments. Statistical significance was evaluated with unpaired Student two-tailed t test for (A–C, G and J). For (E, F and I), statistical significance was evaluated with unpaired Student’s two-tailed t test for parametric data and a Mann–Whitney U test for nonparametric data. For (H), Two-way analysis of variance with Šidák correction was used. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$, ∗∗∗∗$p \leq 0.0001.$ CV, central vein; ER, endoplasmic reticulum; FIT2, fat storage–inducing transmembrane protein 2; FIT-LKO, FIT2 knockout mice; PT, portal triad; TG, triglyceride. As with chow feeding, FIT2-LKO mice showed evidence of hepatic ER stress and injury. Levels of both mRNA transcripts and phosphorylated protein markers of the unfolded protein response were increased in the FIT2-LKO mice to an even greater degree than under standard chow feeding (Fig. 4, F and G). Moreover, plasma ALT and AST markers of liver injury were further increased in the FIT2-LKO mice after the HFD challenge (Fig. 4H). Although apoptotic, inflammation, and fibrosis often accompany such severe ER stress and liver damage, these markers were not substantially altered between genotypes (Fig. 4I). ## Discussion Previous data showed that the ER-resident FIT2 has acyl-CoA diphosphatase activity in vitro and is important for maintaining ER homeostasis in human and yeast cells [14]. We now show that hepatic deficiency of FIT2 results in increased acyl-CoA levels in vivo, which are linked to increased ER stress and signs of liver injury, as manifested by elevated circulating transaminase levels. The latter findings for hepatic FIT2 deficiency were exacerbated with HFD feeding. Although it is uncertain whether humans with FIT2 deficiency exhibit similar hepatocyte defects [10, 11], our studies highlight the crucial importance of FIT2 in lipid and ER homeostasis in vivo. In contrast to what was found in FIT2-deficient cultured cells [14], we found no gross morphological changes in the ER or ER whorls in hepatocytes of FIT2-LKO mice. A possible explanation for the absence of ER morphology changes could be that increased autophagic flux cleared such structures, particularly since autophagy ameliorates liver damage in certain contexts [22]. Consistent with this notion, FIT2 interacts genetically with autophagic pathways; FIT2 deletion sensitizes Renca cancer cells to cell death from IFNγ and inactivation of autophagy reverses this phenotype [12]. The reduction in TG storage under HFD feeding conditions is similar to what has been reported for FIT2 deficiency in cells that have been cultured with excess fatty acids [1, 14]. However, unexpectedly, chow-fed FIT2-LKO mice accumulated neutral lipids and TGs in hepatocytes. The modest level of steatosis in chow-fed FIT2-LKO mice may be at least partially explained by reductions in TG secretion and fatty acid oxidation capacity. With respect to TG secretion, our results are consistent with the hypothesis that FIT2 deficiency impairs the loading of nascent lipoproteins with TGs. Since FIT2 is hypothesized to act on the luminal leaflet of the ER, FIT2 deficiency may lead to acyl-CoA accumulation at this leaflet and interfere with the lipidation of the nascent apoB particles in the ER lumen. Similarly, changes in ER phospholipids can have marked effects on TG secretion, although we did not find changes in 20:4-PC, which has been directly implicated in this process [17, 23, 24]. The reduction in fatty oxidation capacity in lysates of the FIT2-LKO livers was substantial and may also contribute to the TG accumulation in chow-fed mice. We found no differences in mitochondrial content, gene expression, or protein levels, suggesting that FIT2 deficiency adversely affects fatty acid oxidation through an as-yet-unknown mechanism. Of note, altered mitochondrial biology is consistent with the human FIT2 deficiency phenotype; patients with FIT2 mutations present with deafness–dystonia symptoms similar to those afflicted with Mohr-Tranebjaerg syndrome, which is caused by defects in mitochondrial function [20, 21]. Our findings highlight that the phenotypes of ER stress and lipid accumulation with FIT2 deficiency can be dissociated. We found ER stress to be a consistent observation in all our studies of FIT2 deficiency in cells and mice, on either chow or HFD. In contrast, the lipid accumulation phenotype in mice appears to be contextual and depends on the dietary status. This supports the hypothesis that lipid storage phenotypes are a secondary consequence and not a primary role for FIT2 in LD formation [14]. In support of this, loss of FIT2 in pancreatic β cells is accompanied by ER stress, and FIT2 deficiency resulted in several fold increased levels of tissue ceramides [9]. We also found ceramide levels were increased in FIT2-LKO livers but to a lesser extent (∼$30\%$) (Fig. 1K). The mechanism of how FIT2 deficiency results in ER stress is unclear. Most proximally, the defects associated with FIT2 deficiency are likely due either to accumulation of its substrates (e.g., unsaturated acyl-CoAs) or to deficiency of its products (i.e., 3′,5′-ADP and acyl 4′-phosphopantetheine). Although this remains uncertain at present, FIT2 activity is clearly important for the health of cells. Interestingly, human FIT2 protects Renca cancer cells from IFNγ effects [12]. Additionally, high intratumoral levels of FITM2 expression correlate with decreased survival in human patients with hepatocellular carcinoma [3]. Thus, FIT2 inhibitors may be useful to sensitize specific cancer cells to targeted chemotherapies. Continued studies to elucidate the consequences of FIT2 deficiency will be essential for unraveling why FIT2 activity is so crucial to cell health and, hopefully, for finding therapies for humans suffering from the consequences of FIT2 deficiency. ## Animals Fitm2flox/flox mice (028832, Jackson Laboratory) [8] were crossed with mice expressing Cre recombinase under control of the albumin promoter (B6.Cg-Tg(Alb-Cre)21MGN/J). Mice were housed in the Harvard School of Public Health animal care facility and maintained on a 12-h light–dark cycle. Mice had free access to water and food unless specified otherwise. Mice were weaned to a standard rodent chow diet (PicoLab Rodent Diet 20 #5053). For HFD studies, animals were weaned to a chow diet and then fed TD.88137 ($42\%$ calories from fat) for 11 weeks starting at 7 weeks of age. For food intake measurements, animals were individually caged 1 week prior to data collection. All mice were male and fasted 2 h prior to sacrifice unless specified otherwise. Mice were euthanized with isoflurane, blood was collected via cardiac puncture, and tissues were collected. All in vivo mouse experiments were conducted in accordance with the protocol approved by the Institutional Animal Care and Use Committee at the Harvard University. ## Quantitative PCR For gene expression studies, tissues were homogenized in Qiazol using a Bead Mill Homogenizer (VWR), RNA was isolated using RNeasy kit (Qiagen), and cDNA was synthesized using an iSCRIPT cDNA synthesis kit (Bio-Rad). qPCR was performed using Power SYBR Green PCR Master Mix kit (Applied Biosystems). Primer sequences are listed in Table S4. For the assessment of mitochondrial DNA (mtDNA) content, total DNA was prepared using the QIAmp DNA mini Kit (Qiagen). mtDNA was amplified using primers for Co1 and Nd1 and was normalized to genomic DNA using primers amplifying H19. Primer sequences are listed in Table S4. ## Immunoblotting Livers were homogenized in RIPA buffer (Cell Signaling Technology), supplemented with complete mini EDTA-free protease inhibitor (Sigma Aldrich) and PhosSTOP phosphatase inhibitor (Sigma-Aldrich) using a Bead Mill Homogenizer (VWR). Protein concentrations were measured using a DC Protein Assay (Bio-Rad). Proteins were incubated at 60°C for 15 min in 4× Laemmli sample buffer (Bio-Rad). Liver lysate protein of 20 to 40 μg was separated by SDS-PAGE and transferred to a polyvinylidene fluoride membrane. The membrane was blocked with TBS-T containing $5\%$ nonfat dry milk for 1 h at room temperature and then incubated overnight at 4 °C in primary antibody. Membranes were washed in TBS-T, incubated in secondary antibody, washed with TBS-T, and visualized using SuperSignal Chemiluminescent Substrate (Thermo Scientific). Band intensity was measured using ImageJ software. Antibodies used include total eIF2a (Cell Signaling Technology #9722, 1:1000, in $5\%$ BSA), phosphor-Ser51-eIF2a (Cell Signaling Technology #9721, 1:1000), vinculin (Cell Signaling Technology #4650, 1:1000), ApoB (Abcam ab31992, 1:1000), and FIT2 (a generous gift from David Silver’s laboratory [1], 1:1000) ## Acyl-CoA measurements Cellular and liver acyl-CoA esters were analyzed using a method based on a report by Magnes et al. [ 25] that relies on the extraction procedure described by Deutsch et al. [ 26]. The CoAs were further purified by solid phase extraction as described by Minkler et al. [ 27]. The acyl CoAs were analyzed by flow injection analysis using positive electrospray ionization on Xevo TQ-S, triple quadrupole mass spectrometer (Waters) employing methanol/water ($\frac{80}{20}$, v/v) containing 30 mM ammonium hydroxide as the mobile phase. Spectra were acquired in the multichannel acquisition mode monitoring the neutral loss of 507 amu (phosphoadenosine diphosphate) and scanning from m/z 750 to 1060. Heptadecanoyl CoA was employed as an internal standard. The endogenous CoAs were quantified using calibrators prepared by spiking cell or liver homogenates with authentic CoAs (Sigma) having saturated acyl chain lengths C0-C18.. Corrections for the heavy isotope effects, mainly 13C, to the adjacent m + 2 spectral peaks in a particular chain-length cluster were made empirically by referring to the observed spectra for the analytical standards. Statistical significance was evaluated with multiple unpaired t tests followed by Benjamini, Krieger, and Yekutieli FDR correction of $1\%$ for multiple hypothesis testing, and q values are reported. ## Proteomics Liver (∼20 mg) was homogenized in 800 μl of PBS (supplemented with complete mini EDTA-free protease inhibitor (Sigma Aldrich) and 5 mM EDTA) using a Bead Mill Homogenizer (VWR). The extraction of proteins was performed as described [28]. Mass spectrometry data were analyzed by MaxQuant software version 1.5.2.8 [29] using the following setting: oxidized methionine residues and protein N-terminal acetylation as variable modification, cysteine carbamidomethylation as fixed modification, first search peptide tolerance 20 ppm, and main search peptide tolerance 4.5 ppm. Protease specificity was set to trypsin with up to two missed cleavages allowed. Only peptides longer than six amino acids were analyzed, and the minimal ratio count to quantify a protein is 2. The false discovery rate (FDR) was set to $5\%$ for peptide and protein identifications. Database searches were performed using the Andromeda search engine integrated into the MaxQuant environment [30] against the UniProt-mouse database containing 54,185 entries (December 2018). “ Matching between runs” algorithm with a time window of 0.7 min was utilized to transfer identifications between samples processed using the same nanospray conditions. Protein tables were filtered to eliminate identifications from the reverse database and common contaminants. Fold changes of proteins were calculated by comparing the mean area of log2 intensities between replicates of different genotypes. Statistical significance was calculated using a Student t test followed by Benjamini–Hochberg FDR correction of $5\%$ for multiple hypothesis testing. ## Lipidomics Liver (∼100 mg) was homogenized in 1 ml of PBS using a Bead Mill Homogenizer (VWR). Lipids were extracted, according to the Folch method [31]. Lysis volume was normalized to starting tissue material. The organic fraction containing extracted lipids was subjected to liquid chromatography/tandem mass spectrometry analysis as described in [28]. Mass spectrometry data analysis was performed using LipidSearch version 4.1 SP (Thermo Fisher Scientific). The results were exported to R-Studio where quality control was performed using pairwise correlations between replicates, a principal component analysis comparing sample groups, as well as retention time plot analysis to verify elution clustering within lipid classes. All identified lipids were included for subsequent analyses if they fulfilled the following LipidSearch-based criteria: [1] reject equal to zero, [2] main grade A or main grade B and a p value of <0.01 for at least three replicates, and [3] no missing values across all samples. Statistical significance was calculated using a Student t test followed by a Holm-Sidak test to correct for multiple comparisons. ## Histology Livers were collected and fixed in formalin overnight at 4 °C. Livers were sectioned and stained by the Rodent Histopathology Core at Harvard Medical School. Frozen sections were used for Oil Red O staining, which were unbiasedly scored for steatosis by a histopathologist [28]. Paraffin-embedded tissue was used for H&E staining. Sections were imaged on a ZEISS light microscope. ## Electron microscopy Mice were anesthetized with isoflurane and then perfused with 10 ml of PBS followed by 10 ml of $2.5\%$ glutaraldehyde, $2.5\%$ paraformaldehyde in 0.1 M sodium cacodylate buffer (pH 7.4). Liver pieces of 1 to 2 mm were fixed in the fixative overnight, washed several times in 0.1 M cacodylate buffer, osmicated in $1\%$ osmium tetroxide/$1.5\%$ potassium ferrocyanide (final solution) for 3 h, and followed by several washes of dH2O. $1\%$ uranyl acetate in maleate buffer was added for 1 h and then washed several times with maleate buffer (pH 5.2). This was followed by a graded cold ethanol series up to $100\%$, which is changed 3× over 1 h, followed by propylene oxide, changed 3× over 1 h. The sample was then placed in ½ and ½ propylene oxide with a plastic mixture including a catalyst overnight. The following day, samples were polymerized in Taab 812 Resin (Marivac Ltd) at 60°C for 24 to 48 h. Sections of 80 nm were cut with a Leica ultracut microtome, picked up on 100 mesh formvar/carbon-coated copper grids, stained with $0.2\%$ lead citrate, and viewed, and imaged with a JEOL 1200X electron microscope equipped with an MP 2k CCD camera. For ER dilation quantification, three images (representative of ER dilation for that animal) were selected per mouse, and the distance across the ER lumen (bilayer center-to-bilayer center) was measured using ImageJ. At least 25 lumen measurements were calculated per image and averaged to provide the representative ER dilation for that mouse. For total ER quantification, three images (representative of ER content for that a) were selected per mouse. Using ImageJ, the total cell area was traced and calculated (nucleus excluded due to variability in nuclear size), and the ER was manually traced. ER content was calculated as nm ER length divided by μm2 available cell area. Four flox and seven FIT2-LKO animals were assessed using this method, and the data depict the average and standard deviation of these biological samples. ## TG and apoB secretion measurements Mice were fasted for 4 h and injected intravenously with 1000 mg/kg body weight with the lipoprotein lipase inhibitor, Poloxamer-407. Tail vein blood was collected at $t = 0$, 1, 2, and 4 h. Plasma was supplemented with complete mini EDTA-free protease inhibitor (Sigma Aldrich) and snap frozen. Plasma TG was measured using Infinity TG kit (Thermo Scientific). ApoB-100 protein levels were measured by immunoblotting, as described earlier. At 1 h, the sample was diluted 1:5. At 2 and 4 h, they were diluted 1:10. 1.5 μl of plasma (or 1.5 μl of diluted plasma) was heated at 95°C for 5 min in 2× denaturing sample buffer. Plasma samples from each time point were run on the same gel. Equal loading was confirmed by visualization of albumin with Ponceau staining. To compare between gels, a sample from each time point was run on the $t = 0$ gel (Fig. S3A). ## VLDL particle size measurements Mice were fasted 4 h prior to sacrifice; 2 × 10 μl was removed for density profiling by isopycnic ultracentrifugation [32]. For each plasma sample, the d < 1.063 g/ml fraction was prepared by ultracentrifugation in a Beckman Coulter Optima MAX-XP benchtop ultracentrifuge in an MLA-55 rotor (18 h × 172,301g at 14 °C). This fraction contains virtually all of the apo-B100 in the plasma [33]. Lipoprotein particle diameters were determined by dynamic light scattering analysis with a Microtrac Series 150 Ultrafine particle analyzer fitted with a flexible conduit-sheathed probe tip (UPA-150; Microtrac) [34, 35]. Raw particle-size distributions from number distributions were converted to population percentiles, which were used to calculate the median particle diameter for each decile of lipoprotein size distribution. ## Ex vivo fatty acid oxidation assay Mice were fasted for 4 h and euthanized with isoflurane. Liver was collected and processed via Dounce homogenization in 2 ml of sucrose-Tris-EDTA buffer as detailed [36]. Liver homogenates were centrifuged at 450g for 10 min at 4 °C. Supernatants were collected and incubated for 1 h in the presence of fatty acid oxidation substrate (300 μM palmitic acid with 0.4 μCi 1-14C-palmitic acid). 1 mM rotenone, an inhibitor of oxidative phosphorylation, was used as a control. The radioactivity of trapped CO2 and acid-soluble metabolites were measured using a liquid scintillation counter. Fatty acid oxidation rates were calculated as [(counts per minute-blank)/reaction mixture specific activity]/g tissue. ## Liver biochemical assays Liver (∼50 mg) was homogenized in 500 μl of lysis buffer (250 mM sucrose, 50 mM Tris HCl, pH 7.4). Lipids were extracted using a modified Bligh and Dyer method and solubilized in $0.1\%$ Triton-X-100 by sonication (three rounds of 2 s at 30 mA). TG and cholesterol were quantified using Infinity Triglyceride and Cholesterol Reagents (Thermo Scientific). Glycogen was measured from ∼20 mg of liver using EnzyChrom Glycogen Assay Kit (BioAssay Systems), according to the manufacturer’s instructions. ## Plasma analyses Plasma TG and total plasma cholesterol were measured from 2 and 10 μl of plasma, respectively, using Infinity Triglyceride and Cholesterol Reagents (Thermo Scientific). For high-density lipoprotein (HDL) cholesterol measurements, non-HDL was precipitated by incubating 20 μl of plasma with precipitation buffer containing 0.44 mM phosphotungstic acid and 20 mM MgCl2 for 10 min at room temperature, followed by centrifugation. Cholesterol was measured from the resulting supernatant. Plasma ALT, AST, bilirubin, ALP, albumin, and total protein were measured with Piccolo Liver Panel Plus discs used with a Piccolo Xpress chemistry analyzer (Abaxis). ## Statistical analyses Results are expressed as mean ± standard deviation. Statistical significance was evaluated with unpaired Student two-tailed t test (if data passed a Shapiro-Wilk test for normality) or a Mann–Whitney U test (for nonparametric data, which did not pass test for normality). For experiments with multiple readouts, statistical significance was evaluated with two-way analysis of variance (ANOVA) with post hoc Šidák test, or repeated-measures ANOVA for time course experiments. Analyses were performed using GraphPad Prism 7. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$, ∗∗∗∗$p \leq 0.0001.$ ## Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository [37] with the dataset identifier PXD033884. The mass spectrometry lipidomics data are available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001476. ## Supporting information This article contains supporting information. Supporting Tables S1–S4 and Figures S1–S4 ## Conflict of interest T. C. W. is an investigator of the Howard Hughes Medical Institute. ## Author contributions L. M. B., R. V. F. Jr, and T. C. W. conceptualization and methodology; L. M. B. and A. I. formal analysis and investigation; Z. W. L., R. L. W., and O. R. I. investigation; R. T. B. formal analysis; L. M. B., A. 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--- title: Indirect epigenetic testing identifies a diagnostic signature of cardiomyocyte DNA methylation in heart failure authors: - Christian U. Oeing - Mark E. Pepin - Kerstin B. Saul - Ayça Seyhan Agircan - Yassen Assenov - Tobias S. Merkel - Farbod Sedaghat-Hamedani - Tanja Weis - Benjamin Meder - Kaomei Guan - Christoph Plass - Dieter Weichenhan - Dominik Siede - Johannes Backs journal: Basic Research in Cardiology year: 2023 pmcid: PMC10027651 doi: 10.1007/s00395-022-00954-3 license: CC BY 4.0 --- # Indirect epigenetic testing identifies a diagnostic signature of cardiomyocyte DNA methylation in heart failure ## Abstract Precision-based molecular phenotyping of heart failure must overcome limited access to cardiac tissue. Although epigenetic alterations have been found to underlie pathological cardiac gene dysregulation, the clinical utility of myocardial epigenomics remains narrow owing to limited clinical access to tissue. Therefore, the current study determined whether patient plasma confers indirect phenotypic, transcriptional, and/or epigenetic alterations to ex vivo cardiomyocytes to mirror the failing human myocardium. Neonatal rat ventricular myocytes (NRVMs) and single-origin human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and were treated with blood plasma samples from patients with dilated cardiomyopathy (DCM) and donor subjects lacking history of cardiovascular disease. Following plasma treatments, NRVMs and hiPSC-CMs underwent significant hypertrophy relative to non-failing controls, as determined via automated high-content screening. Array-based DNA methylation analysis of plasma-treated hiPSC-CMs and cardiac biopsies uncovered robust, and conserved, alterations in cardiac DNA methylation, from which 100 sites were validated using an independent cohort. Among the CpG sites identified, hypo-methylation of the ATG promoter was identified as a diagnostic marker of HF, wherein cg03800765 methylation (AUC = 0.986, $P \leq 0.0001$) was found to out-perform circulating NT-proBNP levels in differentiating heart failure. Taken together, these findings support a novel approach of indirect epigenetic testing in human HF. ### Supplementary Information The online version contains supplementary material available at 10.1007/s00395-022-00954-3. ## Introduction Heart failure (HF) is a multifaceted clinical syndrome that is diagnosed based on clinical evidence of hemodynamic insufficiency. Patients with HF initially present with nonspecific symptoms of fatigue and exertional dyspnea, warranting a broad diagnostic workup to identify the underlying cause(s). Despite its widespread use, the poor specificity of elevated circulating BNP or NT-proBNP levels limits its use as a diagnostic tool to “ruling-out” the presence of HF [54]. Techniques to characterize the functional consequences of cardiac dysfunction, including non-invasive imaging and functional tests, provide some prognostic insights, but no molecular tests are yet available to diagnose HF. A new approach to diagnose HF and predict outcome is therefore needed, one which reflects the molecular foundations of its pathogenesis. Although lifestyle and genetic factors have been shown to confer HF risk, their convergence onto epigenetic machinery presents an opportunity for diagnostic testing. Genome-wide association studies have uncovered thousands of causal genetic mutations [4], but the clinical value of these discoveries is limited by both the relative infrequency and pleiotropy of monogenic cardiomyopathies [25]. Environmental and behavioral factors such as obesity [1], diabetes mellitus [18, 19], and hypertension [39] are far more prevalent risk factors for HF, though the synergistic effects of environmental exposures and the plethora of mediators remain largely unknown. Recent studies have therefore begun to study the molecular basis of gene-environment or epigenetic interactions as underlying determinants of HF susceptibility and pathogenesis [42]. Unlike the direct epigenetic profiling of solid tumors, which has already shown promise in precision-based oncology [52], diagnostic access to myocardial tissue remains comparably limited. Epigenetic modifications, whether directly to DNA via CpG methylation or to ancillary structures including histone proteins, have been linked to pathogenesis of cardiovascular disease [12, 20, 26, 44, 49, 53]. Recent studies have uncovered robust differences in cardiac DNA methylation in patients with end-stage heart failure [11, 13, 28, 35], displaying both etiology-specific [36] and socioeconomically driven [37] effects on cardiac metabolic programs. Hence, DNA methylation may encode the complex environmental exposures, including circulatory milieu, which lead to cardiac dysfunction. Therefore, the current study employs a novel diagnostic approach via indirect epigenetic testing to determine whether circulating factors are capable of driving epigenetic reprogramming of cardiomyocytes. The current study treated human inducible pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) with plasma collected from patients with non-ischemic HF caused by dilated cardiomyopathy (DCM, $$n = 13$$) and healthy donors ($$n = 10$$) (Fig. 1). Genome-wide analysis of array-based CpG methylation identified 49 “indirect” epigenomic markers of DCM, which were validated in a larger published cohort. Therefore, we offer preliminary evidence to support the feasibility of indirect epigenetic testing of DCM using hiPSC-CMs. Fig. 1Graphical overview. Human inducible pluripotent stem cells (iPSC-CMs) were treated with plasma from either DCM ($$n = 13$$) or healthy ($$n = 10$$) subjects for 48 h. Samples were then analyzed for cell size using InCell Analyzer and submitted for methylation analysis with the Illumina™ Beadchip HumanMethylation450k (m450k) Array platform. Data were then cleaned and analyzed in comparison to m450k analysis of human cardiac biopsies from explanted hearts of DCM patients ($$n = 7$$) and non-failing donor controls ($$n = 3$$) ## Ethics statement Human studies were approved by the ethics committee and medical faculty at the Heidelberg University Hospital (Heidelberg, Germany; appl. no. S-$\frac{390}{2011}$). Informed consent was obtained for the procurement of left ventricular assist device core biopsies, and a waiver of consent was granted for tissue samples received from non-failing hearts of organ donors. Control blood samples were obtained according to the protected health information 45 C.F.R. 164.514 e2 (Bioserve) and the BCI informed consent F-641-5 (Biochain). Patient health information was acquired at time of tissue acquisition, and all human RNA-sequencing and DNA methylation array data are available upon request. ## Patient samples All samples were obtained from and authorized by the Heidelberg University Hospital Biobank (Heidelberg, Germany). Biopsies were selected according to age and gender matching with reduced systolic left ventricular ejection fraction (LVEF) and dilatation (Supplemental Table 1). Exclusion criteria included evidence of coronary artery disease or other clinically relevant cardiac conditions. Human myocardial biopsies were obtained from patients with DCM ($$n = 7$$) or from non-failing donor hearts ($$n = 3$$), as described previously [41]. ## Differentiation of human induced pluripotent stem cells into cardiomyocytes To determine whether cardiomyocytes exhibit differences in DNA methylation in vitro, hiPSC-CMs were differentiated using an established protocol [29, 41]. Briefly, hiPSCs were harvested from Matrigel (BD Bioscience; 354,277) coated 6-well plates (Corning) and cultured with Essential 8™ medium (Thermo Fisher Scientific; A1517001) and ROCK inhibitor (Tocris; 1254). The hiPSCs were cultured for 3 days or until achieving a confluence of 70–$90\%$. The medium was then replaced by RPMI1640 (Thermo Fisher Scientific; 21875-034), insulin-free B27 Supplement (Thermo Fisher Scientific; A1895601) and 10 μM CHIR99021 (Tocris; 4423) for 24 h. The next day (Day 1), the medium was changed to RPMI1640 and insulin-free B27 Supplement. 24 h later (Day 2), cells were treated with 5 μM IWP2 (Tocris, 3533) in RPMI1640 with B27 Supplement minus insulin. On Day 5, the medium was again changed to RPMI1640 plus insulin-free B27 Supplement. After Day 7 the medium was changed every two days with RPMI1640 with B27 Supplement (Thermo Fisher Scientific; 17,504,044) until day 15. To enrich cardiomyocytes, metabolic stress was induced using 4 mM lactate as described by Tohyama et al. [ 48]. Quality of isolation, and purity of hiPSC-CMs were assessed using cardiac troponin (cTNT) positivity versus negative control after maturation (Supplemental Fig. S1A) and after plasma treatment (Supplemental Fig. S1B). Briefly, hiPSC-CM were fixed, washed and were incubated with the primary antibody (Troponin T, Cardiac Isoform Ab-1 (Clone 13–11)) (Thermo Fischer Scientific; MS-295-P1) over night and incubated with the secondary antibody (Alexa 488 Goat anti- Ms. IgG1; Thermo Fisher Scientific A21121). Negative control is missing the first antibody (Troponin T) to show specificity of antibody binding. Quantification is performed using an automated high-throughput algorithm with InCell® microscope (Supplemental Fig. S1C). ## Isolation of neonatal rat ventricular cardiomyocytes (NRVMs) Heart pieces of 1- to 2-day-old Wistar rats were digested by a mix of collagenase (CellSystems Biotechnologie Vertriebs GmbH) and pancreatin (Sigma-Aldrich) and incubated at 37 °C for 20 min. The supernatant containing the NRVMs was sequentially collected. NRVMs were pelleted by centrifugation and re-suspended in a salt balanced solution. NRVMs were finally purified using a discontinuous Percoll gradient (GE Healthcare). Cells were re-suspended in DMEM (Sigma-Aldrich) with supplements and plated on collagen (Sigma-Aldrich) coated cell culture plates (Greiner Bio-One) [40]. ## Cardiomyocyte plasma treatments For cell size and perinuclear atrial natriuretic peptide (ANP) staining measurements, hiPSC-CMs were plated in octuplets on 96-well black µClear plates (Greiner Bio-One) with Matrigel (BD Bioscience) coating and NRVMs were plated on collagen. For DNA isolation, cells were plated on 12-well plates. After 24-h starvation with FCS-free medium, NRVMs and hiPSC-CMs were treated for 48 h with $5\%$ patient plasma from DCM or non-failing control (CON) subjects instead, or with fetal calve serum (FCS) or FCS-free medium (“starve”). ## Cardiomyocyte immunofluorescence staining Cardiomyocytes were fixed with paraformaldehyde (Sigma-Aldrich) after 48-h treatment. Antibodies against cardiac α-actinin (Sigma-Aldrich) and ANP (Peninsula Lab) were used sequentially overnight at 4 °C. Secondary antibodies (Thermo Fisher Scientific) were incubated for 1 h at room temperature. Nuclei were stained with DAPI (Thermo Fisher Scientific). Histological imaging and analyses were performed using an InCell Analyzer 2200 (GE Healthcare), where cell size and perinuclear ANP intensity could be measured using the automated HTS approach, which has been developed and validated by the InCell investigator software (GE Healthcare). Cell sorting results for troponin is shown in Supplemental Fig. 1A. As a proxy of stable purity after treatment of hiPSC-CMs, viable cells were quantified using the same HTS approach by counting all DAPI + cells and actinin overlay (see Supplemental Fig. 1B–C). Reproducibility of cell size measurements in different hiPSC-CM cell lines is shown in Supplemental Fig. 2A. ## HumanMethylation450k BeadChip (m450k) Array Epigenome-wide DNA methylation studies were performed using the Illumina® Beadchip HumanMethylation450k (m450k) array platform, as previously described [36]. For each assay, 500 ng DNA was bisulfite-treated before amplification, hybridization, and imaging standard to the Illumina® protocol. Briefly, frozen biopsies were disrupted using the TissueRuptor (Qiagen). DNA isolation of disrupted biopsies or pelleted NRVMs and hiPSC-CMs was done using the QIAamp DNA Blood and Tissue Kit (Qiagen) according to the manufacturer’s protocol. DNA integrity was monitored by gel electrophoresis. Array intensity data generated via iScan® were preprocessed and normalized using quantile normalization to adjust for technical differences in Type I/II array designs [23]. Total (methylated + unmethylated) signal intensity for each probe was weighed against the background signal via negative control probes to provide a statistical (P value) detection threshold (Supplemental Fig. S3). Possible confounding of differential methylation via overlapping SNPs was evaluated using MethylToSNP (0.99.0), removing 1494 CpG probes from the analysis of cardiac biopsy samples (Supplemental Fig. S4); no SNPs were detected among iPSC-CMs. ## RNA-sequencing RNA sequencing analysis was performed as previously outlined [36], with detailed methods available as an online supplement. Briefly, RNA was isolated from iPSC-CMs using Qiazole™ reagent (Qiagen Inc., Hilden, Germany) and validated via fragment analysis (Agilent) to ensure RNA quality. Sample B2 was removed (RIN = 2.5) and was identified owing to RNA Integrity Numbers (RINs) which were 9.2 ± 1.5, with all samples achieving RINs > 7 (Supplemental Table 2). Samples were then submitted for paired-end 100 bp RNA sequencing which was performed at BGI Tech Solutions (Hong Kong, CN), where high-throughput next-generation RNA-sequencing was performed using the DNBSEQ™ G400 platform. Prior to alignment, adapters and low-quality (PHRED < 20, or $1\%$ sequencing error rate) sequences were trimmed from reads files using trimgalore (0.5.0). ## Bioinformatics All coding scripts used in the current study are available as an online supplement via GitHub data repository: https://github.com/mepepin/Indirect.Epigenomics. Differential methylation analysis was performed as previously described [36]. Differential methylation analysis was completed by fitting probe-wise linear models to the normalized log-ratios, followed by an empirical Bayesian shrinkage of probe-wise sample variance via Minfi (1.40.0) within the R (4.1.2) statistical computing environment [43]. For RNA-sequencing analysis, alignment of reads to the hg19 genome was accomplished using STAR (v2.7.9a), yielding ~ $95\%$ uniquely mapped reads for all samples. Raw counts were generated using Samtools [21], with differential gene expression performed using DESeq2 [22] (1.34.0) within the R (4.1.2) computing environment [38]. Dispersion estimates were determined via maximum-likelihood, which were shrunken according to an empirical Bayes approach to provide normalized count data for genes proportional to both the dispersion and sample size. Differential expression was then determined from normalized read counts via Log2(fold-change) using the Wald test followed by Bonferroni-adjusted P value for each aligned and annotated gene. From this, 2077 genes were found to be differentially expressed at $P \leq 0.05.$ ## Statistical analysis For all pairwise comparisons, the Shapiro–Wilk test for normality was performed to determine the most appropriate statistical test. Statistical comparisons were achieved using two-tailed t tests between DCM and CON in the cell size and ANP intensity as well as qPCR experiments. All data are reported as mean ± standard deviation unless otherwise specified. ## DCM patients’ plasma increases cardiomyocyte size and perinuclear ANP To determine whether 48-h exposure to human plasma impacts cardiomyocyte morphology in accordance with the patients’ diagnosis of HF, cell size was quantified using the InCell™ automated high-content screening (HTS) assay for NRVMs (Fig. 2A) and iPSC-CMs (Fig. 2B). In both NRVMs and hiPSC-CMs, exposure to plasma from DCM patients conferred a $22\%$ ($$P \leq 0.004$$) and $27\%$ ($P \leq 0.001$) increase in cell size, respectively. Cardiomyocyte hypertrophy was reproducible, seen in repeated experiments with hiPSC-CMs from two additional independent cell lines (Suppl. Figure 2A). To determine whether exposure to plasma from DCM patients could reproduce pathological hallmarks of cardiac stress, an HTS approach was used to quantify both ANP abundance and its subcellular distribution within hiPSC-CMs. Immunohistochemical staining demonstrated greater abundance of perinuclear ANP staining in the hiPSC-CMs treated with DCM plasma relative to CON plasma (Fig. 2D), though neither ANP abundance nor cell size correlated with circulating NT-proBNP levels (Suppl. Figure 2B–C).Fig. 2DCM patients’ plasma increases cardiomyocyte size. After 48 h of treatment with $5\%$ plasma from dilated cardiomyopathy (DCM, $$n = 13$$) or healthy control (CON, $$n = 10$$) subjects, cell size was measured for A NRVMs and B hiPSC-CMs. C Representative immunocytochemistry-based quantification of atrial natriuretic peptide (ANP) performed in DCM plasma-treated (DCM) relative to control plasma-treated hiPSC-CMs co-stained for α-Actinin and DAPI ($$n = 4$$). Starvation vs. FCS is represented as a mean value of each well count with each approximately 1300 cells counted per well. In contrast, CTR vs. DCM is represented as a mean value of octuplets with each well counting approximately 1300 cells, hence a mean of a mean of 8 wells (a mean of 8 means, derived from approx. 1300 cells each). Student’s t-test reporting mean ± S.E.M. ($P \leq 0.05$, $P \leq 0.01$, *$P \leq 0.001$) ## DNA methylation changes in cardiac biopsies The Illumina® Beadchip HumanMethylation450k array was used to quantify CpG methylation intensity of DNA isolated from biopsies of DCM ($$n = 7$$) and non-failing control hearts (CON, $$n = 3$$). Unsupervised multi-dimensional scaling (MDS) of the 10,000 most-variable CpG probes revealed a marked separation in cardiac DNA methylation signature between DCM and CON samples (Fig. 3A). Differential quantification of DCM and CON identified 84,024 differentially methylated CpG sites (DMPs) ($P \leq 0.05$), with the most robust alterations seen in cg02459042 (NXN, $63.6\%$ hyper-methylated, $$P \leq 1.3$$ × 10–8) (Fig. 3B). Because DNA methylation is known to regulate gene expression in a site-dependent manner [3, 17], DMP distribution was performed according to where plotted onto both annotated gene regions (promoter, 5’UTR, gene body, and 3’UTR) as well as according to their distance from CpG Islands (CGIs) (Fig. 3C); the resulting distribution revealed that, although the greatest overall number of DMPs were located within gene bodies, a disproportionate percentage of DMPs were found within "North Shore”-associated CpG sites within the proximal promoter of adjacent genes (Fig. 3C–D). Nevertheless, strong heart failure-associated signatures of differential methylation were seen throughout the annotated genomic regions (Fig. 3E). Taken together, these findings support previously published evidence of robust epigenomic shifting in end-stage human heart failure [13, 28, 35–37].Fig. 3Cardiac DNA methylation in cardiac biopsies. A Multidimensional scaling (MDS) of top-10,000 CpG probes within the Illumina® HumanMethylation450k array performed on cardiac left ventricle samples from patients with end-stage heart failure (DCM) or non-failing donor control hearts (CON). The two principal components that account from the largest variance in DNA methylation were used to generate a scatterplot, flanked by density plots of each principal component. B Volcano plot illustrating the robustness of CpG methylation differences, plotting (– log10[P value]) as a function of percent difference in methylation (%) in DCM vs. CON, probes exceeding $P \leq 0.05$ and \|methylation %\|> 5 highlighted in yellow. Labelled are the 10 most-robustly hyper-methylated and hypo-methylated CpG probes by % methylation. C Distribution of differential methylation via three-dimensional contour plot of differentially methylated CpG probes (DMPs) categorized according to their presence within genomic (Promoter, 5’ UTR, Body, Exon–Intron boundary, or 3’ UTR) and CpG (Shelf, Shore, and Island) regions. Bar graph depicting the number of DMPs within each genomic region. D proportional distribution of CpG Island-associated DMPs. E Heatmap and hierarchical clustering of DMPs according to each genomic region. * $P \leq 0.05$ ## DNA methylation changes detected in the indirect cardiomyocyte test To determine whether circulating factors are sufficient to trigger alterations in cardiac DNA methylation reminiscent of failing hearts, hiPSC-CMs were exposed to plasma obtained from patients with DCM or age-matched healthy control (CON) subjects. Unlike in cardiac biopsies, unsupervised clustering failed to differentiate between iPSCs exposed to DCM plasma ($$n = 13$$) and those with CON plasma ($$n = 10$$) (Fig. 4A). Nevertheless, a robust signature of differential methylation was seen between DCM and CON plasma treated hiPSC-CMs, with 28,381 DMPs ($P \leq 0.05$) detected. Of these, five DMPs achieved genome-wide significance (Fig. 4B): cg03800765 (ATG7, $32.4\%$, $$P \leq 8.6$$ × 10–6), cg14156314 (C9orf140, – $0.7\%$, $$P \leq 4.1$$ × 10–6), cg18502522 (SCAMP2, – $24.5\%$, 2.2 × 10–6), cg07561469 (CCNF, – $31.1\%$, $$P \leq 1.2$$ × 10–6), and cg05274755 (NPAS3, – $19.0\%$, $$P \leq 1.3$$ × 10–7). Furthermore, the highest proportion of DMPs relative to the m450k array were associated with promoter-associated CGIs, stressing a potential regulatory influence on adjacent coding regions (Fig. 4C). Among the CGI-associated DMPs, most were found within the promoter of adjacent coding regions (Fig. 4D), although robust differences in methylation were seen across genomic regions, as visualized via heatmap and hierarchical clustering (Fig. 4E). Taken together, these observations support that, although a global shift in DNA methylation does not distinguish between hiPSC-CMs treated with DCM versus CON plasma, robust alterations in DNA methylation still occur within promoter-associated CGIs. Fig. 4DNA methylation changes detected in the indirect cardiomyocyte test. A MDS plot of top-10,000 CpG probes within the Illumina® HumanMethylation450k array performed on inducible pluripotent stem cell (iPSC)-derived cardiomyocytes exposed to plasma from patients with end-stage heart failure (DCM; $$n = 13$$) relative to plasma from healthy (CON; $$n = 10$$) patients. B Volcano plot illustrating the robustness of CpG methylation differences, plotting (- log10[P value]) as a function of percent difference in methylation (%) in DCM vs. CON, probes $P \leq 0.05$ and \|methylation %\|> 5 are highlighted in yellow. Labelled are the 10 most-robustly hyper-methylated and hypo-methylated CpG probes by % methylation. C Distribution of differential methylation via three-dimensional contour plot of differentially methylated CpG probes (DMPs)* categorized according to their presence within genomic (Promoter, 5’ UTR, Body, Exon–Intron boundary, or 3’ UTR) and CpG (Shelf, Shore, and Island) regions. Bar graph depicting the number of DMPs within each genomic region. D proportional distribution of CpG Island-associated DMPs. E Heatmap and hierarchical clustering of DMPs according to each genomic region. * DMPs defined via $P \leq 0.05$ ## Common epigenetic changes detected in cardiac biopsies and by the indirect approach To identify “indirect” epigenetic loci in plasma-treated iPSC-CMs, we compared DMPs found in both myocardial and iPSC-CM analyses (Fig. 5A). Albeit a minority of co-methylated CpG sites, 389 concordant DMCs (coDMCs) associated with 426 genes were found between cardiac biopsies and iPSC-CMs. Gene set enrichment revealed disproportionate differential methylation proximal to genes associated with “Apoptosis” ($$P \leq 0.007$$, 9 DMCs), “Myogenesis” ($$P \leq 0.01$$, 10 DMCs), “Epithelial-Mesenchymal Transition” ($$P \leq 0.01$$, 10 DMCs), and “Heme Metabolism” ($$P \leq 0.01$$, 10 DMCs) pathways (Fig. 5B).Fig. 5Concordant epigenetic signature of iPSC-CMs and cardiac biopsies. A Hierarchical clustering and heatmap visualization of 389 concordantly methylated DMPs (coDMPs)* in both cardiac tissue (red) and iPSCs (blue) treated with plasma from DCM (cyan) or healthy (grey) subjects. RNA-sequencing log2Fold-Change plotted alongside DNA methylation B Gene-set enrichment analysis of the 426 proximal genes associated with at least one of the coDMCs, using the KEGG 2020 molecular signatures database with statistical enrichment calculated using enrichR. C Venn diagram illustrating the shared DMCs between the 389 coDMPs, m450k analysis of cardiac biopsies for DCM vs. CON ($$n = 41$$), and m450k analysis of buffy coat for DCM vs. CON ($$n = 31$$). D Top 5 most differentially-methylated CpG sites in iPSC-CMs that could be validated using the Meder et al. dataset. E bar plot of the top 5 most robust DMCs that were present in the validation datasets. Each dot represents methylation levels of 1 well of approx. 1 million hiPSC-CMs treated with plasma, or of the available amount of myocardial tissue from patients. * $P \leq 0.01$ To validate DNA methylation differences observed in our cohort of human cardiac biopsies, the overlapping 389 coDMCs were compared those of a testing cohort of cardiac and blood samples from DCM ($$n = 41$$) and non-failing ($$n = 31$$) control subjects from Meder et al. [ 28] (Fig. 5C); 100 DMCs were validated in cardiac biopsies ($25.7\%$ overlap, $P \leq 0.043$), and 115 DMCs were also seen in blood ($29.6\%$, $P \leq 0.01$). Examination of the top 5 most robustly differentially methylated CpGs in iPSC-CMs that were validated uncovered CpG island-associated CpGs located at – or near – the promoter regions for ATG7 (cg03800765, – $32.4\%$, $$P \leq 9.0$$ × 10–6), DZIP1L (cg09151521, $30.7\%$, $$P \leq 0.007$$), ZNF397OS (cg26141063, – $29.3\%$, $$P \leq 0.005$$), TGFBR3 (cg17074213, – $28.4\%$, $$P \leq 0.004$$), and POL2A (cg21257117, $25\%$, $$P \leq 0.005$$) (Fig. 5D). Plotting of each DMC revealed equivalent degrees of differential methylation at these sites between cardiac biopsies and iPSC-CMs (Fig. 5E). To determine whether any of these CpG sites of iPSC-CMs are associated with differences in transcriptional activity, next-generation RNA-sequencing analysis was performed on the samples submitted for DNA methylation analysis. Among the 2,077 differentially expressed genes (DEGs), 49 were accompanied by proximal differential methylation (Table 1, Fig. 5C). Therefore, although the exposure of hiPSC-CMs to human plasma does not comprehensively recapitulate the transcriptional alterations seen in the failing myocardium, the indirect measurement of CpG methylation permits a differentiation between DCM and CON biopsies and impacts pathways known to contribute to cardiac dysfunction. Table 1Differentially methylated genomic regions ## ATG7 as a putative epigenetic biomarker of DCM in iPSC-CMs To better understand the transcriptional potential of single-site CpG methylation on associated gene expression, the most robustly differentially methylated CpG was taken as a use-case scenario (Fig. 6A), which displayed a strong correlation (spearman ρ = 0.61, $$P \leq 0.0026$$) between methylation at cg03800765 and expression of the adjacent gene ATG7. Area under the receiver operating characteristics (ROC) curves (AUCs) were computed for cg03800765 methylation intensity or ATG7 expression for each dataset (Fig. 6B), revealing markedly higher AUCs for cardiac biopsy (AUC = 1.0, $$P \leq 0.0167$$) and iPSC-CM (AUC = 0.986, $P \leq 0.0001$) methylation relative to circulating cells (AUC = 0.789, $P \leq 0.0001$), iPSC-CM mRNA (AUC = 0.639, $$P \leq 0.264$$), and circulating NT-proBNP levels (AUC = 0.75, $$P \leq 0.05$$).Fig. 6ATG7 as an indirect candidate biomarker of CREB1 activity in plasma-treated iPSCs. A Scatterplot correlation between CpG methylation of iPSC-CMs treated with plasma from DCM (cyan) control (grey) patients at cg03800765 and RNA-sequencing based gene expression of ATG7 (normalized counts). Also illustrated is the negative linear trend (blue line, $R = 0.61$, $$P \leq 0.0026$$) with $95\%$ confidence region (gray). B Location of the CpG site cg03800765 in a CpG island adjacent to the ATG7 gene, demonstrating overlap with the CREB1 motif (MEME suite). C Putative downstream DMCs overlapping CREB1 response element To identify putative upstream signaling that could be impacted by ATG7 methylation at cg03800765, motif enrichment was performed using the MEME suite for CpG site-specific motif discovery at this DMC locus (± 10 BP). This approach identified CREB1 as a likely upstream transcriptional regulator (Fig. 6C), consistent with published evidence [32]. Downstream scanning of all DMCs for CREB1 response elements in DCM plasma-treated iPSC-CMs identified 117 overlapping DMCs; of these, 46 ($39\%$) were located within the proximal promoter of adjacent genes (Fig. 6D). Taken together, these observations suggest that epigenetic competition of CREB1 binding may influence ATG7 expression in DCM. ## Discussion As a molecular readout for gene-environment interactions, epigenomic profiling offers potential for precision-based clinical diagnostics [7, 9, 24, 47, 52, 56]. For conditions in which tissue is difficult to access, including cardiovascular and neurologic diseases, clinical decision-making is forced to rely on indirect measurements, though no epigenetic biomarkers have yet been identified for diagnostic or prognostic purposes. Myocardial epigenetics has mostly been studied using biopsies from end-stage failing or post-mortem “healthy” hearts [5, 14, 31, 49, 51], thereby missing the early stages of HF in which manifestations of cardiac dysfunction may be reversible. In this study, we demonstrate the usefulness of routinely acquired blood plasma to circumvent these problems via indirect epigenetic testing of DCM patients. ## Indirect model of epigenetic testing Although genetic heterogeneity is known to confound DNA methylation analyses, the hiPSC-CMs used in this study were generated from a single healthy adult of European ancestry, thereby circumventing genetic confounding. Treatment of iPSC-CMs with patient plasma induced both cellular hypertrophy and perinuclear ANP accumulation, both of which reflect properties of failing myocardium. Similarly, DNA methylation analysis identified 389 concordant DMPs (Fig. 5A), enriching pathways known to be disrupted in HF (Fig. 5B); among these, 100 DMPs ($25.7\%$) were validated in a larger independent cohort of DCM ($$n = 41$$) [28]. Although we identify many promising candidates (Table 1), cg03800765 methylation exhibited superior diagnostic performance to both circulating NT-proBNP levels and ATG7 expression in our cohort (Fig. 6B). Therefore, although future studies are needed to establish its clinical usefulness, we provide the conceptual basis for indirect epigenetic testing in HF. ## Circulating factors in heart failure Despite the robust phenotypic and epigenetic consequences that were observed following plasma treatments, it remains unknown which circulating factor(s) is/are ultimately responsible. Their identification could enable direct measurement of plasma; however, we hypothesize that cardiomyocyte phenotype is dictated by a circulatory milieu that converges onto epigenetic machinery. Cytokines have been found to predict cardiac functional improvement on mechanical circulatory support [8]. MicroRNAs have been implicated as mediators of circulating cardiovascular risk [10]. Cardiac exosomes have also emerged as possible molecular vehicles that facilitate crosstalk between the heart and end-organ tissues [16]. A recent study by Mentowski et al. demonstrated that engineered exosomes can stimulate cardiomyocyte hypertrophy [30]. Therefore, the indirect testing of cardiomyocyte epigenetics may permit a collective assessment of these factors and potentially influence myocardial disease fate. Therefore, we hypothesize that the measurement of epigenetic consequences may be superior in predicting cardiovascular disease. ## DNA methylation as a proxy of HF diagnosis and outcome Our analysis uncovered robust differential methylation cg03800765 in both iPSC-CMs (– $32.4\%$, $$P \leq 9.0$$ × 10–6) and cardiac biopsies (– $25.2\%$, $$P \leq 0.004$$), a CpG site located within a promoter-associated CpG island upstream of ATG7. Although methylation at this site was negatively correlated with ATG7 expression ($$P \leq 0.0026$$), only cg03800765 methylation was significantly predictive of patient diagnosis with HF in iPSC-CMs ($P \leq 0.0001$), cardiac biopsies ($$P \leq 0.0167$$), and circulating cells ($P \leq 0.0001$); by contrast, ATG7 expression failed to provide any diagnostic benefit ($$P \leq 0.264$$). Furthermore, cg03800765 methylation in iPSC-CMs out-performed circulating NT-proBNP levels as a diagnostic marker, underscoring its potential usefulness via indirect epigenetic testing (Fig. 6B). Although larger clinical cohorts are needed to evaluate the potential of indirect epigenetics to predict HF risk, cg03800765 is a promising candidate. ## Autophagy and ATG7 The genomic region adjacent to cg03800765 encodes the ubiquitin-like modifier-activating enzyme ATG7, a protein involved in phagolysosome formation and mitophagy [6]. Autophagy is essential to maintaining the regenerative potential of hematopoietic progenitor cells, and controls metabolic activity via epigenetic regulation, the dysregulation of which leads to heart failure [15, 33, 34]. Although no studies have yet explored the consequences of disrupted cardiac ATG7 expression, familial ATG5 mutations are associated with severe cardiac hypertrophy leading to dilated cardiomyopathy by 10 months [55]. In mice, ATG7−/− or ATG5−/− leads to cardiomyopathy characterized by inhibited autophagy and induced mesenchymal transition and apoptosis [45, 46, 50, 57]. Conversely, in vivo overexpression ATG7 in mice improves autophagic capacity that ameliorates desmin-related cardiomyopathy [2]. Therefore, the differential methylation of ATG7 may represent a phenotypically pertinent observation. However, it remains to be shown whether perturbation of the ATG7 promoter methylation indeed causes alterations in gene expression. ## Limitations Although the current study and analysis provide novel insights into the diagnostic potential of indirect epigenomic testing, some limitations must be considered. First, DCM etiology and medication history in our cohort could not be standardized with control subjects owing to limited supply of clinical data and tissue, respectively (see Suppl. Table 1). Although the current descriptive study uncovers an indirect epigenetic signature in iPSC-CMs following treatment with plasma of HF patients, future studies should consider early, etiology-specific signatures of DNA methylation in larger cohorts to understand its diagnostic, and possibly predictive, potential in human heart failure. Different etiologies of HF (e.g. HF with preserved ejection fraction) are possibly marked by a more systemic dysregulation of circulating metabolic factors, and thus might be even more suitable for indirect testing. Lastly, incorporation of other epigenetic marks, including histone modifications that are thought to be more signal responsive [27], may further improve the clinical precision of epigenetic testing. ## Conclusion In the current study, we provide the first evidence that circulating factors drive indirect epigenomic alterations of iPSC-CMs and may therefore be useful for diagnostic testing. Diagnostic screening of cardiac biopsies is unfeasible, whereas development and standardization of indirect epigenomic testing using blood plasma or serum may circumvent this limitation. ## Supplementary Information Below is the link to the electronic supplementary material. Supplemental Figure S1.: Purity of hiPS-CMs. ( A) FACS sorting for cTNT shows high purity after differentiation. ( B) Cell count under control conditions and after plasma treatment shows similar results suggesting stable cell purity. 1-way ANOVA was performed. Supplementary file1 (PDF 3901 KB)Supplemental Figure S2: (A) Cell size differences measured in 2 more human inducible pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) cell lines to confirm that cellular hypertrophy is not cell-line dependent. 1-way ANOVA was performed. ( B) Correlation of cell size of plasma-treated neonatal rat ventricular myocytes (NRVMs) and hiPSC-CMs with NT-proBNP of respective patients. ( C) Correlation of ANP intensity with hiPSC-CM cell size. Supplementary file2 (PDF 289 KB)Supplemental Figure S3: Mean detection P-values and beta value distribution for (A) cardiac biopsies from DCM (orange) and CON (green) subjects and (B) iPSC-CMs treated with plasma from DCM (orange) or CON (reen) subjects. Figures generated using Methylkit (1.20.0) in in R (4.0.5). Supplementary file3 (PDF 236 KB)Supplemental Figure S4: (A) Putative SNPs. Computational identification of putative single-nucleotide variants (SNPs) was accomplished using the MethylToSNP (0.99.0) algorithm in R (4.0.5). Supplementary file4 (PDF 131 KB)Supplementary file5 (DOCX 22 KB)Supplemental Table 2: Fragment Analysis of RNA. Supplementary file6 (CSV 1 KB) ## References 1. Alpert MA, Lavie CJ, Agrawal H, Aggarwal KB, Kumar SA. Obesity and heart failure: epidemiology, pathophysiology, clinical manifestations, and management. Transl Res (2014) 164 345-356. DOI: 10.1016/j.trsl.2014.04.010 2. Bhuiyan MS, Pattison JS, Osinska H, James J, Gulick J, McLendon PM, Hill JA, Sadoshima J, Robbins J. Enhanced autophagy ameliorates cardiac proteinopathy. J Clin Invest (2013) 123 5284-5297. DOI: 10.1172/JCI70877 3. Bird AP. CpG-rich islands and the function of DNA methylation. Nature (1986) 321 209-213. DOI: 10.1038/321209a0 4. Burke MA, Cook SA, Seidman JG, Seidman CE. 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--- title: Outcomes after coronary artery bypass grafting and percutaneous coronary intervention in diabetic and non-diabetic patients authors: - Hanna-Riikka Lehto - Klas Winell - Arto Pietilä - Teemu J Niiranen - Jyri Lommi - Veikko Salomaa journal: European Heart Journal. Quality of Care & Clinical Outcomes year: 2021 pmcid: PMC10027652 doi: 10.1093/ehjqcco/qcab065 license: CC BY 4.0 --- # Outcomes after coronary artery bypass grafting and percutaneous coronary intervention in diabetic and non-diabetic patients ## Abstract ### Aims To assess the prognosis of patients with coronary heart disease (CHD) after first myocardial revascularisation procedure in real-world practice and to compare the differences in outcomes of coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) among diabetic and non-diabetic patients. ### Methods and results A database was compiled from the national hospital discharge register to collect data on all cardiac revascularisations performed in Finland in 2000–2015. The outcomes (all-cause deaths, cardiovascular (CV) deaths, major CV events and need for repeat revascularisation) after the first revascularisation were identified from the national registers at 28 day, 1 year, and 3 year time points. A total of 139 242 first-time revascularisations (89 493 PCI and 49 749 CABG) were performed during the study period. Of all the revascularised patients, $24\%$ had diabetes, and $76\%$ were non-diabetic patients. At day 28, the risk of fatal outcomes was lower after PCI than after CABG among non-diabetic patients, whereas no difference was seen among diabetic patients. In long-term follow-up the situation was reversed with PCI showing higher risk compared with CABG for most of the outcomes. In particular, at 3 year follow-up the risk of all-cause deaths was elevated among diabetic patients [HR 1.30 ($95\%$ CI 1.22–1.38) comparing PCI with CABG] more than among non-diabetic patients [HR 1.09 (1.04–1.15)]. The same was true for CV deaths [HR 1.29 (1.20–1.38) among diabetic patients, and HR 1.03 (0.98–1.08) among non-diabetic patients]. ### Conclusion Although PCI was associated with better 28 day prognosis, CABG seemed to produce better long-term prognosis especially among diabetic patients. ## Graphical Abstract Graphical Abstract ## Introduction Globally, approximately 463 million adults are estimated to have diabetes. The prevalence of diabetes has been steadily increasing over the past few decades, and the global prevalence has been projected to further increase by $51\%$ by the year 2045.1 The incidence of coronary heart disease (CHD) is higher among patients with diabetes than in the general population, and cardiovascular diseases (CVDs), mainly CHD, are the most common cause of death among diabetic patients.2–4 Diabetes is known to cause more generalized, diffuse atherosclerosis, and consequently, multivessel coronary artery disease (CAD) is detected more often among patients with diabetes than in normoglycemic subjects.2,5–8 Survival after revascularisation is known to be worse for patients with diabetes than in patients without diabetes.9 Hence, the choice of optimal revascularisation method for diabetic patients with CHD has been debated over the past few decades, and numerous randomized controlled trials (RCTs) have been carried out to compare the outcomes of coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI).9–14 In diabetic patients with multivessel CAD, CABG has been shown to associate with better long-term survival compared to PCI with bare metal stents (BMSs)9 as well as with drug eluting stents (DESs).10–14 However, when analyzing a composite outcome of death, myocardial infarction (MI) and stroke among diabetic patients with 1- or 2-vessel disease,14 or when analyzing individual components of a composite outcome measure among diabetic patients with left main CAD or multivessel disease,15 no long-term differences have been observed between CABG and PCI. Furthermore, PCI has challenged CABG with its wider availability and lower risk for stroke, and with the development of DESs.10,16,17 In an RCT comparing CABG and PCI with a newer generation everolimus-eluting stent, no difference was observed in the composite outcome of death, MI and stroke between these methods among diabetic patients with low or intermediate complexity left main CAD at 3 years.18,19 The latest recommendation in the European clinical practice guidelines is that in less complex, 1- to 2-vessel disease without left anterior descending (LAD) coronary artery involvement, PCI is recommended; whereas with LAD disease, both revascularisation methods can be implemented. Further, in intermediate or high complexity LAD and 3-vessel CAD, CABG is recommended for patients with diabetes.20 The aforementioned RCTs provide rather clear evidence for choosing the treatment strategies. However, due to the specific inclusion criteria used in RCTs, the generalizability of their results into real-world practice can be limited. Furthermore, by using real-world data from the hospital discharge register (HDR), we have previously shown that PCI was the more commonly used revascularisation procedure among both diabetic and non-diabetic patients.21 Thus, the aim of this study was to compare the prognosis among diabetic and non-diabetic patients with CHD after the first revascularisation procedure, either CABG or PCI, and to compare the differences in the short- and long-term outcomes between the two revascularisation methods in real-world practice. ## Study design and data sources We used the nationwide HDR to identify all patients who had their first cardiac revascularisation carried out in Finland during the years 2000–2015 (Supplementary Figure S1). The data on revascularisations has been collected using electronic templates filled out by the treating physician for every invasive cardiac procedure performed. The HDR and Finnish Drug Reimbursement Register (DRR) were used to identify diabetes and other pre-existing cardiovascular comorbidities. The outcomes were identified in the HDR and causes of death register (CDR). Registration in these electronic health care registers is mandatory by law, and these registers cover the whole country. They have been described in detail in earlier publications.21,22 The outcomes after the first revascularisation were evaluated at the time points of 28 days, 1 year, and 3 years. If repeat revascularisations were carried out within 28 days from the incident procedure, these patients were excluded from the 1 year and 3 year analyses regarding the repeat revascularisations. Finally, we further analyzed all the other 1 and 3 year outcomes separately for those patients who had an event-free 28 day survival. ## Definitions of revascularisation procedures Information on the revascularisation procedures is recorded using codes defined by the Nordic Medico-Statistical Committee (NOMESCO) from 1996 onwards. Coronary artery bypass grafting codes collected for the study were FNA, FNB, FNC, FND, and FNE; and PCI codes FN1AT, FN1BT, FN1YT, FNF, FNG, and TFN40 (for detailed description of the NOMESCO codes, see Supplementary Table S1). If CABG followed PCI within 7 days, then CABG was considered to be the first revascularisation overriding the PCI procedure. A procedure was defined urgent if it was necessary to be performed as an emergency procedure or within 7 days after hospitalization. After that the procedure was considered elective. ## Definitions of diabetes and pre-existing comorbidities The diagnoses in the HDR are recorded according to the International Classification of Diseases (ICDs) codes as defined in the Finnish version of ICD-10 applied from 1996 onwards. To better capture the diabetic patients and the comorbidities of all patients, we also used data on special reimbursements from the Finnish DRR. A patient was considered to have diabetes if she/he had either an ICD-10 code E10 or E11 in the HDR, or was entitled to special reimbursements for hypoglycaemic medications in the DRR. A patient was considered to have hypertension if she/he had an ICD-10 code I10-I13 and I15 in the HDR or was entitled to special reimbursements for antihypertensive medications; and to have chronic heart failure (CHF) if she/he had the ICD-10 code I11 or I50 in the HDR, or was entitled to special reimbursements for CHF medications. Previous MI, CHD, or cardiomyopathy were identified with ICD-10 codes I21–I23, I25, I42–I43, respectively, in the HDR. Valvular defects were recognized based on the ICD-codes I05–08, I34–37, I39.0–I39.4, and atrial fibrillation on ICD-10 code I48. Previous strokes were identified with ICD-10 codes I61 and I63 (excluding codes I63.6, I64, and I60.0–I60.9) and peripheral artery disease (PAD) with the ICD-code I70.2. The used ICD-10 codes are described in detail in Supplementary Table S2. ## Definitions for outcome events during follow-up To obtain data on fatal cases, causes of death were collected from the CDR. A death was considered to be due to CVD if the cause of death was determined by any of the following ICD-10 codes in the CDR: I20–I25, I61–I64, R96, or R98 (Supplementary Table S2). For cause-specific outcomes, ACS were identified from electronic health registers using the ICD-codes I20.0, I21, and I22 (Supplementary Table S2). Stroke, CHF, and any CVD (either ACS, stroke, or CHF) were identified from HDR using the same criteria as for the pre-existing events. Repeat revascularizations were considered with the same NOMESCO codes as defined above. ## Statistical methods The data were pooled from all first cardiac revascularisation procedures performed between the years 2000 and 2015. Baseline variables were summarized using descriptive statistics presented as means and standard deviations (SDs). Comparisons of the baseline characteristics were performed using Student's t-test for continuous variables, and chi-square test for categorical variables. Kaplan–Meier curves and log-rank tests were used for comparing event-free survivals after the first revascularisation procedure between diabetic and non-diabetic patients. Similarly, event-free survivals among diabetic patients were compared between those who had PCI and those who had CABG as the revascularisation method. The same comparison was repeated for non-diabetic patients. Cox proportional hazards regression analyses were used for computing hazard ratios (HRs) of outcome events comparing different revascularisation methods, i.e. PCI with CABG. The multivariate model was adjusted for age, gender, year of procedure, region of residence, valvular defects, ACS/CHD/cardiomyopathy, hypertension, stroke, atrial fibrillation, peripheral arterial disease, and duration of diabetes. These analyses were carried out separately for diabetic and non-diabetic patients. Schoenfeldt residuals were used to ascertain validity of the proportional hazards assumption. We considered $P \leq 0.05$ to be statistically significant for all analyses. When appropriate, $95\%$ confidence intervals (CIs) are presented. All statistical analyses were carried out using R statistical software version 3.6.0 (R Core Team, 2019). ## Patient characteristics and overall event-free survival A total of 139 242 patients were revascularised during the study period. PCI was more common than CABG as a revascularisation procedure both among patients with and without diabetes, in both genders and all comorbidity subgroups (Table 1). Altogether 49 749 CABG ($35.7\%$ of total revascularisations) and 89 493 PCI ($64.3\%$) procedures were performed. Among all revascularisations, men accounted for a total of 99 437 ($71.4\%$) cases and women for 39 805 ($28.6\%$). Urgent procedures constituted 59 224 ($42.5\%$) of all revascularisations. Of the revascularised patients, 33 018 ($23.7\%$) had diabetes, whereas 106 224 ($76.3\%$) did not. Coronary artery bypass grafting constituted $39.1\%$ of all revascularisations among patients with diabetes and $34.7\%$ among non-diabetic patients. Patients with diabetes were slightly older compared to non-diabetic patients and presented more often with a history of previous ACS and stroke, and had a higher prevalence of CHF and hypertension than non-diabetic patients (Table 1). Table 1 \| Unnamed: 0 \| DM \| Non-diabetic \| P* \| \| --- \| --- \| --- \| --- \| \| N \| 33 018 \| 106 224 \| \| \| Age \| 68.2 (10.1) \| 66.4 (11.0) \| <0.0001 \| \| Women \| 10 647 (32.2) \| 29 158 (27.4) \| <0.0001 \| \| Urgent procedure \| 13 591 (41.2) \| 45 633 (43.0) \| <0.0001 \| \| Previous MI \| 9603 (29.1) \| 22 714 (21.4) \| <0.0001 \| \| Previous stroke \| 3044 (9.2) \| 5496 (5.2) \| <0.0001 \| \| CHF \| 19 858 (60.1) \| 61 588 (58.0) \| <0.0001 \| \| Hypertension \| 17 038 (51.6) \| 32 679 (30.8) \| <0.0001 \| \| ACS/CHD/Cardiomyopathy \| 26 943 (81.6) \| 83 936 (79.0) \| <0.0001 \| \| AF \| 4050 (12.3) \| 9078 (8.5) \| <0.0001 \| \| Valvular deficiency \| 2661 (8.1) \| 7584 (7.1) \| <0.0001 \| \| PAD \| 2932 (8.9) \| 3659 (3.4) \| <0.0001 \| \| Duration of diabetes \| 7.8 (6.0) \| \| \| \| CABG \| CABG \| CABG \| CABG \| \| N \| 12 900 \| 36 849 \| \| \| Age \| 67.6 (9.1) \| 67.2 (9.6) \| 0.0003 \| \| Women \| 3638 (28.2) \| 8675 (23.5) \| <0.0001 \| \| Urgent procedure \| 3763 (29.2) \| 10 271 (27.9) \| <0.0001 \| \| Previous MI \| 4593 (35.6) \| 11 121 (30.2) \| <0.0001 \| \| Previous stroke \| 1183 (9.2) \| 2164 (5.9) \| <0.0001 \| \| CHF \| 6891 (53.4) \| 16 887 (45.8) \| <0.0001 \| \| Hypertension \| 6279 (48.7) \| 11 044 (30.0) \| <0.0001 \| \| ACS/CHD/Cardiomyopathy \| 9727 (75.4) \| 25 536 (69.3) \| <0.0001 \| \| AF \| 1351 (10.5) \| 3084 (8.4) \| <0.0001 \| \| Valvular deficiency \| 1732 (13.4) \| 5289 (14.4) \| 0.0050 \| \| PAD \| 1232 (9.6) \| 1549 (4.2) \| <0.0001 \| \| Duration of diabetes \| 7.4 (5.6) \| \| \| \| PCI \| PCI \| PCI \| PCI \| \| N \| 20 118 \| 69 375 \| \| \| Age \| 68.6 (10.7) \| 65.9 (11.6) \| <0.0001 \| \| Women \| 7009 (34.8) \| 20 483 (29.5) \| <0.0001 \| \| Urgent procedure \| 9828 (48.9) \| 35 362 (51.0) \| <0.0001 \| \| Previous MI \| 5010 (24.9) \| 11 593 (16.7) \| <0.0001 \| \| Previous stroke \| 1861 (9.3) \| 3332 (4.8) \| <0.0001 \| \| CHF \| 12 967 (64.5) \| 44 701 (64.4) \| <0.0001 \| \| Hypertension \| 10 759 (53.5) \| 21 635 (31.2) \| <0.0001 \| \| ACS/CHD/Cardiomyopathy \| 17 216 (85.6) \| 58 400 (84.2) \| <0.0001 \| \| AF \| 2699 (13.4) \| 5994 (8.6) \| <0.0001 \| \| Valvular deficiency \| 938 (4.7) \| 2295 (3.3) \| <0.0001 \| \| PAD \| 1700 (8.5) \| 2110 (3.0) \| <0.0001 \| \| Duration of diabetes \| 8.0 (6.2) \| \| \| When examining the whole 3-year period after the first revascularisation procedure using Kaplan–Meier curves, patients with diabetes had lower event-free survival compared to non-diabetic patients when the data from both CABG and PCI were pooled (Figure 1A). When comparing the two revascularisation methods over the whole 3 year period for all outcome events, event-free survival of patients treated with CABG was better than those treated with PCI in both diabetic and non-diabetic patients (Figure 1B, C). Figure 1: Kaplan–Meier estimates of cumulative incidences of composite outcome event of all-cause death, any cardiovascular event, and repeat revascularisation procedures. The cumulative incidences among (A) diabetic and non-diabetic patients for all revascularisation procedures, and in (B) diabetic patients and (C) non-diabetic patients for coronary artery bypass grafting and percutaneous coronary intervention separately. P-values are for log-rank tests, statistically significant if P < 0.05. ## Short-term outcomes The numbers of different outcome events at 28 days among diabetic and non-diabetic patients grouped by CABG and PCI are shown in detail in Table 2. In multivariate adjusted analyses, non-diabetic patients treated with PCI had lower risk of all-cause deaths and CVD deaths when compared to patients treated with CABG, whereas no significant differences were observed between the two revascularisation methods among patients with diabetes. Both among patients with diabetes and non-diabetic patients, PCI was associated with lower risk for all other outcome events when compared to CABG except for repeat revascularisation procedures, where CABG was associated with lower risk in both patient groups (Table 3). ## Fatal long-term outcome events A total of 7750 and 14 113 all-cause deaths were observed at 1 year and 3 year time points, respectively (Table 2). The fatal long-term outcomes after CABG and PCI differed between the diabetic and non-diabetic groups. When evaluated at the time point of 1 year, PCI was associated with higher risk for all-cause deaths than CABG among diabetic patients, whereas no difference was observed between the revascularisation methods among non-diabetic patients (Table 3). At the 3 year time point, however, PCI was associated with higher risk for all-cause deaths than CABG in both diabetic and non-diabetic patients. Moreover, the excess risk in the PCI group was even higher among diabetic patients than among non-diabetic patients (Table 3). Correspondingly, there were differences in the long-term incidence of cardiovascular death by diabetes status. At the 1 year time point, non-diabetic patients treated with PCI had lower risk of CVD death than those treated with CABG. However, at the 3 year time point, there were no longer significant differences between PCI and CABG among the non-diabetic patients. Conversely, patients with diabetes treated with CABG had lower risk of CVD death than those treated with PCI at both the 1 year and 3 year time points (Table 3). We analysed further the fatal long-term outcomes for those subjects, who had remained event-free for 28 days following the first revascularisation procedure. Altogether, 31 657 ($95.9\%$) diabetic patients and 103 229 ($97.2\%$) non-diabetic patients had an event-free 28 day survival. Among these patients, CABG was associated with lower risk for all-cause and cardiovascular death when compared to PCI both at 1 year and 3 year time points in diabetic as well as in and non-diabetic patients. At the 3 year time point, the risk of CVD death after PCI when compared to CABG was more elevated in patients with diabetes than in non-diabetic patients (Table 4). Table 4 \| Unnamed: 0 \| 1-year \| 1-year.1 \| 3-year \| 3-year.1 \| \| --- \| --- \| --- \| --- \| --- \| \| \| HR (95% CI)b \| P* \| HR (95% CI)b \| P* \| \| All-cause deaths \| All-cause deaths \| All-cause deaths \| All-cause deaths \| All-cause deaths \| \| Non-diabetic \| 1.22 (1.11–1.33) \| <0.001 \| 1.29 (1.22–1.37) \| <0.001 \| \| DM \| 1.33 (1.18–1.49) \| <0.001 \| 1.47 (1.37–1.58) \| <0.001 \| \| Cardiovascular deaths \| Cardiovascular deaths \| Cardiovascular deaths \| Cardiovascular deaths \| Cardiovascular deaths \| \| Non-diabetic \| 1.13 (1.02–1.25) \| 0.018 \| 1.24 (1.16–1.32) \| <0.001 \| \| DM \| 1.32 (1.17–1.50) \| <0.001 \| 1.48 (1.37–1.61) \| <0.001 \| \| Acute coronary syndrome \| Acute coronary syndrome \| Acute coronary syndrome \| Acute coronary syndrome \| Acute coronary syndrome \| \| Non-diabetic \| 1.34 (1.17–1.53) \| <0.001 \| 1.45 (1.32–1.59) \| <0.001 \| \| DM \| 1.72 (1.42–2.08) \| <0.001 \| 1.56 (1.38–1.77) \| <0.001 \| \| Stroke \| Stroke \| Stroke \| Stroke \| Stroke \| \| Non-diabetic \| 0.78 (0.68–0.90) \| 0.001 \| 0.89 (0.81–0.98) \| 0.013 \| \| DM \| 0.92 (0.74–1.14) \| 0.432 \| 1.04 (0.91–1.19) \| 0.606 \| \| Any CVD \| Any CVD \| Any CVD \| Any CVD \| Any CVD \| \| Non-diabetic \| 1.51 (1.36–1.69) \| <0.001 \| 1.61 (1.49–1.73) \| <0.001 \| \| DM \| 1.58 (1.34–1.87) \| <0.001 \| 1.68 (1.50–1.88) \| <0.001 \| \| Chronic heart failure \| Chronic heart failure \| Chronic heart failure \| Chronic heart failure \| Chronic heart failure \| \| Non-diabetic \| 1.73 (1.53–1.94) \| <0.001 \| 1.94 (1.78–2.11) \| <0.001 \| \| DM \| 1.96 (1.64–2.34) \| <0.001 \| 2.00 (1.77–2.26) \| <0.001 \| ## Cause specific long-term outcome events When evaluating the risk for other outcome events, including non-fatal outcomes, at 1 and 3 year time points from the revascularisation, the risk for CHF and any CVD event was significantly lower after CABG than PCI in both diabetic and non-diabetic patients. There were no differences in these risks between the diabetic and non-diabetic patients (Table 3). Similarly, repeat revascularisation procedures were significantly less frequent after CABG than after PCI in both patient groups at the 1 year and 3 year time points. On the other hand, the risk for stroke was significantly lower after PCI compared to CABG in non-diabetic patients at both 1 and 3 year time points; whereas among patients with diabetes, no differences were observed in the long-term stroke risk between the revascularisation methods in neither of these time points (Table 3). Regarding the risk for ACS at the 1 year time point, CABG was associated with lower risk for ACS than PCI among diabetic patients, whereas no difference was observed in non-diabetic patients (Table 3). However, at 3 years, the risk for ACS was lower after CABG when compared to PCI among both patient groups (Table 3). When we analyzed patients with event-free survival for the first 28 days at the 1 year and 3 year time points, we observed lower risk of ACS, heart failure, and any CVD event after CABG than PCI among both diabetic and non-diabetic patients (Table 4). ## Discussion Our large register-based study reflecting real-world clinical practice showed that the long-term prognosis was better with CABG than with PCI in both non-diabetic and diabetic patients. At the 3 year time point, CABG was associated with lower risk for all-cause death compared to PCI in both non-diabetic and diabetic patients. Coronary artery bypass grafting was also associated with lower long-term risk for CVD death among patients with diabetes at 3 years. As for non-diabetic patients in regard to the fatal CVD outcomes, CABG was associated with better prognosis than PCI at 3 years among those individuals, who had remained event-free for the first 28 days. Importantly, the better prognosis after CABG than after PCI regarding these long-term fatal CVD outcomes was even more evident among diabetic patients than among non-diabetic patients. For those patients who had remained event free for the first 28 days after revascularisation, CABG was associated with lower risk for CHF and all cardiovascular events, except for stroke among patients with diabetes, when compared to PCI. Regarding all fatal outcomes, the short-term prognosis at 28 days was better after PCI than after CABG in non-diabetic patients, whereas no difference was observed between the methods among patients with diabetes. However, the short-term prognosis was better after PCI than CABG for all other cause-specific outcomes in both patient groups except for the need of repeat revascularisations. The better long-term outcomes after CABG compared to PCI in our study are in line with another observational study (ASCERT), which reported an adjusted 4 year mortality of $16.4\%$ for CABG and of $20.8\%$ for PCI with first-generation DESs among patients aged 65 or older, and an adjusted risk ratio favoring CABG both in non-diabetic and diabetic patients.23 Our real-life results on non-selected patients are also consistent with the findings on diabetic patients with multivessel disease in original RCT studies9–11 and pooled analyses of RCT studies.16 FREEDOM trial, the first large RCT on revascularisation methods for diabetic patients published in 2012, compared PCI with DESs and CABG among diabetic patients with stable-ischemic multivessel CHD. This study showed that in a 3.8-year follow-up, CABG was superior to PCI, with all-cause mortality among patients with CABG being $10.9\%$ compared to $16.3\%$ in patients with PCI ($$P \leq 0.049$$).10 These results were further confirmed after 7.5 years of follow-up, where all-cause mortality after CABG was $18.3\%$ compared to $24.3\%$ after PCI ($$P \leq 0.01$$).11 Better long-term survival after CABG compared to PCI was confirmed also by a pooled analysis in a systematic review (powered to detect all-cause mortality), which reported a $10.0\%$ all-cause mortality after a 5 year follow-up among diabetic patients with multivessel disease treated with CABG compared to $15.5\%$ in patients treated with PCI (with either BMS or DES). In contrast to our results, this pooled analysis did not find differences in all-cause mortality among non-diabetic patients.16 Interestingly, we found no differences in the risk for fatal all-cause and CVD deaths in 28 days between the revascularisation methods among diabetic patients, whereas non-diabetic patients who underwent PCI had better short-term prognosis when compared to CABG. Similar to our results, the FREEDOM study10 showed no differences in the short-term CVD mortality between PCI and CABG in diabetic patients. However, on the contrary to the results in the FREEDOM study, we showed better short-term prognosis regarding the risk for ACS and stroke after PCI when compared to CABG, both in diabetic and non-diabetic patients; whereas the FREEDOM study10 showed no differences in the risk for major cardiovascular and cerebrovascular events. This discrepancy may be explained by differences in patient selection in real-world clinical practice when compared to RCTs. In observational studies, differences have been observed in the characteristics of the patients about to undergo either CABG or PCI. On average, patients treated with PCI have been shown to be older and more often women, whereas patients selected for CABG have more often generalized and 2-3 vessel disease.23 However, for the long-term prognosis, it may be that patient selection does not fully explain the outcome differences: Tam et al.24 examined outcomes after CABG and PCI using clinical and administrative databases in Canada, and performed a 1:1 propensity score matching of 23 baseline characteristics for 4301 pairs of patients with diabetes and 2–3 vessel CHD. After matching for comorbidities, CABG was associated with lower mortality and lower risk for major adverse cardiac and cardiovascular events (MACCEs) when compared to PCI in 8 year follow-up.24 In addition, different mechanism of achieving the revascularisation has been suggested to explain the superiority of CABG over PCI. Namely, CABG may provide additional protection from those lesions that are non-flow limiting at the time of the procedure, and thus not treated with PCI but bypassed with CABG and resulting in a wider area with collateral circulation for CABG compared to PCI.25 It could also be speculated that differences in the long-term outcomes between revascularisation methods could arise from differences in secondary prevention after revascularisation. However, evidence suggests that the differences herein would be in favor of PCI, and thus, not explaining the differences in the outcomes. In the FREEDOM study, controlled optimal medical treatment was provided after both PCI and CABG.10 In addition, observational studies have shown that patients who had undergone CABG filled fewer prescriptions for statins, ACE inhibitors, and ARB blockers compared to those patients for whom PCI was performed.26 Moreover, compliance with guideline-directed medical therapy after revascularisation has been shown to be worse after CABG than PCI in clinical trials.27 Further studies are needed to clarify whether these findings apply also to revascularised patients with diabetes. Some criticism has also been raised against the outcome measures used in revascularisation trials. A recently published meta-analysis concluded that PCI was associated with higher all-cause, cardiac, and non-cardiac mortality when compared to CABG at 5 years.28 Due to the higher non-cardiac mortality with PCI, the authors suggested that all-cause mortality should be used as the primary end point for revascularisation trials in CHD. Our overall results of the lower all-cause long-term mortality after CABG when compared to PCI are in line with the meta-analysis by Gaudino et al.28 In our previous study, we have shown that only approximately $27\%$ of diabetic patients and $22\%$ of non-diabetic patients in Finland in 2012–2015 had received CABG as their first revascularisation procedure.21 This may imply that some of the patients eligible for CABG might have received PCI instead. Since, as shown in this study, the long-term prognosis seems to be better after CABG especially for patients with diabetes; a balanced evaluation of the optimal revascularisation strategy for an individual patient, performed by a multidisciplinary heart team, would be vital for achieving the best individual outcome.20 ## Strengths and limitations The major strength in our study is that due to the mandatory documentation protocols, the Finnish HDR covers all hospitalisations and nearly all revascularisations in Finland including the follow-up.29 We were also able to identify all known diabetic patients by using the HDR and DRR. With these registers we identified the important comorbidities. We also acknowledge limitations in our study. Firstly, we did not have access to individual patient data depicting the clinical characteristics and detailed clinical situation leading to revascularisation or concurrent medications, nor data on the extent of the coronary atherosclerosis. We had neither access to the data on the types of the stents used in PCI nor on the detailed descriptions of the bypass technique in CABG. ## Conclusions Although PCI was associated with better or non-different short-term prognosis, CABG was associated with better long-term prognosis especially among patients with diabetes at the 3 year time point. Our results provide further support to earlier findings suggesting better long-term prognosis after CABG than after PCI in diabetic patients. However, further randomized trials are warranted to determine in more detail the clinical characteristics that influence the choice of the revascularization method in diabetic individuals. ## Funding VS was supported by the Finnish Foundation for Cardiovascular Research. TN was supported by the Finnish Foundation for Cardiovascular Research, the Academy of Finland (Grant no 321351), and the Emil Aaltonen Foundation. ## Unknown Conflict of interest: KW has received honoraria from Novo Nordisk and AstraZeneca for lecturing, and from Sanofi for consulting, all unrelated to the present study. JL has received honoraria from Bayer Ltd. and Boehringer Ingelheim for lecturing. VS has received honoraria from Novo Nordisk and Sanofi for consulting. He also has ongoing research collaboration with Bayer Ltd. (all unrelated to the present study). H-R L, AP, and TN have none to declare. ## Data availability Due to the privacy and legal restrictions, individual level data used in this article are not available. ## References 1. 1. International Diabetes Federation . IDF Diabetes Atlas. 9th ed. Brussels, Belgium: 2019. https://www.diabetesatlas.org.. IDF Diabetes Atlas (2019.0) 2. 2. WHO . Global report on Diabetes 2016. World Health Organization 2016, France. https://apps.who.int/iris/bitstream/handle/10665/204871/9789241565257_eng.pdf;jsessionid=A1A22BF336B5A4AABD252D1E11890094?sequence=1. 3. Morrish NJ, Wang SL, Stevens LK, Fuller JH, Keen H. WHO multinational study group. Mortality and causes of death in the WHO multinational study of vascular disease in diabetes. Diabetologia (2001.0) 44 S14-S21. PMID: 11587045 4. Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S. Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation (2018.0) 137 e67-e492. PMID: 29386200 5. Bauters C, Lemesle G, de Groote P, Lamblin NA.. A systematic review and meta-regression of temporal trends in the excess mortality associated with diabetes mellitus after myocardial infarction. Int J Cardiol (2016.0) 217 109-121. PMID: 27179900 6. Ledru F, Ducimetiere P, Battaglia S, Courbon D, Beverelli F, Guize L. New diagnostic criteria for diabetes and coronary artery disease: insights from an angiographic study. J Am Coll Cardiol (2001.0) 37 1543-1550. PMID: 11345363 7. Donahoe SM, Stewart GC, McCabe CH, Mohanavelu S, Murphy SA, Cannon CP. Diabetes and mortality following acute coronary syndromes. JAMA (2007.0) 298 765-775. PMID: 17699010 8. Natali A, Vichi S, Landi P, Severi S, L´Abbate A, Ferrannini E.. Coronary atherosclerosis in type II diabetes: angiographic findings and clinical outcome. Diabetologia (2000.0) 43 632-641. PMID: 10855538 9. The final 10-year follow-up results from the BARI randomized trial. J Am Coll Cardiol (2007.0) 49 1600-1606. PMID: 17433949 10. Farkouh ME, Domanski M, Sleeper LA, Siami FS, Dangas G, Mack M. Strategies for multivessel revascularization in patients with diabetes. N Engl J Med (2012.0) 367 2375-2384. PMID: 23121323 11. Farkouh ME, Domanski M, Dangas GD, Godoy LC, Mack MJ, Siami FS. Long-term survival following multivessel revascularization in patients with diabetes: the FREEDOM Follow-On Study. J Am Coll Cardiol (2019.0) 73 629-638. PMID: 30428398 12. Frye RL, August P, Brooks MM, Hardison RM, Kelsey SF, MacGregor JM. A randomized trial of therapies for type 2 diabetes and coronary artery disease. N Engl J Med (2009.0) 360 2503-2515. PMID: 19502645 13. Park SJ, Ahn JM, Kim YH, Park DW, Yun SC, Lee JY. Trial of everolimus-eluting stents or bypass surgery for coronary disease. N Engl J Med (2015.0) 372 1204-1212. PMID: 25774645 14. Mancini GB, Farkouh ME, Brooks MM, Chaitman BR, Boden WE, Vlachos H. Medical treatment and revascularization options in patients with type 2 diabetes and coronary disease. J Am Coll Cardiol (2016.0) 68 985-995. PMID: 27585501 15. Kappetein AP, Head SJ, Morice MC, Banning AP, Serruys PW, Mohr FW. Treatment of complex coronary artery disease in patients with diabetes: 5-year results comparing outcomes of bypass surgery and percutaneous coronary intervention in the SYNTAX trial. Eur J Cardiothorac Surg (2013.0) 43 1006-1013. PMID: 23413014 16. Head SJ, Milojevic M, Daemen J, Ahn JM, Boersma E, Christiansen EH. Mortality after coronary artery bypass grafting versus percutaneous coronary intervention with stenting for coronary artery disease: a pooled analysis of individual patient data. Lancet (2018.0) 391 939-948. PMID: 29478841 17. Cardiac procedures. Health at a Glance: Europe 2016: State of Health in the EU Cycle (2016.0) 170-171 18. Stone GW, Sabik JF, Serruys PW, Simonton CA, Généreux P, Puskas J. Everolimus-eluting stents or bypass surgery for left main coronary artery disease. N Engl J Med (2016.0) 375 2223-2235. PMID: 27797291 19. Milojevic M, Serruys PW, Sabik JF, Kandzari DE, Schampaert E, van Boven AJ. Bypass surgery or stenting for left main coronary artery disease in patients with diabetes. J Am Coll Cardiol (2019.0) 73 1616-1628. PMID: 30947913 20. Cosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V. 2019 ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J (2020.0) 41 255-323. PMID: 31497854 21. Lehto HR, Pietilä A, Niiranen TJ, Lommi J, Salomaa V.. Clinical practice patterns in revascularization of diabetic patients with coronary heart disease: nationwide register study. Ann Med (2020.0) 52 225-232. PMID: 32429711 22. Sund R.. Quality of the Finnish Hospital Discharge Register: a systematic review. Scand J Public Health (2012.0) 40 505-515. PMID: 22899561 23. Weintraub WS, Grau-Sepulveda MV, Weiss JM, O'Brien SM, Peterson ED, Kolm P. Comparative effectiveness of revascularization strategies. N Engl J Med (2012.0) 366 1467-1476. PMID: 22452338 24. Tam DY, Dharma C, Rocha R, Farkouh ME, Abdel-Qadir H, Sun LY. Long-term survival after surgical or percutaneous revascularization in patients with diabetes and multivessel coronary disease. J Am Coll Cardiol (2020.0) 76 1153-1164. PMID: 32883408 25. Doenst T, Haverich A, Serruys P, Bonow RO, Kappetein P, Falk V. PCI and CABG for treating stable coronary artery disease: JACC review topic of the week. J Am Coll Cardiol (2019.0) 73 964-976. PMID: 30819365 26. Hlatky MA, Solomon MD, Shilane D, Leong TK, Brindis R, Go AS.. Use of medications for secondary prevention after coronary bypass surgery compared with percutaneous coronary intervention. J Am Coll Cardiol (2013.0) 61 295-301. PMID: 23246391 27. Pinho-Gomes AC, Azevedo L, Ahn JM, Park SJ, Hamza TH, Farkouh ME. Compliance with guideline-directed medical therapy in contemporary coronary revascularization trials. J Am Coll Cardiol (2018.0) 71 591-602. PMID: 29420954 28. Gaudino M, Hameed I, Farkouh ME, Rahouma M, Naik A, Robinson NB. 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--- title: Cardiovascular disease preventive effects of aspirin combined with different statins in the United States general population authors: - Tao Liu - Ronghua Zuo - Jia Wang - Zixuan Huangtao - Bing Wang - Lifang Sun - Shasha Wang - Baoyin Li - Zhijian Zhu - Yesheng Pan journal: Scientific Reports year: 2023 pmcid: PMC10027662 doi: 10.1038/s41598-023-31739-w license: CC BY 4.0 --- # Cardiovascular disease preventive effects of aspirin combined with different statins in the United States general population ## Abstract The purpose of this study was to explore the use of aspirin in conjunction with various statins for cardiovascular disease (CVD) prevention in the general population of the United States (U.S.). A total of 3778 people from the National Health and Nutrition Examination Surveys from 2011 to 2018 were included in our analysis. After adjusting for sociodemographic and common cardiovascular risk factors, we used multivariable logistic regression analysis to determine aspirin should be combined with which type of statin for better CVD preventive effects. Subgroup analyses were carried out subsequently. In comparison to the aspirin use alone, the odds ratios with $95\%$ confidence intervals for CVD were 0.43 (0.33, 0.57), 0.69 (0.42, 1.13), 0.44 (0.31, 0.62), 0.34 (0.23, 0.50) and 0.64 (0.49, 0.84) for the combination use of aspirin and atorvastatin, lovastatin, pravastatin, rosuvastatin as well as simvastatin, respectively, in the fully-adjusted model. Aspirin combined with rosuvastatin was more effective in the prevention of individual CVD, including congestive heart failure, coronary heart disease, angina pectoris and heart attack, than aspirin combined with other statins. In conclusion, statins combined with aspirin have a clear advantage over aspirin alone in preventing CVD. In addition, when various sex, age, and fitness levels were considered, as well as with and without diabetes mellitus, the combination usage of aspirin and rosuvastatin had the greatest CVD preventive effects than aspirin coupled with other statins. ## Introduction Cardiovascular disease (CVD), including atherosclerosis, heart failure, cerebrovascular disease, peripheral vascular disease and other cardiac abnormalities, is the leading cause of death worldwide, and both its prevalence and fatality rate are increasing1. Every year, approximately 655,000 Americans die as a result of cardiovascular disease, resulting in a massive economic burden2. Aspirin, a nonsteroidal anti-inflammatory drug, has been shown to inhibit the formation of various inflammatory mediators and adhesion molecules, resulting in anti-atherosclerosis3,4. Aspirin reduces the risk of major vascular events by $15\%$ to $20\%$ when used for primary CVD prevention5. As a result, the role of aspirin in the prevention and treatment of CVD is widely acknowledged in the medical community6. HMG-CoA reductase enzyme is the starting point of cholesterol synthesis, and statins compete with the enzyme and thus impede the beginning of cholesterol production7. Statins, a class of lipid-lowering drugs, have a substantial impact on decreasing lipids and delaying plaque development, thus lowering morbidity and death in individuals with atherosclerotic CVD8. Statin therapy has been shown to reduce the risk of cardiovascular events by $30\%$ to $40\%$ when used for primary prevention of CVD5. Therefore, in the prevention of primary CVD, the combination of statins with aspirin is more beneficial than aspirin alone. However, it is unclear which combination of aspirin and statins provides the greatest protection against cardiovascular events. Therefore, we used the data from the National Health and Nutrition Examination Survey (NHANES) database to investigate which type of statin, when combined with aspirin, has the best effect for CVD prevention. ## Study population The NHANES database is a complex survey that combines interviews and physical examinations to obtain a nationally representative sample of the civilian, noninstitutionalized United States (U.S.) population (https://www.cdc.gov/nchs/nhanes/)9. In this study, we analyzed NHANES data from four 2-year cycles (2011–2018). Participants with missing CVD and information on the aspirin and statin medication questionnaires ($$n = 16$$,090 and 16,449, respectively) were excluded. Finally, a total of 3378 participants took part in this study. Because the current study relied on existing data from the NHANES database and did not involve the collection of new data, no ethical approvals were required. The National Center for Health Statistics Institutional Review Board approved all NHANES procedures, and all participants provided written informed consent10. The NHANES website (https://www.cdc.gov/nchs/nhanes/) contains complete information about the survey design, methodology, and data. ## Aspirin and statin use The use of aspirin and various statins was determined using data from the standardized NHANES 2011–2018 questionnaire. To determine the use of aspirin, participants were asked the following questions: “Do your doctors or other health care providers advise you to take a low-dose aspirin every day to prevent heart attacks, strokes, or cancer? Have you ever been told to do this?”, “ Are you now doing this?”, and “Are you taking a low-dose aspirin every day on your own to prevent heart attacks, strokes, or cancer?” Using these questionnaires, a binary variable (yes or no) was created to indicate participants’ current aspirin use. In terms of statin use, participants were asked if they had used any medications that required a prescription in the previous month. Those who said yes were then asked to show the investigator the medication containers for all of the products they had used. Statins with the generic name’s atorvastatin, lovastatin, pravastatin, rosuvastatin, simvastatin, and combination products were studied. The NHANES website (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx) contains detailed information and procedures. ## Covariates The following covariates were downloaded from the NHANES database: age, sex, race/ethnicity, family poverty income ratio (PIR), education level, marital status, the history of hypertension, and diabetes mellitus (DM), smoker, alcohol user, body mass index (BMI), physical activity (PA), mean energy intake, hemoglobin (HB), high-density lipoprotein-cholesterol (HDL-C), total cholesterol (TC), triglyceride (TC), blood urea nitrogen (BUN), uric acid (UA), serum creatinine (Scr). During the home interview, the following data were self-reported by the participants: age, sex, race/ethnicity, education level, marital status, smoking status, drinking status, and mean energy intake. Sex was dichotomized into two groups (male and female). Education was categorized into five groups (< 9th grade, 9–11th grade, high school, college, and graduate). Self-identified race/ethnicity was grouped as Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other race. Marital status was categorized into three groups (married, separated, and never married). Family poverty-to-income was defined as the total family income divided by the poverty threshold11. In addition, data on Hb, HDL-C, TC, TG, BUN, UA, and Scr were obtained from laboratory tests. Frequency of aspirin use includes ‘one every day of aspirin’, and ‘one every other day of aspirin’. Details of all variables are available online at https://www.cdc.gov/nchs/nhanes/. ## CVD ascertainment The primary outcome for the study was CVD which defined as a composite of five self-reported outcomes (congestive heart failure (CHF), coronary heart disease (CHD), angina pectoris, heart attack and stroke)12. The participant was recorded as having CVD if she/he answered “yes” to the following question: “Has a doctor or other health professional ever told you that you had congestive heart failure/coronary heart disease/angina pectoris/stroke?”. A standardized medical condition questionnaire administered during the personal interview provides more detailed information (www.cdc.gov/nchs/nhanes/). Primary prevention was defined in this study as the prevention of the first occurrence of a cardiovascular event, such as self-reported CHD, CHF, angina/angina pectoris, heart attack, or stroke. ## Statistical analysis Continuous variables were presented as means standard deviation and categorical variables as frequency or percentage. Furthermore, we built three models: model 1, which adjusted for age and sex; model 2, which adjusted for age, sex, race, educational level, marital status, family PIR, smoker, alcohol user, hypertension, DM, and BMI; and model 3, which adjusted for all of the potential confounding factors listed in Table 1. The multivariable logistic regression models were used to investigate the use of aspirin in combination with various statins for primary CVD prevention. The stratified and interaction models were used to perform subgroup analysis. All the analyses were performed with R version 3.6.4 (R Foundation for Statistical Computing, Vienna, Austria), SPSS version 22.0 (SPSS Inc., Chicago, IL, USA) and EmpowerStats software (http://www.empowerstats.com). P-value < 0.05 was regarded as statistically significant. Table 1Demographic characteristics of the study participants. VariablesOverall ($$n = 3778$$)CVD ($$n = 2520$$)Non-CVD ($$n = 1258$$)P-valueAge, years66.45 ± 10.2267.58 ± 9.9963.75 ± 10.04 < 0.001Gender, % < 0.001 Male2026 ($53.6\%$)1267 ($33.5\%$)759 ($20.1\%$) Female1752 ($46.4\%$)1253 ($33.2\%$)499 ($13.2\%$)Race, % < 0.001 Mexican American381 ($10.1\%$)284 ($7.5\%$)97 ($2.6\%$) Other Hispanic343 ($9.1\%$)247 ($6.5\%$)96 ($2.5\%$) Non-Hispanic White1746 ($46.2\%$)1112 ($29.4\%$)634 ($16.8\%$) Non-Hispanic Black901 ($23.8\%$)606 ($16.0\%$)295 ($7.8\%$) Other race407 ($10.8\%$)271 ($7.2\%$)136 ($3.6\%$)Family poverty-income ratio2.54 ± 1.612.64 ± 1.603.17 ± 1.65 < 0.001Education level, % < 0.001 < 9th grade645 ($17.1\%$)386 ($10.2\%$)259 ($6.9\%$) 9–11th grade746 ($19.7\%$)467 ($12.3\%$)279 ($7.4\%$) High school1425 ($37.7\%$)958 ($25.3\%$)467 ($12.4\%$) College525 ($13.9\%$)373 ($9.9\%$)152 ($4.0\%$) Graduate437 ($11.6\%$)336 ($8.9\%$)101 ($2.7\%$)Marital status, % < 0.001 Having a partner2270 ($60.1\%$)1555 ($41.2\%$)715($18.9\%$) No partner1383 ($36.6\%$)866 ($22.9\%$)517 ($13.7\%$) Unmarried125 ($3.3\%$)99 ($2.6\%$)26 ($0.7\%$)Hypertension, % < 0.001 No1050 ($27.8\%$)810 ($21.4\%$)240 ($6.4\%$) Yes2728 ($72.2\%$)1710 ($45.3\%$)1018 ($26.9\%$)DM, % < 0.001 No2423 ($64.1\%$)1682 ($44.5\%$)741 ($19.6\%$) Yes1355 ($35.9\%$)838 ($22.2\%$)517 ($13.7\%$)Smoker, % < 0.001 No1834 ($48.5\%$)1327 ($35.1\%$)507 ($13.4\%$) Yes1944 ($51.5\%$)1193 ($31.6\%$)751 ($19.9\%$)Alcohol user, %0.319 No1018 ($26.9\%$)683 ($18.1\%$)335 ($8.9\%$) Yes2760 ($73.1\%$)1837 ($48.6\%$)923 ($24.4\%$)PA, %0.913 Vigorous564 ($14.9\%$)381 ($10.1\%$)183 ($14.5\%$) Moderate788 ($20.9\%$)520 ($13.8\%$)268 ($7.1\%$) Never2426 ($64.2\%$)1619 ($42.9\%$)807 ($21.4\%$)BMI, kg/m230.31 ± 6.7430.50 ± 6.6230.41 ± 6.600.684Mean energy intake (kcal/day)1880.75 ± 721.711919.57 ± 730.931997.12 ± 706.790.006Hb, g/dL13.80 ± 1.5113.92 ± 1.6014.13 ± 1.33 < 0.001BUN, mg/dL17.07 ± 7.5718.09 ± 7.8616.19 ± 5.89 < 0.001UA, mg/dL5.77 ± 1.485.92 ± 1.555.58 ± 1.36 < 0.001Scr, mg/dL1.03 ± 0.651.05 ± 0.500.93 ± 0.49 < 0.001HDL-cholesterol, mg/dL52.20 ± 15.9349.83 ± 16.4654.56 ± 17.60 < 0.001TC, mg/dL182.63 ± 45.11171.14 ± 43.87192.99 ± 43.35 < 0.001TG, mg/dL163.76 ± 118.50167.58 ± 112.94167.63 ± 124.160.992CVD medications, % < 0.001 Aspirin alone1587 ($42.0\%$)1245 ($33.0\%$)342 ($9.0\%$) Atorvastatin and aspirin combination778 ($20.6\%$)425 ($11.3\%$)353 ($9.3\%$) Lovastatin and aspirin combination135 ($3.6\%$)88 ($2.4\%$)47 ($1.2\%$) Pravastatin and aspirin combination290 ($7.7\%$)169 ($4.5\%$)121 ($3.2\%$) Rosuvastatin and aspirin combination233 ($6.2\%$)120 ($3.2\%$)113 ($3.0\%$) Simvastatin and aspirin combination755 ($20.0\%$)473 ($12.5\%$)282 ($7.5\%$)Frequency of aspirin use, % < 0.001 One every day3543 ($93.8\%$)2323 ($61.5\%$)1220 ($32.3\%$) One every other day235 ($6.2\%$)197 ($5.2\%$)38($1.0\%$)Aspirin dose, mg111.51 ± 82.38128.52 ± 96.73108.81 ± 80.87 < 0.001Data are presented as mean ± SD or n (%).CVD cardiovascular disease, DM diabetes mellitus, PA physical activity, BMI body mass index, Hb hemoglobin, Scr serum creatinine, BUN blood urea nitrogen, UA uric acid, TC total cholesterol, TG triglycerides, HDL-cholesterol high density lipoprotein-cholesterol. ## Ethical approval and consent to participate All NHANES participants provided written informed consent and the National Center for Health Statistics obtained institutional review board approval prior to data collection. Because NHANES data are de-identified and publicly available, the analysis presented here was exempt from IRB review. ## Baseline characteristics Table 1 shows the baseline characteristics of the study participants. In total, 3778 people (aged 66.45 ± 10.22 years) were included in our study. According to weighted analysis, the number of people included in our study represents the overall U.S. population of 2,468,896. CVD was present in $27.7\%$ of this population. There were significant differences in baseline characteristics between the CVD group and non-CVD group, with the exception of the alcohol user, PA, BMI, and TG. ## Association between aspirin and different statins use and total CVD Table 2 shows the results of multivariable logistic regression analyses for the association between aspirin and different statin use and total CVD. After controlling for underlying cofounders, the odds ratios (ORs) with $95\%$ confidence intervals (CIs) for CVD were 0.43 (0.33, 0.57), 0.69 (0.42, 1.13), 0.44 (0.31, 0.62), 0.34 (0.23, 0.50) and 0.64 (0.49, 0.84) for aspirin and different statin combinations (atorvastatin, lovastatin, pravastatin, rosuvastatin and simvastatin).Table 2Associations of aspirin alone compared to aspirin plus statin with the risk of total CVD.CVD medicationsModel 1Model 2Model 3OR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)Aspirin aloneRef. Ref. Ref. Atorvastatin and aspirin combination0.36 (0.30, 0.44)*0.36 (0.28, 0.45)0.43 (0.33, 0.57)Lovastatin and aspirin combination0.55 (0.38, 0.80)0.65 (0.42, 1.01)0.69 (0.42, 1.13)Pravastatin and aspirin combination0.40 (0.31, 0.52)*0.43 (0.31, 0.58)0.44 (0.31, 0.62)Rosuvastatin and aspirin combination0.31 (0.23, 0.41)0.34 (0.25, 0.48)0.34 (0.23, 0.50)Simvastatin and aspirin combination0.53 (0.43, 0.64)0.54 (0.43, 0.68)0.64 (0.49, 0.84)Model 1: age and gender. Model 2: Model 1 variables plus education level, race/ethnicity, family poverty-income ratio, hypertension, diabetes mellitus, smoker, alcohol user, and body mass index. Model 3 was adjusted for Model 2 variables plus physical activity, total energy intake, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, uric acid, creatinine, blood urea nitrogen, and hemoglobin. CVD cardiovascular disease, OR odd ratio, CI confidence interval.$P \leq 0.01$, $P \leq 0.001.$ ## Association between aspirin and different statins use and individual CVDs Separate analyses were conducted to examine the relationship between aspirin and various statins use and individual CVDs such as CHF, CHD, angina pectoris, heart attack, and stroke. The findings revealed that there was a strong link between the use of aspirin and different statins and the prevalence of individual CVDs such as CHD, CHF, angina pectoris, and heart attack. The ORs ($95\%$ CIs) of individual CVDs were 0.47 (0.27, 0.84), 0.24 (0.15, 0.39), 0.24 (0.13, 0.42), 0.30 (0.19, 0.49), and 0.98 (0.54, 1.81) in the fully adjusted model for rosuvastatin and aspirin combination, respectively, compared to aspirin use alone (Table 3).Table 3Associations of aspirin alone compared to aspirin plus statin with the risk of individual CVD.CVD medicationsCHFCHDAnginaHeart attackStrokeOR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)Aspirin alone1.001.001.001.001.00Atorvastatin and aspirin combination0.51 (0.34, 0.77)0.28 (0.19, 0.39)*0.37 (0.23, 0.59)0.37 (0.26, 0.54)*0.90 (0.60, 1.35)Lovastatin and aspirin combination0.91 (0.39, 2.13)0.50 (0.26, 0.96)0.64 (0.26, 1.60)0.74 (0.368, 1.482)0.63 (0.33, 1.22)Pravastatin and aspirin combination0.60 (0.36, 1.02)0.38 (0.24, 0.60)*0.34 (0.19, 0.60)0.47 (0.297, 0.749)*0.58 (0.36, 0.92)Rosuvastatin and aspirin combination0.47 (0.27, 0.84)0.24 (0.15, 0.39)0.24 (0.13, 0.42)0.30 (0.189, 0.488)*0.98 (0.54, 1.81)Simvastatin and aspirin combination0.65 (0.42, 0.99)0.40 (0.28, 0.57)*0.42 (0.26, 0.67)0.48 (0.334, 0.685)*1.07 (0.71, 1.62)Analysis was adjusted for age, gender, education level, race/ethnicity, marital status, family poverty-income ratio, hypertension, diabetes mellitus, smoker, alcohol user, body mass index, physical activity, total energy intake, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, uric acid, creatinine, blood urea nitrogen, and hemoglobin. CVD cardiovascular disease, CHF congestive heart failure, CHD coronary heart disease, OR odd ratio, CI confidence interval.$P \leq 0.05$, $P \leq 0.01$, $P \leq 0.001.$ ## Subgroup analyses Subgroup analyses were carried out based on age, sex, hypertension, DM, and BMI (Table 4). With respect to the subgroup analyses (age, sex, DM, and BMI), rosuvastatin combined with aspirin was shown to be more effective for the prevention of CVD. However, in populations without hypertension, the combination of atorvastatin plus aspirin was more effective in preventing CVD. Furthermore, there were significant differences in the use of different statins and aspirins in preventing CVD in terms of age, hypertension, and DM.Table 4Subgroups analysis for the associations of aspirin alone compared to aspirin plus statin with the prevalence of total CVD.Aspirin aloneAtorvastatin and aspirin combinationLovastatin and aspirin combinationPravastatin and aspirin combinationRosuvastatin and aspirin combinationSimvastatin and aspirin combinationP for interactionOR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)Sex0.126Male1.000.32 (0.22, 0.46)0.55 (0.28, 1.07)0.36 (0.22, 0.59)0.21 (0.12, 0.37)*0.44 (0.31, 0.64) Female1.000.59 (0.39, 0.89)0.81 (0.38, 1.73)0.51 (0.31, 0.83)*0.50 (0.28, 0.89)0.98 (0.64, 1.49)Age < 0.001 < 601.000.41 (0.23, 0.74)0.94 (0.24, 3.74)0.28 (0.14, 0.57)0.23 (0.10, 0.52)0.68 (0.35, 1.33) ≥ 601.000.44 (0.32, 0.60)0.65 (0.38, 1.11)0.51 (0.34, 0.80)0.38 (0.24, 0.60)0.63 (0.47, 0.85)Hypertension < 0.001 No1.000.27 (0.15, 0.47)*0.41 (0.14, 1.20)0.36 (0.17, 0.76)0.43 (0.16, 1.14)0.50 (0.28, 0.88)* Yes1.000.50 (0.37, 0.68)*0.78 (0.44, 1.37)0.47 (0.32, 0.70)0.33 (0.21, 0.51)*0.68 (0.50, 0.93)DM0.879 No1.000.39 (0.27, 0.55)**0.51 (0.26, 0.97)0.37 (0.24, 0.57)*0.31 (0.19, 0.53)0.51 (0.37, 0.73)** Yes1.000.57 (0.37, 0.88)1.02 (0.46, 2.27)0.594 (0.33, 1.06)0.42 (0.23, 0.79)1.00 (0.64, 1.56)BMI0.019 < 301.000.33 (0.23, 0.49)0.51 (0.26, 1.00)0.39 (0.23, 0.64)0.26 (0.15, 0.44)0.50 (0.35, 0.72)* ≥ 301.000.55 (0.37, 0.81)1.07 (0.51, 2.24)0.49 (0.30, 0.79)0.41 (0.23, 0.74)*0.85 (0.57, 1.26)Analyses was adjusted for age, gender, education level, race/ethnicity, marital status, family poverty-income ratio, hypertension, diabetes mellitus, smoker, alcohol user, body mass index, physical activity, total energy intake, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, uric acid, creatinine, blood urea nitrogen, and hemoglobin when they were not strata variables. CVD cardiovascular disease, DM diabetes mellitus, BMI body mass index, OR odd ratio, CI confidence interval.$P \leq 0.05$, $P \leq 0.01$, $P \leq 0.001.$ ## Discussion With the aging of the population, the incidence of cardiovascular events is gradually increasing in the elderly13. Meanwhile, various risk factors such as unhealthy lifestyle and environmental pollution also make the incidence of cardiovascular events in the young population gradually increased14. The most important way to prevent CVD is to promote a healthy lifestyle throughout life, besides, drug control is another important method revealing from the ACC/AHA guideline15. Thus, it is of great importance to focus on the primary drug prevention of cardiovascular events in the general population. As the most commonly used drugs in cardiovascular system, several consensuses have acknowledged the benefits of aspirin in CVD primary prevention, especially its prominent function in reducing the risk of nonfatal myocardial infarction and stroke16, meanwhile, the use of aspirin will also lead to the increased incidence of bleeding, as shown by a large-scale meta-analysis17. Gaziano et al. constructed a large, randomized, multicenter clinical trial and discovered that based on the low CVD risk participants, the CVD events rate was much lower than expected when the participants were administrated with aspirin, which illustrated the benefits of aspirin in CVD primary prevention18. Statins are the first choice for reducing high blood cholesterol, a risk factor for CVD events, mainly due to the reduction of cholesterol biosynthesis19. Taylor et al. summarized that people without evidence of CVD treated with statins showed reductions in all-cause mortality, major vascular events and revascularizations with no excess of adverse events20. Researchers have constructed several clinical trials to evaluate whether the combination use of aspirin and statins could be beneficial for patients. Athyros et al. found that comparing with statins or aspirin used alone, combination usage of aspirin and statin significantly reduced the CVD events in dyslipidaemic patients21. A Korean national cohort study22 evaluating the efficacy of aspirin and statins in primary prevention of cardiovascular mortality showed that the combination use of aspirin and statins could benefit the participants. Statin therapy can improve peripheral atherosclerosis and reverse atherosclerotic plaques. However, rare research focused on the choice of statins with the combination use of aspirin in CVD primary prevention. Here, based on the large-scale population integrated from NHANES database, we reported that the combination of aspirin and rosuvastatin could remarkably reduce the incidence of total and individual CVDs comparing with the combination use of aspirin and other kinds of statins, revealing the advantage of rosuvastatin. Rosuvastatin is the latest and most potent statin currently on the market. Compared with other statins, rosuvastatin is a fully synthetic statin which acts by interfering with the endogenous synthesis of cholesterol through competitively inhibiting the 3-hydroxy-3-methylglutaryl coenzyme23. In primary prevention populations, all statins reduced the risk of all-cause, and CVD mortality, but some harm risks also increased. Different statin types had different benefit-harm profiles. A drug-level network meta-analysis showed that atorvastatin and rosuvastatin reduced CVD events most effectively24. And, Liping also found that the risk of adverse reactions was not increased with rosuvastatin compared with atorvastatin25. In addition, in an intermediate-risk, ethnically diverse population without CVD, rosuvastatin at 10 mg per day significantly decreased cardiovascular events compared with placebo26. Compared to other statins, rosuvastatin has been shown to reduce LDL cholesterol more effectively than most other statins27 and has the best efficacy in reducing total cholesterol and low-density lipoprotein cholesterol (LDL-C), and also increases HDL-C more than atorvastatin28. In East Asian patients with hypercholesterolemia, Zhang and his team revealed that rosuvastatin was more effective than atorvastatin29. However, Perez-Calahorra has found that there is no difference in ASCVD recurrence between high doses of rosuvastatin and atorvastatin30. ## Strengths and limitations Strengths of this study included the relatively large sample size to investigate the use of aspirin in conjunction with various statins for CVD prevention. Despite the fact that our research found that combining aspirin and statins is advantageous to the cardiovascular system in the general population. The most important option for primary prevention is still lifestyle modification. More specific group classifications, which NHANES did not offer, should be explored, such as whether individuals were at low, moderate, or high CVD risk, to better provide drug usage evidence. To evaluate the advantages, the adverse effects of aspirin and different stains should also be considered. ## Conclusion Our results demonstrate that the combination use of aspirin and statins are more effective than using aspirin alone in CVD prevention based on the large-scale U.S. general population. Moreover, rosuvastatin showed more remarkable effects in total and individual CVDs prevention comparing with other statins when combining use with aspirin. ## References 1. Al-Mallah MH, Sakr S, Al-Qunaibet A. Cardiorespiratory fitness and cardiovascular disease prevention: An update. Curr. Atheroscler. Rep.* (2018.0) 20 1. DOI: 10.1007/s11883-018-0711-4 2. Rhee TG, Kumar M, Ross JS, Coll PP. Age-related trajectories of cardiovascular risk and use of aspirin and statin among US adults aged 50 or older, 2011–2018. J. Am. Geriatr. Soc. (2021.0) 69 1272-82. DOI: 10.1111/jgs.17038 3. Hybiak J, Broniarek I, Kiryczyński G, Los LD, Rosik J, Machaj F. Aspirin and its pleiotropic application. 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--- title: Association between handgrip strength and heart failure in adults aged 45 years and older from NHANES 2011–2014 authors: - Run-Min Li - Guo-Hua Dai - Hui Guan - Wu-Lin Gao - Li-Li Ren - Xing-Meng Wang - Hui-Wen Qu journal: Scientific Reports year: 2023 pmcid: PMC10027666 doi: 10.1038/s41598-023-31578-9 license: CC BY 4.0 --- # Association between handgrip strength and heart failure in adults aged 45 years and older from NHANES 2011–2014 ## Abstract Growing evidence indicates that handgrip strength (HGS) is a conspicuous marker for assessing some diseases affecting middle-aged and elderly individuals. However, research regarding HGS and heart failure (HF) is sparse and controversial. Hence, we aimed to investigate the association between HGS and HF among adults aged 45 years and older in the United States. In this cross-sectional study, we included 4524 adults older than 45 years who were part of the National Health and Nutrition Examination Survey. A generalized additive model was used to estimate the association between HGS and HF. Age, gender, race, income, education, body mass index, smoking status, drinking status, diabetes, hypertension, stroke, vigorous physical activity, total energy intake, total protein intake, total sugars intake, and total fat intake covariates were adjusted using multiple regression models. And further subgroup analysis was conducted. We documented 189 cases of HF, including 106 men and 83 women. HGS was negatively associated with HF after adjusting for all the covariates (odds ratio = 0.97, $95\%$ confidence interval = 0.96–0.99; $P \leq 0.001$). Compared with the lowest quintile, the highest quintile was associated with an $82\%$ lower incidence of HF (odds ratio = 0.18, $95\%$ confidence interval = 0.08–0.43; $P \leq 0.001$). Subgroup analysis showed that the results remained stable. In US adults older than 45, HGS was negatively associated with HF after adjusting for covariates. This finding had the potential to draw attention to the physiological and pathological effects of decreased muscle function on HF and may influence further prospective studies with intervention trials. ## Introduction Along with the global social structure of population aging, the trend of aging brings about an increase in the overall incidence and prevalence of heart failure (HF). Currently, the incidence of HF in Europe is about $\frac{3}{1000}$ person-years (all age groups) or about $\frac{5}{1000}$ person-years in adults, the increase in overall incidence is mainly associated with people over 85, with a limited contribution from people under 551,2. At the same time, data from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC) showed an overall HF prevalence of 17 cases per 1000 people, the prevalence is approximately $1\%$ for those aged < 55 years and > $10\%$ for those aged 70 years or older1,3. In the United States, HF currently affects 6 million people, and the direct costs of HF treatment are expected to cost up to $30.7 billion and will double by 20304,5. Importantly, studies have shown that even patients with mild symptoms may still have a high risk of hospitalization and death6. A study also showed that the most clinically stable HF patients, who had never had a prior or remote HF hospitalization, still had high absolute rates of cardiovascular death and hospitalization during the course of trial7. There is a need to explore more prevention strategies for HF, which necessitates a better understanding of the association between risk factors and HF. Handgrip strength (HGS) is a quick and straightforward measure of muscle function and is closely related to overall muscle strength8. Age-related reductions in overall muscle strength are associated with all-cause mortality and other adverse clinical events in middle-aged or elderly people and can be characterized by HGS9–11. For example, studies have shown that reduced HGS increases the risk of all-cause mortality and cancer12. Overall, HGS is associated with cardiovascular mortality and the incidence of cardiovascular disease12–14, but data on the association between HGS and HF remains sparse and controversial. For instance, Segar et al. suggested that decreased HGS had a nonsignificant association with a higher incident risk of both reduced and preserved ejection fraction heart failure15. At the same time, Hauptman et al. found that changes in HGS were not associated with 30-day HF readmission16. In contrast, a cohort study showed that relative HGS (absolute values of HGS divided by weight in kilograms) was inversely associated with the risk of heart failure17. A case–control study suggested that HGS is an ideal indicator of risk stratification in patients with HF18. In a meta-analysis, HGS was considered to be an independent predictor of admissions for HF19. Therefore, in this study, we examined the associations of HGS with HF among US adults aged 45 years and older using samples from a database of a multiracial population and tried to explain the mechanism. ## Ethics statement The use of the dataset from the National Health and Nutrition Examination Survey (NHANES) was approved by the National Center for Health Statistics (NCHS) Institutional Review Board in compliance with the revised Declaration of Helsinki. Informed consent was obtained from all participants before data collection. All the methods were carried out in accordance with the relevant guidelines and regulations of the NCHS Institutional Review Board. ## Data sources The NHANES is a nationally representative survey conducted by the NCHS. It adopted a stratified, multistage probability cluster sampling design to select representative samples from United States civilians and assess their health or nutritional status. The survey data and methodological details about the NHANES are available at www.cdc.gov/nchs/nhanes/. ## Study design and participants This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. As a retrospective cross-sectional study, no direct contact was performed with the participants, so the privacy risk was minimal. The deidentified data were extracted from the 2011–2014 NHANES cycles since the information on HGS measurement was only provided in these cycles. In order to strictly screen the included participants, the following exclusion criteria were used: participants with cognitive impairment and depression were excluded because these conditions may cause abnormal HGS data20,21; participants missing data for HF, HGS, or other covariates; the age of participants under 45 years old. Data were analyzed from May to July 2022. ## HGS measurement and diagnosis of HF The exposure variable was HGS, which was measured using a handgrip dynamometer (Model T.K.K.5401). The HGS measurement protocol was explained and demonstrated to the participant by a qualified examiner. The examiner next adjusted the handgrip size of the dynamometer to the participant’s hand size and requested the participant to try squeezing the dynamometer for a practice test. The purpose of the practice test was to check whether the participant understood the procedure and whether the handgrip size was properly adjusted. After practice, the participant was instructed to squeeze the dynamometer as hard as possible with one hand while exhaling to prevent intrathoracic pressure buildup. The test was then repeated for the other hand. Each hand was evaluated three times, with a 60-s interval of rest between measurements on the same hand. The HGS was calculated as the sum of the largest reading from each hand and expressed in kilograms. This variable was not calculated for participants who only performed the test on one hand, such as participants with “missing arm, hand, or thumb,” “hand paralysis,” “wearing a cast on wrist or hand” or “other (specify)”. Unless the participant was physically disabled, the handgrip test was conducted in a standing posture. If the paraplegic participant is not able to obtain the proper testing form in the assigned sitting position, they will be excluded from the handgrip test. Detailed descriptions of the HGS measurement protocol are provided in the NHANES “Muscle Strength Procedures Manual”22. The outcome variable was HF. In the NHANES, HF data were provided by a self-reported personal interview. Participants were considered to have HF if they answered yes to the question “Has a doctor or other health professional ever told you that you had heart failure23? Although the method of defining the main outcome with the results of the questionnaire survey had certain ambiguity. However, given the lack of B-type natriuretic peptide (BNP), N-terminal pro-B-type natriuretic peptide (NT-proBNP), cardiac troponin, electrocardiogram, or cardiac imaging in the NHANES database, it was difficult to make a definite diagnosis of heart failure. Some existing literature also supported the use of questionnaires as a diagnostic method for heart failure in NHANES participants24–26. ## Covariates Variables thought to be confounders based on existing literature and clinical judgment were included4,20,23,27,28. In this study, covariates included demographic data (age, gender, race, education, income, body mass index), a questionnaire on medical history (diabetes mellitus, hypertension, stroke), lifestyle characteristics (smoking status, drinking status, vigorous physical activity), and nutrient intake situation (total energy intake, total protein intake, total sugars intake, and total fat intake). In NHANES, information on self-reported race and ethnicity was derived from responses to survey questions on race. Educational was divided into 3 levels (high school or less, some college, and college graduate or higher). BMI is calculated from a given height and weight of participants. As used by US government departments to report NHANES dietary and health data, we categorized family income into the following 3 levels based on the family poverty income ratio: low income (≤ 1.3), medium income (> 1.3 to 3.5), and high income (> 3.5)29. Diabetes mellitus, hypertension, stroke, and vigorous physical activity were defined based on the self-reported questionnaire. Smoking status was categorized into the following 3 groups: never smoked (or smoked < 100 cigarettes), former smoker (smoked at least 100 cigarettes but has quit), and current smoker. Drinking status was determined by the survey question, “In any 1 year, have you had at least 12 drinks of any type of alcoholic beverage?” Participants who answered “yes” were defined as alcohol drinkers30. The vigorous physical activity was determined by a questionnaire, “Do you do any vigorous-intensity sports, fitness, or recreational activities that cause large increases in breathing or heart rate like running or basketball for at least 10 min continuously?” The option was “Yes” or “No”. Estimates of dietary intake data were assessed from the 24-h dietary recall, which utilized the Automated Multiple Pass Method performed by trained interviewers in the Mobile Examination Center31. This multi-step procedure provided for enhanced accuracy of the intakes of foods and beverages reported as ingested from midnight to midnight of the prior day32,33. Nutrient intakes were estimated from the Food and Nutrient Database for Dietary Studies as well as the Food Patterns Equivalents Database, respectively, to estimate intakes on the day of record34. The total energy intake was measured in kilocalories (kcal), total protein intake, total sugars intake, and total fat intake were measured in grams (gm). The data acquisition process for all the covariates can be found at www.cdc.gov/nchs/nhanes/. ## Statistical analysis Descriptive analysis was applied to all participants’ data. Categorical variables are expressed as proportions (%). Continuous variables are expressed as the mean and standard deviation (SD) or median and interquartile range (IQR), as appropriate. Student’s t-test and the chi-square test were used for continuous variables and categorical variables, respectively, to assess differences in clinical characteristics. Odds ratios (ORs) and $95\%$ confidence intervals (CIs) were calculated for HGS with HF using multiple logistic regression models. Age, gender, race, income, education, BMI, smoking status, drinking status, hypertension, diabetes, stroke, vigorous physical activity, total energy intake, total protein intake, total sugars intake, and total fat intake covariates were adjusted. A generalized additive model was used to study the association between HGS and HF. Age levels were classified into ≥ 60 years old and < 60 years old, according to the World Health Organization’s definitions of middle age and old age. BMI levels were divided into two groups by 25. Each nutrient intake was divided into three equal subgroups. Subgroup analysis of age levels, gender, race, income levels, education levels, BMI levels, smoking status, drinking status, hypertension, diabetes, stroke, vigorous physical activity, total energy intake levels, total protein intake levels, total sugars intake levels, and total fat intake levels covariates were performed using stratified logistic regression models. Interaction across subgroups was tested using the likelihood ratio test. A $P \leq 0.05$ was considered statistically significant. Relevant methodological descriptions could be found in the literature30,35. The import and mergence of original data were performed with the statistical software R packages (“foreign” and “dplyr”) (http://www.R-project.org, The R Foundation). The Free Statistics software version 1.4 (Freeclinical Medical Technology Co, Ltd. http://www.clinicalscientists.cn/freestatistics/) was utilized for descriptive analysis, multiple logistic regression analysis, and subgroup analysis. By setting the function menu and calling the built-in R language packages, the software performed data analyses. The initialization file (settings file) was where the software stored the outcomes of repeated data processing. Figure 1 depicted the main data analysis process. Figure 1The block diagram of the main data analysis process. ## Baseline characteristics of the study participants by categories of HGS The flowchart for participant enrollment is presented in Fig. 2. Participants with cognitive impairment ($$n = 74$$) and depression ($$n = 840$$) were excluded. A total of 4524 participants aged 45 years and older remained after the exclusion of 4372 subjects with missing handgrip strength data, 4922 subjects with missing HF data, and 356 subjects with missing other covariates. Figure 2Flow chart of sample selection from the NHANES 2011–2014. Among the 4524 participants from the study, 189 participants were diagnosed with HF. The baseline characteristics of all participants are shown in Table 1. The average age of the study participants was 61.78 years. Males accounted for $50.22\%$ of the total study population. All the variables were significantly different among persons classified into the different quintiles of HGS. The lowest quintile of HGS was ≤ 48.40 kg; the 2nd quintile was ≥ 48.50 and < 58.10 kg; the 3rd quintile was ≥ 58.20 and < 70.20 kg; the 4th quintile was ≥ 70.30 and < 85.70 kg; and the highest quintile was ≥ 85.80 kg. Compared with participants in the HGS quintile 1 group, the other quintile groups were younger, more likely to be male, had higher income, education, BMI, and nutrient intake, and had lower rates of diabetes, hypertension, and stroke. However, smoking, drinking, and vigorous physical activity rates were higher. Table 1Description of 4524 participants included in the present study. VariableAll participantsHandgrip strength (kg)P valueQ1(≤ 48.40)Q2(48.50–58.10)Q3(58.20–70.20)Q4(70.30–85.70)Q5(≥ 85.80)Participants (n)4524902895917905905Heart failure n(%) < 0.01 No4335 (95.82)845 (93.68)851 (95.08)874 (95.31)870 (96.13)895 (98.90) Yes189 (4.18)57 (6.32)44 (4.92)43 (4.69)35 (3.87)10 (1.10)Age (years)61.78 ± 10.6768.74 ± 10.1362.28 ± 10.2560.47 ± 10.5360.91 ± 10.2156.53 ± 8.25 < 0.01Gender n(%) < 0.01 Female2252 (49.78)826 (91.57)734 (82.01)545 (59.43)140 (15.47)7 (0.77) Male2272 (50.22)76 (8.43)161 (17.99)372 (40.57)765 (84.53)898 (99.23)Race n(%) < 0.01 Mexican American428 (9.46)90 (9.98)92 (10.28)78 (8.50)82 (9.06)86 (9.50) Non-Hispanic White2037 (45.03)462 (51.22)406 (45.36)392 (42.75)367 (40.55)410 (45.30) Non-Hispanic Black1127 (24.91)134 (14.85)193 (21.57)258 (28.14)248 (27.40)294 (32.49) Other932 (20.60)216 (23.95)204 (22.79)189 (20.61)208 (22.99)115 (12.71)BMI (kg/m2)29.21 ± 6.4828.58 ± 6.8429.07 ± 6.6929.62 ± 6.9529.03 ± 6.0829.75 ± 5.69 < 0.01Income n(%) < 0.01 PIR ≤ 1.31342 (29.67)327 (36.25)265 (29.61)258 (28.13)270 (29.84)222 (24.53) 1.3 < PIR ≤ 3.51589 (35.12)344 (38.14)310 (34.64)331 (36.10)308 (34.03)296 (32.71) PIR > 3.51593 (35.21)231 (25.61)320 (35.75)328 (35.77)327 (36.13)387 (42.76)Education n(%) < 0.01 High school or less1005 (22.21)263 (29.16)168 (18.77)194 (21.16)208 (22.98)172 (19.01) Some college2323 (51.35)471 (52.22)475 (53.07)480 (52.34)432 (47.74)465 (51.38) College graduate or higher1196 (26.44)168 (18.62)252 (28.16)243 (26.50)265 (29.28)268 (29.61)Diabetes n(%) < 0.01 No3705 (81.90)701 (77.72)733 (81.90)768 (83.75)734 (81.10)769 (84.97) Yes819 (18.10)201 (22.28)162 (18.10)149 (16.25)171 (18.90)136 (15.03)Hypertension n(%) < 0.01 No2739 (60.54)464 (51.44)585 (65.36)577 (62.92)555 (61.33)558 (61.66) Yes1785 (39.46)438 (48.56)310 (34.64)340 (37.08)350 (38.67)347 (38.34)Stroke n(%) < 0.01 No4304 (95.14)813 (90.13)862 (96.31)878 (95.75)869 (96.02)882 (97.46) Yes220 (4.86)89 (9.87)33 (3.69)39 (4.25)36 (3.98)23 (2.54)Smoking status n(%) < 0.01 Never smoker2360 (52.17)560 (62.08)528 (58.99)485 (52.89)377 (41.66)410 (45.31) Former smoker1420 (31.38)253 (28.05)249 (27.83)270 (29.44)358 (39.56)290 (32.04) Current smoker744 (16.45)89 (9.87)118 (13.18)162 (17.67)170 (18.78)205 (22.65)Drinking status n(%) < 0.01 Never drinker1243 (27.48)411 (45.57)304 (33.97)264 (28.79)167 (18.45)97 (10.72) Former drinker3103 (68.59)453 (50.22)556 (62.12)609 (66.41)708 (78.24)777 (85.85) Current drinker178 (3.93)38 (4.21)35 (3.91)44 (4.80)30 (3.31)31 (3.43)Vigorous physical activity n(%) < 0.01 No3868 (85.50)864 (95.79)827 (92.40)802 (87.46)745 (82.32)630 (69.61) Yes656 (14.50)38 (4.21)68 (7.60)115 (12.54)160 (17.68)275 (30.39)Total energy intake (kcal)1988.52 ± 893.591644.10 ± 745.071742.65 ± 693.671936.20 ± 767.732196.17 ± 955.572420.31 ± 1022.18 < 0.01Total protein intake (gm)78.31 ± 39.5763.88 ± 31.9868.20 ± 30.4275.85 ± 36.3387.79 ± 42.7395.71 ± 45.03 < 0.01Total sugars intake (gm)102.37 ± 67.0790.09 ± 58.8793.16 ± 57.6098.91 ± 62.93108.76 ± 69.47120.86 ± 79.41 < 0.01Total fat intake (gm)76.47 ± 43.5462.79 ± 37.3167.09 ± 36.1874.64 ± 38.6884.37 ± 47.6893.36 ± 48.87 < 0.01Data presented are mean ± SD or n(%).BMI body mass index, PIR poverty income ratio. The P value < 0.05 represents the significance of the comparison among groups. ## Association between HGS and HF The generalized additive model was utilized to test the non-linearity of HGS and HF. As shown in Fig. 3, there was a linear association between HGS and HF (P for non-linearity = 0.438 > 0.05).Figure 3The non-linearity test of handgrip strength and heart failure. Table 2 shows the ORs and $95\%$ CIs for the risk of HF determined by HGS. The unadjusted model omits any adjustment for covariables. The adjusted model I adjusts for age, race, gender, income, and education. The adjusted model II adjusts for age, race, gender, income, education, BMI, smoking status, drinking status, diabetes, hypertension, stroke, vigorous physical activity, total energy intake, total protein intake, total sugars intake, and total fat intake. When analyzed in continuous form, HGS was significantly associated with the incidence of HF. This association was found in the unadjusted model (OR = 0.98, $95\%$ CI = 0.97–0.98), adjusted model I (OR = 0.97, $95\%$ CI = 0.96–0.98), and adjusted model II (OR = 0.97, $95\%$ CI = 0.96–0.99).Table 2Association of HGS with HF.Unadjusted modelAdjusted model IAdjusted model IIHGS (kg)0.98 (0.97–0.98)0.97 (0.96–0.98)0.97 (0.96–0.99)HGS quintiles Q1 (≤ 48.40)1(Ref)1(Ref)1(Ref) Q2 (48.50–58.10)0.77 (0.51–1.15)0.99 (0.64–1.54)1.12 (0.70–1.77) Q3 (58.20–70.20)0.73 (0.49–1.10)0.75 (0.46–1.23)0.89 (0.53–1.49) Q4 (70.30–85.70)0.60 (0.39–0.92)0.47 (0.27–0.84)0.53 (0.29–0.98) Q5 (≥ 85.80)0.17 (0.08–0.33)0.16 (0.07–0.36)0.18 (0.08–0.43)P for trend < 0.001 < 0.001 < 0.001Data presented are ORs and $95\%$ CIs. The adjusted model I adjusts for age, race, gender, income, and education. Adjusted model II adjusts for adjust I + BMI, smoking status, drinking status, diabetes, hypertension, stroke, vigorous physical activity, total energy intake, total protein intake, total sugars intake, and total fat intake. “ P for trend” is mainly used to test whether there is a certain linear change trend between the change in the exposure variable of HGS and the change in the outcome variable of HF. When treated as a categorical variable, in the unadjusted model, there was a decreasing risk of developing HF as the quintile of HGS increased (P for trend < 0.001). Compared with those in the lowest quintile, participants who had a measurement of HGS in the highest quintile had an $83\%$ decreased risk of the development of HF (OR = 0.17, $95\%$ CI = 0.08–0.33). After adjustment for age, race, gender, income, education, BMI, smoking status, drinking status, diabetes, hypertension, stroke, vigorous physical activity, total energy intake, total protein intake, total sugars intake, and total fat intake, the odds ratios were (OR = 1.12, $95\%$ CI = 0.70–1.77), (OR = 0.89, $95\%$ CI = 0.53–1.49), (OR = 0.53, $95\%$ CI = 0.29–0.98), and (OR = 0.18, $95\%$ CI = 0.08–0.43) for HGS quintiles 2–5, respectively (P for trend < 0.001). ## Subgroup analyses of the association between HGS and HF To determine whether the association between HGS and HF was stable in different subgroups, we performed stratified analyses and interactive analyses (Table 3). No interactive role was found in the association between HGS and HF (P for interaction > 0.05).Table 3Subgroup analyses of the association between HGS and heart failure. Confounding factorHGS quintilesP for trendP for interactionCategoryQ1Q2Q3Q4Q5(≤ 48.40)(48.50–58.10)(58.20–70.20)(70.30–85.70)(≥ 85.80)Age levels (years)0.18 n ≤ 601(Ref)0.71 (0.21–2.40)0.35 (0.09–1.30)0.18 (0.04–0.83)0.01 (0–0.12) < 0.01 n > 601(Ref)1.15 (0.69–1.91)1.04 (0.58–1.86)0.64 (0.32–1.26)0.36 (0.14–0.91)0.03Gender n(%)0.51 Female1(Ref)0.88 (0.49–1.59)0.64 (0.28–1.44)0.27 (0.03–2.15)0 (0-Inf)0.15 Male1(Ref)2.72 (0.95–7.83)2.02 (0.73–5.60)1.08 (0.38–3.03)0.34 (0.10–1.14) < 0.01Race n(%)0.20 Mexican American1(Ref)1.03 (0.15–6.84)1.38 (0.17–11.02)0.21 (0.02–2.63)0 (0–Inf)0.04 Non-Hispanic White1(Ref)0.93 (0.49–1.77)0.98 (0.48–2.00)0.43 (0.18–1.03)0.11 (0.03–0.46) < 0.01 Non-Hispanic Black1(Ref)0.67 (0.25–1.83)0.51 (0.18–1.43)0.68 (0.22–2.12)0.39 (0.10–1.55)0.18 Other1(Ref)4.02 (0.65–24.81)0.24 (0.01–4.42)1.69 (0.14–19.91)0 (0-Inf)0.20Income levels n(%)0.20 PIR ≤ 1.31(Ref)0.99 (0.43–2.32)1.75 (0.73–4.21)1.36 (0.46–4.00)0.80 (0.20–3.24)0.82 1.3 < PIR ≤ 3.51(Ref)1.15 (0.57–2.32)0.62 (0.27–1.43)0.27 (0.09–0.76)0.11 (0.02–0.50) < 0.01 PIR > 3.51(Ref)1.22 (0.40–3.69)0.55 (0.16–1.87)0.37 (0.10–1.35)0.05 (0.01–0.38) < 0.01Education levels n(%)0.74 High school or less1(Ref)1.08 (0.46–2.55)0.87 (0.31–2.43)0.36 (0.10–1.31)0.46 (0.10–2.09)0.15 Some college1(Ref)1.15 (0.61–2.15)0.93 (0.46–1.87)0.68 (0.30–1.56)0.11 (0.03–0.40) < 0.01 College graduate or higher1(Ref)0.56 (0.14–2.15)0.44 (0.11–1.81)0.20 (0.04–1.01)0.09 (0.01–0.78)0.02BMI levels (kg/m2)0.73 n ≤ 251(Ref)0.75 (0.28–2.04)0.34 (0.10–1.20)0.32 (0.08–1.26)0.21 (0.04–1.22)0.04 n > 251(Ref)1.29 (0.75–2.20)1.15 (0.64–2.09)0.59 (0.29–1.20)0.18 (0.06–0.49) < 0.01Diabetes n(%)0.96 No1(Ref)1.30 (0.71–2.37)1.10 (0.56–2.16)0.60 (0.27–1.34)0.25 (0.08–0.73)0.01 Yes1(Ref)0.78 (0.37–1.65)0.64 (0.27–1.53)0.43 (0.16–1.16)0.11 (0.02–0.50) < 0.01Hypertension n(%)0.52 No1(Ref)1.35 (0.70–2.61)0.75 (0.35–1.64)0.53 (0.22–1.30)0.12 (0.03–0.52) < 0.01 Yes1(Ref)0.90 (0.46–1.75)0.98 (0.48–1.99)0.48 (0.21–1.14)0.21 (0.07–0.63) < 0.01Stroke n(%)0.79 No1(Ref)1.12 (0.68–1.84)0.77 (0.43–1.37)0.53 (0.28–1.02)0.17 (0.07–0.42) < 0.01 Yes1(Ref)1.11 (0.26–4.69)2.00 (0.45–8.82)0.52 (0.06–4.23)0.18 (0.01–3.46)0.40Smoking status n(%)0.54 Never smoker1(Ref)0.71 (0.37–1.37)0.41 (0.18–0.91)0.20 (0.07–0.53)0.07 (0.02–0.27) < 0.01 Former smoker1(Ref)1.51 (0.66–3.50)1.98 (0.84–4.66)1.22 (0.46–3.27)0.33 (0.07–1.53)0.38 Current smoker1(Ref)2.58 (0.59–11.23)1.09 (0.21–5.54)0.76 (0.13–4.64)0.31 (0.04–2.57)0.13Drinking status n(%)0.95 Never drinker1(Ref)0.95 (0.46–1.99)1.02 (0.42–2.47)0.21 (0.04–1.10)0.55 (0.11–2.74)0.23 Former drinker1(Ref)1.19 (0.63–2.25)0.90 (0.45–1.80)0.62 (0.29–1.33)0.15 (0.05–0.43) < 0.01 Current drinker1(Ref)1.69 (0.26–10.75)0.86 (0.11–6.40)0.62 (0.05–7.19)0 (0-Inf)0.19Vigorous physical activity n(%)0.57 No1(Ref)1.07 (0.66–1.73)0.84 (0.48–1.45)0.59 (0.31–1.11)0.17 (0.06–0.46)0.01 Yes1(Ref)2.50 (0.20–30.53)1.76 (0.13–23.32)0.41 (0.03–6.61)0.20 (0.01–4.01) < 0.01Total energy intake levels (kcal)0.25 n ≤ 15401(Ref)0.66 (0.32–1.37)0.84 (0.36–2.00)0.87 (0.32–2.34)0.21 (0.04–1.16)0.28 1540 < n ≤ 22061(Ref)3.06 (1.24–7.51)1.71 (0.63–4.66)1.38 (0.43–4.44)0.52 (0.10–2.63)0.32 n > 22061(Ref)1.00 (0.34–2.91)0.56 (0.19–1.64)0.13 (0.04–0.48)0.06 (0.01–0.28) < 0.01Total protein intake levels (gm)0.85 n ≤ 57.791(Ref)0.80 (0.39–1.62)1.21 (0.52–2.83)0.87 (0.29–2.60)0.39 (0.07–2.24)0.62 57.79 < n ≤ 87.361(Ref)1.75 (0.76–4.05)0.88 (0.35–2.22)0.44 (0.15–1.29)0.11 (0.02–0.55) < 0.01 n > 87.361(Ref)0.98 (0.32–3.04)0.67 (0.22–2.06)0.38 (0.11–1.28)0.13 (0.03–0.61) < 0.01Total sugars intake levels (gm)0.70 n ≤ 65.821(Ref)0.96 (0.45–2.04)1.07 (0.45–2.54)1.21 (0.44–3.34)0.11 (0.01–0.99)0.39 65.82 < n ≤ 115.171(Ref)0.81 (0.35–1.86)0.72 (0.29–1.80)0.24 (0.08–0.72)0.08 (0.02–0.39) < 0.01 n > 115.171(Ref)2.47 (0.91–6.70)0.98 (0.32–2.99)0.37 (0.10–1.39)0.29 (0.06–1.28) < 0.01Total fat intake levels (gm)0.47 n ≤ 52.931(Ref)0.84 (0.42–1.69)0.71 (0.30–1.67)0.69 (0.25–1.88)0.15 (0.03–0.85)0.10 52.93 < n ≤ 86.141(Ref)1.08 (0.43–2.72)1.32 (0.50–3.49)1.00 (0.32–3.07)0.26 (0.05–1.29)0.25 n > 86.141(Ref)1.71 (0.61–4.79)0.71 (0.23–2.14)0.21 (0.06–0.76)0.11 (0.02–0.53) < 0.01Adjusted for age, gender, race, income, education, BMI, smoking status, drinking status, diabetes, hypertension, stroke, vigorous physical activity, total energy intake, total protein intake, total sugars intake, and total fat intake. “ P for trend” is mainly used to test whether there is a certain linear change trend between the change in the exposure variable of HGS and the change in the outcome variable of HF among different subgroups. Interaction refers to the situation where the effect of one risk factor (A) on a certain disease outcome is different across strata of another risk factor (B), or vice versa. This means that if the interaction between A and B is present, A and B are not independent in causing a certain disease. “ P for interaction” is mainly used to test whether the negative association between HGS and HF remains constant throughout all age groups. A P for interaction > 0.05 represents no interaction. ## Discussion We comprehensively analyzed the covariates that may obstruct the identification of the association between HGS and HF, based on existing literature, clinical experience, and NHANES data. In this cross-sectional study, we revealed a negative association between HGS and HF in US adults older than 45, independent of age, gender, race, income, education, BMI, smoking status, drinking status, diabetes, hypertension, stroke, vigorous physical activity, total energy intake, total protein intake, total sugars intake, and total fat intake. No interactive role was found in the association between HGS and HF, suggesting that the above conclusions remained stable in the different subgroups. Given the sensitivity to physiological changes, HGS was used as a valid marker of muscle function36. Although HGS has been found to be associated with mortality and the incidence of some cardiovascular diseases, the relationship between HGS and HF remains unclear37. Some studies have supported that HGS has no effect on the incidence of HF15,16. However, similar to our conclusion, a cohort study from England suggested that participants with a higher HGS had a lower incidence of HF. This conclusion was more obvious among participants aged > 65 years than among those aged ≤ 65 years in the subgroup analysis. However, the interaction between age and HGS for HF was not statistically significant38. The results of other studies based on the UK Biobank and Swedish National Inpatient Registry also revealed that objective measurements of HGS are strongly and independently associated with a lower HF incidence39,40. These conflicting conclusions may be attributed to the heterogeneity among these studies, including differences in participant selection, study size, study designs, and controlled covariates. Based on previous literature, our study excluded groups with depression and cognitive impairment, fully considered confounding factors, and strictly limited the age of the included population, making the conclusion reliable and filling in the gaps of current research from different perspectives. Currently, no conclusive statement can be made about how muscle function decline could lead to the incidence of HF. From the existing literature, we speculate that the possible mechanism is as follows. First, inflammation and oxidative stress might be an underlying mechanism for both muscle function decline and HF41. Inflammatory cytokines could alter blood vessel dynamics, which might result in alterations to muscle metabolism and muscle loss. For example, Wingless/Integrated (Wnt) signaling pathway molecules were found to play a critical role in tissue-specific stem cell aging and an increase in tissue fibrosis with age, involved in both calcification and loss of muscle mass, have been proposed as potential mediators42,43, In addition, the oxidative stress theory of aging suggested that age-associated functional losses were closely related to the accumulation of reactive oxygen and species (ROS)-induced damages37. Oxidative stress is involved in several age-related conditions including muscle function decline and HF44,45 The mechanism may be related to mitochondrial dysfunction leading to limited oxygen availability and subsequent reliance on anaerobic metabolism46,47. Second, apoptosis may be another important underlying mechanism. Several apoptotic pathways have been linked with age-related muscle function48 and a higher frequency of myonuclear apoptosis has also been found in the muscle of patients with HF relative to age-matched healthy controls49. Thirdly, abnormal glucose metabolism may be a common risk factor for HF and muscle function decline. Considering that skeletal muscle is the main site for insulin-mediated glucose disposal and that insulin resistance is strongly associated with HF, it could be hypothesized that insulin resistance plays a main role in both HF and muscle function decline50. In addition, it has been reported that muscular strength was demonstrated to be associated with a reduced risk of long-term development of diabetes mellitus51, which is known to be a major risk factor for the development of HF52,53. Finally, muscle acted as a paracrine and exocrine organ, and the myokines may act in an autocrine, paracrine, and endocrine manner. The release of myokines from skeletal muscle preserves or augments cardiovascular function, in the meanwhile, increased muscle strength may provide capabilities for more active lifestyles that are related to a lower HF risk40. To the best of our knowledge, our study is the first to provide evidence that HGS is negatively associated with HF among American middle-aged and elderly adults after properly identifying and adjusting covariates. The data collected in the NHANES is carried out following standardized protocols, and the NHANES is designed to provide nationally representative estimates. Therefore, the current findings have ideal generalizability. It is helpful for clinicians to identify groups at high risk for HF. To get higher-quality evidence, in the future, we intend to perform a cohort study utilizing local medical resources as well as a systematic review or meta-analysis to further investigate the association between HGS and HF. There are also some limitations in our study. First, limited by the cross-sectional study design, this study had less power regarding the determination of causal relationships between HGS and HF. Second, since the study was conducted in a population of middle-aged and elderly individuals, the findings are only generalizable to relatively healthy adults. Third, although the NHANES considerably enhanced the reliability of the questionnaire survey by developing strict protocols, regular training of investigators, and other measures, recall bias, and self-report bias were still inescapable54. Fourth, while we controlled for a broad range of lifestyle and health-related factors, correcting for possible covariates remains challenging. Due to the limitations of the database, additional data on HF were not available to further stratify the patients. For example, ejection fraction, disease course, NT-proBNP, BNP, cardiac troponin, electrocardiogram, cardiac imaging data, and other diagnostic, and therapeutic indicators. In future studies, researchers should fully consider the above defects to provide higher-quality medical evidence. ## Conclusions Overall, this cross-sectional study indicated that HGS was negatively associated with HF. This conclusion remained stable in participants aged ≥ 45 years, with different genders, races, incomes, education, BMI, smoking status, drinking status, diabetes status, hypertension status, stroke status, vigorous physical activity, total energy intake, total protein intake, total sugars intake, and total fat intake. 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--- title: Probing the behavior and kinetic studies of amphiphilic acrylate copolymers with bovine serum albumin authors: - Shehla Mushtaq - Muhammad Asad Abbas - Habib Nasir - Azhar Mahmood - Mudassir Iqbal - Hussnain A. Janjua - Nasir M. Ahmad journal: Scientific Reports year: 2023 pmcid: PMC10027669 doi: 10.1038/s41598-023-27515-5 license: CC BY 4.0 --- # Probing the behavior and kinetic studies of amphiphilic acrylate copolymers with bovine serum albumin ## Abstract This article presents that acrylate copolymers are the potential candidate against the adsorption of bovine serum albumin (BSA). A series of copolymers poly(methyl methacrylate) (pMMA), poly(3-sulfopropyl methacrylate-co-methyl methacrylate) p(SPMA-co-MMA), and poly(dimethylaminoethyl methacrylate-co-methyl methacrylate) p(DMAEMA-co-MMA) were synthesized via free radical polymerization. These amphiphilic copolymers are thermally stable with a glass transition temperature (Tg) 50–120 °C and observed the impact of surface charge on amphiphilic copolymers to control interactions with the bovine serum albumin (BSA). These copolymers pMD1 and pMS1 have surface charges, − 56.6 and − 72.6 mV at pH 7.4 in PBS buffer solution that controls the adsorption capacity of bovine serum albumin (BSA) on polymers surface. Atomic force microscopy (AFM) analysis showed minimum roughness of 0.324 nm and 0.474 nm for pMS1 and pMD1. Kinetic studies for BSA adsorption on these amphiphilic copolymers showed the best fitting of the pseudo-first-order model that showed physisorption and attained at 25 °C and pH 7.4 within 24 h. ## Introduction Protein interaction and adsorption on the surface of polymeric materials affect the functionality of materials and their performance. Protein adsorption on polymeric materials has biological applications and demand while the dispersal of these proteins played a substantial role in the biological response1–4. Morphology and characteristics of the material surface control the deposition and film formation with intervening protein5–8. Poly methyl methacrylate with its unusual physicochemical qualities and structural characteristics has attracted a lot of interest as a building block for the production of hybrid polymeric materials9. PMMA is utilized in a variety of medical devices, including blood pumps and dialyzers, due to its superior biocompatibility, hemocompatibility, and simplicity of manipulation10. The glass transition temperature (Tg) of MMA after copolymerization with hydrophilic monomers, as well as hydrogen-bonding interactions between these two monomer segments, improved its thermal stability11,12. These thermally stable amphiphilic copolymers are useful in controlling protein adsorption and can be helpful in degrading the analytical performance of devices and body implants13. Designing material for protein adsorption, on the other hand, is significantly more difficult, thus the adsorbent material should be thoroughly defined14. The conversion of hydrophobic polymers to amphiphilic and hydrophilic is a common, albeit non-restrictive theme among the strategies of resistance towards protein adsorption15,16. Biological molecules interaction and film formation on materials controlled via charge-like proteins undergo a conformational change that associate with material and its amphiphilic, hydrophobic and hydrophilic domain17–19. BSA protein is highly demanded by researchers and frequently used because of its high purity and water solubility20. BSA has been the most studied protein in adsorption–desorption process as a function of charge, protein concentration, pH, substrate charge and polarization potential, permitting studies in the presence and absence of charge21,22. Bovine serum albumin (BSA) has poorer internal stability than other proteins, it was selected as the model protein for the adsorption studies23. Higher internal stability proteins exclusively adsorb onto hydrophilic surfaces via electrostatic interactions, whereas lower internal stability proteins adsorb onto any surface independent of electrostatic connections24. However, because of the complex structure of protein and the sort of interaction it involves, the surface quality of the adsorbent material is critical25. Inspired by these properties to control protein adsorption on the surface of polymers we synthesized the amphiphilic copolymers changing the concentration of different monomers. These acrylate copolymers showed good antifouling properties through controlling their surface energies as reported in our previous work26–30. Amphiphilic acrylate copolymers are obtained through direct polymerization of hydrophobic monomers with hydrophilic monomers by radical polymerization31,32. Zho et al. synthesized a number of amphiphilic copolymers by radical polymerization of 2-hydroxyethyl methacrylate and 2-perfluorooctylethyl. These polymers have better antifouling properties as compared to homopolymers when they have 4–7 and 4–$14\%$ hydrophobic and hydrophilic groups on their surface33. Yufei Wang et al. and Befeng et al. have studied antifouling and adsorption properties but through the modification of polymeric surfaces by embedding different groups16,22. In this study, intrinsic amphiphilic copolymers composed of hydrophobic and hydrophilic acrylate monomers that were synthesized by single-step free radical polymerization. A series of amphiphilic compositions was prepared by varying the monomers ratio of SPMA, DMAEMA and MMA in copolymers. The synthesis of homopolymer and copolymers was confirmed by FTIR and 1H-NMR while thermal stability of the polymers was examined by TGA and DSC. In this study bovine serum albumin protein was selected as a model protein to check the adsorption on the surface of polymers. Protein film formation on the surface of these copolymers was characterized by SEM and AFM. Zeta potential of copolymers was performed to measure surface charge of amphiphilic copolymers at polymers constant temperature 25 °C and pH 7.4. Protein adsorption on the surface of polymers was physiosorption that confirmed after obeying pseudo first order kinetics, adsorption capacity (Qo) was found maximum, 9 mg/g for pMMA and minimum 0.9 mg/g for pMD1 through UV–Vis spectroscopy. ## Materials All chemicals were of analytical grade and used in chemical synthesis without further purification. Dimethyl aminoethyl methacrylate (DMAEMA, $98\%$) (Sigma-Aldrich, Germany), methyl methacrylate (MMA, $99\%$) (Sigma, USA), 3-sulfopropyl methacrylate (SPMA $99\%$) (Sigma-Aldrich, Germany) 2,2-azobisisobutyronitrile (AIBN, $98\%$) (Sigma, USA), N,N-dimethyl formamide (DMF, $99\%$) (Aldrich, USA), ethanol, phosphate buffer solution (PBS) (Amersco, Belgium) and bovine serum albumin (BSA) (Aldrich, USA). ## Method Both the homopolymer and copolymers were synthesized through free radical polymerization with 62–$65\%$ yield26,30. ## Synthesis of homopolymer pMMA was synthesized under inert atmosphere via free radical polymerization in DMF solvent in the presence of 2,2′-azobisisobutyronitrile (AIBN) as initiator at 70 °C. Molar ratios of monomer, solvent and initiator is 10:100: 0.02. MMA (10 g, 99.9 mmol), were introduced in the reaction flask. The polymerization process was allowed to run for 5 h with continuous stirring under nitrogen purging through the Schlenk line at 70 °C. ## Synthesis of copolymers Synthesis of amphiphilic copolymers of p(MMA-co-SPMA) and p(MMA-co-DMAEMA) was done by free radical polymerization. Copolymerization was done by using the molar ratio of 10:100:0.02 of monomer:solvent:initiator in inert atmosphere at 70 °C in the reaction flask (Fig. 1). Polymerization was done with continuous nitrogen gas purging and stirring for 5 h.Figure 1Synthesis of (A) pMMA; (B) p(DMAEMA-co-MMA) and (C) p(SPMA-co-MMA). ## Instrumentations used for characterization A Bruker ALPHA-P FTIR equipment was utilized to identify the functional groups. Bruker, ASCEND 400-MHz NMR spectrometer was utilized to collect 1H-NMR spectra30. TGA was done by using a Mettler Toledo STARe thermogravimetric system in a N2 atmosphere throughout a temperature range of 0–550 °C at a scan rate of 10 °C/min and 10–12 mg polymer samples were used. DSC was performed by a Mettler Toledo DSC STAR system under nitrogen purge and hermetic pans were used with sample weight of 10–12 mg. A Jasco UV–Vis (model V-530) spectrophotometer was used to measure the concentration of BSA in solution. For UV phosphate buffer solution was used to maintain pH 7.4, polymer and BSA ratio were taken 6 g/L and 0.60 g/L. Systronic microprocessor pH meter (model l-362) was used for the measurement of pH. Scanning electron microscope (JSM-7500F, JEOL Ltd., Japan) operated in secondary electron mode at a 5 kV acceleration voltage was used to examine the surface morphology of polymers. Before scanning electron microscopy (SEM) samples were dried and there was no moisture. Sample for SEM prepared through platinum sputtering and then analyzed for morphology after adsorption. Atomic force microscopy, AFM (Asylum—Cypher™ AFM) was used to measure the roughness caused by BSA adsorption. All samples with size 1 × 1 used and samples were dried before performing AFM. Zeta plus analyzer (Malvern) was used to measure the zeta potential of polymer solutions with concentration of 0.02 wt% in water by electrophoresis light scattering at pH 7.4 and 25 °C. ## Adsorption of BSA The BSA protein adsorption studies were performed in a batch process of adsorption for specific contact time at 25 °C at pH 7.4 in phosphate buffer solution. Here polymer and BSA ratio were taken 6 g/L and 0.60 g/L, after centrifugation the solvent was filtered and then analyzed using the Lowery technique. The adsorption efficiency of the prepared materials was calculated using the following equations:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left(qe\right)= \frac{[\left(Co-Ce\right) x V}{(m)}$$\end{document}qe=[Co-CexV(m)2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{Ce}{Qe} = \frac{1}{{K}_{L}x {q}_{m}} + \frac{Ce}{{q}_{m}}$$\end{document}CeQe=1KLxqm+Ceqm3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{log}\left(q\right)=\mathrm{log}\left({K}_{f}\right) + \frac{1}{n}(Ce)$$\end{document}logq=logKf+1n(Ce)where “qe” is the adsorption capacity of BSA (mg/g), “V” is the volume of solution (L), “m” is the mass of adsorbent (g), and “Co” and “Ce” are the initial and final BSA concentrations (mg/L), respectively. The process of adsorption has also been expressed by Langmuir and Freundlich relations [Eqs. [ 1] and [2]]. The logarithmic form of the Freundlich equation is expressed in Eq. [ 3], where “Kf” having unit (mg/g) is the Freundlich constant and “n” is the Freundlich exponent.4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{ln}\left({q}_{e} - {q}_{t}\right) = \mathrm{ln}\left({q}_{e}\right) - {K}_{1}t$$\end{document}lnqe-qt=lnqe-K1t5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{t}{{q}_{t}} = \frac{1}{{K}_{2}{q}_{e}^{2}} + \frac{1}{{q}_{e}}t$$\end{document}tqt=1K2qe2+1qet To explore BSA adsorption kinetics, pseudo first order and pseudo second order kinetics [Eqs. [ 4] and [5]] were also applied, whereas Eq. [ 6] was used to investigate the maximum adsorption. The BSA concentration of the adsorbent phase (qe, mg/g) was determined using,6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$qe=\frac{(Co-C)}{w}$$\end{document}qe=(Co-C)wwhere “qe” is equilibrium adsorption of BSA, “Ci” and “Ce” are initial and equilibrium concentration (mg/L), respectively, “V” is the volume of solution (L), and “W” is the weight of dry adsorbent (g)34. ## Ethical approval This study does not involve any experimentation related to vertebrates and invertebrates. ## Infrared spectroscopy FTIR analysis of the polymers pMMA, pMD1 and pMS1 is shown in Fig. 2. Here the band at 1433 cm−1 (spectrum A) was assigned to the asymmetric bending vibration of the pMMA CH3 group16. The absorbance value of 1381 cm−1 was caused by the deformation of OCH3 of pMMA30. Stretching and bending of C–O–C was represented by the typical signals detected at 1265 cm−1 and 1145 cm−1, respectively35. The band at 1193 cm−1 was due to stretching vibration of –OCH3 and vibrations of –CH2 at 977 cm−1 and 716 cm−1 were assigned for wagging and rocking modes of pMMA, respectively36.Figure 2FTIR spectra of polymers pMMA, pMD1 and pMS1. The copolymer pMD1 in spectrum B, contains the DMAEMA and MMA distinctive bands and for C-N, band at 1020 cm−1 assigned to stretching vibration of tertiary amine that confirms the presence of DMAEMA segment into copolymers35. MMA moiety for C=O bond belongs to ester that shows absorption band at 1730 cm−1, –CH2 group gives band at 1450 cm−1 for bending vibration30. The band at 3000–3500 cm−1 is owing to the O–H group due to moisture present in DMAEMA because of its hydrophilic nature as shown in Fig. 2. The stretching vibration at 749 cm−1 is due to –CH2 group and –SO3 showed asymmetric vibration of pMS1 in the spectrum C. The bands at 1354 cm−1 and 1145 cm−1 are due to C–O stretching vibration of the ester30,35. ## 1HNMR study of copolymers The 1HNMR spectrum (Fig. 3A) of the homopolymer of pMMA shows a signal at 3.6 ppm because of resonance of –OCH3 protons30.Figure 31HNMR spectra of polymers: (A) pMMA; (B) pMD1 and (C) pMS1. The 1HNMR spectrum of p(SPMA-co-MMA) is presented in Fig. 3B that demonstrates the peaks for chemical shifts related to variant protons surroundings in diverse chemical moieties enclosed in the copolymer37. The spectrum clearly shows MMA and SPMA signals with significant differences of protons dynamic mobility that validate structure of the copolymer and effect of the association process that displayed spectrum30. In the p(MMA-co-SPMA) copolymer the peaks at 4.1 ppm and 3.35 ppm correspond to the –OCH2 and OCH3 protons, respectively. The peak for –CH2 protons in the vicinity of -SO3K is observed at 2.5 ppm slightly upward for SPMA38. Furthermore, CH2SO3K group shows a signal at 2.72 ppm due to resonance and the peaks at 3.1–3.5 ppm show the indication of copolymer formation30. The methyl groups along the chains show peaks at 1.1 ppm and 1.8 ppm that are enclosed by the different environments. The composition of two monomers in the copolymer, p(MMA-co-DMAEMA) indicate peaks at 4.1 ppm (due to the –OCH2 protons in the DMAEMA moiety) and at 3.6 ppm (corresponding to the –OCH3 protons in the MMA moiety) (Fig. 3C)39. In the copolymers, compositions of the monomers were calculated by division through peak intensities at 2.5 ppm for protons of –OCH2 group of DMAEMA to the peak intensity at 2.3 ppm for protons of –OCH3 group of MMA. In the diblock the peaks are observed at 4.1 ppm for the protons of N-CH2 and for the protons of tertiary amine, –N(CH3)2 at 3.59 ppm40. ## Thermal analysis of polymers The findings of thermal analysis of pMMA, p(MMA-co-DMAEMA) and p(MMA-co-SPMA) by TGA are shown in Fig. 4A. In this thermal study temperature range was up to 550 °C at which pMMA was decomposed leaving no residual weight. The pyrolysis of the p(MMA-co-SPMA) and p(MMA-co-DMAEMA) gave small yields of residual weight. The pyrolysis of the p(MMA-co-SPMA) and p(MMA-co-DMAEMA) gave small yields of residual weight. These polymers showed weight loss in different stages and different temperatures between 150 and 470 °C41. pMMA showed first decomposition of $10\%$ at 280 °C, $40\%$ at 380 °C and $100\%$ decomposition at 440 °C. Some research has been conducted to explore the thermal degradation of pMMA, which indicates degradation in the first stage between 200 and 280 °C and this was happened because of head to head scission of linkages. The scission in the second stage (300–375 °C) of the unsaturated chain ends due to weight loss and disproportionation occurs $60\%$41.Figure 4TGA(3A) & DSC (3B) analysis of polymers: pMMA, pMD1, pMD2, pMS1 and pMS2. The copolymer of p(MMA-co-DMAEMA) start to melt at temperature of 100 °C and could be attributed to the decomposition with the loss of functionality of end groups above 100 °C. Here 30–40 % weight loss between 220 °C and 270 °C is significantly more than expected due to the end group loss, as stated in previous literature41. At 440 °C, copolymers pMD1 and pMD2 lost 75 percent of their weight, demonstrating that copolymers are more stable than homopolymers. The decomposition of p(MMA-co-SPMA) happens in two stages, the first of which occurs in the temperature ranging from 320 to 360 °C and corresponds to the decomposition of the polymer caused by the breaking of weak head-to-head links, resulting in a $10\%$ weight loss. The second phase occurs in the 360–440 °C range due to the breakdown of the unsaturated polymer chain ends caused by the disproportionation termination reaction. According to Kashiwagi et al., the first step could be attributable to the breakdown of the unsaturated polymer chain ends42. The second stage at 380 °C is most likely related to the random breakdown of the polymers major chains. In p(MMA-co-SPMA) copolymers, $60\%$ weight loss was reported, with beginning points of weight loss appearing at higher temperatures than in pMMA, and thermal degradation occurs in two phases. Further homopolymer and copolymers were characterized for thermal properties by DSC as shown in Fig. 4B, DSC thermograms of pMMA, p(MMA-co-DMAEMA) and p(MMA-co-SPMA) copolymers. The glass transition temperature of pure pMMA is 120 °C, which is consistent with the reported Tg value11,43. On copolymerization of MMA with DMAEMA and SPMA obtained single glass transition (Tg) point that indicate copolymers are homogeneous. Lower Tg recommend the increase in the segmental mobility of the DMAEMA chains in p(MMA-co-DMAEMA). pMD1 and pMD2 had Tg, 50 °C and 65 °C with varying concentration of DMAEMA and MMA. On the other hand, pMS1 and pMS2 showed greater Tg and endothermic flow of heat around 110–117 °C that showed rigidity of copolymers with increasing content of 3-sulfopropyl methacrylate content because of introduction of sulfonyl groups in the copolymers. SPMA concentration into the copolymers enhance the bonding and attraction between the chains of polymer that increase the crystallization and stability of polymers as shown in Table 1.Table 1TGA and DSC of copolymers. SamplesStages of TGA-weight loss temperature (°C)Tg (°C)1st stage2nd stage3rd stagepMMA200–280280–380380–480120p(MMA-co-SPMA)320–360360–440–110–117p(MMA-co-DMAEMA)150–200240–360370–43550–65 ## Zeta potential of copolymers Figure 5 shows the zeta potential curves of the pMMA, pMD1, pMD2, pMS1 and pMS2 in buffer solution at pH 7.4 and 25 °C. Pure pMMA shows the zeta potential of 18 mV which implies that pMMA at ionic strength of 0.01 at pH 7.4 is negatively charged44. The copolymers of pDM1 and pDM2 show the zeta potential of − 56.1 and − 45.4 mV that indicates the decrease in zeta potential with the increase in the concentration of DMAEMA (Table 2). DMAEMA is pH responsive and normally it has positive charge due to the amino group. This explains the solubility of DMAEMA in water, as the concentration increases, the polymer chains get more dissociated. pSM1 and pSM2 showed the zeta potential of -72.2 and -56.6 mV and the increase in zeta potential was due to the increase in the concentration of 3-sulfopropyl methacrylate45.Figure 5Zeta potential of pMMA, pMS1, pMS2, pMD1 and pMD2.Table 2Concentration of monomers in amphiphilic copolymers, zeta potential (mV), roughness (nm) and BSA adsorption capacity (%).SamplesConcentration (g)Zeta potential (mV) at pH 7.4Roughness, R (nm)MMADMAEMASPMApMMA20––18 ± 16.85pMS110–10− 72.7 ± 10.324pMS2146− 56.6 ± 11.85pMD11010–− 56.1 ± 10.474pMD2146–− 45 ± 10.871 ## Morphology of the homopolymer and copolymers Copolymers, as opposed to the homopolymers, are chemically diverse which triggers different behavior for the adsorption of protein on the surface. The SEM images of pMMA, pMD1, pMD2, pMS1 and pMS2 of homopolymer and copolymers are shown in Fig. 6. Here SEM images, show the surface morphology of the pMMA and other copolymers of MMA with DMAEMA and SPMA after adsorption of BSA at 25 °C and pH 7.4. As pMMA has hydrophobic nature and showed greater BSA adsorption as compared to amphiphilic copolymers. In pMMA thick film of BSA is formed because of opposite charges on the surface of pMMA and BSA. On the other hand, amphiphilic copolymers pMD1, pMD2, pMS1 and pMS2 and BSA have negative charges and minimum adsorption was observed46.Figure 6SEM of polymers, (A) pMMA; (B) pMS1; (C) pMS2; (D) pMD1 and (E) pMD2. Here both copolymers pMS1 and pMD1 have comparatively high concentration of hydrophilic monomers (3-sulfopropyl methacrylate and dimethyl aminoethyl methacrylate) than pMS2 and pMD2 which showed low adsorption of BSA. These copolymers have sulfate and amino functional that control their amphiphilic character and interaction of BSA with the polymers26–30. ## AFM analysis of copolymers AFM analysis of the polymers was performed at pH 7.4 in order to observe the film formation upon the surface of the polymers. BSA adsorption on the surface of polymers was performed and pMMA showed maximum roughness of 6.85 nm and least roughness of 0.374 nm and 0.474 was observed in pMS1 and pMD1, respectively (Table 2). pMMA has positive charge while pMD1, pMS1 and BSA have negative charges as observed through zeta potential. Because of similar charges amphiphilic copolymers showed minimum adsorption of BSA on the surface of polymers (Fig. 7).Figure 7AFM analysis of (A) pMMA; (B) pMS1; (C) pMS2; (D) pMD1 and (E) pMD2. Protein adsorption is directly related to the hydrophobic property of the material that reverses to the hydrophilic character of polymers. This observation supports the prior findings on homogeneous polymer surfaces that protein non-specific adsorption prefers more hydrophobic surfaces. This might be explained by the combined impact of the protein surface's amphiphilicity and the chemical heterogeneity of the copolymer interfaces. As a consequence, the total protein adsorption density increased with the density of chemical interfaces on the surface, and protein adsorbed on the pMMA surface was shown to be denser than that on the amphiphilic surfaces containing copolymers. ## Kinetic studies and adsorption modelling Adsorption kinetics for BSA was performed to check the adsorption phenomenon on the surface of polymers, Fig. 8A and B indicate the pseudo first and second order curves. The kinetic curves showed that initially the adsorption of BSA on the polymer was rapid that decreases with time. By comparing the R2 values of both pseudo first and second order for pMD1, pMD2, pMS1, pMS2 and pMMA, it is evident that all these polymers follow pseudo first order kinetics which means there is predominantly physisorption on the polymers. Moreover, the rate constant values k1 are very small in each polymer that indicates low adsorption possibility on the polymers. It is due to the antifouling behavior of these polymers as indicated in our previous studies28,30,35. These results are in accordance with the zeta potential values at pH 7.4 the BSA has a low negative charge as well as pMD1, pMD2, pMS1 and pMS2 also have a negative charge that causes repulsion to the incoming BSA molecules and results in low adsorption on the polymer surface. Figure 8(A) Pseudo 1st order, (B) Pseudo 2nd order rate curves obtained from adsorption of BSA on polymers, (C) Langmuir isotherm and (D) Freundlich isotherm. pMMA has low positive charge and due to attraction BSA adsorbs on pMMA, hence it shows higher adsorption than the other amphiphilic copolymers47–49. Therefore, by copolymerizing MMA with DMAEMA and SPMA, the charge on the polymer changed to negative that results in the rejection of BSA, hence increasing the antifouling characteristic. The process of adsorption has also been expressed by Langmuir and Freundlich relations [Eqs. [ 1] and [2]] in Fig. 8C and D50–52. According to the data and the regression coefficient values, it is clear that the polymers follow the Freundlich isotherm which means that the adsorption of BSA takes place as the multilayer. ## Effect of time and BSA adsorption Figure 9 represents the effect of time on the adsorption of BSA on polymers. Initially, there is a rapid adsorption of BSA onto the polymers. Adsorption greatly decreases as most of the adsorption sites were occupied initially, which lowers the adsorption of BSA and finally the steady state adsorption was achieved in 4 hrs20. Initially, 50–$55\%$ of BSA was rejected by the polymers, as time passed the adsorption sites were occupied and there was no further adsorption at the equilibrium so at this stage up to 80–$85\%$ of BSA was rejected. These results were significant as, 50–$55\%$ of BSA was rejected at the early stage that indicating the antifouling behavior of the polymers53. The maximum BSA adsorption, q in the case of pMD1 is 338.7, pMD2 is 331, pMS1 is 291, pMS2 is 301 and pMMA is 459 mg.g−1.Figure 9BSA adsorption on the surface of polymers with time. ## Conclusion In this study the homopolymer, pMMA and copolymers p(MMA-co-SPMA), p(MMA-co-DMAEMA) were synthesized with various composition. The copolymer p(MMA-co-SPMA) showed a better thermal stability than that of p(MMA-co-DMAEMA) and pMMA. Monomers concentration effected the thermal stability and glass transition temperature (Tg) of copolymers, pMS1 and pMS2 were endowed with a higher heat distortion resistance by the introduction of SPMA in pMMA. Zeta potential measurements were performed at pH 7.4 to recognize the protein interactions on surface of polymers at room temperature. SEM and AFM analysis showed significant deposition of BSA protein on the surface of pMMA as compared to amphiphilic copolymers. In this study all the polymers followed the pseudo first order rate that showed physiosorption and Freundlich isotherm that indicates the multilayer adsorption. The adsorption of BSA decreases with time and the maximum adsorption was found in pMMA, while the copolymers showed lower adsorption than the pMMA that implies the higher antifouling characteristic of copolymer than pMMA. ## References 1. 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--- title: Geriatric nutritional risk index as a prognostic marker for patients with upper tract urothelial carcinoma receiving radical nephroureterectomy authors: - Li-Wen Chang - Sheng-Chun Hung - Chuan-Shu Chen - Jian-Ri Li - Kun-Yuan Chiu - Shian-Shiang Wang - Cheng-Kuang Yang - Kevin Lu - Cheng-Che Chen - Shu-Chi Wang - Chia-Yen Lin - Chen-Li Cheng - Yen-Chuan Ou - Shun-Fa Yang journal: Scientific Reports year: 2023 pmcid: PMC10027676 doi: 10.1038/s41598-023-31814-2 license: CC BY 4.0 --- # Geriatric nutritional risk index as a prognostic marker for patients with upper tract urothelial carcinoma receiving radical nephroureterectomy ## Abstract To investigate the prognostic value of the geriatric nutritional risk index (GNRI) in patients with upper tract urothelial cell carcinoma (UTUC) receiving radical nephroureterectomy (RNU). Between January 2001 and December 2015, we enrolled 488 patients with UTUC underwent RNU in Taichung Veterans General Hospital. GNRI before radical surgery was calculated based on serum albumin level and body mass index. The malnutritional status was defined as GNRI < 92.0. Using Kaplan–Meier analyses and Cox proportional hazards models to analyze the risk factors on disease-free survival (DFS), cancer-specific survival (CSS) and overall survival (OS). 386 patients were categorized as normal nutritional status (GNRI ≥ 92) and 102 patients as malnutritional status (GNRI < 92). We used the receiver operating characteristic (ROC) curve for determined the association between GNRI and OS, with area under the curve (AUC) being 0.69. The 5-year survival rate of DFS, CSS and OS were $48.6\%$, $80.5\%$ and $80.5\%$ in the normal nutritional group and $28.0\%$, $53.2\%$ and $40\%$ in the malnutritional group. Using the multivariate analysis, malnutritional status was found as an independent risk factor for OS (hazard ratio [HR] = 3.94, $95\%$ confidence interval [CI] 2.70–5.74), together with age (HR = 1.04, $95\%$ CI 1.02–1.06), surgical margin positive (HR = 1.78, $95\%$ CI 1.13–2.82), pathological T3 (HR = 2.54, $95\%$ CI 1.53–4.21), pathological T4 (HR = 6.75, $95\%$ CI 3.17–14.37) and lymphovascular invasion (HR = 1.81, $95\%$ CI 1.16–2.81). We also found GNRI index as independent risk factor in DFS (HR = 1.90, $95\%$ CI 1.42–2.54) and CSS (HR = 5.42, $95\%$ CI 3.24–9.06). Preoperative malnutritional status with low GNRI is an independent marker in predicting DFS, CSS and OS in UTUC patients underwent RNU. ## Introduction Urothelial carcinoma (UC) is a urothelial originated malignant disease, involving mostly the low urinary tract (bladder and urethral) while upper tract urothelial carcinoma (UTUC) is relative uncommon, accounting for only 5–$10\%$ of UCs1. Compared with western countries, the incidence of UTUC in Taiwan is much higher due to arsenic water contamination, herb consumption and prevalence hemodialysis, constituting $40.2\%$ of all UCs2,3. Radical nephroureterectomy (RNU) with bladder cuff excision is the standard treatment for clinical localized UTUCs4. Despite staging and surgical refinements, oncology outcome after RNU remain unchanged over the past decades5. Tumor stage and grade are the main prognostic factors. Analyses of the SEER database on 5-year cancer specific survival (CSS) showed $86\%$ for T1N0, $77\%$ for T2N0, $63\%$ for T3N0 and $39\%$ for locally advanced6. Other tumor-related factors that affect oncology outcomes include variant histology, lymph node involvement, lymphovascular invasion, surgical margins, extensive tumor necrosis and hydronephrosis7. Patients’ factors such as comorbidity, American Society of Anesthesiologists (ASA), performance status (PS) and Charlson Comorbidity Index are also associated with survival outcome on top of the disease stage8,9. Malnutrition is a common problem in cancer patients that may progress to cachexia, leading to poor response to therapy, relative poor prognosis and lower quality of life10. The geriatric nutritional risk index (GNRI), which consists of serum albumin level and the ratio of actual and ideal body weights, is a simple and accurate screening method initially designed to predict outcomes in hospitalized elderly patients11. Low GNRI is associated with poor prognosis in many human malignancies regarding less treatment response and shorter survival time12. The index could also be used in predicting perioperative and oncological outcomes for patients with esophageal cancer, gastric cancer, colorectal cancer, bladder cancer and kidney cancer who have received definitive radical surgery13–17. In metastatic urothelial carcinoma, GNRI index is known to be a useful predictive biomarker for chemotherapy and immune checkpoint inhibitors and poor nutrition status associated with less treatment response and survival18–20. No study has yet been published regarding the association between the GNRI and localized UTUC. Here, we aim to investigate the impact of GNRI on survival outcomes of UTUCs receiving RNU. ## Patient selection This study was retrospective chart reviewed analysis and it was approved by the Institutional Review Broad of Taichung Veterans General Hospital (IRB No. CE13240A-3) and informed consents were obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations. From 2001 to 2015, 728 patients with pathological confirmed UTUC underwent RNU with bladder cuff excision at Taichung Veterans General Hospital. Initially, 520 patients with primary UTUC and available medical record were included in the study. 13 patients were excluded due to loss of follow-up within the first year after operation and 2 patients were excluded due to died related to surgery. 15 patients were excluded due to concurrent radical cystectomy and 2 patients were excluded due to no albumin report. Finally, 488 patients were enrolled in the analysis. RNU approached included traditional open nephroureterectomy through thoracoabdominal incision ($$n = 67$$), laparoscopic transperitoneal nephroureterectomy ($$n = 403$$) and retroperitoneoscopy nephroureterectomy ($$n = 18$$). We performed hilar lymph node dissection only in patients clinically suspicious lymph node metastasis before 2007. Since 2008, hilar lymph node dissection with or without regional lymph node dissection was routinely performed during RNU. The templates of regional lymph node dissection included para-aortic and peri-caval lymph node for renal pelvis and proximal ureter tumor and pelvic lymph node for distal ureter tumor. Adjuvant chemotherapies with cisplatin-based regimens were performed for those with advanced tumor feature (T$\frac{3}{4}$ or lymph node positive) but not routinely practiced according to clinicians preference21. Tumor staging followed the American Joint Committee on Cancer and the International Union for Cancer Control updated tumor-node-metastasis (TNM) cancer staging system22. Tumor grade was determined in accordance with the 2004 World Health Organization/International Society of Urologic Pathology consensus classification23,24. ## Surveillance protocol All patients were under periodic monitoring protocol: every 3 months during the first two years after operation, every 3 or 6 months during the third year in the case of no evidence of recurrence or progression. The follow-up protocol included laboratory studies, urine cytology, computed tomography (or magnetic resonance imaging) and cystoscope evaluation. ## The geriatric nutritional risk index The nutritional status with GNRI values was calculated as follows: GNRI = 1.489 × serum albumin level (g/L) + 41.7 × (actual body weight [kg]/ideal body weight [kg])25. The ideal body weight was identified as [height (m)]2 × 22 (kg/m2). The value of the actual body weight/ideal body weight was set to 1 when the actual body weight exceeded the ideal body weight. Malnutritional status was defined as a GNRI < 92.0, according to previous literature15,20. Patients were divided into either the normal nutrition group (GNRI ≥ 92.0) and malnutrition group (GNRI < 92.0). ## Patient characteristics Patient characteristics included the following: gender, age at operation, Eastern Cooperative Oncology Group (ECOG) performance status, albumin, hypertension, diabetes mellitus, coronary artery disease, Body Mass Index (BMI, kg/m2), smoking status, renal function, surgical modality, tumor location, surgical margin status, pathological TNM stage, tumor grade, concomitant carcinoma in situ (CIS), lymphovascular invasion and adjuvant chemotherapy. ## Outcome assessment and statistical analysis End point assessment included: Disease Free Survival (DFS), Cancer Specific Survival (CSS) and Overall Survival (OS), as counted from the date of the RNU. Receiver operating characteristic (ROC) curve was used to determine the cut-off value for overall survival using the Youden index. Mann–Whitney U test was used for continuous variables, and Pearson’s chi-squared test was used for categorical variables. The Kaplain − Meier survival curve and log-rank test was used to determine survival outcomes. For the association between the variables, we used univariate and multivariate Cox hazard regression models to analyze the hazard ratio (HR) and $95\%$ Confidence Interval (CI). Analyses were conducted using the Statistical Package for Social Sciences (SPSS), version 22.0. ## Ethics statement The studies involving human participants were reviewed and approved by certification at Taichung Veteran General Hospital, Taiwan, with Certification of approval with IRB: CE13240A-3. The patients/participants provided their written informed consent to participate in this study. ## Patient and characteristics A total of 488 patients were enrolled in the study.102 patients were in the malnutrition group (GNRI < 92) and 386 patients were in the normal nutrition group (GNRI ≥ 92) (Table 1). The median age was 70.0 years (range 63.8–76.0) in the malnutrition group, and 67.0 years (58.0–76.0) in the normal nutrition group ($$p \leq 0.023$$). The median GNRI was 86.8 (range 83.4–89.3) in the malnutrition group, and 101.3 (range 96.9–104.3) in the normal nutrition group ($p \leq 0.001$). There was no statistical difference between the two groups in terms of comorbidity, smoking status, preoperative renal function, history of uremia, surgical modality and tumor location. The median follow-up period was 23.2 months (range 11.3–36.1) in the malnutrition group and 41.2 months (range 27.0–65.0) in the normal nutrition group ($p \leq 0.001$).Table 1Demographic ($$n = 488$$).GNRI < 92 ($$n = 102$$)GNRI ≥ 92 ($$n = 386$$)P-valueGender0.846 Male44 ($43.1\%$)160 ($41.5\%$) Female58 ($56.9\%$)226 ($58.5\%$)Age70.0 (63.8–76.0)67.0 (58.0–76.0)0.023BMI (kg/m2)22.9 (19.9–24.7)24.2 (21.8–26.2) < 0.001Albumin (g/dL)3.1 (2.9–3.3)4.0 (3.8–4.3) < 0.001GNRI86.8 (83.4–89.3)101.3 (96.9–104.3) < 0.001*Performance status ECOG0.002** 014 ($13.7\%$)49 ($12.7\%$) 158 ($56.9\%$)279 ($72.3\%$) 2–430 ($29.4\%$)58 ($15.0\%$)ComorbidityHTN65 ($63.7\%$)233 ($60.4\%$)0.613DM27 ($26.5\%$)78 ($20.2\%$)0.217COPD/asthema5 ($4.9\%$)12 ($3.1\%$)0.369CAD6 ($5.9\%$)13 ($3.4\%$)0.252Creatinine > 1.5 mg/dL30 ($29.4\%$)99 ($25.6\%$)0.522HBV or HCV carrier14 ($13.7\%$)42 ($10.9\%$)0.531Previous UCUB21 ($20.6\%$)66 ($17.1\%$)0.501Hydronephrosis11 ($10.8\%$)39 ($10.1\%$)0.986Smoking status0.695 Never77 ($75.5\%$)284 ($73.6\%$) Current/former25 ($24.5\%$)102 ($26.4\%$)Preoperative renal function0.157 eGFR ≥ 30 ml/min/1.73m271 ($69.6\%$)295 ($76.4\%$) eGFR < 30 ml/min/1.73m231 ($30.4\%$)91 ($23.6\%$)History of uremia0.887 Negative86 ($84.3\%$)330 ($85.5\%$) Positive16 ($15.7\%$)56 ($14.5\%$)Surgical modality0.187 Open19 ($18.6\%$)48 ($12.4\%$) Transperitoneal laparoscopy78 ($76.5\%$)325 ($84.2\%$) Retroperitoneoscopy5 ($4.9\%$)13 ($3.4\%$)Tumor locationCalyx24 ($23.5\%$)90 ($23.3\%$)1.000Renal pelvis56 ($54.9\%$)241 ($62.4\%$)0.203Promixal ureter40 ($39.2\%$)120 ($31.1\%$)0.151Middle ureter29 ($28.4\%$)80 ($20.7\%$)0.126Distal ureter22 ($21.6\%$)97 ($25.1\%$)0.538Surgical margin0.026* Negative85 ($83.3\%$)353 ($91.5\%$) Positive17 ($16.7\%$)33 ($8.5\%$)Pathological T0.067 T146 ($45.1\%$)204 ($52.8\%$) T213 ($12.7\%$)46 ($11.9\%$) T330 ($29.4\%$)115 ($29.8\%$) T413 ($12.7\%$)21 ($5.4\%$)Pathological N < 0.001 N083 ($81.4\%$)362 ($93.8\%$) N15 ($4.9\%$)11 ($2.8\%$) N2–314 ($13.7\%$)13 ($3.4\%$)Tumor grade0.090 Low5 ($4.9\%$)40 ($10.4\%$) High97 ($95.1\%$)346 ($89.6\%$)Concomitant CIS0.082 Negative79 ($77.5\%$)329 ($85.2\%$) Positive23 ($22.5\%$)57 ($14.8\%$)Lymphovascular invasion0.001 Negative67 ($66.3\%$)314 ($81.3\%$) Positive35 ($34.3\%$)72 ($18.7\%$)Adjuvant Chemotherapy25 ($24.5\%$)92 ($23.8\%$)0.991F/u time (month)23.2 (11.3–36.1)41.2 (27.0–65.0) < 0.001*GNRI geriatric nutritional risk index, BMI body mass index, ECOG Eastern Cooperative Oncology Group, HTN hypertension, DM diabetes mellitus, COPD chronic obstructive pulmonary disease, CAD coronary artery disease, HBV Hepatitis B virus, HCV Hepatitis C virus, UCUB urothelial carcinoma in urinary bladder, eGFR estimated Glomerular filtration rate, CIS carcinoma in situ. Chi-square test. Mann–Whitney Test, Median (IQR). $P \leq 0.05$, *$P \leq 0.01.$ We hypothesized poor nutrition with low GNRI not only result from the patient himself but also the sequela of the more advanced malignant disease. In Table 1, we found that more advanced tumor in malnutrition group than in normal nutrition group such as pathological T stage ($$p \leq 0.067$$), pathological N stage ($p \leq 0.001$), positive surgical margin ($16.7\%$ vs. $8.5\%$, $$p \leq 0.026$$) and lymphovascular invasion ($34.3\%$ vs. $18.7\%$, $$p \leq 0.001$$). Additionally in supplementary Fig. 1 to 4, we found that high pathological T stage, high pathological N stage, lymphovascular invasion and surgical margin positive were associated with lower GNRI index score. ## GNRI cut-off value The ROC curve was plotted for GNRI as a predictive factor for OS and revealed the area under curve (AUC) was 0.69 with a cut-off value 93.58 months (Fig. 1a). When using the cut-off value as 93.58 months, the sensitivity was $48.33\%$ and the specificity was $83.15\%$ (AUC 0.657). When using the cut-off value as 92, the sensitivity was $43.33\%$ and the specificity was $86.41\%$ (AUC 0.649), respectively. We used Delong test to examine the two cut-off and showed no significant difference in predicting overall survival ($$p \leq 0.430$$) (Fig. 1b; Table 2). Thus, we think GNRI cut-off value 92 and 93.58 were both efficacy for patients with UTUC receiving RNU. Additionally, we used the cut-off value of 93.58 to categorize patient characteristics and their demographic as shown in Supplementary Table 1 and to exam the predict value in Fig. 3.Figure 1ROC for GNRI as a predictive factor for overall survival. The cut-off value was 93.58 with an AUC of 0.69 (sensitivity: 0.48, specificity: 0.83).Table 2Sensitivity and specificity of different GNRI cut-off value. VariablesAUC($95\%$ CI)DeLong test pSensitivity (%)Specificity (%)GNRI 920.649(0.61–0.69)0.43043.3386.41GNRI 93.580.657(0.61–0.70)48.3383.15Outcome: death. ## Overall survival We compared OS between the malnutrition and normal nutrition groups. Using GNRI 92 as cut-off value, the median OS was 30.16 months in the malnutrition group and the median OS could not be calculated due to half of patients were survive in the normal nutrition group ($p \leq 0.001$) (Fig. 2a). Using GNRI 93.58 as the cut-off value, the median OS was 34.83 months in patients with GNRI < 93.58 and half of patients were survived in GNRI ≥ 93.58 ($p \leq 0.001$) (Fig. 3a). Univariate and multivariate analyses with COX regression revealed GNRI < 92 being an independent risk factor for OS (HR = 3.94, $95\%$ CI 2.70–5.74, $p \leq 0.001$) (Table 3). We also found other independent risk factors for OS including the following: age (HR = 1.04, $95\%$ CI 1.02–1.06, $p \leq 0.001$), surgical margin positivity (HR = 1.78, $95\%$ CI 1.13–2.82, $$p \leq 0.013$$), pathological T3 (HR = 2.54, $95\%$ CI 1.53–4.21, $p \leq 0.001$), pathological T4 (HR = 6.75, $95\%$ CI 3.17–14.37, $p \leq 0.001$) and lymphovascular invasion (HR = 1.81, $95\%$ CI 1.16–2.81, $$p \leq 0.008$$).Figure 2Kaplan–Meier curve for UTUC patients comparing normal nutrition group and malnutrition group according to GNRI = 92. ( a) Overall survival, median 30.16 months in malnutrition group and half of patients were survive in normal nutrition group, $p \leq 0.001.$ ( b) Disease free survival, median 10.97 months in malnutrition group and 52.93 months in normal nutrition group, $p \leq 0.001.$ ( c) Cancer specific survival, median time were not calculated due to half of patients were survive in both group, $p \leq 0.001.$Figure 3Kaplan–Meier curve for UTUC patients according to cut-off value of GNRI by 93.58. ( a) Overall survival, median 34.83 months in GNRI < 93.58 and half of patients were survive in GNRI ≥ 93.58, $p \leq 0.001.$ ( b) Disease free survival, median 12.81 months in GNRI < 93.58 and 52.93 months in GNRI ≥ 93.58, $p \leq 0.001.$ ( c) Cancer specific survival, median time were not calculated due to half of patients were survive in both group, $p \leq 0.001.$Table 3Cox regression—overall survival. UnivariateMultivariableHazard ratio$95\%$ CIp-valueHazard ratio$95\%$ CIp-valueGenderFemaleReferenceReferenceReferenceReferenceMale1.44(1.01–2.06)0.0460.75(0.52–1.09)0.135Age1.03(1.02–1.05) < 0.0011.04(1.02–1.06) < 0.001GNRI ≥ 92ReferenceReferenceReferenceReference < 924.47(3.10–6.43) < 0.0013.94(2.70–5.74)< 0.001Performance status ECOG0ReferenceReference10.73(0.44–1.20)0.2092–41.43(0.82–2.51)0.209ComorbidityHTN1.09(0.75–1.58)0.647DM1.57(1.05–2.33)0.0270.92(0.60–1.41)0.699COPD/asthema1.75(0.72–4.30)0.220CAD1.89(0.92–3.87)0.082Creatinine > 1.5 mg/dL1.38(0.94–2.05)0.104Smoking statusNeverReferenceReferenceCurrent/former1.39(0.95–2.03)0.094Preoperative renal functioneGFR ≥ 30 ml/min/1.73m2ReferenceReferenceeGFR < 30 ml/min/1.73m21.42(0.96–2.10)0.077History of uremiaNegativeReferenceReferencePositive1.19(0.73–1.92)0.487Surgical marginNegativeReferenceReferenceReferenceReferencePositive4.62(3.05–7.02)< 0.0011.78(1.13–2.82)0.013Pathological TT1ReferenceReferenceReferenceReferenceT22.33(1.20–4.52)0.0131.51(0.77–2.99)0.232T34.19(2.64–6.64)< 0.0012.54(1.53–4.21) < 0.001T418.21(10.43–31.79)< 0.0016.75(3.17–14.37) < 0.001Pathological NN0ReferenceReferenceReferenceReferenceN13.15(1.53–6.50)0.0021.23(0.57–2.63)0.597N2–36.27(3.84–10.22)< 0.0011.08(0.56–2.09)0.811Tumor gradeLowReferenceReferenceReferenceReferenceHigh15.65(2.18–112.03)0.0067.22(0.99–52.96)0.052Lymphovascular invasionNegativeReferenceReferenceReferenceReferencePositive4.39(3.06–6.31) < 0.0011.81(1.16–2.81)0.008GNRI geriatric nutritional risk index, ECOG Eastern Cooperative Oncology Group, HTN hypertension, DM diabetes mellitus, COPD chronic obstructive pulmonary disease, CAD coronary artery disease, eGFR estimated Glomerular filtration rate. Cox proportional hazard regression. $p \leq 0.05$, *$p \leq 0.01.$ ## Disease free survival Using GNRI 92 as the cut-off value, the median DFS was 10.97 months in the malnutrition group, and 52.93 months in the normal nutrition group, ($p \leq 0.001$) (Fig. 2b). Using GNRI 93.58 as the cut-off value, the median DFS was 12.81 months in patients with GNRI < 93.58 and was 52.93 months in patients with GNRI ≥ 93.58 ($p \leq 0.001$) (Fig. 3b). Univariate and multivariate analyses with COX regression revealed GNRI < 92 as an independent risk factor of DFS (HR = 1.90, $95\%$ CI 1.42–2.54, $p \leq 0.001$) (Table 4). We also found other independent risk factors for DFS including the following: surgical margin positivity (HR = 1.68, $95\%$ CI 1.13–2.49, $$p \leq 0.010$$), pathological T4 (HR = 1.89, $95\%$ CI 1.06–3.37, $$p \leq 0.031$$) and lymphovascular invasion (HR = 1.67, $95\%$ CI 1.19–2.35, $$p \leq 0.003$$).Table 4Cox regression—disease free survival. UnivariateMultivariableHazard ratio$95\%$ CIp-valueHazard ratio$95\%$ CIp-valueGenderFemaleReferenceReferenceReferenceReferenceMale1.38(1.08–1.77)0.0111.22(0.94–1.58)0.131Age1.01(1.00–1.02)0.090GNRI ≥ 92ReferenceReferenceReferenceReference < 922.19(1.65–2.90)< 0.0011.90(1.42–2.54) < 0.001Performance status ECOG0ReferenceReference11.15(0.78–1.68)0.4842–41.35(0.86–2.11)0.193ComorbidityHTN1.02(0.79–1.31)0.898DM1.36(1.02–1.82)0.0361.19(0.88–1.61)0.271COPD/asthema2.12(1.21–3.71)0.0092.06(0.98–3.70)0.113CAD1.28(0.73–2.23)0.394Creatinine > 1.5 mg/dL1.28(0.97–1.69)0.078Smoking statusNeverReferenceReferenceCurrent/former1.13(0.86–1.50)0.372Preoperative renal functioneGFR ≥ 30 ml/min/1.73m2ReferenceReferenceeGFR < 30 ml/min/1.73m21.01(0.76–1.35)0.942History of uremiaNegativeReferenceReferencePositive1.08(0.77–1.53)0.644Surgical marginNegativeReferenceReferenceReferenceReferencePositive3.11(2.22–4.36)< 0.0011.68(1.13–2.49)0.010Pathological TT1ReferenceReferenceReferenceReferenceT21.35(0.89–2.02)0.1541.15(0.75–1.75)0.520T31.67(1.25–2.22)< 0.0011.14(0.82–1.60)0.436T44.46(2.94–6.76) < 0.0011.89(1.06–3.37)0.031Pathological NN0ReferenceReferenceReferenceReferenceN11.64(0.89–3.01)0.1100.99(0.53–1.86)0.984N2–32.90(1.90–4.44)< 0.0011.14(0.68–1.89)0.626Tumor gradeLowReferenceReferenceReferenceReferenceHigh2.37(1.35–4.14)0.0031.61(0.90–2.89)0.110Lymphovascular invasionNegativeReferenceReferenceReferenceReferencePositive2.53(1.94–3.31) < 0.0011.67(1.19–2.35)0.003GNRI geriatric nutritional risk index, ECOG Eastern Cooperative Oncology Group, HTN hypertension, DM diabetes mellitus, COPD chronic obstructive pulmonary disease, CAD coronary artery disease, eGFR estimated Glomerular filtration rate. Cox proportional hazard regression. $p \leq 0.05$, $p \leq 0.01.$ ## Cancer specific survival Using GNRI 92 as the cut-off value, the median CSS could not be calculated due to half of patients were survive in both group and showed significant difference ($p \leq 0.001$) (Fig. 2c). Similar result was found when using GNRI 93.58 as the cut-off value ($p \leq 0.001$) (Fig. 3c). Univariate and multivariate analyses with COX regression revealed GNRI < 92 as an independent risk factor of CSS (HR = 5.42, $95\%$ CI 3.24–9.06, $p \leq 0.001$) (Table 5). We also found other independent risk factors for CSS including the following: male gender (HR = 1.93, $95\%$ CI 1.15–3.23, $$p \leq 0.012$$), age (HR = 1.04, $95\%$ CI 1.01–1.07, $$p \leq 0.005$$), surgical margin positivity (HR = 2.16, $95\%$ CI 1.26–3.71, $$p \leq 0.005$$), pathological T3 (HR = 6.30, $95\%$ CI 2.57–15.43, $p \leq 0.001$) and pathological T4 (HR = 24.79, $95\%$ CI 8.21–74.87, $p \leq 0.001$).Table 5Cox regression—cancer specific survival. UnivariateMultivariableHazard ratio$95\%$ CIp-valueHazard ratio$95\%$ CIp-valueGenderFemaleReferenceReferenceReferenceReferenceMale1.90(1.19–3.03)0.0071.93(1.15–3.23)0.012Age1.03(1.00–1.05)0.0221.04(1.01–1.07)0.005GNRI ≥ 92ReferenceReferenceReferenceReference < 926.14(3.84–9.82)< 0.0015.42(3.24–9.06)< 0.001**Performance status ECOG0ReferenceReference10.48(0.27–0.88)0.5882–41.00(0.50–1.99)0.996ComorbidityHTN1.09(0.67–1.77)0.720DM1.05(0.59–1.86)0.867COPD/asthema1.66(0.52–5.28)0.390CAD1.52(0.55–4.16)0.418Creatinine > 1.5 mg/dL1.47(0.89–2.41)0.130Smoking statusNeverReferenceReferenceCurrent/former1.56(0.96–2.54)0.073Preoperative renal functioneGFR ≥ 30 ml/min/1.73m2ReferenceReferenceeGFR < 30 ml/min/1.73m20.79(0.44–1.42)0.435History of uremiaNegativeReferenceReferencePositive0.55(0.24–1.27)0.163Surgical marginNegativeReferenceReferenceReferenceReferencePositive8.31(5.15–13.41)< 0.0012.16(1.26–3.71)0.005Pathological TT1ReferenceReferenceReferenceReferenceT23.26(1.03–10.29)0.0442.38(0.72–7.81)0.154T310.21(4.54–22.96)< 0.0016.30(2.57–15.43)< 0.001T456.80(24.07–134.02) < 0.00124.79(8.21–74.87)< 0.001Pathological NN0ReferenceReferenceReferenceReferenceN16.23(2.93–13.24) < 0.0011.50(0.65–3.47)0.344N2–311.39(6.60–19.64) < 0.0011.23(0.59–2.55)0.577Tumor gradeLowReferenceReferenceReferenceReferenceHigh8.56(1.19–61.64)0.0331.84(0.23–14.52)0.565Lymphovascular invasionNegativeReferenceReferenceReferenceReferencePositive7.07(4.39–11.38) < 0.001*1.55(0.86–2.81)0.148GNRI geriatric nutritional risk index, ECOG Eastern Cooperative Oncology Group, HTN hypertension, DM diabetes mellitus, COPD chronic obstructive pulmonary disease, CAD coronary artery disease, eGFR estimated Glomerular filtration rate. Cox proportional hazard regression. $p \leq 0.05$, $p \leq 0.01.$ ## Patients age > 70 years old Because GNRI index was initially designed for elderly patients, survival analysis was performed in patients elder than 70 years old11. In Fig. 4, GNRI index < 92 was associated with shorter OS ($p \leq 0.001$, Fig. 4a), DFS ($p \leq 0.001$, Fig. 4b) and CSS ($p \leq 0.001$, Fig. 4c) in patients age > 70 years old. Figure 4Kaplan–Meier curve for overall survival in UTUC patients elder than 70 years old comparing normal nutrition group and malnutrition group according to GNRI = 92. ( a) Overall survival, median 25.36 months in GNRI < 92 group and it couldn’t be calculated due to more than half of them were survive in GNRI ≥ 92 group, $p \leq 0.001$ (b) Disease free survival, median 9.79 months in GNRI < 92 group and 63.44 months in GNRI ≥ 92 group, $p \leq 0.001$ (c) Cancer specific survival, median 37.09 months in GNRI < 92 group and it couldn’t be calculated due to more than half of them were survive in GNRI ≥ 92 group, $p \leq 0.001$. ## Perioperative complications Perioperative complications were no significant differences among the two groups. A total 11 complications ($13.7\%$) were found in malnutrition group, and 33 complications ($8.5\%$) in the normal nutrition group ($$p \leq 0.868$$) (Supplementary table 2). Among these complications, 3 vascular injuries ($2.9\%$) were in the malnutrition group, and 7 ($1.8\%$) in the normal nutrition group ($$p \leq 0.506$$). There were 3 wound infections ($2.9\%$) in the malnutrition group, and 8 ($2.1\%$) in the normal nutrition group ($$p \leq 0.162$$) and 8 cases of ileus ($7.8\%$) in the malnutrition group, and 18 ($4.7\%$) in the normal nutrition group ($$p \leq 1.000$$). ## Discussion Our principal finding is that preoperative GNRI, as a nutritional status evaluation tool, is an independent prognostic factor for UTUC patients receiving RNU. Age, surgical margin positive, pathological T stage and lymphovascular invasion are also independently affect the overall survival under a long-term follow-up. Literature suggests that GNRI index < 92 as clinical trigger for nutritional support in institutionalised elderly26. We also used GNRI < 92 to verify the prognostic value. Additionally, based on the ROC curve analysis in our study, the cut-off value 93.58 may be more sensitive and specific in predicting survival outcome, although cut-off value 92 may be more convenient to use in clinical practice. In our analysis, we found that low GNRI index was not only associated with individual physical condition, but also due to advanced tumor behavior. In the supplementary Fig. 1 to 4, we found that advanced pathological features including higher pathological T stage, pathological N stage, surgical margin positive and lymphovascular invasion had significant lower GNRI index value. As the result, this may explain why it was associated with poorer survival outcome. Malnutrition is a common problem in hospitalized elderly patients, and it is associated with their functional decline and higher mortality rate27. Malnutrition may be characterized by loss of muscle or fat mass causing body weight loss, which is common for cancer patients as the result of cachexia and is responsible for $22\%$ of deaths28. Pretreatment serum albumin levels provide useful nutritional assessment and prognostic significance for cancer patients. Serum albumin levels may drop due to tumor progression, immune response to tumor and anticancer therapy29. The nutritional risk index is first used to evaluate nutritional status and postoperative outcome which is calculated by albumin content, in terms of present body weight and usual body weight30. However, this index is not widely used because most elderly patients do not remember their usual body weight, and their weight loss may require correcting multiple contributing factors31. GNRI was proposed by Bouillanne et al., and with the usual body weight replaced by ideal body weight. It became a simplified and more convenient predictive tool11. Although albumin is a well-known index of nutrition status affecting wound healing and postoperative complication, it will be altered by digestive function and systemic inflammation32,33. In contrast, GNRI calculated by albumin, actual body weight and ideal body weight is more objective and easily determined and it was associated with risk of deaths in many human diseases such as diabetes mellitus, cardiovascular disease, end stage renal disease and cancers34. Several studies reported the value of GNRI in predicting oncologic outcome and comorbidity in cancer patients receiving curative surgical treatment. Kubo et al., found that low GNRI are associated with high incidences of preoperative dysphagia, postoperative lung complications and 5-year overall survival in esophagus cancer13. Similar to our finding, they found that incidence of nodal metastasis and pathological stage were significantly higher in the GNRI-low group than in the GNRI-high group which contributed to the inferior survival outcomes after esophagectomy. Most of reported literatures focused on the gastrointestinal tract malignancies. Hirahara et al. and Sasaki et al. also reported that GNRI is an independent prognostic factor for OS in gastric patients underwent laparoscopic gastrectomy and in colorectal cancer patients after curative surgery14,15. Significantly higher incidence of postoperative complications was found in low-GNRI group, including surgical site infection, ileus, anastomotic leakage, intra-abdominal abscess, colitis, pneumonia, and urinary infection. The difference of post operative complications was not seen in our population. The possible reason is that no intestinal reconstruction during RNU and anastomotic leakage may cause subsequent complications. Malnutrition is relative less common in genitourinary tract malignancy, and it may present as a sequela of advanced disease or paraneoplastic syndrome35. In a large-scale retrospective study, Kang et al. found that low values of GNRI are associated with aggressive pathologic characteristics and poor survival in patients with renal clear cell carcinoma who have nephrectomy17. Riveros et al. found that in bladder cancer patients receiving radical cystectomy, GNRI independently predicts mortality, blood transfusion, pneumonia, extended length of stay and non-home discharge16. Additional to survival and perioperative complications outcome, they also suggest that low GNRI was associated with extended length of hospital stay and nonhome discharge. Moreover, GNRI may being part of Enhanced Recovery after Surgery (ERAS) protocols for nutritional risk screening before radical cystectomy36. Our present study is the first to investigate the relationship between GNRI and survival outcomes in UTUC patients receiving RNU, not only elderly but also young age populations. It could be the sequela of advanced tumor stage causing cancer cachexia or paraneoplastic syndrome and advanced malignant features were associated with low GNRI score. Additionally in multivariate analysis, we confirmed GNRI is the independent risk factor for OS, CSS and DFS. Treatment protocol for UTUC changed over time. For example, whether performing template lymph node dissection during RNU was inconsistent in our populations. Meta-analysis for retrospective articles suggested templated-based lymph node dissection improve cancer specific survival in high-stage UTUC and reduces the risk of local recurrence37. Additionally, population-based cohort studies found that lymph node dissection improved survival outcomes not only in clinical lymph node negative but also pathological lymph node negative UTUC patients38,39. Nevertheless, template lymph node dissection was only routinely performed in our populations since 2008 and it may influence the survival outcome. Similarly, the POUT trial suggests that adjuvant gemcitabine-platinum combination chemotherapy significantly improved disease free survival40. However, only $23.9\%$ patients in our population received adjuvant chemotherapy and this may influence the conclusion of our analysis. Below are some limitations of our study. First, retrospective design had selection and information bias that had restricted the power of the prognostic role. Prospective cohort study is needed to overcome the limitations of the potential bias. Second, the surgical method is a possible confounder that impacts oncologic outcomes. The difference of surgical approach and template lymph node dissection may influence the outcome. Third, reports in the literature suggested that the nutrition status is associated with physical performance and the quality of life41. However, the research approach using questionnaires was not feasible due to the retrospective nature of our study. Finally, we did not assess the impact of neoadjuvant or adjuvant therapy which may have systemic impacts leading to malnutrition and influence the survival result. In the present era of using immune check-point inhibitors, further large scaled prospective cohort studies are needed to verify the association between GNRI and malignance. ## Conclusions Preoperative malnutritional status with low GNRI is an independent marker in predicting DFS, CSS and OS in UTUC patients underwent RNU. Age, surgical margin positivity, advanced tumor stage and lymphovascular invasion are also independent prognostic factors. ## Supplementary Information Supplementary Legends. Supplementary Figure 1.Supplementary Figure 2.Supplementary Figure 3.Supplementary Figure 4.Supplementary Tables. The online version contains supplementary material available at 10.1038/s41598-023-31814-2. ## References 1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J. Clin. (2021.0) 71 7-33. 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Role of surgical approach on lymph node dissection yield and survival in patients with upper tract urothelial carcinoma. Urol. Oncol. (2018.0) 36 e1-9.e9. DOI: 10.1016/j.urolonc.2017.09.001 40. Birtle A. Adjuvant chemotherapy in upper tract urothelial carcinoma (the POUT trial): A phase 3, open-label, randomised controlled trial. Lancet (2020.0) 395 1268-1277. DOI: 10.1016/s0140-6736(20)30415-3 41. Beberashvili I. Geriatric nutritional risk index, muscle function, quality of life and clinical outcome in hemodialysis patients. Clin. Nutr. (2016.0) 35 1522-1529. DOI: 10.1016/j.clnu.2016.04.010
--- title: High-resolution separation of bioisomers using ion cloud profiling authors: - Xiaoyu Zhou - Zhuofan Wang - Jingjin Fan - Zheng Ouyang journal: Nature Communications year: 2023 pmcid: PMC10027677 doi: 10.1038/s41467-023-37281-7 license: CC BY 4.0 --- # High-resolution separation of bioisomers using ion cloud profiling ## Abstract Elucidation of complex structures of biomolecules plays a key role in the field of chemistry and life sciences. In the past decade, ion mobility, by coupling with mass spectrometry, has become a unique tool for distinguishing isomers and isoforms of biomolecules. In this study, we develop a concept for performing ion mobility analysis using an ion trap, which enables isomer separation under ultra-high fields to achieve super high resolutions over 10,000. The potential of this technology has been demonstrated for analysis of isomers for biomolecules including disaccharides, phospholipids, and peptides with post-translational modifications. Ion mobility is used in mass spectrometers for structure analysis of biomolecules. Here, the authors show that ion mobility analysis in an ion trap under ultra-high fields enables isomer separation at resolutions over 10,000, wich they demonstrate for isomers of disaccharides, phospholipids, and peptides. ## Introduction Biomolecules, such as glycans, lipids, and peptides, play vital roles in biological systems1–3. They exist in forms of a wide variety of isomers or isoforms, which have identical chemical formulas and molecular weights but different structures and biological functions4. The glycans5–9 and lipids10–12 have complex configurations, while the structural characterization of peptides and proteins can be complicated with post-translational modifications (PTMs) and higher-order conformations, all resulting in a variety of isomers and isoforms13–15. The significance of identifying isomers is well recognized for almost all disciplinaries16. As one example, isomers such as cis or trans unsaturated fatty acids in food can have very different effects on human health17. Ionic forms of the molecules have been used as surrogates for species identification as well as structural characterization. While mass spectrometry (MS) is often used to obtain the compositions of molecular ions18–21, ion mobility (IM) has been widely employed for differentiation of the isomers and isoforms of biomolecular ions22–24. The combination of these two technologies in the form of IM-MS has been a major direction in mass spectrometry development during the last decade. IM techniques separate ions based on the mobility differences due to the collisions between the ions and the background neutral molecules. Taking the most classic method with drift-tube ion mobility as an example, it uses ion-neutral collisions in an electric field (with a field strength E) to identify the differences in collision cross-section (CCS), which is associated with the variations in structures of molecular ions. Applications of IM have led to the separation of the isomers or isoforms for disaccharides, phospholipids, and peptides with post-translational modifications25–27. The separation efficiency is affected by both the electric field E and the number density (N) of the collision gas. The mobility of the ions is the result of the balance between the motion driven by the electric field E and the collisions with the background gas molecules. Typically, IM is performed under a low electric field E/N < 30 Td (Townsend number, 1 Td = \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1\times {10}^{-21}$$\end{document}1×10−21 V m2)28, such as for implementations with drift time22, traveling wave29, trapped30, 31, and differential modes32. With a long separation time30 or path33–36, separation resolutions of several hundreds have been achieved (Supplementary Fig. 1 and Supplementary Table 1), which, however, is still inadequate for differentiating bioisomers of high structural complexities. Operating the IM at higher E/N ratios, for example, applying high-field asymmetric waveform at ~100 Td, shows enhanced isomer separation;37 however, researchers have not discovered an effective method for implementation with higher E/N ratios. The main challenge is to prevent the loss of the ions while they are driven by a high electric field through the collisions over a relatively long time or long path. Here, we report the development of ion cloud profiling technology to enable high-resolution IM analysis in a radiofrequency (RF) electromagnetic field with E/N > 1 MTd. Isomer differentiation was achieved at a resolution better than 10,000, which allowed us to attack some challenging issues in the structural analysis of biomolecules. ## Ion cloud profiling: instrumentation and performance The ion cloud profiling was performed in a dual-LIT (linear ion trap) miniature mass spectrometer modified from a Mini β instrument (PURSPEC Technology (Beijing) Ltd., Beijing China) (Fig. 1a), which is also capable of performing tandem MS analysis38. Ions of isomers were generated by a nano-electrospray ionization (nESI) source, mass selected in the LIT I, and transferred to the LIT II for final structural analysis (Fig. 1b). The physics employed here was that under forced oscillations, ion clouds of the isomeric ions were separated due to their difference of damping cross-sections (DCSs, Fig. 1c)39–41. For experimental implementations, an auxiliary alternative-current (AC) was employed for resonance excitation of the isomeric ions in the LIT; by scanning the AC voltages in different scan rates depending on the species, the isomeric ions with different DCSs were ejected sequentially according to the ion cloud profile sizes (Fig. 1c and Supplementary Fig. 2). When the AC scanning speed was below 5000 mV/s, a good correlation between AC ejection voltages and DCSs could be established and an ion cloud profiling spectrum with a resolution over 10000 (Fig. 1d) was produced. More specifically, the AC scanning speed was 53 mV/s for the analysis of disaccharides, and 88 mV/s for both phospholipids and peptides (Fig. 1d).Fig. 1Instrumental setup, principal, and performance characterization of the ion cloud profiling technology. Schematics of a the miniature MS system used in this work and b its key components for isomer structural analysis. c Simulated ion trajectories for two isomeric ions characterized by reduced damping coefficients: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}^{{\prime} }\,$$\end{document}b′= 0.0010 (blue) and 0.0012 (purple). Here, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}^{{\prime} }=2b/\varOmega m$$\end{document}b′=2b/Ωm, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varOmega$$\end{document}Ω is the angular frequence of the RF field, m is ion mass, b is the damping coefficient of the ions. Insets, zoom-in plots of the ion trajectories at the beginning [1] and ejection [2] stages of the AC excitation. Isomeric ions are ejected sequentially according to their DCSs when their oscillation amplitudes exceed the trap geometry, r0, as indicated by the blue dashed lines. d Ion cloud profiling spectra of three biomolecules, lactose (m/z 365, CCS 177.6 Å2), phosphatidylcholine (PC) 18:$\frac{1}{16}$:0 (m/z 761, CCS 296.2 Å2), and an acetylated peptide (m/z 542, CCS 357.9 Å2), superimposed in one spectrum. The CCS values of lactose and peptide are measured by timsTOF (Bruker Daltonics, Bremen, Germany). The CCS value of phosphatidylcholine is taken from Groessl’s work42. The resolution here is defined as, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R={V}_{{AC}}/\triangle {V}_{{AC}}$$\end{document}R=VAC/△VAC, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${V}_{{AC}}$$\end{document}VAC and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\triangle {V}_{{AC}}$$\end{document}△VAC are the AC ejection amplitude of analyte ions and the full width at half maximum (FWHM) of the peak, respectively43. ## Analysis of biomolecules To show the enhanced separation capability of the technology, we first analyzed glycans with complexed isomeric structures. Four disaccharides, including trehalose, maltose, cellose, and lactose, were considered. They present differences in the structure include composition, connectivity, and configuration (Fig. 2a). The disaccharides are composed of basic building blocks, the monosaccharides. Each monosaccharide contains multiple hydroxyl groups, which could be connected to form a glycosidic bond with another monosaccharide. Through linking different hydroxyl groups, carbohydrates normally have branched structures with diverse regiochemistry. In addition, each glycosidic bond formation is accompanied by creation of a stereocenter, because two monosaccharides could be connected via two different configurations. Using ion cloud profiling method developed here, the four isomeric disaccharides were baseline resolved (Fig. 2b) and the experimental results agreed well with the simulation (Supplementary Fig. 3). A mixture of lactose and cellose was also analyzed, by both IM through ion cloud profiling (Fig. 2c) and MS/MS analysis (Top, Fig. 2d). While MS/MS normally is powerful for distinguish isomers through characteristic fragmentation patterns, in this case identical patterns were observed for these two isomers. The results here suggest that the IM using ion cloud profiling technology is complementary to the MS or MS/MS analysis and has the potential to achieve high-resolution structural analysis of isomers, leading to isomeric-specific selection (Supplementary Fig. 4) and quantification analysis (Figs. 2e and 2f).Fig. 2Structural analysis of glycans.a Structures of four isomeric disaccharides, which have isomerization of composition, connectivity, and configuration in pairs. b Ion cloud profiling spectra of the four disaccharides: trehalose (red), maltose (orange), cellose (green), lactose (blue), and the mixture of these four (black). c Ion cloud profiling spectrum of lactose and cellose mixture. d Tandem MS spectra of lactose and cellose mixture (top), pure lactose (middle), and pure trehalose (bottom). Lactose (blue) and cellose (green) have identical mass to charge ratio, m/z 365, and fragment, m/z 305, in tandem MS spectra. e Calibration curves for pure lactose and cellose. For quantitative analysis of pure sample, concentrations varied from 5 μM to 50 μM. Each value represents the mean ± s.d. ( $$n = 10$$). f Calibration curves for the mixture of lactose and cellose. For quantitative analysis of mixture, concentration of cellose was 5 μM, and concentration ratios of lactose to cellose varied from 0.5 to 10. Each value represents the mean ± s.d. ( $$n = 15$$). Source data are provided as a Source Data file. To show the universality of the technology, we further demonstrated the structural separation of phospholipids (Fig. 3a) and peptides (Fig. 3e). Phospholipids consist of two fatty acyl chains, a phosphate head group, and a glycerol backbone, for which the isomerization arises from a number of variants in structure including sn- positions of the fatty acyl chains (Fig. 3b), C=C locations (Fig. 3c) and configurations (cis/trans, Fig. 3d) of C=C in each fatty acyl chain, derivatization sites, and etc. These structure features could be well characterized using the ion cloud profiling (Fig. 3b–d). For proteins and peptides, PTMs contribute significantly to the structure complexity. In this study, peptides SGKLRASHKG with methylation in K3 or K9 (Fig. 3f), acetylation in K3 or K9 (Fig. 3g), and phosphorylation in S1 or S7 (Fig. 3h) were analyzed for demonstration. As shown in Fig. 3f–h, the isomers of the peptides could all be clearly resolved in the ion cloud profiling spectra. This method has also been applied for distinguishing different conformations of protein ions, for which baseline separation was obtained for charge states of +9 and +13 of myoglobin as well as +12 and +13 for cycohcrome c (Supplementary Fig. 5).Fig. 3Structural analysis of lipids and peptides.a Structure of phospholipids with isomerization of sn position, carbon-carbon double bond location, and cis/trans structure due to the double bond. Ion cloud profiling spectra of b PC 18:1(9Z)/16:0 (blue), PC 16:$\frac{0}{18}$:1(9Z) (green), and their mixture (red); c PC 18:1 (11Z)/18:1(11Z) (blue), PC 18:1 (9Z)/18:1(9Z) (green), and their mixture (red); d PC 18:1 (9E)/18:1(9E) (blue), PC 18:1 (9Z)/18:1(9Z) (green), and their mixture (red). e Structure of peptide, SGKLRASHKG, with different types of PTMs. Ion cloud profiling spectra of the peptide with f methylation in K3 (blue), K9 (green), and their mixture (red); g acetylation in K3 (blue), K9 (green), and their mixture (red); h phosphorylation in S1 (blue), S7 (green), and their mixture (red). In summary, we show an ion mobility technology for high-resolution structural separation of bioisomers. The DCS-based ion cloud profiling could serve as an alternative means for structural separation of biomolecules. The technology developed here showed distinct advantages with a resolution over 10,000, which represents a significant improvement in analytical technology for biological studies (Supplementary Fig. 1). Moreover, the implementation is very simple with the use of an ion trap, which can perform MS/MS analysis at the same time. Ion trap also is a popular ion processing device in modern hybrid mass spectrometers and the high E/N IM separation performed at low pressure make it highly compatible with coupling to mass analyzers such as Orbitrap and TOF. It is expected that this method can be readily applied for a broad range of applications for biological study. ## Materials Trehalose (200 μM) was purchased from Klamar reagent (Shanghai, China), maltose (200 μM) was purchased from Meryer (Shanghai, China), cellose (200 μM) was purchased from Macklin (Shanghai, China), lactose (200 μM) was purchased from Aladdin (Shanghai, China). Synthetic lipids standards, PC 16:$\frac{0}{18}$:1(9Z) (200 μM), PC 18:$\frac{0}{16}$:1(9Z) (200 μM), PC 18:$\frac{1}{18}$:1(9Z) (200 μM), PC 18:$\frac{1}{18}$:1(9E) (200 μM) and PC 18:$\frac{1}{18}$:1(11Z) (200 μM), were purchased from Avanti Polar Lipids (Alabaster, AL, USA). Six kinds of peptides (50 μM), SGKLRASHKG with phosphorylation in S1 and S7, methylation in K3 and K9, acetylation in K3 and K9, were synthesized and purchased from Sangon Biotech (Shanghai, China). These samples were used directly without further purification. Methanol and water, purchased from Fisher Scientific (Fairlawn, NJ, USA) were used for preparing the sample solvents. Trehalose, maltose, cellose, lactose and six peptides were solvated in methanol and water ($\frac{50}{50}$, v/v). Lipids standards were solvated in methanol with $0.1\%$ acetic acid. ## MS instrumentation The experiments were performed in a home-made dual-linear ion trap (dual-LIT) miniature MS system (Fig. 1a)38, which includes a nano-electrospray (nESI) ion source for sample ionization in atmosphere, a discontinuous atmospheric pressure interface (DAPI) connecting the atmosphere and the vacuum, and dual-LIT mass analyzers, LIT I and LIT II, as well as three gates for ion processing. The DAPI uses a pinch valve to control the ion introduction during each analysis. The DAPI typically opens 5-30 ms to allow the ion introduction and then closes to allow the pressure drop back from 0.1 Torr (13.3 Pa) to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1\times {10}^{-5}$$\end{document}1×10−5 Torr. Each of the LITs had a nominal radius r0 of 4 mm, and a length z0 of 51 mm, driven by a dual-phase RF at a frequency of 1 MHz for ion trapping and mass analysis. The two LITs were separated by three mesh gates, which were applied with direct-current (DC) voltages to tune the ion transfer along the z direction (Table S2). With a small alternating-current (AC) voltages coupled with the RF, resonance excitation of the ions in the LITs was achieved to allow ion isolation, ion activation, and ion ejection (Table S3). Air was used as the buffer for ion cooling. For IM technique, typically the detector response speed is not the limiting factor for the resolution and the instrument operation status is not approaching the detector response speed limit. All data were collected and processed by SpecMS from PURSPEC. All data processing were performed using MATLAB 2017b from MathWorks. ## Theoretical modeling and numeric simulations Theoretical modeling and numeric simulations were employed for the understanding and optimization of the profiling process. When the ions were excited by an AC, the motion of the ions in the LIT II was described by a forced oscillation. The equation yields:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$m\frac{{d}^{2}x}{d{t}^{2}}+b\frac{{dx}}{{dt}}+{kx}=C{{\sin }}\left(\omega t\right)$$\end{document}md2xdt2+bdxdt+kx=Csinωtwhere m is ion mass, x is ion displacement in the x-coordinate, t is time. C represents the excitation strength and is defined as2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C=\alpha {V}_{{AC}}/2{r}_{0}$$\end{document}C=αVAC/2r0where VAC is AC voltage with an angular frequency ω and α is calibration coefficient. k is the spring constant due to the effective RF field and is defined as3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$k = 2$e{V}_{{eff}}/{r}_{0}^{2}$$\end{document}$k = 2$eVeff/r02where Veff is the effective trapping depth of the RF field, e is electron charge, and r0 is the field radius of the LIT II. b is the damping coefficient of the ion motion and is defined as4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b=e{K}^{-1}$$\end{document}b=eK−1 K is the ion mobility in the RF field and is defined as5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K=\frac{3e}{16N}\sqrt{\frac{2\pi }{\mu {k}_{B}T}}\frac{1}{{\varOmega }_{D}}$$\end{document}$K = 3$e16N2πμkBT1ΩDwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varOmega }_{D}$$\end{document}ΩD is the DCS of the ions,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\,{k}_{B}$$\end{document}kB is Boltzmann constant, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document}μ is the reduced mass of ions and collision gas, T is temperature. Therefore, the damping coefficient b is proportional to the damping cross-section from Eq. 4, and Eq. 5:6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b=\frac{16N}{3}\sqrt{\frac{\mu {k}_{B}T}{2\pi }}{\varOmega }_{D}$$\end{document}$b = 16$N3μkBT2πΩD The solution of Eq. 1 yields:7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$x=\gamma {e}^{-\frac{b}{2m}t}\sin \left(\sqrt{{\omega }_{0}^{2}-\frac{{b}^{2}}{4{m}^{2}}}t+\delta \right)+\left[\frac{\left({\omega }_{0}^{2}-{\omega }^{2}\right){{\sin }}\left(\omega t\right)-\frac{b\omega }{m}{{\cos }}\left(\omega t\right)}{{\left({\omega }_{0}^{2}-{\omega }^{2}\right)}^{2}+{\left(\frac{b\omega }{m}\right)}^{2}}\right]\frac{C}{m}$$\end{document}x=γe−b2mtsinω02−b24m2t+δ+ω02−ω2sinωt−bωmcosωtω02−ω22+bωm2Cmwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\omega }_{0}^{2}=\frac{k}{m}$$\end{document}ω02=km, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta$$\end{document}δ are arbitrary constant depending on the initial conditions of the ions. The first term represents a damped ion motion, whose oscillation amplitude goes to zero within a few milliseconds. The second term represent the forced oscillation of the ions. At resonance, i.e., \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\omega {=\omega }_{0}$$\end{document}ω=ω0 (resonance frequency), the maximum displacement of the ion motion, A, as a function of the damping term, b, yields:8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$A=\frac{C}{b{\omega }_{0}}=\sigma {b}^{-1}\propto {\varOmega }_{D}^{-1}$$\end{document}A=Cbω0=σb−1∝ΩD−1where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma=C\sqrt{\frac{m}{k}}$$\end{document}σ=Cmk is a constant for a specific ion trapping condition. Equation 8 is the theoretical basis of the ion cloud profiling technology for performing structural analysis of isomeric ions. Under the same AC excitation, the size of the ion cloud, A, becomes different for isomers according to the damping term b or DCS \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varOmega }_{D}$$\end{document}ΩD, as shown in Fig. 1c. Especially, if the displacement of the ion motion is equal to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${r}_{0}$$\end{document}r0, we can obtain the correlation between scanning AC voltage and DCS \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\varOmega }_{D}$$\end{document}ΩD, which has9\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${V}_{{AC}}=\frac{32N}{3}\sqrt{\frac{\mu {k}_{B}T}{2\pi }}{\alpha }^{-1}{r}_{0}^{2}{\omega }_{0}{\Omega }_{{{{{{\rm{D}}}}}}}$$\end{document}VAC=32N3μkBT2πα−1r02ω0ΩD The DCS of ions is proportional to the AC ejection voltage. And the larger the DCS is, the greater the AC ejection voltage is required. To obtain the high E/N condition, the pressure for the analysis was set below 1 × 10−5 Torr. Adequate separation of isomers could be achieved, while the ion pack being maintained compact for each species (Supplementary Fig. 2) to allow high resolutions to be achieved through the ion cloud profiling method. 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--- title: Development and validation of new predictive equations for resting energy expenditure in physically active boys authors: - Edyta Łuszczki - Paweł Jagielski - Anna Bartosiewicz - Katarzyna Dereń - Piotr Matłosz - Maciej Kuchciak - Łukasz Oleksy - Artur Stolarczyk - Artur Mazur journal: Scientific Reports year: 2023 pmcid: PMC10027683 doi: 10.1038/s41598-023-31661-1 license: CC BY 4.0 --- # Development and validation of new predictive equations for resting energy expenditure in physically active boys ## Abstract Measurement or estimation of resting energy expenditure (REE) should be the first step in determining energy demand in physically active boys. The purpose of this study was to develop and validate new equations for resting energy expenditure in male children and adolescents practicing soccer. The cross-sectional studywas carried out among 184 boys in the derivation group and 148 boys in the validation group (mean age 13.20 ± 2.16 years and 13.24 ± 1.75 years, respectively). The calorimeter and device for assessing body composition by bioelectrical impedance analysis (BIA) were used. Model of multiple regression showed that REE can be predicted in this population with Eq. [ 1] (with height and weight data) or Eq. [ 2] (with age, height, and fat free mass data). Predictive Eq. [ 1] had an average error of 51 ± 199 kcal and predictive Eq. [ 2] − 39 ± 193 kcal. Cohen's d coefficient was 0.2, which confirms the small difference. The bias was $4.7\%$ and $3.9\%$, respectively. The accuracy was $61.2\%$ in the population for predictive Eq. [ 1] and $66.2\%$ for predictive Eq. [ 2]. Therefore, the new equations developed and validated in this study are recommended for the estimation of REE in physically active boys, when the use of IC is not feasible or available. ## Introduction The energy expenditure values among young people were developed in 2008, and the compilation provides values of metabolic equivalents (MET) for each type of activity, averaging them for age, sex, and other characteristics1. In some cases, adult values have been considered2. Nevertheless, children's physical and mental characteristics vary compared to adults. Physical activity (PA) is crucial for both the health and correct development of children and adolescents. Therefore, from a public health perspective, increasing PA is one of the most important ways to improve overall health3. The proportions of the body compartments: fat mass (FM) and fat free mass (FFM) change during growth and development. Consequently, these components can be determinant when analyzing the physical fitness of children and adolescents4. In this context, physical fitness has been documented as a key determinant of a healthy lifestyle5. Research shows that of the top ten extracurricular sports among children and adolescents, the most popular is soccer. It is followed by swimming, running, and cycling6, and boys and girls are very different in this respect. In Poland and Europe, the popularity of sports schools for the young generation is growing. Students in sports championship schools attend at least 16 h of sports activities per week. Many sports championship schools, which educate in team disciplines, such as soccer or volleyball, field teams of outstanding students in league competitions. Appropriate energy intake in the diet is a key for the population of young athletes. Most of these children eat at school, some live in a boarding school, and eat all their meals there. The measurement or estimation of resting energy expenditure (REE) should be the first step in determining the energy demand of these children and adolescents7. REE constitutes 60–$70\%$ of the total energy requirement for most people8. However, the REE level should be set accordingly as a valuable tool in the development of food rations, the menu in schools, and nutrition plans. It improves athletic performance and prevents weight loss in children and adolescents. Energy balance is integral for boys to sustain optimal growth and development, with additional nutritional intake required to offset the increased energy cost of training. Studies have investigated the nutritional intake in adult professional soccer players9,10. However, a relatively limited number of studies have investigated the nutritional intake of children and adolescents soccer players11. The findings of these studies have presented suboptimal energy intake relative to estimates of energy expenditure11. Therefore, correctly indicating the REE and then increasing it by the expenditure of physical activity will correctly estimate the total daily energy demand. Despite REE can be calculated very precisely using laboratory methods such as indirect calorimetry (IC), for children and adolescents in sports predictive equations from the literature are also used. There are many equations created for children in the literature, however, these were not formulated for physically active boys or young athletes. In addition, the method for evaluating REE is time-consuming, cost-prohibited, and requires sophisticated tools; therefore, sports trainers, teachers, or people related to sports use the predictive equations developed. The creation of new predictive equations will be very useful for small sports clubs where, due to lack of funds and equipment, the use of IC is not possible. It is widely known that the accuracy of REE predictive equations is characteristic of the population for which they were formulated, and should not be used for groups different from what was originally intended12. The results of the literature agree that REE is elevated with excess weight13,14. Observation presents the FFM as the strongest indicator that affects the REE15. FM is also a component of body composition, which could affect REE, but the research is inconclusive16,17. In 2020, we conducted a cross-sectional study that compares the precision of REE known from previous publications in the literature with the values derived from IC measurement among boys who play soccer. Our results showed that most ready-made equations underestimate REE, which can be a problem, especially if we define the total energy demand of children, who, due to intensive physiological development and regular training, require more calories per day. In conclusion, to date, the best predictive equation has not been created for boys activity undertaking intense physical activity18. As a result of the still low availability and high cost of IC devices, further research was needed that could allow the formation of a special equations for physically active boys. Therefore, the objective of this study was to validate the new predictive equations for active male children and adolescents training soccer. ## Subjects and new predictive equations In 2020 we conducted a coss-sectional study among 184 boys aged 10 to 16 using a calorimeter and a device for assessing body composition by means of electrical bioimpedance using a segment analyzer. A detailed description of the group and the methodology has already been published18. In September 2021 we conducted a a cross-sectional validation study among 148 boys aged 10–16 years. With the start of school classes without restrictions and online classes, invitations were sent to all Sports Championship School directors (13 schools) in the Podkarpackie Voivodeship (south-eastern Poland). Of the six schools that agreed, an invitation was sent to all parents or guardians of boys attending these schools. However, due to the ongoing pandemic in Poland, we obtained fewer research approvals than in previous years (before the pandemic). Of the six selected schools, 183 parents agreed to have their children examined. Of these, 35 children did not complete the study due to injury or discontinued the study for any reason (e.g. illness). The inclusion criteria were as follows: male, playing soccer, age between 9 and 16, training for minimum 2 years, training 3 times a day/match once a week, and with the consent of parents/guardians to participate in the study. The exclusion criteria were: female, age < 9 and > 16 years, training < 2 years, training < 3 times a day, a functional state that does not allow for self-maintenance of a standing position, illness or injury, a lack of desire to participate in the study or strong pre-test anxiety, and being absent from school on assessment days. 183 parents/guardians agreed to participate in the study. Of these, 35 children did not complete the study due to injury or discontinued the study for any reason. All subjects were healthy. In the last 6 months, no weight loss or infection with increased fever were reported. They did not use any drugs. Parents/guardians and participants gave their informed consent to participate in the study. ## Assessments All examinations were conducted in the laboratories of Rzeszow University by experienced researchers between January and May 2022. Participants came forward to the laboratory from 7:00 to 10:00 a.m. at controlled temperature. The temperature at the location of the measurements was controlled (22–25 °C). ## Anthropometric measurements, body composition Before taking the measurements, the participants received precise information about the course of the study. To minimize the risk of bias in body composition analysis, the bladder was emptied. Height measurement was made with a height meter (Seca 213) with an accuracy of 0.5 cm. The boys took off their shoes and stood with their backs to the stadiometer in an upright position. The average of three measurements was used for analysis. Body composition was measured using bioelectrical impedance analysis (BIA, 6.25 kHz/50 kHz, 90 µA). The TANITA MC-980 MA (Tanita, Tokyo, Japan) was used. The analyzer is equipped with 8 electrodes, of which 4 are built into the platform, while the others are placed in the handles. Participants were asked to remove footwear and socks. Measurements were made in underwear, standing in designated places on the platform. According to the Tanita MC-980 PLUS MA manual, accurate measurement requires setting up the machine as level as possible. The adjustable feet were rotated in 4 positions so that the bubble of the level indicator was in the middle. Participants stood upright on the platform with their legs extended, placing their feet so that they touched the front and rear electrodes, ensuring that the weight was evenly distributed on both feet. The person examined held handles in their hands that were taken from the body at an angle of 35–40. ## Resting energy expenditure Resting energy expenditure (kcal/day) was measured using a Cosmed Quark RMR indirect calorimeter (Rome, Italy) with a ventilated canopy hood and a disposable antibacterial filter. Full service of all measurement instruments was performed prior to the study, and daily calibration was performed according to the manufacturer's instructions. The evidence-based protocol for measurement of resting energy expenditure by IC was adopted in the study and clearly discussed (Table 1).Table 1Evidence-based guidelines for measurement of resting metabolic rate with IC.CriteriaGuidelines for measurementStudy group recommendationFasting (thermic effect of food)Minimum fast 5 h after meals or snacks (Grade II), 4 h after small meal if longer fast is clinically inappropriate (Grade II)All recommendations concerning preparations for the study were outlined, including: having rest minimum for 20 min, abstention from nicotine for minimum 2 h, refraining from the consumption of meals 12 h before the test, refraining from drinking beverages with caffeine and alcohol content for the last 48 h before the test, as well as refraining from participation in a physical activity for the previous 14 hThe method of conducting the study was explained in detail and each study participant had the opportunity to visit the test rooms beforehand and familiarize themselves with the equipment so that it did not raise concerns or cause anxiety in the researched groupAlcohol ingestionMinimum abstention from alcohol for 2 h (Grade III)Nicotine ingestionMinimum abstention from nicotine for 2 h (Grade II)Caffeine ingestionMinimum abstention from caffeine for 4 h (Grade II)Rest periodsRest 10–20 min (Grade III)Physical activity restrictionMinimum abstention from moderate aerobic or anaerobic exercise for 2 h before test (Grade II), for vigorous resistance exercise abstention of at least 14 h (Grade III)Environmental conditionsAllow a room temperature of 20–25 °C (68–77°F) (Grade III) Ensure each individual is physically comfortable with measurement position during the test and repeated measures are in the same reclined position (Grade V)The rooms had a controlled temperature between 22 and 25 °CIn addition, each participant had the opportunity to acclimatize in the environment by lying flat for 30 minGas collection devicesUse rigorous adherence to prevent air leaks (Grade III) Further studies comparing modern gas collection devices are needed in healthy and clinical populations (Grade V)With these devices, exhaled gas was captured by a canopy (ventilated hood system) or a face mask connected to oxygen and carbon dioxide analyzers mounted on a metabolic cart. This is essential for correct measurementSteady-state conditions and measurement intervalDiscard initial 5 min. Then achieve a 5-min period with $10\%$ CVb for VO2c and VCO2d (Grade II)We use a 20-min protocol in which the first 5 min of data are discarded and the remaining 15 min of data have a coefficient of variation of no more than $10\%$No. of measures/24 hAchieve steady state and one measure is adequate; if not, two to three nonconsecutive measures improve accuracy (Grade II)1 measure/24 hRepeated measures (daily to monthly variation)Repeated measures vary $3\%$-$5\%$ over 24 h (Grade II) and vary up to $10\%$ over weeks to months (Grade II)–Respiratory quotient (RQ)RQ measures 0.70 or 1 suggest protocol violations or inaccurate gas measurement (Grade II)The Quark RMR is a state-of-the-art metabolic system designed for accurate measurement of Resting Energy Expenditure (REE) and respiratory ratio (R), in a non-invasive way, through the measurement of oxygen consumption (VO2) and carbon dioxide production (VCO2) together with other ventilatory parameters. RQ was between 0.7 and 1.0aGrade I—strong, consistent evidence; Grade II—somewhat weaker evidence and disagreement among authors may exist; Grade III—limited design quality; Grade IV—professional opinion only, no clinical trials; Grade V—no available studies.bCV—coefficient of variation (standard deviation [mean of individual replicate measures] × 100).cVO2—oxygen consumption.dVCO2—carbon dioxide production. The following statistical methods were used. Descriptive statistics are presented in Table 2 (number (n), Me—median and standard deviation (SD)). Shapiro–Wilk test, allowed to test the compliance of the tested variable with the normal distribution and the Student's t-test or Mann–Whitney U test was used to check the differences between both groups for the analyzed quantitative or ordinal variables. The bias was calculated as the mean difference between the predicted value and the measured REE.Table 2Anthropometric characteristics of the study participants. VariablesDerivation group (2020 year) $$n = 184$$Validation group (2022 year) $$n = 148$$MeanSDMeMinMaxMeanSDMeMinMaxpAge [years]13.202.1613.0010.0016.0013.241.7513.2410.0116.430.8289Height [cm]162.9114.90166.00132.00191.00161.0113.51160.50128.00193.000.2310Weight [kg]52.3714.4453.8524.80102.2050.8413.3849.6526.9287.880.3233BMI [kg/m2]19.272.5719.0513.6028.0019.232.4219.1113.7926.530.8259Fat %17.583.7616.908.8034.0018.384.0317.6810.4040.800.0597FFM [kg]43.1712.0244.4020.6079.6041.5211.2139.7022.7072.400.2017Me median, SD standard deviation, p t Student test, BMI body mass index, FFM fat free mass. The agreement between measured and estimated REE values was evaluated by determining the bias in absolute values and as a percentage of the measured value and the corresponding limit of agreement (upper limit of agreement (ULA) = bias + 1.96 × SD; lower limit of agreement (LLA) = bias − 1.96 × SD). Additionally, Pearson’s product-moment correlation coefficient (r) and the coefficient of determination (R2) were calculated. The concordance correlation coefficient (CCC) as a measure of agreement was also determined. The percentage of participants whose predicted REE value was within ± $10\%$ of the measured REE was used as a precision measure. The heteroscedasticity was tested using the Bland–Altman method. The plots presented the difference between predicted and measured REE versus the mean of predicted and measured REE. The PS IMAGO PRO 8.0 software (IBM SPSS STA-TISTICS 28) and the MedCalc software were used. The adopted level of statistical significance was $p \leq 0.05.$ ## Ethical consent This research project obtains acceptance of Institutional Bioethics Committee at the University of Rzeszów (Resolution No. $\frac{2}{01}$/2019). Parents/guardians and participants gave their informed consent to participate in the study. ## Characteristics of the study group 184 boys aged 10–16 years partake in the study when the new predictive equations were developed and 148 subjects aged 10–16 years in 2022 to validate the new equations. The descriptive characteristics of the study group are presented in Table 2. ## The findings Model of multiple regression showed that REE can be predicted in this population with Eqs. [ 1] or [2], as follows: Predictive Eq. [ 1]:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{REE }}\left({{\text{kcal}}/{\text{d}}} \right) \, = \, - {196}.{49 } + { 9}.{25 }{\text{ Height }}\left({{\text{cm}}} \right) \, + { 1}0.{2}0 \, {\text{ Weight }}\left({{\text{kg}}} \right)$$\end{document}REEkcal/d=-196.49+9.25∗Heightcm+10.20∗Weightkg ($R = 0.84$, $p \leq 0.001$; bias = 0, $$p \leq 0.93$$). Predictive Eq. [ 2]:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{REE }}\left({{\text{kcal}}/{\text{d}}} \right) \, = { 359}.{45 }{-}{ 23}.{69}{\text{ Age }}\left({{\text{years}}} \right) \, + { 5}.{64 }{\text{ Height }}\left({{\text{cm}}} \right) \, + { 2}0.{36 }{\text{ FFM }}\left({{\text{kg}}} \right)$$\end{document}REEkcal/$d = 359.45$-23.69∗Ageyears+5.64∗Heightcm+20.36∗FFMkg∗ ($R = 0.86$, $p \leq 0.001$; bias = 0, $$p \leq 0.9992$$). For the first, the following data were used: height and weight (basic anthropometric data), and for the second, age, height, and fat free mass (body mass composition data). The R correlation coefficient indicates that there is a strong correlation between the results obtained from the developed predictive equation and the results measured by the IC method. REE calculated by IC in the first group averaged 1844 ± 328 kcal and in the second group 1760 ± 357 kcal. The validation group consisted of 148 boys. The results showed that both Eqs. [ 1] and [2] had a very low error in relation to the REE measured with the IC. Predictive Eq. [ 1] had an average error of 51 ± 199 kcal and predictive Eq. [ 2] − 39 ± 193 kcal. Cohen's d coefficient was 0.2, which confirms the small difference. The bias was $4.7\%$ and $3.9\%$, respectively. These values are a limit value that does not exceed $10\%$ of the error; therefore, predictive Eq. [ 1] and [2] seem to be appropriate to be used in young sports people. The accuracy of the prediction was on a very high level in both two equations. The accuracy was $61.2\%$ in the population for predictive Eq. [ 1] and $66.2\%$ for predictive Eq. [ 2]. This means that both equations are consistent (with an error of ± $10\%$) for more than $60\%$ of the population. Accuracy of the predictive models is critical as it determines the quality of their predictions that form the scientific evidence. In our study $61.2\%$ and $66.2\%$ represent a high accuracy (Table 3).Table 3Validity of the resting energy expenditure. REE (kcal/d)T-testBias kcal/dLLA kcal/dULA kcal/dBias (%)rp-valueR2p-value (linear regression)CCCPredictionMeanSDp-valueMeanSDMeanSD(correlation)Accurate %Under %Over %REE derivation group1844328100 Equation [1]18442790.93450172− 3373370.89.00.84 < 0.00010.73 < 0.00010.8473.410.915.7 Equation [2]18441670.99920167− 3273270.89.10.86 < 0.00010.74 < 0.00010.8576.010.413.7REE validation group1760357100 Equation [1]18112560.002251199− 4413394.712.50.84 < 0.00010.70 < 0.00010.7861.28.829.9 Equation [2]17992660.015439193− 4173393.912.10.85 < 0.00010.72 < 0.00010.8166.28.825.0REE measured resting metabolic rate by IC, SD standard deviation, Bias difference between the predicted value and the measured REE, LLA lower limit of agreement, ULA upper limit of agreement, bias% bias in %, CCC concordance correlation coefficient, Accurate % percentage of subjects in which the error of the predictive equation was within $10\%$ of the measured value, Under % percentage of subjects underestimated by the predictive equation with an error > $10\%$ of the measured value, Over % percentage of subjects overestimated by the predictive equation with an error > $10\%$ of the measured value.Cohen's $d = 0.26.$*Cohen's $d = 0.20.$ The mean value of the differences between measured and predicted REE (bias) is indicated by the solid line in the Fig. 1. The dashed lines delimit the $95\%$ confidence interval. All regression lines were statistically significant at $p \leq 0.0001$, indicating a systematic bias. Figure 1Agreement between measured and predicted resting energy expenditure (REE) using the Eqs. [ 1] and [2]—Bland Altman plot. ## Discussion In the present study, two new specific equations were developed and validated to predict REE in physically active male children and adolescents. A correct estimate of resting energy expenditure is essential for nutritional management and calculates total energy. When IC measurement is not feasible or available, ready equations became a valuable tool for estimating REE. However, the optimal predictive accuracy of these equations is obtained when used in subjects with the same characteristics as those in whom the equations were developed19. To our knowledge, this is the first study available in the literature on the development and validation of new predictive equations to estimate REE in healthy boys who practice sports regularly. This is a very important issue because optimizing energy consumption is of fundamental importance in the population of young athletes for whom, in addition to energy expenditure associated with the development of the body, the energy demand due to intense physical activity also increases. The mean REE value for physically active boys was 1844 kcal/day in the derivation groupand 1760 kcal in the validation group. It seems to be representative of other active male children and adults examined so far. The literature presented that among active men, REE values were 1858 kcal/day, 1788 kcal/day, 1808 kcal/day20–22 and 1834 kcal/day among 10 soccer players23. In one study investigating the dietary and activity regimes of adolescent soccer players in the UK, the mean daily energy deficit of − 3299 ± 729 kJ was observed24. The researchers noticed that the comparison of ready-to-use formulas to calculate REE in physically active people with IC shows that several of these equations underestimate or overestimate REE by up to 300 kcal25. The literature shows that this bias may be caused by differences in the metabolic activity of FM and FFM because most of these predictive equations are not based on body composition, but on total body weight26. The literature indicates that formulas having the smallest error deviations in the population of children and adolescents are the formulas of Schofield and FAO/WHO/UNU based on body weight and height. Rodriguez et al. observed that the FAO/WHO/UNU and Schofield formulas were suitable for predicting REE in children and adolescents27. Hofsteenge et al. showed that it was not a proper equation for obese adolescents and presented overestimation with a deviation of + $10.7\%$ and mean squared prediction error 276 kcal/day28. On the other hand, a meta-analysis from 2020 presented that the Mifflin equation shows the highest precision in the 11–18 years old obese children and adolescents29. According to our previous study, we found that the predictive equation for boys who are physically active regularly has not been developed to date. Although the use of the Institute of Medicine of the National Academies (IMNA) predictive equation gave the smallest error in the REE estimates, this equation and all the predictive equations used in these studies underestimated the REE of children and adolescents. The mean error ranged from 477 kcal/day for the Maffeis predictive equation to 182 kcal/day for the IMNA predictive equation18. Similar results were found in studies checking the accuracy of REE equations in sporting populations, especially in endurance sports25,30,31. De Lorenzo et al.30 found that the Cunningham equation overestimates in about 59 kcal/day measured REE. Harris Benedict and Mifflin formulas underestimated REE in 51 male athletes in various types of sports. Thompson et al.25 noticed that all mean predicted REE values were lower than those measured with IC, except for the Cunningham formula* in male and female endurance athletes31. Due to the still low availability of IC devices, we decided that further research is needed that will allow us to generate special equations for physically active boys. In our study, we identified appropriate predictive equations for the population of physically active boys who play male soccer. A linear regression analysis was performed to obtain two new predictive equations for REE in the study group. It is well known that predictive equations cannot replace REE measurements by IC, but the large, homogeneous group in our study allows us to conclude that these predictive equations can be used among young male soccer players. The results of the study can be applied with caution to similar groups of the population, taking into account the limitations of this study and the factors that affect the REE of athletes. The first prediction equation, based on anthropometric parameters (body weight and height), is easier to use by dietitians and physicians during medical examination because it only needs a typical scale and growth meter/stadiometer. The second equation, based on body composition (FFM), age and height, is more population-specific than the predictive Eq. [ 1] because in most studies FFM was the main significant determinant of REE in the population32–34. However, it involves specific equipment and more time to measure body composition. Nevertheless, in a similar random sample of 148 boys (validation group), both new equations predicted REE with a bias of $4.7\%$ and $3.9\%$ and were accurate for $61.2\%$ [Eq. [ 1]] and $66.2\%$ of the population. Furthermore, when an external validation was performed in an independent group of physically active boys, the REE estimated by both equations was significantly different from the measured REE, but the prediction was very high and similar to different studies that validated new predictive equations35. In the validation cohort, the two new equations present the same small mean difference and a similar SD of the differences between measured and predictive REE, although FFM has been shown to be the best predictor of REE in many studies. In the literature, it is observed that FFM explains REE better compared to body weight. Differences in REE are known to be related to FFM, but genetics could also explain the difference in REE between populations. Therefore, the predictive equations for REE based on body composition are generally population-specific and therefore should be more appropriate36. Together with age and gender, physical activity has a significant influence on FFM. The issue of body composition and the influences of physical activity is quite different from REE. However, there is limited evidence on the evaluation of body composition and its dynamic changes in children and adolescents who train sports regularly37. ## Strengths and limitations The study was the first known to develop and validate REE in physically active boys and represents two population groups, the derivation group (for which two prediction equations are developed) and the validation group. This is the first known study to create new equations to estimate REE in boys who play soccer regularly. Groups 1 and 2 were relatively large and representative. The results should be considered to estimate REE only in a specific population of soccer, boys, Caucasian race, and at a specific age of 10–16 years. The group consisted of young boys who played sport regularly; therefore, our results could be confounded by overweight, obesity, and unintentional weight gain, therefore, our findings could not be generalized. The age range of the subjects in the experiment is from 10.01 to 16.43 years old, and their hydration status are always changing with their maturations, and it could influence on fat free mass measured by BIA. ## Conclusions Our study allowed us to collect data to develop new predictive equations for male children and adolescents who play soccer. This is very important because REE is an essential element in evaluating and determining the diet of boys with high physical activity in the context of balancing their daily energy expenditure. It affects not only the maintenance of normal body weight composition and is related to the proper development and maintenance of health, but also the maintenance of optimal physical fitness in boys. Most ready-made predictive equations underestimate REE, which can be a problem, especially if we define the total energy demand of children, who due to intensive physiological development and regular training, require more calories per day. Therefore, the new predictive equations could be more appropriate for predicting REE in this population. These predictive equations can be very useful for small sports clubs where, due to lack of funds and equipment, the use of IC is not possible. ## References 1. Ridley K, Ainsworth BE, Olds TS. Development of a compendium of energy expenditures for youth: A second update of codes and MET values. Int. J. Behav. Nutr. Phys. Act. (2008) 5 45. DOI: 10.1186/1479-5868-5-45 2. Ainsworth B. Compendium of physical activities. Med. Sci. Sports Exerc. 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--- title: 'The gut microbiome in social anxiety disorder: evidence of altered composition and function' authors: - Mary I. Butler - Thomaz F. S. Bastiaanssen - Caitriona Long-Smith - Sabrina Morkl - Kirsten Berding - Nathaniel L. Ritz - Conall Strain - Dhrati Patangia - Shriram Patel - Catherine Stanton - Siobhain M. O’Mahony - John F. Cryan - Gerard Clarke - Timothy G. Dinan journal: Translational Psychiatry year: 2023 pmcid: PMC10027687 doi: 10.1038/s41398-023-02325-5 license: CC BY 4.0 --- # The gut microbiome in social anxiety disorder: evidence of altered composition and function ## Abstract The microbiome-gut-brain axis plays a role in anxiety, the stress response and social development, and is of growing interest in neuropsychiatric conditions. The gut microbiota shows compositional alterations in a variety of psychiatric disorders including depression, generalised anxiety disorder (GAD), autism spectrum disorder (ASD) and schizophrenia but studies investigating the gut microbiome in social anxiety disorder (SAD) are very limited. Using whole-genome shotgun analysis of 49 faecal samples (31 cases and 18 sex- and age-matched controls), we analysed compositional and functional differences in the gut microbiome of patients with SAD in comparison to healthy controls. Overall microbiota composition, as measured by beta-diversity, was found to be different between the SAD and control groups and several taxonomic differences were seen at a genus- and species-level. The relative abundance of the genera Anaeromassillibacillus and Gordonibacter were elevated in SAD, while Parasuterella was enriched in healthy controls. At a species-level, Anaeromassilibacillus sp An250 was found to be more abundant in SAD patients while Parasutterella excrementihominis was higher in controls. No differences were seen in alpha diversity. In relation to functional differences, the gut metabolic module ‘aspartate degradation I’ was elevated in SAD patients. In conclusion, the gut microbiome of patients with SAD differs in composition and function to that of healthy controls. Larger, longitudinal studies are warranted to validate these preliminary results and explore the clinical implications of these microbiome changes. ## Introduction Social anxiety disorder (SAD) is one of the most common psychiatric conditions with estimated lifetime prevalence rates as high as $13\%$ reported in the United States [1] and similarly high prevalence rates across Europe [2]. The current Diagnostic and Statistical Manual of Mental Disorders – Fifth Edition (DSM-V) describes SAD as a condition characterised by ‘marked fear or anxiety about one or more social situations in which the individual is exposed to possible scrutiny by others.’ These fears generally extend across a variety of situations, including social interactions (e.g., having a conversation, meeting unfamiliar people), being observed (e.g., eating or drinking), and performing in front of others (e.g., giving a speech) [3]. SAD typically begins early in life and tends to run a chronic, often lifelong, course [4]. It is associated with serious functional disability and markedly reduced quality of life [5] with up to $69\%$ of sufferers experiencing another lifetime major comorbid disorder [6]. In particular, SAD markedly increases the risk of subsequent depression [7] which is associated with a poorer prognosis and greater risk of suicide attempts [8]. Thus, timely intervention in this early-onset disorder has the potential to not only reduce substantial disability, but to markedly reduce the psychiatric disease burden later in life. Current first-line treatments include selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs) and cognitive behavioural therapy [9]. Unfortunately, a significant proportion of patients fail to adequately respond to first-line pharmacotherapy [10] and even fewer patients will respond to subsequent second-line treatments. In a large randomised controlled trial (RCT) investigating augmentation and switch strategies for refractory SAD (defined as more than two unsuccessful adequate pharmacological treatment trials), only $46\%$ of patients demonstrated a response to treatment, while only $21\%$ of all patients achieved remission at the 12-week endpoint [11]. Thus, there is a great necessity for an improved understanding of the neurobiological basis for this condition and the development of alternative therapeutic strategies. The microbiome-gut-brain axis may represent one such potential avenue for investigation. The human gastrointestinal tract (GIT) harbours a vast assembly of microorganisms, predominantly bacteria but also fungi, viruses, protozoa and archaea. While the term gut microbiota refers to the assemblage of living organisms present within the gut, the term microbiome encompasses the micro-organisms and their ‘theatre of activity’ i.e., their structural elements, genomes and metabolites, as well as the surrounding environmental conditions [12, 13]. It is estimated that the number of bacteria in the human gut is slightly in excess of the total number of human cells, at approximately 3.8 ×10 13 [14] and that the collective genome of these bacterial cells vastly exceeds the amount of human DNA present in the body [15]. Given this enormous, modifiable reservoir of genetic potential, it is unsurprising that there is keen interest in the potential role of the gut microbiome in the aetiology and treatment of many disease processes. The microbiome is recognised as a key player in bidirectional signalling between the gut and brain, with the term ‘microbiome-gut-brain’ (MGB) axis describing this communication network. Many physiological systems relevant to psychiatric disorders come under the influence of the gut microbiome, including the immune system, vagal neurotransmission, tryptophan metabolism, endocrine function and the stress response system, making the MGB axis an attractive new therapeutic target in psychiatry [16]. Despite a growing interest in the role of the gut microbiome in the neurobiology of the stress response [17] and social behaviour [18, 19], there has been very limited investigation of the gut microbiome in relation to SAD. Indeed, apart from a few small studies in generalised anxiety disorder (GAD) [20–22], and in co-morbid irritable bowel syndrome (IBS) [23], gut microbiome composition or function has remained largely unexplored in patients with clinical anxiety disorders, including SAD, panic disorder and agoraphobia. This is despite an abundance of preclinical studies demonstrating that anxiety-like behaviours are altered in animal models following a variety of microbiota manipulations [24] and clear evidence that certain probiotics can reduce self-reported anxiety levels in healthy human volunteers [25] The past decade has also seen a spate of preclinical studies revealing a role for the gut microbiome in social development and social behaviour in animals [26, 27] and this has extended to human studies in recent years. Differences in gut microbiota composition and diversity have recently been linked to personality traits such as sociability in the general population [28]. Additionally, altered gut microbiota composition has been demonstrated in adults experiencing social exclusion [29], a psychological phenomenon to which those with SAD are particularly sensitive [30]. Interestingly, a potential therapeutic role for the microbiota in SAD is supported by a cross-sectional study which reported that higher intake of fermented, probiotic-containing foods by healthy students appeared to be protective against developing SAD in those who were at higher genetic risk, as measured by trait neuroticism [31]. Here we report on compositional and functional differences in the gut microbiome of patients with SAD using whole-genome shotgun analysis of 49 faecal samples (31 cases and 18 controls). The functional differences, based on Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologues, are explored using the recently described gut-brain module (GBM) [32] and gut metabolic module (GMM) [33] analysis. We hypothesise that the gut microbiome is compositionally and functionally altered in those with SAD. ## Participants Patients with a clinical diagnosis of SAD were recruited through local general practitioners, psychologists and outpatient psychiatric clinics. The study was also advertised through local and national SAD support groups and by an online website (www.sadgut.ie). Eligibility was limited to men and women aged between 18–65 years with a clinical diagnosis of SAD. Exclusion criteria included any significant acute or chronic medical illness (including functional gastrointestinal disorders such as irritable bowel syndrome); the presence of any condition or medication which the investigator believed would interfere with the objectives of the study or confound the interpretation of the study, including anticonvulsants, centrally acting corticosteroids, opioid pain relievers, laxatives, enemas, anti-coagulants, over-the counter non-steroidal anti-inflammatories (NSAIDS); the use of probiotics, prebiotics or antibiotics in the previous 4 weeks; females who were pregnant or breastfeeding; subjects who were vegetarian or adhering to a strict specific diet. Specific psychiatric exclusion criteria included a lifetime diagnosis of psychotic disorder, intellectual disability, bipolar disorder, dementia or ASD and a current diagnosis of major depressive disorder (MDD), eating disorder, alcohol or substance abuse or dependence. A past history of depression was permitted, as was the presence of a comorbid anxiety disorder, provided the clinician was satisfied that SAD was the primary diagnosis. SAD participants were permitted to continue taking their regular psychotropic medication. Healthy controls were recruited through email and print advertising in University College Cork. Controls were required to have no past or current psychiatric diagnosis along with the other general exclusion criteria outlined for the SAD participants. ## Procedures All study procedures were approved by the Clinical Research Ethics Committee of the Cork Teaching Hospitals (Study number APC085) and the study was conducted in accordance with the ICH Guidelines on Good Clinical Practice, and the Declaration of Helsinki. All participants provided written informed consent. All participants were interviewed by an experienced psychiatrist using the MINI International Neuropsychiatric Interview (Version 7.0) [34] to confirm the diagnosis of SAD based on DSM-V criteria and assess for any relevant comorbidities. No patient met the criteria for the ‘performance only’ specifier and all experienced social anxiety symptoms across a range of situations. Social anxiety symptoms were assessed using the Liebowitz Social Anxiety Scale-Self Report (LSAS), a 24-item scale which was initially developed as a clinician-rated instrument [35, 36] but was later shown to have excellent psychometric properties as a self-report scale [37–39] To quantify nutrient intake, participants completed the self-administered 152-item SLAN-06 (Survey of Lifestyle, Attitudes and Nutrition in Ireland) food frequency questionnaire (FFQ), which is adapted from the EPIC Norfolk questionnaire [40] and validated to be used in an Irish population [41]. Participants were asked to estimate the frequency with which they consumed a specified portion size of each of the foods listed over the preceding year. The FFQs were analysed for nutrient intake using the FETA software [42]. Stool consistency was assessed using the Bristol Stool Chart (BSC) [43]. Exercise levels were measured using the International Physical Activity Questionnaire (self-administered short form) [44] and sleep using the Pittsburgh Sleep Quality Index [45]. ## Biological/faecal samples Freshly voided faecal samples were collected from study participants into plastic containers containing an AnaeroGen sachet (Oxoid AGS AnaeroGen Compact, Fischer Scientific, Dublin) to generate anaerobic conditions within the container. Participants were instructed to collect the faecal sample as close to the study visit as possible and to keep the sample containers in a refrigerator at 4 °C until delivery to the study site. A cool pack was used to transport the sample to the study site, where it was immediately stored at − 80 °C for later analysis. ## Microbiome sample preparation and whole genome shotgun sequencing Total bacterial metagenomics DNA was extracted using the QIAmp Fast DNA Stool Mini kit (Qiagen, UK) with a modified protocol combined with repeated bead beating method (Zhongtang Yu & Mark Morrison 2018). Briefly, 1 ml of lysis buffer (500 mM NaCl, 50 mM Tris-HCl pH8.0, 50 mM EDTA and $4\%$ sodium dodecyl sulphate) was added to the stool sample in the bead-beating tube. The samples were homogenized using a mini beadbeater (BioSpec) and incubated at 70 °C for 15 minutes (for cell lysis) followed by centrifugation at 4 °C. The supernatant was removed and the bead-beating step was repeated. Ammonium acetate (Sigma Aldrich, Ireland) was added to the pooled supernatant and incubated on ice. Following a centrifugation step the supernatant was transferred to Eppendorf tubes containing iso-propanol. The following day, DNA was pelleted and washed with $70\%$ ethanol and dissolved in Tris-EDTA. The DNA was then RNAse and proteinase-K treated and purified according to the manufacturer’s instructions (QIAmp Fast DNA Stool Mini kit; Qiagen, UK). The DNA was quantified using Qubit and stored at −30 °C. Whole genome shotgun sequencing was performed using Nextera XT kit. Library prep was done following the Nextera XT DNA Library Preparation Guide from Illumina. Quality of the library was evaluated using the Agilent High Sensitivity DNA chip and running it on the Bioanalyzer and the DNA was quantified using Qubit DNA High sensitivity kit read on a qubit fluorometer 3.0. The samples were pooled and sequencing was carried out on the NextSeq500 using a 300 cycle High Output v2 kit. ## Taxanomic and functional analysis We performed quality checks on raw sequences from all faecal samples using FastQC [46]. Shotgun metagenomic sequencing data were then processed through analysis workflow that utilizes Huttenhower Biobakery pipeline [47], including Kneaddata [48], MetaPhlAn3 [49] and HUMAnN3 [50] to obtain species, genes and pathways abundance matrix. Briefly, quality filtering and host genome decontamination (human) was performed using Trimmomatic [51] and Bowtie2 [52] via Kneaddata wrapper program with following parameters: ILLUMINACLIP:/NexteraPE-PE.fa:2:30:10, SLIDINGWINDOW:5:25, MINLEN:60, LEADING:3, TRAILING:3. Taxonomic and functional profiling of the microbial community was performed using MetaPhlan3 and HUMAnN3 using default parameter. Next, gene abundance matrix was further collapsed by KEGG Orthology (KO) term and Gene Ontology (GO) term mapping via “humann_regroup_table” function provided within HUMAnN3. Further data-handling was undertaken in R (version 4.03) using the Rstudio GUI (version 1.4.1103). In all microbiome analysis with the exception of alpha diversity, taxa with a prevalence of <$5\%$ of samples at the genus level were excluded from analysis as ratios are invariant to sub-setting and this study employs compositional data analysis techniques [53, 54]. Principal component analysis was performed on centred log-ratio transformed (clr) values using the ALDEx2 library [55]. The number of permutations was always set to 1000. Beta diversity was computed in terms of Aitchison distance, or Euclidean distance between clr-transformed data. Alpha diversity was computed using the iNEXT library [56]. KEGG orthologues were used as features to compute functional alpha diversity. Gut-Brain Modules (GBMs) and Gut-Metabolic Modules (GMMs) were calculated from HUMAnN3 output using the R version of the Gomixer tool [32]. Stacked barplots were generated by normalising counts to 1, generating proportions. Genera that were never detected at a $10\%$ relative abundance or higher were aggregated and defined as rare taxa for the purposes of the stacked barplots. Differential abundance of both microbes and functional modules were calculated using implementations of the ALDEx2 library. Effect sizes were calculated using Cohen’s D statistic. A p value of <0.05 was deemed significant in all cases. To correct for multiple testing in tests involving microbiome features, the Benjamini-Hochberg (BH) post-hoc was performed with a q-value of 0.1 used as a cut-off for species and 0.2 for functional modules. Plotting was done using the ggplot2 [57] and patchwork [58] libraries in R. Custom R scripts and functions are available online at https://github.com/thomazbastiaanssen/Tjazi [59]. A linear modelling approach was used to test for a group effect on taxonomic and functional differences, whilst adjusting for covariates including age, sex, BMI, exercise and dietary differences. ## Statistical analysis of metadata All metadata were analysed using SPSS 25 (IBM, Armonk, NY, USA). Visual inspection of box plots was used to identify outliers and consideration given to removal of those lying more than three times the interquartile range (IQR) below the first quartile or above the third quartile. Missing values were excluded from analysis. Normality of data was assessed by visual inspection of histograms along with examination of skewness and the Shapiro-Wilk statistic. Differences in demographic data, FFQ and LSAS scale scores between the SAD and control group were assessed using Chi-squared or Fisher’s Exact test for categorical variables, and independent t-tests or non-parametric Mann-Whitney U tests for continuous variables. Data are presented as mean ± SD unless stated otherwise. ## Demographics Based on previous microbiota studies from our laboratory [60], a sample size of 30 participants was estimated to achieve significant changes in microbiota composition. Thirty-one patients with social anxiety disorder (SAD) and eighteen healthy controls participated in the study. There were no significant differences between patients and controls in relation to age, sex, race, years of education, birth delivery mode, alcohol consumption, smoking status, or stool consistency (Table 1). Individuals in the SAD group had higher BMI scores compared to controls (t[46]=2.65, $$p \leq 0.01$$). SAD patients had significantly lower exercise levels than controls, based on mean IPAQ scores (t[45] = −2.125, $$p \leq 0.04$$), the difference when looking at exercise categories being at the level of a trend (X2[2] = 5.822, $$p \leq 0.054$$). Almost three-quarters ($74.2\%$) of SAD patients had a past history of MDD and $35.5\%$ had a comorbid secondary anxiety disorder. Just over two-thirds ($67.7\%$) of patients were taking psychotropic medication. The majority of these patients were prescribed an SSRI ($48.4\%$) (8 taking Escitalopram, 2 taking Vortioxetine, 2 taking Sertraline, 1 taking Citalopram, 1 taking Paroxetine, 1 taking Fluoxetine) or SNRI ($9.7\%$) (3 taking Venlafaxine) with $22.6\%$ ($$n = 7$$) taking an alternative regular psychotropic medication (1 patient taking Pregabalin, 1 taking Agomelatine, 1 taking Bupropion, 2 taking Trazadone and 2 taking low-dose (50 mg) Quetiapine).Table 1Demographic Characteristics and Psychological Scales. SAD ($$n = 31$$)Controls ($$n = 18$$)p valueAge (years); mean (SD)36.0 (11.96)41.7 (10.79)0.10Gender; % female (n)48.4 [15]66.7 [12]0.25Race; % Caucasian (n)100 [31]94.4 [17]0.37Years of Education; mean (SD)17.32 (4.32)19.25 (4.89)0.16Delivery mode at birth • Vaginal74.2 [23]83.3 [15]0.74 • Caesarean section6.5 [2]5.6 [1] • Unknown19.3 [6]11.1 [2]BMI (kg/m2); mean (SD)28.02 (5.0)24.22 (4.49)0.01 Alcohol (units per week); mean (SD)5.52 (7.4)2.63 (3.1)0.78Alcohol categories; % (n) • 0–3 units/week58.1 [18]72.2 [13]0.21 • 4–9 units/week16.1 [5]22.2 [4] • ≥ 10 units/week25.8 [8]5.6 [1]Smoking status; % smokers (n)12.9 [4]5.6 [1]0.64Exercise (IPAQ score); mean (SD)3143.89 (3769.34)5816.17 (4650.98)0.04 Exercise (IPAQ category); % (n) • Low25.8 [8]5.6 [1]0.054 • Moderate35.5 [11]16.7 [3] • High38.7 [12]66.7 [12]Bristol Stool Chart; % (n) • Score 10 [0]0 [0]0.50 • Score 222.6 [7]11.1 [2] • Score 332.3 [10]38.9 [7] • Score 429.0 [9]38.9 [7] • Score 56.4 [2]11.1 [2] • Score 69.7 [3]0 [0]Comorbidity: % (n)Past history of MDD74.2 [23]0 [0]<0.0005 Other anxiety disorder (total)35.5 [11]0 [0] • Agoraphobia22.6 [7]<0.0005 • GAD6.5 [2] • Panic Disorder3.2 [1] • Multiple3.2 [1]Psychotropic medication % (n) • No medication32.3 [10]100 [18]<0.0005 * • Taking medication67.7 [21]0 [0]◦ SSRI48.4 [15]◦ SNRI9.7 [3]◦ Other regular anxiolytic22.6 [7]◦ As required Beta-blocker12.9 [4]◦ As required Benzodiazepine6.4 [2]Social anxiety scale scoresLSAS; mean (SD) • Fear Subscale44.94 (11.91)10.67 (10.31)<0.0005 * • Avoidance subscale38.16 (13.47)8.33 (10.54)<0.0005 * • Social interaction subscale39.13 (13.19)9.2 (10.38)<0.0005 * • Performance subscale43.68 (14.04)9.67 (9.71)<0.0005 * • Total83.13 (24.77)19 (19.61)<0.0005 BMI Body Mass Index, GAD Generalised Anxiety Disorder, IPAQ International Physical Activity Questionnaire, LSAS Liebowitz Social Anxiety Scale, MDD: Major Depressive Disorder, PSQI Pittsburgh Sleep Quality Index, SAD Social Anxiety Disorder, SD Standard Deviation, SNRI Serotonin and Norepinephrine Reuptake Inhibitor, SSRI Selective Serotonin Reuptake Inhibitorp values based on Fisher’s exact test for categorical variables and Student’s T-tests for all continuous variables apart from alcohol (units per week) which was a nonparametric Mann-Whitney U test. p-values reaching statistical significance are accompanied by an asterisk. ## Dietary intake Based on FFQ analysis, the only significant difference in nutrient intake seen between the SAD patients and controls was in relation to carbohydrates (Table 2). SAD patients had greater intake of total carbohydrates ($U = 154$, z = −1.983, $$p \leq 0.047$$), which appeared to be driven by higher total sugar intake ($U = 140$, z = −2.31, $$p \leq 0.021$$) as other carbohydrate groups, starch and fibre, were equivalent in patients and controls. No other differences in nutrient groups, vitamins or minerals were seen between patients and controls. Table 2Dietary intake (median (IQR)) obtained from analysis of food frequency questionnaires. Recommended daily intakeControlSADp valueMedian (IQR)Median (IQR)(% total energy)(% total energy)Kilocalories2000–2400 (males; depending on activity level)1493 [1011]2255 [1771]0.12Protein (g)10–$35\%$ of total energy69 [41]($18.5\%$)90 [58]($16\%$)0.17Fat (g)20–$35\%$ of total calories60 [35]($36\%$)67 [78]($27\%$)0.66Carbohydrate (g)45–$65\%$ of total calories171 [123]($46\%$)258 [276]($46\%$)0.047 Monounsaturated fatty acids (g)>$12\%$ of total energy25 [14]($15\%$)26 [28]($10\%$)0.45Polyunsaturated fatty acids (g)>$6\%$ of total energy12 [11]($7\%$)13 [15]($5\%$)0.91Saturated fatty acids (g)<$10\%$ of total energy20 [15]($12\%$)21 [27]($8\%$)0.52Cholesterol (mg)300 mg225 [170]251 [153]0.58Total sugar (g)<$10\%$ of total energy80 [70]($21\%$)118 [87]($21\%$)0.02 Starch (g)107 [79]159 [171]0.08Fibre (g)>25 g16 [20]21 [21]0.60Vitamin A (µg)800 µg291 [686]189 [245]0.48Thiamine (mg)1.1 mg1.6 (1.1)2 (1.8)0.12Riboflavin (mg)1.4 mg1.3 [1]1.6 (2.8)0.17Niacin (mg)16 mg22.9 (15.7)31.5 (25.8)0.10Vitamin B6 (mg)1.4 mg2.3 (1.3)2.9 [3]0.17Vitamin B12 (µg)2.5 µg5.5 (4.7)5.2 (5.3)0.60Folate (µg)200 µg277 [266]350 [445]0.60Vitamin C (mg)80 mg90 [125]120 [121]0.61Vitamin D (µg)5 µg2.7 (3.4)2.8 (4.2)0.41Vitamin E (mg)12 mg9.8 (9.7)12.8 (13.3)0.60Phosphorous (mg)700 mg1020 [681]1310 [1402]0.38Calcium (mg)1000 mg464 [319]547 [438]0.78Iron (mg)7 mg12 [8]14 [20]0.48Selenium (µg)55 µg53 [38]66 [55]0.76Zinc (mg)10 mg7.8 (4.5)10.4 [8]0.32Sodium (mg)1600 mg2599 [1597]2877 [2293]0.33Potassium (mg)2000 mg3043 [1742]3422 [3731]0.61Magnesium (mg)375 mg302 [190]321 [296]0.50Copper (mg)1 mg1.1 (0.9)1.3 (1.4)0.52Chloride (mg)800 mg3713 [2413]4338 [3609]0.29Manganese (mg)2 mg3.6 (2.5)3.2 (3.1)0.68Iodine (µg)15 µg101 [59]117 [79]0.70(P-values based on results of non-parametric Mann-Whitney U tests. p values reaching statistical significance are accompanied by an asterisk). ## Compositional differences in the gut microbiota of SAD patients The gut microbiota of patients with SAD differed from those of healthy controls in terms of overall composition as well as in relation to specific genus- and species-level differentially abundant features. Beta diversity was found to be different between the two groups as measured by PERMANOVA ($$p \leq 0.038$$, R2 = 0.028) using the compositionally appropriate Aitchison distance metric (Fig. 1A). No differences were found between groups in alpha diversity, based on the Chao1, Shannon or Simpson indices (Fig. 1B).Fig. 1Gut Microbiota differences between SAD and control groups. A Beta diversity between SAD and healthy control groups, as measured by Aitchison Distance. p-value based on PERMANOVA test. B Alpha-diversity between SAD and healthy controls, as measured by Chao1, Simpson and Shannon indices. p-values based on Student’s t-tests. C Relative abundance of species-level taxa for each participant. Each column represents one participant. Genera that were never detected at a $10\%$ relative abundance or higher are aggregated and defined as rare taxa for the purposes of the stacked barplots. (* p = <0.05) (HC: Healthy Control, SAD: Social Anxiety Disorder). A total of 73 genera and 159 species were identified (Fig. 1C). Of these, three genera and two species were found to show significant differences in relative abundance after false discovery rate (FDR) correction using the Benjamini-Hochberg procedure. At the genus level, Anaeromassilibacillus and Gordonibacter were enriched in SAD while Parasutterella was more abundant in the control samples (Fig. 2A). At the species level, Anaeromassilibacillus sp An250 was found to be more abundant in SAD patients (padj = 0.024, effect size = −1.036). Specifically, this species was present in $48.4\%$ ($\frac{15}{31}$) of the SAD samples but only found in $5.6\%$ ($\frac{1}{18}$) of the control samples. Conversely, the bacterial species, Parasutterella excrementihominis was found to be enriched in healthy controls (padj = 0.042, effect size = 1.120) (Fig. 2B). After adjusting for age, sex, BMI, exercise and dietary differences (total carbohydrates), these genus- and species-level relative abundance differences remained significant. We found no statistically significant differences in the relative abundance of any microbial taxa between unmedicated SAD patients and those taking psychotropic medication or between those SAD participants with or without a history of MDD.Fig. 2Genus and species level differences between SAD and healthy controls. A Genus-level differences in relative abundance between SAD and controls seen in three genera; Anaeromassillibacillus and Gordonibacter are enriched in SAD while Parasutterella is enriched in healthy controls. B Species-level differences in relative abundance between SAD and controls; Anaeromassilibacillus sp An250 is increased in SAD while Parasuterella excrementihominis is enriched in healthy controls. (* p = <0.05) (Clr centred log-ratio transformed, HC Healthy Control, SAD Social Anxiety Disorder). ## Functional differences in the gut microbiome of SAD patients No differences were found in functional diversity between the two groups (Fig. 3A). We identified 69 of the 103 Gut-Metabolic Modules (GMMs) curated in the current database [33] and 26 of the 56 Gut-Brain Modules (GBMs) characterised by Valles-Colomer and colleagues [32]. No GBMs reached our threshold for significance after FDR correction. However, one GMM, Aspartate Degradation I, describing the capacity of the microbiome to degrade aspartate by the enzyme aspartate aminotransferase (AspAT), was found to be significantly more abundant in patients with SAD (padj = 0.150, effect size = −1.032) (Fig. 3B). This functional difference remained between the two groups after adjusting for age, sex, BMI, exercise and dietary differences (total carbohydrates).Fig. 3Functional differences between SAD and control groups. A One gut metabolic module, Aspartate Degradation I, was found to be increased in SAD patients. B Functional diversity, between SAD and healthy controls, as measured by Chao1, Simpson and Shannon indices. p values based on Student’s t-test. No differences seen between the groups. (* p = <0.05) (Clr centred log-ratio transformed, HC Healthy Control, SAD Social Anxiety Disorder). We then set out to investigate whether any microbial taxa or functional modules were associated with LSAS scores. After controlling the FDR, we did not detect any such associations (data not shown). ## Discussion This study demonstrates, for the first time, that the gut microbiome is compositionally and functionally altered in people with social anxiety disorder (SAD) compared with healthy controls. Moreover, it increases the growing evidence linking social brain function and the microbiome [27]. Firstly, we show that beta diversity, an indicator of overall microbiota composition, was significantly different between the two groups. The relative abundance of three genera, Anaeromassilibacillus, Gordonibacter and Parasutterella, and two corresponding species, Anaeromassilibacillus sp An250 and Parasutterella excrementihominis differed significantly between SAD patients and controls. Additionally, functional differences were evident with the microbiome of SAD patients enriched for the gut metabolic pathway, aspartate degradation I. Strikingly, we found Anaeromassilibacillus sp An250 to be present in almost half of our SAD group but in only one healthy control. Anaeromassilibacillus is a newly-discovered genus, which was first isolated in 2017 from the faecal sample of a 1-yo Senegalese patient with kwashiorkor [61]. Several strains of Anaeromassilibacillus, including sp An250 have since been identified from the caecal microbiome of chickens [62, 63]. Anaeromassilibacillus is a member of the Clostridiales order and Clostridiaceae family of bacteria [61], taxonomic groups which are increased in the gut microbiota of patients with autism spectrum disorder (ASD) [64], depression [65] and schizophrenia [66]. Conversely, various genera from the Clostridiales order were found to be reduced in the faecal microbiota of people with GAD [21] although Clostridiales was positively correlated with anxiety scores in a study analysing serum microbial DNA composition in patients with mood disorders [67]. Despite such inconsistencies, significant shifts in the abundance of Clostridiales taxa appears to be common to many psychiatric disorders and may represent disease-shared microbial responses [68]. Furthermore, preclinical studies suggest a link between Clostridiales and social behaviour. In a recent study, mice subjected to early life social isolation stress showed a significantly increased abundance of Clostridiales. These mice subsequently demonstrated reductions in sociability and social novelty behaviours, which negatively correlated with Clostridiales abundance [69]. In another study, mice exposed to a social stressor had increased relative abundance of the genus Clostridium [70]. However, it is difficult to extrapolate findings from animal studies to humans [71] especially with regards to a process as complex as social behaviour. Given the very recent addition of Anaeromassilibacillus to human microbiome databases, there is little in the existing literature about its role in human health and disease. It was one of several genera found to be enriched in untreated patients with MDD compared to those receiving antidepressant treatment, suggesting that it could be altered by psychotropic medication or be an indicator of treatment response [72]. Additionally, the relative abundance of Anaeromassilibacillus reduced significantly in the faeces of children with ASD after guar gum prebiotic supplementation, which was associated with reduced irritability and improved constipation [73], again suggesting that reduction of this genus may be associated with improved psychopathology. Thus, although the literature is sparse, Anaeromassilibacillus appears be of relevance in ASD and depression, psychiatric conditions which are highly comorbid with SAD [74, 75] and which may involve shared pathophysiological processes. Of note, we did not see a difference in the relative abundance of Anaeromassilibacillus in medicated, compared to, unmedicated SAD patients; although given the small sample size, this should be interpreted with caution. Gordonibacter is another genus about which relatively little is known. It is a member of the Eggerthellaceae family and Coriobacteriia class [76] and is notable in its ability to produce urolithins from the metabolism of polyphenols [77], which may have an impact on mental health [78]. Parasutterella has been more extensively studied. It is a member of the Sutterellaceae family and in humans, is largely represented by a single species, Parasutterella excrementihominis [79]. Similar to our finding of lower Parasutterella levels in SAD, members of this genus have also been found to be reduced in ASD [80]. Weight and dietary factors appear to be important influences. Parasutterella is negatively associated with BMI and waist circumference [81] and conversely, can be induced by high sugar [82] and high-fat diets [83]. Our SAD group had elevated sugar intake and did not differ in terms of fat intake but, although the group difference for Parasutterella excrementihominis remained after adjusting for BMI, increased BMI in the SAD group could contribute to its reduced abundance in SAD patients. It is difficult to interpret the importance and relevance of specific bacterial taxa differences in a patient group. The gut microbiome is a highly complex and dynamic ecosystem where microbes continuously interact with, and impact, one another and the host [84]. Attempts are underway to characterise microbial community structures and gain insights into the many complex microbe-microbe and host-microbe networks and interactions [85, 86]. Some human gut microbial groups appear to be highly influential and exert a metabolic impact on a substantial number of other microbial entities, so-called ‘network influencers’ [85]. None of our differentially expressed genera or species have been reported as being such core taxa or ‘influencers’ and it is unclear what these over- and under-represented taxa mean in the overall context of the gut microbial environment of SAD patients. With this in mind, exploring microbial function may offer deeper insights than relying on composition alone in an ever-changing ecosystem. Using GBMs and GMMs, which are microbiome-related functional pathways that have been manually curated from existing literature [32, 33], we identified one functional pathway that was enriched in the SAD group – aspartate degradation I. According to MetCyc, a comprehensive reference database of metabolic pathways and enzymes [87], this pathway involves the conversion of the amino acid, L-aspartate to the corresponding keto acid, oxaloacetate, by the enzyme, aspartate aminotransferase (AspAT). Several bacteria and archaea have demonstrated this enzymatic ability including Haloferax mediterranei [88], Pseudoalteromonas translucida TAC125 [89], Saccharolobus solfataricus [90] and Escherichia coli [91, 92]. Interestingly, bacterial AspAT enzyme activity may represent a link between gut microbiome function and the tryptophan-kynurenine pathway, a key physiological system in psychiatric disorders. There are significant interactions between tryptophan metabolism and the MGB axis [93–96] and the gut microbiome may influence host diet selection behaviour by mediating the availability of essential amino acids such as tryptophan [97]. Tryptophan metabolism involves the downstream conversion of kynurenine to kynurenic acid (KYNA) by the enzyme kynurenine aminotransferase (KAT). KYNA is an important neuroactive substance, which is elevated by chronic stress in animal models [98, 99] as well as in psychiatric conditions such as schizophrenia [100, 101] and SAD [102]. Notably, KAT activity has been detected in E. coli cells in vitro, and authors suggested that the source of KYNA detected in the rat small intestine could be the gut bacteria [103]. This bacterial KAT enzyme protein has been identified as being identical to the bacterial AspAT enzyme [104] and thus, an elevation in the ‘aspartate degradation I’ functional pathway may represent increased KAT, as well as AspAT potential, by the microbiome. While currently a hypothetical supposition, it is possible that the elevated peripheral KYNA which we previously reported in SAD patients [102] may be linked with the key functional difference seen in the microbiome of this group. In support of this hypothesis is the fact that D-cycloserine, an orally-administered, broad-spectrum antibiotic, has been found to enhance the treatment response to exposure therapy for SAD [105, 106], an effect which could plausibly be related to its ability to inhibit KAT activity and lower KYNA [107]. This is, to our knowledge, the first study to investigate the composition and function of the gut microbiome in patients with SAD and has several notable strengths. Firstly, our sample consisted of carefully selected patients with a pre-existing clinical diagnosis of SAD who had sought treatment from a mental health professional. Secondly, we used a whole genome shotgun sequencing method, providing information on the functional capacity of the microbiome, as well as offering greater resolution of bacterial species identification, than the more commonly used 16 S rRNA gene sequencing [108, 109]. Thirdly, we took into account many of the important host variables known to confound gut microbiota studies in human disease [110]. Stool quality is a particularly strong source of gut microbiota variance [110, 111] which has often been neglected in psychiatric microbiota studies. Stool consistency, as measured by the BSC, was matched between our groups, as were other important variables, including age, sex, birth delivery mode, smoking status and alcohol. Our groups were not matched in terms of BMI and exercise levels, variables which may be of relevance to the gut microbiome [112, 113]. Although adjusting for these variables did not affect group differences, it would, of course, be preferable to have samples with equivalent BMI and exercise scores. Additionally, we collected detailed dietary information, which has often been lacking in studies of the microbiota in psychiatric conditions. Our groups were well-matched in terms of overall dietary intake. The only difference seen was in relation to carbohydrate consumption, driven by higher sugar intake in the SAD group, and this was adjusted for in our statistical analyses. Study limitations include the small sample size and the single-time point nature of the study, which prevents the establishment of any causal relationships. Additionally, two thirds of our SAD patients were taking psychotropic medication, which may have had an impact on microbiota composition [114, 115]. The majority of our medicated patients were taking an SSRI antidepressant, escitalopram being the most commonly prescribed. Escitalopram has antibacterial activity against some gut commensal strains in vitro [116, 117] although this effect did not translate to an in vivo animal model [117]. Other prescribed SSRIs in our patient group included fluoxetine, citalopram, sertraline, paroxetine and vortioxetine, all of which have shown varying levels of antibacterial activity in vitro [117–120], with in vivo evidence available for fluoxetine [121–123]. The SNRI venlafaxine, conversely, does not appear to impact common gut commensals in vitro [116, 117], although an influence on the microbial richness and on the abundance of certain genera were seen in a mouse model [122]. Thus, the translatability of studies using isolated in-vitro strains to animal models is unclear, with even more uncertainty in relation to their applicability to the human gut microbiome. Limited human data in relation to the effect of antidepressants on the microbiome is available. A small study of 17 depressed patients commenced on escitalopram, found no significant differences in beta-diversity or in any taxa levels between pre-treatment and 6-week post-treatment time-points, although increased alpha diversity was evident [124]. Furthermore, a longitudinal study of 40 patients with depression and/or anxiety revealed no difference in beta diversity between those taking, and not taking, antidepressant medications and no change in alpha diversity in antidepressant-treated patients between baseline and endpoint timepoints. Antipsychotic medications, conversely, did appear to exert an effect on the gut microbiome [125], consistent with previous findings [126]. Two of our patients were prescribed low-dose Quetiapine, a second-generation antipsychotic which thus may have had an impact. Aside from impacting microbiota composition, it is also of course possible that psychotropic medications could have influenced functional pathways. A recent study demonstrated that oral intake of fluoxetine or amitriptyline by rats exposed to chronic unpredictable mild stress resulted in alterations in KEGG metabolic pathways, particularly those pathways concerning carbohydrate metabolism, membrane transport, and signal transduction [127]. However, no such alterations in KEGG pathways were seen in a longitudinal follow-up of psychiatric patients taking antipsychotics, antidepressants and/or anxiolytic medications [125]. In an approach similar to ours, these authors also chose to analyse GBMs in the psychiatric group, although they specifically investigated only 6 of the 56 available GBMs, namely those involving GABA and tryptophan synthesis or degradation. They found alterations in certain GBMs in patients taking antipsychotics and antidepressants but not in those taking anxiolytic medications. All in all, although there is clear evidence that many antidepressants have antibacterial effects, this evidence is based primarily on in-vitro and animal studies, and the impact of these medications on the human gut microbiome structure and function remain largely unknown. Although we did not find any differences in the relative abundance of any taxa between medicated and unmedicated patients, we cannot rule out a potential influence. Finally, some of our SAD patient group had a past history of depression and/or a comorbid anxiety disorder. However, patients with a current depressive episode were excluded and in all, the primary diagnosis was SAD with any comorbid anxiety disorder representing a secondary diagnosis. Although we did not find a difference in the relative abundance of any taxa in those SAD patients with or without a past history of MDD, this must be interpreted with caution given the small numbers of such sub-groups. While it is not possible to disentangle the currently reported observations from the past psychiatric history of study participants, this was a clinically representative sample and we believe that including such patients makes our findings more generalizable considering the significant overlap between depression and anxiety disorders. Given the paucity of studies exploring the gut microbiome in any clinical anxiety disorders, our findings, despite the limitations, are important in generating a foundational base for larger, prospective and interventional microbiome studies in these highly prevalent and disabling psychiatric conditions. Additionally, future preclinical studies and secondary validation experiments would offer a complementary approach to confirm the presence and role of these differentially expressed bacterial taxa and functional pathways. Earlier compositional microbiota studies in depression [60, 128] and ASD [129, 130] have paved the way for a variety of successful interventional studies utilising probiotics [25], dietary change [131] and faecal microbiota transplantation (FMT) [132] in these conditions. It is hopeful that microbiota-based therapeutic interventions may also be realised for patients with clinical anxiety disorders. Indeed, a previous cross-sectional study in university students has suggested that consumption of fermented foods may be protective against the development of social anxiety [31]. In conclusion, the gut microbiome of patients with SAD differs in composition and function to that of healthy controls, raising the possibility that the MGB axis may represent a biomarker reservoir and potential therapeutic target for this early-onset, chronic disorder. 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--- title: An intervention study on a hospital-community integrated management model of tobacco dependence based on a community intervention trial authors: - Kun Qiao - Han Liu - Xingming Li - Qianying Jin - Yao Wang - Mingyu Gu - Xinyuan Bai - Tingting Qin - Yutong Yang journal: Frontiers in Psychiatry year: 2023 pmcid: PMC10027698 doi: 10.3389/fpsyt.2023.1029640 license: CC BY 4.0 --- # An intervention study on a hospital-community integrated management model of tobacco dependence based on a community intervention trial ## Abstract ### Objective To assess the effect of the hospital-community integrated management model of tobacco dependence on smoking cessation among community residents compared with a brief smoking cessation intervention. ### Methods Our study recruited 651 smokers who were willing to quit in 19 communities in Beijing and conducted a 6-month smoking cessation intervention. The control group receiving a brief smoking cessation intervention and the pilot group receiving an integrated smoking cessation intervention. Intention-to-treat analysis (ITT) and generalized estimating equations were used to assess the effects of the integrated intervention and smoking cessation medication on average number of cigarettes smoked per day (ACSD) and smoking cessation rate. ### Results Simple effects analysis showed that smokers taking medication had significantly lower ACSD than those not taking medication at follow-up, the control group reduced smoking by 3.270, 4.830, and 4.760 cigarettes in the first, third and sixth months, respectively, while the pilot group reduced by 6.230, 5.820, and 4.100 cigarettes. The integrated intervention significantly reduced ACSD among medication-taking smokers at 1st month (reduced by 3.420, $P \leq 0.05$) and 3rd month (reduced by 2.050, $P \leq 0.05$), but had no significant effect among non-medication taking smokers. The 3rd month smoking cessation rate among medication-taking smokers was $27.0\%$, which was significantly higher than the smokers with brief smoking cessation intervention. ### Conclusion The integrated hospital-community intervention can significantly promote smoking cessation among smokers taking medication, but the issue of payment for medication and additional labor compensation for medical staff should be addressed before its popularization. ## 1. Introduction Tobacco harm is a significant public health problem in the world today. Smoking can lead to cardiac disease, chronic respiratory disease, cancer, and many other diseases [1]. In 2019, more than 8 million people died from tobacco-related diseases worldwide [2]. China is the world’s largest producer and consumer of tobacco, with nearly 300 million smokers. In China, the smoking rate among people aged 15 years and older was $23.5\%$ in 2020, far above the global average of $17.0\%$ [2]. In addition, more than 1 million people die each year from smoking-related diseases in China, which will increase to 2 million per year by 2030 if no effective action is taken [3]. Smoking cessation is the top priority for preventing smoking-related diseases [4]. The earlier smokers quit smoking and the longer they continue, the greater health gains they will achieve. Despite this, the willingness of Chinese smokers to quit is low [5]. According to the Report of International Tobacco Control (ITC) Policy Evaluation Project China Survey Round 1–5, the proportion of smokers in China who have no intention to quit is as high as $59\%$. Another survey showed that $19.8\%$ of those who had smoked in the past 12 months had tried to quit, $16.1\%$ of current smokers intended to quit within the next 12 months, and only $5.6\%$ of smokers planned to quit within 1 month [6]. Scientific and professional guidance is indispensable to quit smoking. Smokers’ success in quitting is influenced by both individual factors, such as their own health status and economic status, and social factors, such as family environment, interpersonal, relationships and policies. The main smoking cessation interventions commonly used internationally are psychological and behavioral interventions, pharmacological therapies, telephone intervention therapies, and integrated interventions, etc. [ 7]. Smoking cessation clinics are an effective way to implement smoking cessation interventions, and they are irreplaceable in persuading smokers to quit. Some countries and institutions have also started to implement smoking cessation clinics on a large scale, with considerable success [8]. However, in China, the rate of independent smoking cessation among residents is high. The ITC survey showed that the proportion of tobacco-dependent patients in China who did not use any method in quitting smoking was as high as $90.1\%$. In a study by Yang et al. [ 9], $87.6\%$ of smokers had not received help to quit. In addition, although smoking cessation clinics are well equipped with hardware and other aspects, the consultation rate of smoking cessation clinics in China has been very low for a long time [8, 10]. The main reasons for this are the low awareness of smoking cessation clinics, the self-payment for smoking cessation medications, the lack of specific drugs for medications and adverse drug reactions, etc. [ 11]. Tobacco dependence is a chronic disease with a long-term treatment. Community-based smoking cessation interventions can provide comprehensive, continuous, and proactive management for smokers. The main focus of community-based smoking cessation interventions abroad is not limited to community health centers. A study in the United States used external resources such as the media and social organizations to create a social climate that did not support smoking behavior to increase smoking cessation rate [12]. In the UK, pharmacists or pharmacy assistants in community pharmacies provide smokers medication counseling, smoking cessation education, and health behavior advice, combined with smoking cessation medications for intervention [13]. A study in Thailand combined pharmacists and community health workers (CHWs) to deliver smoking cessation interventions to smokers [14]. Compared to smoking cessation clinics in general hospitals, communities have advantages in terms of geography and price. In China, cessation interventions in community clinics mainly consist of providing health education on the dangers of tobacco and brief smoking cessation interventions [15, 16]. The main problem is the lack of long-term effective systematic management, continuous technical support and social support environment for smoking cessation. In conclusion, the integrated hospital-community model has become another feasible way to solve the current problem of smoking cessation in China. Based on the above status, the program team designed a study to optimize the hospital and community-based tobacco dependence management model by focusing on hospital and community strengths. This paper uses adult smokers who participated in the program as the study population to evaluate the effectiveness of this integrated intervention on smoking cessation for community residents in Beijing. ## 2.1. Design Search for Optimization of Tobacco Dependence Management Model Based on Hospital and Community (2017YFC1309404) was funded by the 2017 Key Research and Development Program of the Ministry of Science and Technology of China. This is a community intervention trial implemented in Beijing, with 19 community health service centers selected as investigation and follow-up sites in Beijing by convenience sampling method from December 2019 to June 2020. ## 2.2. Participants Before the implementation of the intervention, this study conducted program publicity in community health centers to recruit smokers who were willing to quit. The general practitioner team helped to mobilize smokers to attend and encourage them to participate in the program. Sample size calculation was based on the 2018 National Adult Tobacco Survey report [6], which showed that the proportion of smokers who tried to quit within the past 12 months was $19.8\%$, the $95\%$ CI corresponded to a Z value of 1.96 and the accuracy δ value was taken as 0.05. And the sample size calculation formula used was: The minimum sample size required was calculated to be 245 each group. In this study, 683 willing quitters were finally recruited by screening according to the following inclusion and exclusion criteria. Inclusion criteria: ➀ Age 18 years or older; ➁ Smokers who are permanent residents of the community and can complete subsequent follow-up; ➂ Patients who can communicate fluently and are willing to participate in; ➃ Willingness to quit smoking; and ➄ Signing an informed consent form. Exclusion criteria: ➀ Below age 18; ➁ Non-smokers; ➂Those with serious mental illness or unable to communicate; and ➃ Smokers who have no intention to quit at all or are unwilling to participate in the trial. At the baseline, smokers who met the inclusion and exclusion criteria had one-to-one questionnaires completed by the investigators. Before the intervention, investigators received unified and systematic training. The training included familiarization with the questionnaire structure, explanation of questionnaire terminology, common problems in questionnaire completion, and the use of the CO monitor (Micro+ Smokerlyzer, produced by Bedfont). After completing the questionnaire, the investigators measured the CO levels of the respondents through the CO monitor and recorded them on the questionnaire. Finally, if the survey respondents’ community was included as the pilot group community, the respondents were added to the WeChat group chat of that community. WeChat is the most popular social networking software in China [17], users can communicate with other users individually or establish a group to communicate together through WeChat. In this study, The WeChat group chat was managed by the program team, and each community established a group chat with members including survey respondents, as well as general practitioner team members, smoking cessation clinic physicians, program team members, and a graduate student majoring in psychology. ## 2.3. Interventions In this study, communities were divided into pilot and control groups with matched socio-economic, population health and community health service characteristics and smokers were allocated to either the pilot or control group depending on their community. A double-blind approach was used throughout the intervention. In the control group, a 5A brief smoking cessation intervention [18] was implemented. After being asked about tobacco use and related health information at the baseline, smokers were given personalized guidance and advice on their smoking cessation plan. If smokers needed the assistance of smoking cessation medication, physician from smoking cessation clinic would decide whether to provide medication based on the smoker’s medical condition and medication use, etc. At the same time, smokers were informed of other ways to obtain smoking cessation support, such as visiting hospital’s smoking cessation clinic, calling the smoking cessation hotline. After that, no additional interventions are made except for a brief intervention for the control group at the follow-up. In addition to brief smoking cessation interventions, the interventions in the pilot group also included online and offline health education activities. Interventions in the pilot and control groups are shown in Table 1. The offline activities were mainly held in community health centers, where smoking cessation clinicians provided medication and counseling guidance to quitters. Special thematic educational activities would also be held, such as lectures on smoking cessation knowledge, peer group activities on smoking cessation, etc. On average, each community in the pilot group offered 1–2 times during the intervention period. The online interventions were mainly implemented through the smoking cessation WeChat groups in each community. Interventions include: TABLE 1 \| Unnamed: 0 \| Baseline \| 1st month \| 3rd month \| 6th month \| \| --- \| --- \| --- \| --- \| --- \| \| Control group \| Questionnaire brief smoking cessation intervention \| Brief smoking cessation intervention follow-up \| Brief smoking cessation intervention follow-up \| Brief smoking cessation intervention follow-up \| \| Pilot group \| Questionnaire brief smoking cessation intervention online activities offline activities \| Brief smoking cessation intervention online activities offline activities follow-up \| Brief smoking cessation intervention online activities offline activities follow-up \| Brief smoking cessation intervention follow-up \| (a) Organizing health education professionals to regularly send out weekly educational materials. (b) Providing online answers to questions and concerns by smoking cessation clinics and general practitioners. (c) Providing psychological coping plans for quitters with withdrawal symptoms by psychological professionals. (d) Notifying activities and follow-up, online discussions among group members, and sharing feelings and experiences about quitting smoking. (e) Providing online professional counseling and guidance for psychological barriers brought on by withdrawal symptoms. At the same time, the program team designed and produced promotional materials such as smoking cessation-related folders, posters, and easy-to-use posters for tobacco-dependent patients at different stages. No further online and offline interventions were conducted in the pilot group after 3 months’ intervention. After conducting the intervention, follow-up was conducted at week 4 (1st month), week 12 (3rd month), and week 24 (6th month). General practitioner in community health institutes and doctors in smoking cessation clinicians of general hospital were involved in the follow-up conducted at community health center. At the follow-up, smoking cessation status of quitters was recorded along with the number of cigarettes smoked in the week before the follow-up. Quitters who were present had the CO testing, and those who were absent were followed up by telephone and their smoking cessation medications were delivered to them by the general practitioners. ## 2.4. Outcomes Average number of cigarettes smoked per day (ACSD) at the 1st, 3rd, and 6th month and smoking cessation rates in the 3rd and 6th month were selected as the evaluation index of the intervention effect. There is no standardized criterion for successful smoking cessation, 7-day point prevalence abstinence and 30-day point prevalence abstinence, as well as long- term/prolonged/sustained rates abstinence to evaluate the effectiveness of smoking cessation interventions (19–21). This paper defines successful smoking cessation as a smoker’s self-reported ACSD of 0 at the follow-up. In addition, the integrated intervention in this study was stopped at 3rd month. Intention-to-treat analysis (ITT analysis) [12, 22] method was used for those who fell off midway through the study. If smokers did not attend the follow-up, their ACSD at the most recent follow-up was used as the outcome of the current follow-up, as was their smoking cessation status. Related studies have shown that using ITT followed by analysis leads to conservative results [23, 24]. ## 2.5. Analysis Statistical Product and Service Solutions 19.0 (SPSS 19.0) was used to process the data. Descriptive statistics were used to report demographic information of the population in the pilot and control groups. Differences in demographic information and smoking cessation rates between the two groups were analyzed using chi-square tests. Measures were expressed as mean ± standard deviation (x^±s). The effects of the intervention and medications taking on ACSD were analyzed using generalized estimating equations [25]. Differences were considered statistically significant at a two-sided P ≤ 0.05. The generalized estimating equations (GEE) were used instead of traditional regression to explore the association between factors [26] or to predict the influencing factors. Its main feature is the introduction of the working correlation matrix, which takes into consideration the correlation between the outcome variables of repeated measures information [27]. The form of the working correlation matrix should be predetermined before fitting the model. Related studies have pointed out that the choice of the working correlation matrix has little effect on parameter estimation [28]. In the present study, which was the same cohort population from intervention to follow-up, the ACSD may be correlated, so an unstructured working correlation matrix was chosen for the analysis. In the analysis of repeated data such as GEE, if there is an interaction effect, then the difference between the main effect and the difference between the overall mean at the corresponding level does not correspond. A further simple effects analysis should be performed to infer whether there is a difference between the corresponding means of a variable at different levels of another variable [29]. ## 3. Result A total of 683 smokers with the intention to quit were recruited in this study. Of these, 32 reported an ACSD of 0 at baseline survey and were not included in this study. Of the remaining 651, 382 were included in the pilot group and 269 in the control group, and the follow-up is shown in Figure 1. The largest number of people were lost at the first follow-up (321, $49.3\%$), and 413 ($63.4\%$) were not shed at the end of the intervention. FIGURE 1: Baseline and follow-up of smokers included in the study. The demographic characteristics of smokers in both groups are shown in Table 2. The main characteristics of the population in this survey were middle-income, urban household, middle-aged, and married males. The chi-square test showed no significant differences in distribution of the pilot and control groups in terms of gender, monthly income level, and average daily smoking ($P \leq 0.05$). There was a significant difference in distribution of smokers in terms of age, marital status, education, work type, registered residence, and health insurance ($P \leq 0.05$). TABLE 2 \| Demographic characteristics \| Demographic characteristics.1 \| Total \| Pilot group (n = 382) \| Pilot group (n = 382).1 \| Control group (n = 269) \| Control group (n = 269).1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| Frequency \| Percent (%) \| Frequency \| Percent (%) \| \| Gender \| Gender \| Gender \| Gender \| Gender \| Gender \| Gender \| \| Male \| Male \| 590 \| 352 \| 92.1 \| 238 \| 88.5 \| \| Female \| Female \| 61 \| 30 \| 7.9 \| 31 \| 11.5 \| \| Age* \| Age* \| Age* \| Age* \| Age* \| Age* \| Age* \| \| Under 39 years old \| Under 39 years old \| 100 \| 49 \| 12.9 \| 51 \| 19.0 \| \| 40–49 years old \| 40–49 years old \| 98 \| 49 \| 12.9 \| 49 \| 18.3 \| \| 50–59 years old \| 50–59 years old \| 192 \| 112 \| 29.5 \| 80 \| 29.9 \| \| 60 years old and above \| 60 years old and above \| 258 \| 170 \| 44.7 \| 88 \| 32.8 \| \| Marriage* \| Marriage* \| Marriage* \| Marriage* \| Marriage* \| Marriage* \| Marriage* \| \| Married \| Married \| 568 \| 342 \| 90.2 \| 226 \| 84.3 \| \| Others \| Others \| 79 \| 37 \| 9.8 \| 42 \| 15.7 \| \| Average monthly income (RMB) \| Average monthly income (RMB) \| Average monthly income (RMB) \| Average monthly income (RMB) \| Average monthly income (RMB) \| Average monthly income (RMB) \| Average monthly income (RMB) \| \| Below 2,000 \| Below 2,000 \| 71 \| 40 \| 11.7 \| 31 \| 13.2 \| \| 2,001–4,000 \| 2,001–4,000 \| 187 \| 111 \| 32.4 \| 76 \| 32.5 \| \| 4,001–6,000 \| 4,001–6,000 \| 152 \| 103 \| 30.0 \| 49 \| 20.9 \| \| 6,001–8,000 \| 6,001–8,000 \| 63 \| 34 \| 9.9 \| 29 \| 12.4 \| \| 8,001–10,000 \| 8,001–10,000 \| 45 \| 23 \| 6.7 \| 22 \| 9.4 \| \| 10,001 and more \| 10,001 and more \| 59 \| 32 \| 9.3 \| 27 \| 11.5 \| \| Education* \| Education* \| Education* \| Education* \| Education* \| Education* \| Education* \| \| Elementary school and below \| Elementary school and below \| 44 \| 16 \| 4.2 \| 28 \| 10.4 \| \| Middle and high school \| Middle and high school \| 345 \| 208 \| 54.7 \| 137 \| 51.1 \| \| College and above \| College and above \| 259 \| 156 \| 41.1 \| 103 \| 38.4 \| \| Work type* \| Work type* \| Work type* \| Work type* \| Work type* \| Work type* \| Work type* \| \| Production staff, operators, or clerical staff \| Production staff, operators, or clerical staff \| 67 \| 37 \| 9.8 \| 30 \| 11.3 \| \| Commercial or service industry personnel \| Commercial or service industry personnel \| 76 \| 33 \| 8.8 \| 43 \| 16.2 \| \| State organs, enterprises, and institutions personnel \| State organs, enterprises, and institutions personnel \| 68 \| 43 \| 11.4 \| 25 \| 9.4 \| \| Professional and technical staff \| Professional and technical staff \| 57 \| 29 \| 7.7 \| 28 \| 10.5 \| \| Military, students, unemployed, and other workers \| Military, students, unemployed, and other workers \| 96 \| 49 \| 13.0 \| 47 \| 17.7 \| \| Retirees \| Retirees \| 279 \| 186 \| 49.3 \| 93 \| 35.0 \| \| Registered residence* \| Registered residence* \| Registered residence* \| Registered residence* \| Registered residence* \| Registered residence* \| Registered residence* \| \| Urban \| Urban \| 528 \| 326 \| 87.6 \| 202 \| 78.3 \| \| Rural \| Rural \| 102 \| 46 \| 12.4 \| 56 \| 21.7 \| \| Health insurance* \| Health insurance* \| Health insurance* \| Health insurance* \| Health insurance* \| Health insurance* \| Health insurance* \| \| Urban employee insurance \| Urban employee insurance \| 377 \| 241 \| 63.6 \| 136 \| 51.1 \| \| Urban residents’ insurance \| Urban residents’ insurance \| 154 \| 90 \| 23.7 \| 64 \| 24.1 \| \| Others \| Others \| 114 \| 48 \| 12.7 \| 66 \| 24.8 \| \| ACSD (Average cigarettes smoked per day) \| ACSD (Average cigarettes smoked per day) \| ACSD (Average cigarettes smoked per day) \| ACSD (Average cigarettes smoked per day) \| ACSD (Average cigarettes smoked per day) \| ACSD (Average cigarettes smoked per day) \| ACSD (Average cigarettes smoked per day) \| \| \| \| 651 \| 17.75 ± 9.509 \| 17.75 ± 9.509 \| 17.17 ± 9.805 \| 17.17 ± 9.805 \| ## 3.1. Analysis of generalized estimating equations of ACSD The model effect test results generalized estimating equation showed that there was a main effect of time, medication taking, and intervention factors ($P \leq 0.05$), as well as an interaction effect of those three factors ($P \leq 0.05$). When there is an interaction effect, the equation analysis is mainly based on the results of simple effect analysis. The results of simple effects analysis of medication taking factor showed that at baseline, there was no significant difference in ACSD in smokers taking the medication compared to smokers not taking the medication in either the control or pilot group (P-values of 0.703 and 0.526, respectively, both greater than 0.05). After 1, 3, and 6 months of intervention, there was a significant difference in ACSD in smokers taking medication compared to those not taking medication ($P \leq 0.05$). In terms of mean differences, at each follow-up, smokers taking medication had lower ACSD than those not taking medication, and the difference decreased over time, as detailed in Table 3. TABLE 3 \| Group \| Time \| Mean difference* \| SE \| P \| \| --- \| --- \| --- \| --- \| --- \| \| Control group \| Baseline \| −0.473 (−2.902, 1.956) \| 1.239 \| 0.703 \| \| \| 1st month \| 3.270 (0.707, 5.829) \| 1.307 \| 0.012 \| \| \| 3rd month \| 4.830 (2.445, 7.224) \| 1.219 \| P < 0.001 \| \| \| 6th month \| 4.760 (2.217, 7.307) \| 1.299 \| P < 0.001 \| \| Pilot group \| Baseline \| −0.669 (−2.736, 1.399) \| 1.055 \| 0.526 \| \| \| 1st month \| 6.230 (4.233, 8.233) \| 1.02 \| P < 0.001 \| \| \| 3rd month \| 5.820 (3.949, 7.684) \| 0.953 \| P < 0.001 \| \| \| 6th month \| 4.100 (2.176, 6.033) \| 0.984 \| P < 0.001 \| The results of time factor simple effects analysis for non-medication-taking group showed that there was a significant difference in ACSD between the pilot and control groups at each follow-up ($P \leq 0.05$). The mean difference showed that ACSD among smokers decreased significantly with the intervention time. The results of time factor simple effects analysis for medication-taking group showed that there was a significant difference ($P \leq 0.05$) in ACSD among smokers in the control group at each follow-up, with a significant reduction of 8.650 cigarettes per day in 6 months of intervention. In addition, the difference between ACSD at the two adjacent follow-up decreased gradually, the reduction in ACSD mainly occurring in the first 3 months after the intervention. The ACSD of smokers in the pilot group at each follow-up after the intervention was significantly different from the ACSD at baseline ($P \leq 0.05$), the ACSD after 1 month of the intervention was significantly different from the ACSD after 3 and 6 months of the intervention ($P \leq 0.05$), and the ACSD after 3 months of the intervention was not significantly different from the ACSD after 6 months of the intervention ($$P \leq 0.708$$), as detailed in Table 4. TABLE 4 \| Unnamed: 0 \| Group \| Time (I) \| Time (J) \| Mean difference (I-J) \| SE \| P \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Non-medication-taking \| Control group \| Baseline \| 1st month \| 1.120 (0.351, 1.884) \| 0.391 \| 0.004 \| \| \| \| Baseline \| 3rd month \| 2.390 (1.011, 3.766) \| 0.703 \| 0.001 \| \| \| \| Baseline \| 6th month \| 3.410 (1.832, 4.992) \| 0.806 \| P < 0.001 \| \| \| \| 1st month \| 3rd month \| 1.270 (0.004, 2.537) \| 0.646 \| 0.049 \| \| \| \| 1st month \| 6th month \| 2.290 (0.786, 3.803) \| 0.77 \| 0.003 \| \| \| \| 3rd month \| 6th month \| 1.020 (0.113, 1.934) \| 0.465 \| 0.028 \| \| \| Pilot group \| Baseline \| 1st month \| 0.890 (0.242, 1.540) \| 0.331 \| 0.007 \| \| \| \| Baseline \| 3rd month \| 2.78 (1.406, 4.145) \| 0.699 \| P < 0.001 \| \| \| \| Baseline \| 6th month \| 4.600 (2.879, 6.323) \| 0.879 \| P < 0.001 \| \| \| \| 1st month \| 3rd month \| 1.880 (0.684, 3.085) \| 0.612 \| 0.002 \| \| \| \| 1st month \| 6th month \| 3.710 (2.086, 5.335) \| 0.829 \| P < 0.001 \| \| \| \| 3rd month \| 6th month \| 1.830 (0.658, 2.994) \| 0.596 \| 0.002 \| \| Medication-taking \| Control group \| Baseline \| 1st month \| 4.860 (3.798, 5.919) \| 0.541 \| P < 0.001 \| \| \| \| Baseline \| 3rd month \| 7.700 (6.444, 8.948) \| 0.639 \| P < 0.001 \| \| \| \| Baseline \| 6th month \| 8.650 (7.334, 9.959) \| 0.67 \| P < 0.001 \| \| \| \| 1st month \| 3rd month \| 2.840 (1.794, 3.880) \| 0.532 \| P < 0.001 \| \| \| \| 1st month \| 6th month \| 3.790 (2.606, 4.970) \| 0.603 \| P < 0.001 \| \| \| \| 3rd month \| 6th month \| 0.950 (0.184, 1.718) \| 0.391 \| 0.015 \| \| \| Pilot group \| Baseline \| 1st month \| 7.790 (6.758, 8.828) \| 0.528 \| P < 0.001 \| \| \| \| Baseline \| 3rd month \| 9.260 (8.057, 10.463) \| 0.614 \| P < 0.001 \| \| \| \| Baseline \| 6th month \| 9.380 (8.124, 10.626) \| 0.638 \| P < 0.001 \| \| \| \| 1st month \| 3rd month \| 1.470 (0.546, 2.388) \| 0.47 \| 0.002 \| \| \| \| 1st month \| 6th month \| 1.580 (0.564, 2.600) \| 0.519 \| 0.002 \| \| \| \| 3rd month \| 6th month \| 0.115 (−0.485, 0.714) \| 0.306 \| 0.708 \| Results of intervention factor simple effects analysis for non-medication taking smokers showed that there was no significant difference in ACSD between the pilot and control groups at each follow up ($P \leq 0.05$). Results of the intervention factor simple effect analysis for medication taking smokers showed that there was no significant difference in ACSD between pilot and control groups at baseline ($P \leq 0.05$). Compared to the control group, the ACSD in pilot group decreased by 3.420 and 2.050 cigarettes after 1 and 3 months of intervention, both of which were significantly different ($P \leq 0.05$). After 6 months of intervention, there was no significant difference between this two groups in terms of ACSD ($$P \leq 0.141$$). The effect of integrated intervention was mainly concentrated in first 3 months, as detailed in Table 5. TABLE 5 \| Unnamed: 0 \| Time \| Mean difference* \| SE \| P \| \| --- \| --- \| --- \| --- \| --- \| \| Non-medication-taking \| Baseline \| 0.680 (−1.930, 3.300) \| 1.332 \| 0.607 \| \| \| 1st month \| 0.460 (−2.220, 3.130) \| 1.365 \| 0.737 \| \| \| 3rd month \| 1.070 (−1.520, 3.670) \| 1.324 \| 0.418 \| \| \| 6th month \| 1.870 (−0.880, 4.630) \| 1.404 \| 0.182 \| \| Medication-taking \| Baseline \| 0.489 (−1.343, 2.320) \| 0.934 \| 0.601 \| \| \| 1st month \| 3.420 (1.580, 5.266) \| 0.94 \| P < 0.001 \| \| \| 3rd month \| 2.050 (0.484, 3.623) \| 0.801 \| 0.010 \| \| \| 6th month \| 1.217 (−0.402, 2.837) \| 0.826 \| 0.141 \| Figure 2 shows the mean marginal estimates of ACSD in the control group and the pilot group at each follow-up after grouping according to whether they took medication. FIGURE 2: Mean marginal estimates by medication-taking subgroup. ## 3.2. Chi-square test of smoking cessation rates of smokers Grouped according to whether they were taking medication or not, the smoking cessation rates were calculated separately for the control and pilot groups after 3 and 6 months’ intervention, and the results are shown in Table 6. The difference in smoking cessation rates of non-medication taking smokers between the pilot and control groups was not significant ($P \leq 0.05$), implying that the brief and integrated interventions were similar in promoting smoking cessation among non-medication smokers. There was a significant difference in smoking cessation rates of medication taking smokers between the pilot and control groups at 3rd month ($$P \leq 0.019$$). However, after stopping the intervention, the difference in smoking cessation rates between the two groups at the 6th month was not significant ($$P \leq 0.231$$). TABLE 6 \| Unnamed: 0 \| Group \| 3rd month \| 3rd month.1 \| 6th month \| 6th month.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| Number of quitters \| Percentage (%) \| Number of quitters \| Percentage (%) \| \| Non-medication-taking \| Control group \| 6 \| 7.1 \| 13 \| 15.3 \| \| \| Pilot group \| 10 \| 7.2 \| 22 \| 15.9 \| \| χ2, p \| \| 0.003, 0.958 \| \| 0.017, 0.897 \| \| \| Medication-taking \| Control group \| 32 \| 17.4 \| 44 \| 23.9 \| \| \| Pilot group \| 66 \| 27.0 \| 71 \| 29.1 \| \| χ2, p \| \| 5.542, 0.019 \| \| 1.435, 0.231 \| \| ## 4. Discussion This study combined a general hospital and a community health centers, taking advantage of the technical strengths of the smoking cessation clinics in general hospital and the distance advantages of the community health centers. The general hospital trained community doctors in smoking cessation skills, and the community smoking cessation clinic conducted follow-up assessments of patients. After the implementation of the general practitioner contracting system in China, the relationship between general practitioners and their contracted families is more stable and communication between doctors and patients is relatively smooth, which is helpful for timely health guidance [30, 31]. Currently, community health workers have more positive attitudes toward tobacco control, but their behavior in delivering smoking cessation interventions is less than ideal [32], providing a favorable opportunity for programme implementation in the community. Therefore, with the concerted efforts of these staff, a better operation mode have been formed, in which the health administration department and social management department provided support, and the research group organized and implemented systematic hospital community smoking cessation intervention, see Figure 3. Studies in the United States [33], the United Kingdom [13], and Thailand [14] have shown that community-based interventions have good effects on smoking cessation, and these studies demonstrate the critical importance of the external environment in helping smokers to quit [34]. In this study, several social organizations and institutions were included in the design of the intervention. For example, the Beijing Association for Tobacco Control assisted in completing social mobilization and recruitment of patients; the China Health Education Center produced smoking cessation publicity materials; and community committees also contributed to the recruitment of smokers and follow-up visits, building a good external support environment for smokers. FIGURE 3: Management pattern of tobacco dependence in hospital and community. First, this study verified the effectiveness of smoking cessation medications for smoking cessation. In both the pilot and control groups, smokers taking the medication had a significantly lower ACSD at each follow-up than smokers not taking medication, and smoking cessation rates were higher than those who did not take medication. Second, this study demonstrated the effectiveness of the brief intervention for smoking cessation. The ACSD at the follow-up was significantly lower than that at the previous follow-up in all groups, except for the test group where the difference between the ACSD at 6th month and 3rd month was not significant. This is because both the pilot and control groups included brief smoking cessation interventions, and various previous studies have demonstrated that brief smoking cessation interventions can be effective in increasing smoking cessation rate [35, 36], with the 6th month smoking cessation rate in the pilot group with medication taking in this study being higher than the $21.6\%$ smoking cessation rate in Lin et al. [ 35]. Finally, this study demonstrates that an integrated intervention can effectively reduce ACSD and increase smoking cessation rate in smokers taking smoking cessation medications. After 1 and 3 months of the intervention, smokers in the pilot group with medication taking had significantly lower ACSD than smokers in the control group with medication taking, had significantly lower smoking cessation rate at 3rd month, and had non-significant differences in ACSD and smoking cessation rate at 6th month between the two groups after stopping the intervention. While the differences in ACSD and smoking cessation rates between smokers in the pilot and control groups without medication taking were consistently non-significant at each follow-up. Related studies have shown that supplementation with medication has better results than intervention alone for smokers who do not want to quit [37]. In the present study, there was no difference in the effect of brief and integrated smoking cessation interventions on ACSD in smokers who did not take medication to quit. In contrast, the integrated intervention was more effective when medication was taken. This seems to suggest that the use of smoking cessation medication is superior to either the brief smoking cessation intervention or the integrated smoking cessation intervention in smoking cessation intervention. Compared to the brief intervention, taking medication means that smokers have to pay for the medication. The cost of smoking cessation medications in this study was covered by program funding and was provided free of charge to smokers. Although smoking cessation medications are cost effective [38] and some studies have shown that the average expenditure on smoking cessation medications for smokers covered by Medicaid is $0.15 per month [39]. However, in a study of smoking cessation clinics in China, up to $62\%$ of smokers attending the clinic did not consider smoking as a disease [40], and they were prone to the perception that they did not need medication to quit smoking. Moreover, smoking cessation medications in China are not included in the health insurance reimbursement list, and the cost of paying for medications may not be lower than the cost of purchasing cigarettes, while medications may also have side effects. These phenomena may lead smokers not to choose smoking cessation medication, which suggests that we should strengthen the publicity on the dangers of smoking and related knowledge, and include smoking cessation medication in the medical insurance reimbursement catalog as soon as possible to reduce the financial burden of smokers. In addition, community-based smoking cessation interventions increase contact with smokers and to some extent improve adherence. Medication adherence can significantly affect the effectiveness of smoking cessation [41], and a lack of awareness of smoking cessation pharmacotherapy, a perception that smoking cessation medications are ineffective, or side effects of taking medications can lead to lower adherence among smokers [42]. In this study, smokers were able to exchange their quit experiences in a WeChat group or consult with professionals, and most of the problems of low medication adherence could be solved through this way. This not only eliminated smokers’ concerns, but also increased their confidence in quitting. This format increases the smoker’s access to the interventionist and increases the intensity of the intervention, which in turn increases the smoker’s adherence to quit. Some studies have shown low adherence among smokers in population-based cessation interventions [43], which may be due to the weak intensity of the intervention [34]. This suggests that when designing community-based interventions, the focus should be on the intensity of the intervention and how to improve smokers’ adherence. There are some limitations in this study. In terms of statistical analysis, firstly, smokers’ adherence was not evaluated and recorded in this study. The loss of smokers to follow up in the first month in the control group resulted in a control group with fewer than the minimum number of people required for the study [245] and may have contributed to bias in the analysis. This may also be a side-effect of the role of integrated interventions in enhancing smokers’ compliance with the intervention. Second, the generalized estimating equation used in this study included only three independent variables for analysis, namely time, group and whether medication was used, and did not control for variables such as age and marriage, which had significantly different distributions in the groups, which may have had some effect on the results of the analysis. In the promotion of interventions, firstly, the intervention forms such as WeChat group chat are additional services provided by physicians, and most responses can only be made during non-working hours. If this integrated intervention is popularized, the physicians involved in the intervention will not only increase their workload, but also receive no additional remuneration, which will affect their enthusiasm over time. The same problem was encountered in the UK study [13]. Secondly, the issue of payment for smoking cessation medications suggests that how to quantify and pay doctors for their extra work, and how to charge smokers for medical behaviors in informal organizations such as WeChat, is an issue that needs to be addressed in popularizing this integrated intervention model. Finally, the design and implementation of this study is mainly based on the good social support environment for tobacco control in Beijing, which is not available in most other provinces in China, which also brings limitations to the promotion of this study conclusion. ## 5. Conclusion An integrated hospital-community tobacco dependence management model can provide a good external supportive environment for smokers and is effective in increasing adherence and reducing ACSD. However, the prerequisite is the need to take smoking cessation medication. In addition, the social organizations involved in the study and the additional services generated by the medical staff need to be further studied through economic evaluation to ensure that the model is cost-effective and worthy of being popularized to other areas. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by the Medical Ethics Committee of Capital Medical University (Z2019SY007) and has been registered in the China Clinical Trials Registry under the name Search for Optimization of Tobacco Dependence Management Model Based on Hospital and Community (ChiCTR1900024991). The patients/participants provided their written informed consent to participate in this study. ## Author contributions KQ: conceptualization, methodology, data analysis, writing–original draft, and writing–review and editing. HL: conceptualization, data collection, and analysis. XL: conceptualization, methodology, investigation, resources, supervision, project administration, and writing–review and editing. QJ: investigation, project management, and data collection. YW: investigation, resources, and project management. MG: investigation and data and project management. XB: writing–review and editing. TQ: conceptualization, investigation, and data management. YY: resources and projects. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Insights into the mechanisms of triptolide nephrotoxicity through network pharmacology-based analysis and RNA-seq authors: - Yue-Ming Luo - Shu-Dong Yang - Miao-Yu Wen - Bing Wang - Jia-Hui Liu - Si-Ting Li - Yu-Yan Li - Hong Cheng - Li-Li Zhao - Shun-Min Li - Jian-Jun Jiang journal: Frontiers in Plant Science year: 2023 pmcid: PMC10027700 doi: 10.3389/fpls.2023.1144583 license: CC BY 4.0 --- # Insights into the mechanisms of triptolide nephrotoxicity through network pharmacology-based analysis and RNA-seq ## Abstract ### Introduction Triptolide (TPL) is a promising plant-derived compound for clinical therapy of multiple human diseases; however, its application was limited considering its toxicity. ### Methods To explore the underlying molecular mechanism of TPL nephrotoxicity, a network pharmacology based approach was utilized to predict candidate targets related with TPL toxicity, followed by deep RNA-seq analysis to characterize the features of three transcriptional elements include protein coding genes (PCGs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) as well as their associations with nephrotoxicity in rats with TPL treatment. ### Results & Discussion Although the deeper mechanisms of TPL nephrotoxcity remain further exploration, our results suggested that c-Jun is a potential target of TPL and Per1 related circadian rhythm signaling is involved in TPL induced renal toxicity. ## Introduction Over 3000 years of constant practice and optimization for the system of Traditional Chinese Medicine (TCM) have endowed its specific tradition that treasures in both scientific and medical fields (Li et al., 2007). A better understanding of therapeutic mechanisms of herb and herbal formulas from TCMs is of great significance for pharmacological study as they have played vital roles in clinical practice (Zuo et al., 2018). As one of the most renowned traditional Chinese medical herbs, Tripterygium wilfordii Hook f. (TWHF) has been applied in the treatment of multiple renal diseases such as membranous nephropathy (MN), nephrotic syndrome (NS) and refractory proteinuria since ancient China. Triptolide (TPL) is a major active component of TWHF as well as a promising compound for cancer therapy (Noel et al., 2019). Increasing evidence suggests that TPL can attenuate the progression of several types of tumor via varieties of approaches including target epigenetic networks (Noel et al., 2020), induce cancer cell apoptosis, enhance the effect of radiotherapy, inhibit metastasis and etc (Meng et al., 2014). TPL also shows potential immunosuppressive effect in autoimmune diseases treatment such as rheumatoid arthritis (Fan et al., 2018). However, the clinical application of TPL is restricted due to its hepatic, nephric, heart and gastrointestinal toxicity (Cheng et al., 2021). The cytotoxic activities of TPL include introducing DNA damage and apoptosis, arresting cell cycle (Park and Kim, 2013), autophagy (You et al., 2018), and it involves in the production of reactive oxygen species (ROS), generation and depolarization of mitochondrial membrane potential (MMP) in different cell lines (Zhang et al., 2019). The advancement of bioinformatics as well as the booming development of compound/drug/diseases databases such as TCMSP (Ru et al., 2014), NIMS (Li et al., 2011) and comCIPHER (Zhao and Li, 2012) have facilitated network pharmacology as a feasible approach to explicate the material composition and molecular mechanism of drugs effectively since it seeks targets by constructing distinct networks and evaluating the molecular connections involved in the process of drug treatment (Li et al., 2019). Network pharmacology has greatly enhanced the investigation of the molecular basis of herbal formula in the past decade (Li and Zhang, 2013). Through network pharmacology, Li et al. revealed the targets and pathways of niacin in the treatment of COVID-19 and colorectal cancer (Li et al., 2021). Niu et al. found that IL6 is potentially regulated by phytochemicals in traditional Chinese medicine for COVID-19 treatment (Niu et al., 2021). On the other hand, RNA-seq has been widely used to affiliate the expression patterns of protein coding gene (PCG), long noncoding RNA (lncRNA) and circular RNA (circRNA). Increasing evidence has shown that lncRNAs and circRNAs are closely related with degenerative diseases (Bhatti et al., 2021), cancers (Anastasiadou et al., 2018) development (Fatica and Bozzoni, 2014; Di Agostino et al., 2020), aging (Jiang et al., 2021; Ge et al., 2022), and they have great potential to be utilized as drug targets in the near future (Matsui and Corey, 2017; He et al., 2021). Although previous renal metabolic analysis revealed that Toll-like receptor signaling pathway and NF-κB signaling pathway played an important role in TPL-induced nephrotoxicity (Huang et al., 2019), the signatures of transcriptional elements are largely unexplored. In this study, we employed deep RNA-seq in female rat kidneys as well as network pharmacology-based analysis, to elucidate the principles of transcriptomic changes (include protein coding genes, lncRNAs and circRNAs) that associated with TPL and identify candidate targets for a better understanding of TPL renal toxicology. ## Animals, pathological measurements and ethic statements Female Sprague-Dawley (SD) rats, weighing 170-190g, were purchased from Guangdong medical laboratory animal center (Guangzhou, China) and housed in the animal facility of our institute under a pathogen-free condition. Rats were fed in an ad arbitrium diet and with free access to water. TPL was purchased from MedChemExpress (New Jersey, USA). Rats were randomly divided into control (Ctrl, $$n = 3$$), low dosage of TPL (L-TPL, $$n = 6$$) and high dosage (H-TPL, $$n = 6$$) groups. The L-TPL and H-TPL rats were separately administrated by oral gavage at a dose of 0.2 mg/kg and 0.4mg/kg for 28 days. Blood samples were collected for testing blood urea nitrogen (BUN) and serum creatinine (Scr) using one-way anova method among three groups. Coronal renal tissue was sectioned for H & E staining following standard protocols. Renal parenchyma was dissected for RNA-seq. The animal protocol of this study was approved by the institutional ethics review board of Shenzhen PKU-HKUST Medical Center (No. 2020252) and the authors declare that all the procedures have carefully followed the animal protocol. This study was in accordance with ARRIVE guidelines (https://arriveguidelines.org). ## RNA isolation and sequencing Three rats per group were randomly selected from Ctrl and H-TPL groups for RNA-seq. Total RNA was extracted from kidney using Trizol reagent (Invitrogen Cat#15596026) following standard protocols and subjected to the preparation of ribosome depletion RNA sequencing library by illumina platform. ## Data availability The annotation files of novel lncRNAs and circRNAs and the raw data were submitted to the Genome Sequence Archive in BIG Data Center, (Beijing Institute of Genomics (BIG), Chinese Academy of Sciences (http://bigd.big.ac.cn/gsa) (Chen et al., 2021), under the bioproject PRJCA010363 with accession No. CRA008544. ## Target prediction by network pharmacology-based analysis A step-wise workflow was utilized to predict candidate targets related with TPL nephrotoxicity. Firstly, TPL related targets (TPL-RT) were collected from TCMSP database (http://tcmspw.com/tcmsp.php) and published studies from PubMed (https://pubmed.ncbi.nlm.nih.gov/) database that related with TPL. Then, we used “nephrotoxicity” as the keyword to acquire the known nephrotoxicity related targets (nephrotoxicity-RT) from GeneCard, OMIN and DRUGBANK databases, respectively. The overlapped targets (OT) between TPL-RT and nephrotoxicity-RT were retained and subjected to STRING database to construct their protein-protein interaction (PPI) networks. Protein pairs with correlation r-value > 0.9 were regarded as high-quality networks (hq-ntw) and were visualized by cytoscape (Shannon et al., 2003). Gene enrichment analysis was employed to classify proteins within hq-ntw. ## Molecular docking analysis The 2D structure of TPL was downloaded from PubChem database (https://pubchem.ncbi.nlm.nih.gov/), then the structure was subjected to optimization by Chem3D software (https://library.bath.ac.uk/chemistry-software/chem3d). PyMOL (https://pymol.org/2/) was utilized to remove the water molecues and small ligands from the protein structures of targets downloaded from PDB database (http://www.rcsb.org/) for subsequent step. The molecular docking was finally performed and visualized using the hydrogen bonded protein structure and optimized TPL structure via AutoDockTools software (https://www.scripps.edu/sanner/software/adt/Tutorial/index.html). ## Western-blot Western-blot assay was performed as we have previously described (Jiang et al., 2021), blots were cut according to the sizes of target proteins prior to hybridisation with antibodies during blotting and exposed by Bio-rad imaging system. Antibody information see Supplementary Table 1. ## Novel lncRNA identification An optimized stepwise filtering workflow that based on our previous studies was used to identify lncRNAs (Jiang et al., 2016; Jiang and Kong, 2020). Briefly, raw data was processed by FastQC (Andrews, 2010) to remove low-quality reads. Stringtie (Pertea et al., 2015) was used for transcript assembly. Transcripts with class code “i” “j” “o” “u” “x”, exon number ≥ 2, and length over 200bp were retained and blast against annotated lncRNAs of rat genome (ALRG) to eliminate redundances. The transcripts were blast to pfam database (Bateman et al., 2004) and assessed by coding potential evaluation tools include CPC (Kong et al., 2007), CNCI (Sun et al., 2013) and CPAT (Wang et al., 2013), respectively. Transcripts that commonly evaluated as “noncoding” by the three analyses were defined as novel lncRNAs. ALRG profiles were acquired from http://ftp.ensembl.org/pub/release-87/fasta/rattus_norvegicus/dna/Rattus_norvegicus.Rnor_6.0.dna.toplevel.fa.gz. ## Novel circRNA identification Candidate circRNA were identified using two tools include find_circ (Memczak et al., 2013) and CIRCexplorer (Zhang et al., 2014) following the standard tutorials with default parameters. Only transcripts that recognized as circRNA by both find_circ and CIRCexplorer were subjected for further analysis. Spliced reads per billion mapping (SRPBM) value for circRNA was calculated through: SRPBM = Circular reads * 109Total mapped reads * read length ## Prediction of interactions between circRNAs and miRNAs The potential interactions between circRNAs and miRNAs were predicted by miRanda (John et al., 2004) using default parameters. ## Identification of differentially expressed PCGs and lncRNAs RPKM (Reads per Kilobase per Million Reads) was calculated via formula: RPKM = Total exon reads mapped reads(millions) * exon length(kb). By comparing the RPKM values, thresholds of \|log(Fold change)\| > 1 and p-value< 0.05 were set to define significantly differentially expressed genes. False discovery rate (FDR) was used for adjusting p-value. Unsupervised clustering was employed to uncover unknown relationships among genes and biological samples. ## Weighted gene co-expression network analysis analysis WGCNA analysis was performed following its official tutorial (Langfelder and Horvath, 2008). Briefly, the normalized FPKM values of PCGs and lncRNAs were pooled and generated to adjacency matrix and subjected to “dynamicTreeCut” package (Langfelder et al., 2008) to filter out outliner samples. Then we used “pickSoftThreshold” function to calculate soft power values for predicting block-wise modules. ## Gene enrichment analysis Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping are useful strategies for gene functional classification (Kanehisa, 2019; Gene Ontology Consortium, 2021). Genes were classified to Gene Ontology and KEGG terms via online tool DAVID (https://david.ncifcrf.gov/) with default parameters. ## Renal pathological changes The serum creatinine (Scr) and blood urea nitrogen (BUN) were tested and we found that H-TPL rats showed significant higher Scr (p-value = 0.0189) than Ctrl and L-TPL, indicated that treatment of high dosage of TPL induced declined renal function (Figures 1A, B). H & E staining found that both L-TPL and H-TPL rats exhibited tubular atrophy and renal blood vessel congestion comparing with Ctrl group (Figures 1C, D),. A total of 10 representative views (3-4 views/rat, $$n = 3$$) were selected for each group for renal tubular injury scores evaluations, non-significant differences was observed between L-TPL and H-TPL(Figure S1, S2). However, wider intercellular gap between renal tubules was observed in H-TPL than L-TPL (Figures 1C, D). Figure 1: Serum creatinine (Scr), BUN and H & E staining in TPL treated rats. (A) Scr levels were significantly elevated in H-TPL groups; (B) BUN levels showed slightly but not statistically significant changes in L-TPL and H-TPL rats comparing with Ctrl group; (C, D) Representative captures of H & E staining of L-TPL (C) and H-TPL (D) kidneys, the yellow box is renal congestion and blue box is renal tubules atrophy. indicates p < 0.05; ns indicates not significant.* ## Potential targets of TPL nephrotoxicity predicted by network pharmacology-based analysis Network pharmacology-based analysis was performed to predict potential targets related with TPL renal toxicity. A total of 537 targets were predicted by GeneCards, OMIM, and DRUGBANK databases and 31 targets by the TCMSP database, we found that 17 were overlapped. The visualization of the interplay among TPL, nephrotoxicity and the 17 overlapped targets (OTs) was shown in Figures 2A, B by cytoscape3 (Shannon et al., 2003). These OTs were ported to STRING database to acquire their protein-protein interaction (PPI) pairs (Figure 2B). Contribution score (CS) was used to assess the importance of interested genes in contributing TPL renal toxicity, it is the number of gene nodes that correlated with each overlapped target (OT) within the PPI network. The contribution scores of 17 OTs were shown in Figure 2C, among which STAT3, TNF and JUN rank the top 3 genes with ≥ 11 gene nodes. By ranking the CSs, genes with CSs ≥ 4 were regarded as candidate targeted genes related to TPL renal toxicity (CTGs-TPL) (Figure 2C). Potential targets were subjected gene enrichment analysis and the top KEGG terms were shown in Figure 2D. Western-blot (WB) assay was employed to validate the association between the top-ranked CTGs-TPL include Stat3 and c-Jun, it showed that c-Jun were decreased in both L-TPL and H-TPL rats (Figure 2E), suggesting that c-Jun is a potential target of TPL. In addition, the activation status of c-Jun, the phosphorylated c-Jun (pc-Jun) was also inhibited slightly (Figure 2E), Considered the vital importance of Stat3 in renal function, two key factors that play essential roles in the upstream and/or downstream of Stat3 signaling include Jak2 and IL17 were selected to investigate their expression levels by WB assays although Stat3 and phosphorylated-Stat3 (pStat3) showed non-significant changes with TPL treatment (Figure S3). Nevertheless, neither Jak2/phosphorylated-Jak2 (p-Jak2) nor IL17 showed response to TPL treatment (Figure S2), demonstrating that TPL may not serve as a potential ligand for either Stat3/IL17 or Stat3/Jak2 signaling in the process of TPL nephrotoxicity. Figure 2: Target genes predicted to be associated with triptolide nephrotoxicity by network pharmacology-based analysis. (A) Visulization of interpalys between TPL and its predicted targets; (B) PPI network of TPL renal toxicity candidate target genes; (C) List of top 15 target genes ranked by contribution scores and they were regarded as candidate targeted genes related to TPL renal toxicity (CTGs-TPL); (D) KEGG terms of potential targets by gene enrichment analysis; (E) Western-blot assay of c-Jun and pc-Jun in Ctrl, L-TPL and H-TPL groups (from left to right, n = 3/group). To discovery the structure-based associations between identified targets and TPL, a molecular docking based strategy was applied to predict the ligand-target interactions between TPL and interested targets include CD86, IL4, CXCL8, STAT3 and CD40. Their interactions were visualized in Figure 3. Figure 3: Visualizations of the interactions between targets and TPL by molecular docking analysis. (A) IL4; (B) STAT3; (C) CD40; (D) CXCL8; (E) CD86. ## Dysregulated protein coding genes As H-TPL rats showed aggravated renal pathological changes with wider intercellular gap between renal tubules, we selected H-TPL renal tissues instead of L-TPL for deep RNA-seq to elucidate the underlying molecular mechanisms of TPL induced renal toxicity. A total of 178 up-regulated and 152 downexpressed PCGs were obtained (Table S2). The gene enrichment analysis (GEA) indicates that up-regulated PCGs are classified (rich factor > 10) to vitamin digestion and absorption (hsa04977), Nitrogen metabolism (hsa00910), glycine, serine and threonine metabolism (hsa00260), steroid biosynthesis (hsa00100), citrate cycle (TCA cycle) (hsa00020) and proximal tubule bicarbonate reclamation (hsa04964) (Figure 4A). The downexpressed PCGs were significantly enriched in circadian rhythm (hsa04710) signaling (rich factor > 10) (Figure 4B). Co-expression correlation of protein-protein pairs was calculated by WGCNA. The networks of protein-protein interaction (PPI) were shown in Figure S4, the core genes include Ptcd3 and Cdk1. Figure 4: Identification of significant dysregulated genes in the renal tissues of H-TPL rats. (A, B) KEGG enrichment analysis of significant overexpressed (A) and downexpressed genes (B, C) Western-blot assay of Per1 in Ctrl, L-TPL and H-TPL groups. Next to liver, kidney exerts the second most robust rhythms of circadian gene expression (Zhang et al., 2014; Myung et al., 2019). Among these dysregulated PCGs in H-TPL rats, we noticed that multiple circadian genes such as Per1, Per2, Per3 and Cry were significantly downexpressed. Previous study found that Per1 in kidney is important for renal sodium handling and necessary for maintaining homeostasis (Douma et al., 2022); therefore, Per1 was selected and validated by western blot assay, it showed that Per1 was decreased in TPL treated kidney (Figure 4C). Although the roles of circadian genes are unknown in the mechanisims of TPL renal toxicity, our results suggested that TPL may relate with the circadian pace of kidney function. It is interesting that we noticed that c-Jun was not among the significant dysregulated genes by TPL treatment, suggesting that c-Jun may involve in TPL toxicity in renal tissues via post-transcriptional regulation. ## Significantly differentially expressed lncRNAs After a strict filtering pipeline, a total of 6061 novel lncRNAs were identified and combined with the annotated lncRNAs of rat genome (ALRG) for next-step analysis. The length distribution and exon number density plots were shown in Figure 5A, B and Figure S5. The majority of novel lncRNAs and ALRG own 2 exons. Unlike ALRG that generally enriched in 200 - 500bp, the length of novel lncRNAs are mostly distributed in 200 - 500, 500 - 1000 and > 3500 bp (Figures 5A, B). A total of 131 up- and 119 down-expressed lncRNAs were identified as significantly differentially expressed lncRNAs (SDElncs) in H-TPL (p-value< 0.05). Increasing studies have demonstrated that lncRNAs usually owns the capacity to regulate their nearby genes (Statello et al., 2021). To illustrate the potential roles of SDElncs, genes that locate within 100kb of SDElncs were acquired and own strong correlations with SDElncRNAs (weight value > 0.8) were defined as target genes (Figure 5C). A total of 26 genes were identified and subjected for GEA. We surprisingly found target genes, alike with the dysregulated PCGs, were also enriched in vitamin digestion and absorption (hsa04977) and metabolic related pathways such as Alanine, aspartate and glutamate metabolism (hsa00250) (Figure 5D), which suggesting that abnormal metabolism of amino acids was potentially related with TPL nephrotoxicity. Figure 5: Features of lncRNA genes in the renal tissues of H-TPL rats. (A, B) Comparison of lengths between ALRGs (A) and novel lncRNAs (B, C) Pairs of SDElncs and 27 candidate target genes predicted by location and WGCNA; (D) KEGG terms of the 27 target genes. ## CircRNA signatures A total of 1529 high-quality novel circRNAs were identified. By calculating the SRPBM values, only 7 circRNAs were found deferentially expressed in H-TPL rats with p-value< 0.05 (Table S1). As circRNAs can be served as miRNA sponges, we utilized miRanda tool (Enright et al., 2003) to predict the potential connections between dysregulated circRNAs and miRNAs. Ranking by tot scores, the top 10 circRNA-miRNA pairs were shown in Table 1. Previous studies reveled that miR-207 was up-regulated in renal and urine of rats with renal fibrosis and decreased in ischemia-reperfusion injury (IRI) model of mouse (Wei et al., 2010; Shi et al., 2018). We found that both circ: Chr 6:124934385-124981303 and circ: Chr11: 66774701-66795896 are strongly targeted with miR-207 (Tot scores > 1000), suggesting these two circRNAs may be involved with mi-207 related renal function regulation although the deep mechanisms is unknown. Table 1 \| miRNA \| circRNA \| Tot_Scores \| Positions \| \| --- \| --- \| --- \| --- \| \| miR-320-5p \| 6:124934385-124981303 \| 1973 \| 1398 1911 2510 1211 1323 2813 1581 2198 1472 2378 2300 1608 1124 \| \| miR-3557-5p \| 11:66774701-66795896 \| 1334 \| 3240 7004 6394 2685 2835 1684 5020 5102 5855 \| \| miR-207 \| 6:124934385-124981303 \| 1253 \| 1577 527 1217 1817 210 1398 1127 722 \| \| miR-337-3p \| 11:66774701-66795896 \| 1203 \| 1929 7259 580 7055 6716 1893 3024 587 \| \| miR-207 \| 11:66774701-66795896 \| 1074 \| 4055 4779 6296 1763 5711 218 6812 \| \| miR-127-5p \| 11:66774701-66795896 \| 1050 \| 125 5264 4301 1792 5339 6845 2597 \| \| miR-3575 \| 5:151944768-151947717 \| 1048 \| 287 312 634 414 579 464 537 \| \| miR-3584-5p \| 4:51682050-51690235 \| 1011 \| 456 539 426 398 366 561 651 \| \| miR-3551-5p \| 9:37960098-38013690 \| 948 \| 278 2478 1751 541 5371 822 \| \| miR-103-1-5p \| 1:134783877-134848903 \| 925 \| 1 3942 1332 3830 2641 3256 \| ## Discussion TPL has been applied as an useful compound for treatment of multiple renal diseases for decades; however its toxicity largely limited its clinical practice. Metabolites has been studied in a broad field such as screening for new therapeutic targets, discovery and validation of disease biomarkers. Multitude studies have applied metabonomics technology to investigate TPL, the regulating mechanisms and the toxicities (Du et al., 2014; Li et al., 2019); however, the transcriptional changes of TPL nephrotoxicity were rarely reported. In this study, a combined approach of network pharmacology method and RNA-seq was used to elucidate the molecular mechanisms of TPL nephrotoxicity. RNA-seq analysis found that a series of circadian genes, such as Per1-3, were significantly dysregulated in renal tissues along with H-TPL treatment. Per1-3 are closely related with renal rhythm. Per1 acts as a circadian clock transcription factor and was regulated by aldosterone, a steroid hormone increases blood pressure via elevating blood volume and Na+ retention (Douma et al., 2022). Myung et al. demonstrated that the mouse kidney of adenine diet induced chronic kidney disease (CDK) model displayed disorganization of Per2 expression (Myung et al., 2019). Per3 exerts dynamic expression patterns in pan renal carcinoma (Liu et al., 2021). Through western blot assay, we validated that Per1 was decreased along with TPL treatment, suggesting that Per1 is involved in the regulation of TPL nephrotoxicity. For identifying candidate targets of TPL nephrotoxicity by network pharmacology based analysis, Huang et al. employed GeneMANIA database and screened out 39 direct-targets in male rats (Huang et al., 2019). Although there is no evidence suggested that TPL toxicity has sexual difference, our study utilized female rats to identify TPL regulated proteins and found that female rats seem to tolerant the renal toxicty under both L-TPL and H-TPL treatment with low renal injure rate. Comparing with Huang’s study, a more strict filtering standard and different databases were used and we gained highly consistent targets. Our further investigations by western-blots validated that c-Jun protein is a potential target of TPL. c-Jun protein is a widely expressed transcription factor associated with a variety of diseases include human renal diseases (Blau et al., 2012). In glomerular and tubular cells, c-Jun was activated and its activation involves in the regulation of renal inflammation and/or fibrosis (De Borst et al., 2007). RNA-seq and network pharmacology are different techniques to elucidate relevant candidate molecular targets from two perspectives. Network pharmacology is a novel approach that widely applied for discovering the targets involved in the process of TCM compounds or modern drugs treatment in a specific disease via integrating biomedical, pharmacological and computational approaches while RNA-seq can gain us a cohort of genes with differential expression directly. A combination of these two techniques definitely provide a more comprehensive knowledge of molecular mechanisms in the process of TPL induced renal toxicity. In this study, Pe1 and c-Jun are two candidates related with TPL nephrotoxicity identified by these two analyses, respectively. Although little evidence has been implied on the connections between Per1 and c-Jun and the mechanisms among c-Jun, Per1, TPL and nephrotoxicity are remain explored, our study suggested that c-Jun protein and Per1 are possibly to be involved in TPL induced renal toxicity via two independent pathways. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material. ## Ethics statement The animal study was reviewed and approved by Shenzhen PKU-HKUST Medical Center. ## Author contributions S-ML, S-DY, J-JJ, and Y-ML conceived and designed this study. Y-ML, M-YW, S-TL, BW, J-HL, Y-YL, HC, L-LZ and J-JJ performed all the experiments and data analysis. J-JJ, S-DY and S-ML wrote and revised this manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1144583/full#supplementary-material ## References 1. Anastasiadou E., Jacob L. S., Slack F. J.. Non-coding RNA networks in cancer. Nat. Rev. Cancer (2018) 18 5-18. DOI: 10.1038/nrc.2017.99 2. Andrews S.. 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--- title: DIA-based technology explores hub pathways and biomarkers of neurological recovery in ischemic stroke after rehabilitation authors: - Wei Hu - Ping Li - Nianju Zeng - Sheng Tan journal: Frontiers in Neurology year: 2023 pmcid: PMC10027712 doi: 10.3389/fneur.2023.1079977 license: CC BY 4.0 --- # DIA-based technology explores hub pathways and biomarkers of neurological recovery in ischemic stroke after rehabilitation ## Abstract ### Objective Ischemic stroke (IS) is a common disease that causes severe and long-term neurological disability in people worldwide. Although rehabilitation is indispensable to promote neurological recovery in ischemic stroke, it is limited to providing a timely and efficient reference for developing and adjusting treatment strategies because neurological assessment after stroke treatment is mostly performed using scales and imaging. Therefore, there is an urgent need to find biomarkers that can help us evaluate and optimize the treatment plan. ### Methods We used data-independent acquisition (DIA) technology to screen differentially expressed proteins (DEPs) before and after ischemic stroke rehabilitation treatment, and then performed Gene Ontology (GO) and pathway enrichment analysis of DEPs using bioinformatics tools such as KEGG pathway and Reactome. In addition, the protein–protein interaction (PPI) network and modularity analysis of DEPs were integrated to identify the hub proteins (genes) and hub signaling pathways for neurological recovery in ischemic stroke. PRM-targeted proteomics was also used to validate some of the screened proteins of interest. ### Results Analyzing the serum protein expression profiles before and after rehabilitation, we identified 22 DEPs that were upregulated and downregulated each. Through GO and pathway enrichment analysis and subsequent PPI network analysis constructed using STRING data and subsequent Cytoscape MCODE analysis, we identified that complement-related pathways, lipoprotein-related functions and effects, thrombosis and hemostasis, coronavirus disease (COVID-19), and inflammatory and immune pathways are the major pathways involved in the improvement of neurological function after stroke rehabilitation. ### Conclusion Complement-related pathways, lipoprotein-related functions and effects, thrombosis and hemostasis, coronavirus disease (COVID-19), and inflammation and immunity pathways are not only key pathways in the pathogenesis of ischemic stroke but also the main pathways of action of rehabilitation therapy. In addition, IGHA1, LRG1, IGHV3-64D, and CP are upregulated in patients with ischemic stroke and downregulated after rehabilitation, which may be used as biomarkers to monitor neurological impairment and recovery after stroke. ## 1. Introduction Ischemic stroke is a common group of diseases that cause functional impairment and severely affect the patient's ability to perform daily life due to its highly disabling nature. Post-stroke rehabilitation is essential to restore the corresponding deficit function as soon as possible. However, as the assessment of neurological recovery after stroke rehabilitation is mainly performed using scales and imaging, it is difficult to provide a timely reference for the development of efficient treatment strategies. Therefore, there is an urgent need to find appropriate biomarkers that can provide valuable information to neurologists and rehabilitation physicians, which can help optimize the treatment plan and can even help find another treatment modality that can benefit patients when they are not sensitive to one treatment method. Given the heterogeneity of ischemic stroke, biomarkers may provide support for developing the field of ischemic stroke rehabilitation and treatment in clinical settings. In this study, we used data-independent acquisition (DIA) proteomics technology to detect differences in serum protein expression before and after treatment in patients with ischemic stroke. Subsequently, advanced Gene Ontology (GO) and pathway enrichment analysis of differentially expressed proteins (DEPs) were performed using bioinformatics tools such as the KEGG pathway, Reactome, and PANTHER. In addition, we identified the hub proteins (genes) and key signaling pathways involved in rehabilitation therapy for neurological recovery by integrating the protein–protein interaction (PPI) network (http://string-db.org) and modularity analysis of DEPs. Based on this, we selected a portion of the proteins of interest for validation using parallel response monitoring (PRM). The identification of DEPs and the enrichment analysis of their biological functions and key pathways were performed to screen for future biomarkers that may be useful for monitoring neurological recovery after ischemic stroke, which may assist in assessing neurological function and improving treatment strategies. ## 2.1. Sample information Blood samples for this study were collected from patients with stroke rehabilitation admitted to the hospital between June 2020 and December 2020. First, screening was performed according to inclusion and exclusion criteria. Inclusion criteria: (a) patients aged ≥18 years who met the diagnostic criteria for ischemic stroke in the Diagnostic Key Points of Various Major Cerebrovascular Diseases in China [2019] and confirmed by imaging techniques; (b) those with neurological dysfunction (NIHSS score ≥1) with a disease duration of <3 months and stable condition; and (c) those who signed informed consent. Exclusion criteria: (a) patients with severe cardiac insufficiency; (b) those with hepatic and renal impairment (ALT, AST, BUN, and Cr ≥ 2 times the high limit of normal); (c) patients with psychiatric disorders, advanced tumors, and hematological diseases; (d) pregnant and lactating women; and (e) those who withdrew from the study for any reason during the study. Calculation of sample size was performed by PASS 15 (version: 15.0.5) Power Analysis and Sample Size Software [2017], with α = 0.05, 1 – β = 0.9, and dropout rates=$20\%$ (NCSS, LLC. Kaysville, Utah, United States, ncss. com/software/pass). Based on the standard clinical treatment, hemiplegic limb function training was given once a day for 30 min for 30 days, and for the presence of speech dysfunction, cognitive dysfunction, or swallowing dysfunction, appropriate rehabilitation treatment was given. To reduce the bias in selecting the study sample, we used the random number table method to randomly select four cases from the 60 total cases for observation. Peripheral blood samples were collected before and after 30 days of rehabilitation treatment. After centrifugation at 3,000 r/min for 15 min, sera were separated and stored in a refrigerator at −80°C for further testing. In addition, to reduce the effect of confounding factors such as age, sex, and race, we matched the selection of healthy controls accordingly. Information on the control group before and after treatment of patients with ischemic stroke is shown in Table 1. This study was supported by the Provincial Department of Science and Technology. Table 1 \| Sample number \| CI-1 \| CI-2 \| CI-3 \| CI-4 \| RT-1 \| RT-2 \| RT-3 \| RT-4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Group \| CI \| CI \| CI \| CI \| RT \| RT \| RT \| RT \| ## 2.2.1. Main instrumentation The main instruments used in this experiment were a Q Exactive HF mass spectrometer (Thermo Fisher Scientific, USA), electronic balance (Yue Ping, Shanghai), EASY-nLC 1,000 liquid chromatograph (Thermo Fisher Scientific, USA), enzyme marker (Kehua, Shanghai), TGL-16A benchtop freezing centrifuge (Luxiang Yi, Shanghai), SDS-PAGE gel electrophoresis instrument (Liuyi, Beijing), Tanon 1600 gel imager (Shanghai Tennant), lyophilizer (Ningbo Scientz), certain LG protein stainer (Nanjing GenScript), and high-pH separation liquid chromatograph (Agilent, USA). ## 2.2.2. Main reagents The main reagents required for this experiment are as follows: iRT standard peptide was purchased from Biognosys; SDS lysis solution was purchased from Beyotime Biotechnology; bicinchoninic acid (BCA) kit, mass spectrometry grade water, and acetonitrile were purchased from ThermoScientific; performed gum was purchased from GenScript Inc. PMSF was purchased from Amresco; disodium hydrogen phosphate dodecahydrate, sodium dihydrogen phosphate monohydrate, NaCl, Tris-HCl (pH 6.8; pH 8.8), Tris, Dithiothreitol (DTT), glycerol, bromophenol blue, trifluoroacetic acid (TFA), indole-3-acetic acid (IAA), urea, and triethylammonium borate; triethylamine-boric acid (TEAB) were purchased from Biotech; glycine and SDS were purchased from Sinopharm; trypsin was purchased from Wallys; anhydrous ethanol and isopropanol were purchased from GENERAL-REAGENT; and de-high abundance kit was purchased from MilliPore. ## 2.3. Sample preparation and LC-MS/MS high-resolution mass spectrometry detection A portion of the prepared total protein solution was subjected to protein concentration determination and SDS-polyacrylamide gel electrophoresis using the BCA method. The other part was first taken to complete the enzymatic digestion by trypsin, and the enzymatically digested peptides were desalted by SOLA™ SPE 96-well plate and subjected to LC-MS/MS high-resolution mass spectrometry. The raw files obtained from the detection were imported into Spectronaut Pulsar software for matching, and the machine signals were transformed into peptide and protein sequence information, and then a library of spectra was built based on the relevant information to complete the subsequent DIA analysis. ## 2.4. Differentially expressed protein screening and GO and biological pathway enrichment analysis Based on the plausible proteins we obtained, differential proteins were screened according to the following criteria: FoldChange of ≥1.2 and a P-value of ≤ 0.05 for upregulated proteins; FoldChange of ≤ 0.833 and a P-value of ≤ 0.05 for downregulated proteins. To further understand the biological functional information of the differential proteins, we performed GO and pathway enrichment analysis of the candidate DEPs using multiple online databases. We submitted DEPs to the DAVID online program (https://david.ncifcrf.gov/; version: 6.8) with a p-value of < 0.05 as the cut-off criterion. In addition, the PANTHER database (http://www.pantherdb.org), the KEGG database (http://www.genome.jp/kegg), BioCyc (http://biocyc.org), and Reactome (http://www.reactome.org) for GO and pathway analysis. Enriched GO terms were ranked according to P-values and displayed as bar graphs; a P-value of < 0.05 was considered to be statistically significant. ## 2.5. PRM-targeted proteomic validation We used parallel reaction monitoring (PRM) targeted proteomics to validate some of the screened proteins of interest to validate our study. First, we screened the differential proteins with no current literature or few expressions with ischemic stroke as representative proteins. Peptide information was collected in data-dependent acquisition (DDA) mode, based on the raw data obtained from mass spectrometry assays and then library search, using the software PD2.2. Then, after identifying the candidate target peptides (selecting proteins with ≥2 peptides), PRM mass spectrometry was performed to validate them, and then Skyline software was used to analyze and complete the quantification of the target proteins. In this study, we strictly followed the SOPs for extraction, quantification, quality control, and subsequent enzymatic digestion and desalting of the sample proteins, and finally completed the PRM assay. ## 3.1. Clinical characteristics of patients Of the four enrolled patients with ischemic stroke, one was men and three were women. The age of the patients at enrollment was 60.0 ± 6.0 years and the median duration of illness was 19.5 days, with a range of 3–30 days. The pre-treatment NIHSS score was 8.3 ± 1.3, and the post-rehabilitation NIHSS was 4.8 ± 1.7, as detailed in Table 2. Table 2 \| No. \| Sex \| Age \| Disease duration (days) \| Combined diseases \| Crisis \| NIHSS score \| NIHSS score.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| Baseline \| Post-RT \| \| 1.0 \| Male \| 63.0 \| 25.0 \| Hypertension, type 2 diabetes \| N \| 7 \| 3 \| \| 2.0 \| Female \| 55.0 \| 30.0 \| Hypertension, hyperlipidemia, chronic bronchitis \| N \| 10 \| 7 \| \| 3.0 \| Female \| 55.0 \| 14.0 \| – \| N \| 8 \| 4 \| \| 4.0 \| Female \| 67.0 \| 3.0 \| Hypertension, type 2 diabetes, hyperlipidemia \| N \| 8 \| 5 \| ## 3.2. Protein concentration measurement results The absorbance and concentration of samples are shown in Table 3. Table 3 \| No. \| Adsorbance 1 \| Adsorbance 2 \| Adsorbance 3 \| Average absorbance \| Measurement concentration (ug/uL) \| Real concentration (ug/uL) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Cl-3 \| 0.18 \| 0.186 \| 0.18 \| 0.182 \| 0.19 \| 1.9 \| \| RT-3 \| 0.187 \| 0.18 \| 0.205 \| 0.191 \| 0.2 \| 2.0 \| \| Cl-4 \| 0.15 \| 0.171 \| 0.181 \| 0.167 \| 0.174 \| 1.74 \| \| RT-4 \| 0.143 \| 0.162 \| 0.18 \| 0.162 \| 0.168 \| 1.68 \| \| Cl-1 \| 0.219 \| 0.216 \| 0.198 \| 0.211 \| 0.222 \| 2.22 \| \| RT-1 \| 0.146 \| 0.159 \| 0.161 \| 0.155 \| 0.161 \| 1.61 \| \| Cl-2 \| 0.304 \| 0.285 \| 0.282 \| 0.29 \| 0.31 \| 3.1 \| \| RT-2 \| 0.06 \| 0.096 \| 0.11 \| 0.089 \| 0.087 \| 0.87 \| ## 3.3. SDS-PAGE results The results of SDS-PAGE are shown in Figure 1. Figure 1: SDS-PAGE electropherogram of samples. ## 3.4. Trusted protein analysis and data quality control Based on the raw data obtained, we retained the proteins with expression values of ≥$50\%$ for any set of samples, and the proteins with missing values <$50\%$ were filled using the k-nearest neighbors (KNN) algorithm ($k = 3$), followed by median normalization and log2 log transformation, to obtain plausible proteins, and the apparent proteins were visualized demonstration. As shown in Figures 2A, B, we performed principal component analysis (PCA) based on the expression of plausible proteins to show the interrelationship between samples from different dimensions, and the results suggested significant differences between the two groups of proteins before and after rehabilitation treatment and good intra-group reproducibility. In the sample correlation analysis of plausible proteins (Figure 2C), by measuring the degree of correlation between samples, the results showed that the two groups of samples were grouped matching their conditions and had good reproducibility. In addition, sample hierarchical clustering analysis was performed in this study and presented in a clustering dendrogram. As shown in Figure 2D, the classification of the samples before and after the rehabilitation treatment matched the clinical one. We standardized the data to minimize the effect of systematic bias on the quantitative protein values of the samples in this study so that the data obtained from each sample and parallel experiments could be at the same level, thus making the subsequent analysis more accurate and reliable, and the median of the data after standardization tended to be in the center. Figure 2: (A, B) are the principal component analysis of the expression of plausible proteins before and after rehabilitation treatment [(A) is a two-dimensional plot, while (B) is a three-dimensional plot]. Each point represents a sample, and the blue represents before treatment, while the orange is after treatment. The more distant the position of the two samples, the more significant the difference is represented. (C) Sample Correlation Analysis. The upper triangle (upper right of the diagonal line), the numbers indicate the correlation values of the two samples, indicates the degree of significance (**p < 0.001); the lower triangle (lower left of the diagonal line), gives the scatter plot of the expression values of the two samples, the red curve is the fitted trend, the greater the slope the stronger the correlation between the two samples; the diagonal line is the expression of the samples themselves distribution graph. (D) Sample hierarchical clustering tree diagram. Each branch end represents a sample, and samples clustered within the same branch are considered as samples expressing similar or close features, and samples not clustered into the same branch can be considered as samples with dissimilar or not close features, as measured by the Euclidean distance of the horizontal coordinate. ## 3.5. Differential protein expression analysis For screening DEPs based on obtaining plausible proteins, we selected both the Foldchange value of the fold change in expression level and the p-value of the significance level. In our study, the differential screening criteria for the project were Foldchange = 1.2 and p-value of < 0.05, where FC = 0 and FC = inf were both with or without differences. After the screening, as shown in Table 4, we obtained a total of 44 DEPS containing 22 upregulated and 22 downregulated proteins. The overall distribution of DEPS was visualized as shown in Figure 3A (volcano plot). In addition to that, we used R language to perform hierarchical clustering on the normalized data. As shown in Figure 3C, the differences in protein expression between the two groups before and after the rehabilitation treatment were obvious, and the samples within the group also appeared in the same cluster by clustering. Moreover, by using the Pearson analysis to analyze the differential proteins, we obtained the corresponding correlation coefficients, the higher the coefficient, the stronger the correlation between the proteins. To demonstrate the association of differential protein expression, we made a TOP50 differential significant protein (p-value ranking) correlation analysis graph, as shown in Figure 3B. ## 3.6.1. GO functional enrichment analysis of differential proteins before and after rehabilitation treatment By using multiple online databases for GO enrichment analysis of differential proteins in this study, we obtained 308 Biological Process (BP) categories, 94 Molecular Function (MF) categories, and 64 Cellular Component (CC) categories. As shown in Figure 4, we filtered to the top 15 (five entries each for each category, ranked from largest to smallest by –log10 P-value) categories obtained from the GO enrichment analysis. In the BP category, differential proteins were mainly enriched in GO:0006958: Complement activation, classical pathway (List Hits = 11, padj = 3.14E-13), GO:0030449: Regulation of complement activation (List Hits = 9, padj = 6. 87E-11), GO:0006898: Receptor-mediated endocytosis (List Hits = 9, padj =2.19E-09), GO:0006956: Complement activation (List Hits = 7, padj =2.09E-08), and GO:0044267: Cellular protein metabolic processes (List Hits = 8, padj = 6. 44E-07). In the CC category, differential proteins were mainly enriched in GO:0005576: extracellular region (List Hits = 32, padj = 1. 71E-21), GO:0070062: extracellular exosome (List Hits = 24, padj = 8.39E-11), GO:0034364: high-density lipoprotein particle (List Hits = 4, padj = 9.08E-06), GO:0005788: endoplasmic reticulum lumen (List Hits = 8, padj = 1.12E-05), and GO:0034366: spherical high-density lipoprotein particle (List Hits = 3, padj = 1. 88E-05). In the MF category, differential proteins were mainly enriched in GO:0003823: antigen binding (List Hits = 7, padj = 2.03E-07), GO:0004252: serine-type endopeptidase activity (List Hits = 6, padj = 5.60E-05), GO:0031210: phosphatidylcholine binding (List Hits = 3, padj = 0.000526458), GO:0030492: hemoglobin binding (List Hits = 2, padj = 0.000617798), and GO:0055102: lipase inhibitor activity (List Hits = 2, padj = 0.000822536). As shown in Figures 4A–C, we also used bubble plots for the presentation of GO enrichment analysis results. In addition, we also used a chord diagram (Figure 4D) to show the relationship between the GO term and the corresponding differential proteins. Figure 4: Gene Ontology (GO) enrichment analysis results. The x-axis Enrichment Score in the bubble diagram is the enrichment score, and the y-axis is the top 5 term information of BP, Biological Process; CC, Cell Component; MF, Molecular Function, respectively. (A) The top 15 GO terms of all differentially expressed proteins. (B) The top 15 GO terms of the up-regulated expressed proteins. (C) The top 15 GO terms of the down-regulated expressed proteins. (D) GO enrichment analysis chord diagram. Protein:gene name on the left, selected GO term on the right, red indicates up-regulated, and blue indicates down-regulated. ## 3.6.2. Pathway enrichment analysis of differential proteins In this study, we performed pathway enrichment analysis by using the online database of KEGG, and the significance of each enriched pathway was expressed by p-value. The p-values were calculated using the hypergeometric distribution test. The results are shown in Figure 5. The differential proteins before and after rehabilitation mainly involved hsa04610: complement and coagulation cascade (pval = 5.37E-10, enrichment score=25.1661442), hsa05322: systemic lupus erythematosus (pval = 8.36E-07, enrichment score=12.74863884), hsa05171: coronavirus disease (COVID-19) (pval = 5.26E-06, enrichment score = 7.743428985), hsa05150: Staphylococcus aureus infection (pval = 9.36E-06, enrichment score = 11.69694026), hsa05143: African trypanosomiasis (pval = 1.08E-05, enrichment score = 16.47783251), hsa04979: cholesterol metabolism (pval = 0.00078561, enrichment score = 16.28397566), hsa04613: neutrophil extracellular trap formation (pval = 0.000848819, enrichment score = 6.559895408), and other pathways. Among them, the upregulated differential protein pathways were enriched in hsa04979: cholesterol metabolism (pval = 0.000127618, enrichment score = 29.51470588), hsa05143: African trypanosomiasis (pval = 0.000560924, enrichment score = 17.91964286), and hsa04145: phagosomes (pval = 0.007285046, enrichment score = 7.307038835), while downregulated differential protein pathways were enriched in hsa04610: complement and coagulation cascade (pval = 2.43E-11, enrichment score = 49.12237762), hsa05322: systemic lupus erythematosus (pval = 2.59E-06, enrichment score = 20.31376518), hsa05171: coronavirus disease (COVID-19) (pval = 2.70E-06, enrichment score = 12.95535234), hsa05150: Staphylococcus aureus infection (pval = 5.93E-05, enrichment score = 17.39544962), and hsa04613: neutrophil extracellular trap formation (pval = 0.004212885, enrichment score = 8.7801677), hsa05133: Pertussis (pval = 0.93E-05, enrichment score = 17.39544962), hsa04613: neutrophil extracellular trap formation (pval = 0.004212885, enrichment score = 8.7801677), hsa05133: pertussis (pval = 0.006614365, enrichment score = 16.03996004), hsa05143: African trypanosomiasis, pval = 0.007830099, enrichment score = 14.7032967), and other pathways. Figure 5: KEGG enrichment top 20 bubble map. The x-axis Enrichment Score is the enrichment score and the y-axis is the pathway information of top 20 in the graph. The larger the bubble the more entries contain the number of differential proteins, the bubble color changes from red-green-blue-purple, and the smaller its enrichment p-value value, the greater the significance. (A) Top 20 KEGG pathways of all differentially expressed proteins. (B) Top 20 KEGG pathways of the up-regulated expressed proteins. (C) Top 20 KEGG pathways of the down-regulated expressed proteins. ## 3.6.3. Differential protein interaction network analysis Using the STRING online database to analyze the differentially expressed proteins in this study, we constructed the interaction network of differential proteins, in which CRP, HP, C4B, C4A, AHSG, APOA1, CFB, CP, AMBP, and SERPINA3 were the 10 proteins with the highest connectivity. In this study, we selected the top 25 proteins in terms of connectivity and used the python package 'networkx 2.1' to draw a protein interaction network map. The top 25 nodes in terms of node connectivity were visualized by the python package 'networkx' and displayed by protein ID and gene name, respectively (as shown in Figure 6). Figure 6: Interaction network analysis of the top 25 differential proteins in terms of connectivity is presented with gene name (A) and protein ID (B), respectively. ## 3.7. PRM validation of target peptides By importing the off-board data into SpectroDive software for analysis, we manually corrected the identification of each peptide in each sample after the peak extraction was finished, adjusted the peptides with offset, and derived the quantitative results of the target peptides. In this experiment, we selected 14 proteins that were differentially expressed before and after rehabilitation treatment, namely IGHV5-51, IGHV3-64D, APOM, CP, F10, IGHA1, APOA1, APOC1, LRG1, TF, CPN1, IGFALS, PLTP, and MEGF8, in addition to QSOX1, IGHA2, KRT14, SLC4A1, IGKV1-16, SERPINA7, KRT9, IGLV3-21, HGFAC, CHIT1, OIT3, NEO1, CNDP1, FUCA2, and MINPP1, and 15 proteins of interest that were differentially expressed in ischemic stroke and healthy subjects in the previous studies and which are less frequently or not. These proteins of interest, which are less frequently or not reported in the literature, were directly associated with ischemic stroke. Based on the derived quantitative information of the target peptides, we used the software built-in mean peptide quantity (i.e., mean peptide quantification) to perform the calculation of the quantitative values of the proteins and used them for subsequent statistical analysis between groups. Then, we performed the analysis of the target protein expression values according to the sample comparison groups separately and plotted the scatter plot of the target protein distribution for each comparison group (Figure 7A). Finally, we then performed expression analysis and visualization based on the data of proteins with consistent expression trends in the comparison groups and plotted the histograms of the target proteins with consistent expression trends, as shown in Figure 7B. After PRM validation, six proteins, including IGHA1, LRG1, IGHV3-64D, IGLV3-21, CP, and SERPINA7, were consistently downregulated in expression after rehabilitation treatment. Figure 7: (A) Scatter plot of target protein expression distribution. The x-axis is the log2 (FC) value of PRM-identified proteins, and the y-axis is the log2 (FC) value of DIA target proteins. Green dots indicate proteins with down-regulated expression trends of both PRM and DIA, orange dots are proteins with up-regulated expression of both PRM and DIA, and gray dots are proteins with inconsistent expression trends of both. (B) Bar chart of target protein expression trend. The x-axis is the ID of the protein with consistent expression trend of PRM and DIA, and the y-axis is the log2 (FC) value of the corresponding protein. log2 (FC) >0 is the protein with up-regulated expression, and log2 (FC) >0 is the protein with down-regulated expression. ## 4. Discussion Ischemic stroke, the most common type of cerebrovascular disease, is characterized by high disability, morbidity, mortality, and recurrence. Although rehabilitation therapy has a good effect on improving neurological function in ischemic stroke, the current assessment of neurological recovery is mainly based on a scale, which is limited by its timeliness and objectivity. Currently, there are two main categories of assessment of neurological recovery, one is the scale category, which is relatively well-established and most widely used. The other category is functional imaging assessment, which is not yet immature, costly, and not widely used. The most commonly used scales are the National Institutes of Health Stroke Scale (NIHSS), the Glasgow Coma Scale (GCS), the Modified Rankin Scale (MRS), and the Barthel index (BI). In addition, there are scales such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) for assessing cognitive function, and the Self-Rating Scale for Depression (SDS) and the Self-Rating Scale for Anxiety (SAS) for assessing mental status. The NIHSS is a 15-item neurological function examination scale designed in 1989 [1]. The NIHSS is currently the most widely used, reliable, valid, and sensitive neurological function assessment scale worldwide. However, the NIHSS is insensitive to posterior circulation stroke and can also underestimate the severity of right hemisphere stroke [2, 3]. The Glasgow Coma Scale (GCS) is used to assess the state of impaired consciousness in stroke, determine the severity of the stroke, and predict the prognosis, and is more appropriate for patients under 55 years of age [4, 5]. The Modified Rankin Scale (MRS) is mainly used to evaluate stroke outcomes and contains only six rating scale items, resulting in poor inter-rater reliability [6, 7]. The Barthel index (BI) consists of 10 items and has good reliability and validity for both face-to-face and telephone assessments, and is often used as a functional indicator of outcome. However, items such as language, cognition, visual acuity, emotional disturbance, and pain were not included, and there was a “ceiling effect” and a “floor effect” on the BI, with a high proportion of cases having a perfect score and a low score in the acute stage, respectively (8–10). Functional neuroimaging assessment includes positron emission tomography (PET), functional magnetic resonance imaging (fMRI), single-photon emission computed tomography (SPECT), magnetic resonance spectroscopy (MRS), and transcranial magnetic stimulation (TMR). Xenon-133 dynamic SPECT, can-18 PET, and [31]P spectroscopic imaging can observe changes in neurological function as it recovers [11]. Functional magnetic resonance imaging (fMRI) can detect changes in brain function after motor recovery in patients with early ischemic stroke [12]. A model of magnetic resonance texture analysis (MRTA) based on ADC maps can be used to assess neurological function in patients with unilateral anterior circulation ischemic stroke [13]. PET provides insight into improvements in speech function by measuring changes in regional cerebral flooding (rCBF) [14]. It is crucial to explore the discovery of biomarkers that can predict or assess neurological recovery on the one hand, and to gain a deeper understanding of the mechanisms associated with rehabilitation therapy, on the other hand, to lay the foundation for exploring optimal treatment plans. In this study, we compared the proteomic changes before and after ischemic rehabilitation treatment, clarified 44 differentially expressed proteins, and found 22 upregulated and downregulated proteins, respectively. Based on this, the differential proteins were subjected to GO enrichment analysis using a bioinformatics approach. The upregulated and downregulated DEPs were further linked to different processes and pathways based on the functions and signaling pathways identified by the significant enrichment analysis. Then, we also constructed a PPI network of DEPs consisting of 44 nodes and 81 edges through the STRING (Version: 11.5) website (the maximum number of linkage demonstrations was chosen to be no more than 10 linkages, and the minimum required interaction confidence was selected to be high confidence 0.700), which had an average node degree of 3.68, an average local clustering coefficient of 0.532, and a predicted number of edges of 14, while the p-value for protein interaction enrichment was <1.0e-16. To obtain more meaningful clusters, we set $k = 7.$ In addition, the four most essential clusters in the PPI network complex, including 17 central node proteins, were screened by Cytoscape (version: 3.9.1) MCODE. Finally, we also validated the 29 proteins of interest using PRM, and the results suggested that among our 29 proteins of interest, PRM validated a total of six proteins including IGHA1, LRG1, IGHV3-64D, IGLV3-21, CP, and SERPINA7 to be consistently downregulated in expression after rehabilitation treatment. The first protein cluster contains eight proteins including C3, C4A, C4B, CFB, CFHR4, CPN1, CRP, and VWF, which are enriched in hsa04610: complement and coagulation cascade (FDR = 6.48E-11), hsa05150: Staphylococcus aureus infection (FDR = 4.85E-06), hsa05133: Pertussis (FDR = 0.00036), hsa05133: pertussis (FDR = 0.00036), and hsa05322: systemic lupus erythematosus (FDR = 0.00053) by the KEGG pathway enrichment analysis. The Reactome pathway enrichment analysis showed that it was mainly enriched in HSA-166658: complement cascade (FDR = 4.93E-14), HSA-977606: regulation of complement cascade (FDR = 8.91E-12), HSA-166663: initial triggering of complement (FDR = 1.36E-10), HSA-174577: activation of C3 and C5 (FDR = 2.05E-09), and HSA-173736: activation of alternative complement (FDR = 0.00095). The WikiPathways enrichment analysis revealed its enrichment in WP2806: complement system (FDR = 3.45E-10), WP545: complement activation (FDR = 5.78E-08), WP558: complement and coagulation cascade (FDR = 1.43E-06), WP5090: complement system in neuronal development and plasticity (FDR = 1.06E-05), WP2328: allograft rejection (FDR = 0.00075), and WP5104: acquired partial steatosis/Barraquer-Simons syndrome (FDR = 0.0011) pathways. Among these proteins, complement C3 is associated with inflammation and ischemia/reperfusion injury and with poor outcomes after ischemic stroke, which may increase its risk within 3 months [15]. Brain endothelial cells are susceptible to direct infection by SARS-CoV-2, which causes upregulation of complement component C3 and increases the association of cerebrovascular events after SARS-CoV-2 infection [16]. Overexpression of C4A decreases the cortical synaptic density, increases microglia phagocytosis of synapses, and affects neurological function [17]. Increased C4B levels reinforce microglia phagocytosis of synapses and synaptic loss in the hippocampal CA3 region [18]. Regulation of CFB and other protein levels may attenuate atherogenic tendencies [19]. CFHR4 expression levels of immune cell infiltration are closely correlated with the levels of infiltrating DCs, neutrophils, Th17 cells, and mast cells [20]. CPN1 may be involved in acute phase response signaling and the development of hypercoagulable and hypofibrinolytic states, and its activation impairs the clearance of impaired mitochondria, leading to mitochondrial dysfunction [21, 22]. Elevated plasma CRP, which may increase the risk of ischemic stroke, is closely associated with cryptogenic stroke with systemic inflammatory features and also increases asymptomatic intracranial or extracranial arterial stenosis in patients with AIS or TIA at 1-year risk of ischemic stroke and risk of ischemic stroke recurrence (23–25). VWF interacts with the circulatory system and platelets in hemostasis and thrombosis by sensing and responding to changes in hemodynamics, which have been associated with atherosclerosis, stroke, and more recently, COVID-19 thrombotic symptoms [26]. The second protein cluster has five proteins, ADH1B, GP1BA, KLKB1, KNG1, and TNXB. No significant pathways were obtained from this cluster after the KEGG pathway enrichment analysis, but the Reactome pathway enrichment analysis showed that it was mainly enriched in HSA-140837: Intrinsic pathway of fibrin clot formation (FDR = 4.51E-05), HSA-9651496: contact activation system (CAS) and defective kallikrein/kinin system (KKS) (FDR = 0.0058). The WikiPathways enrichment analysis showed its enrichment in WP4969: RAS and bradykinin pathway of COVID-19 (FDR =0.0164) and WP558: complement and coagulation cascade (FDR = 0.0311). Of these proteins, ADH1B may improve the cognitive function profile of those who drink moderately [27]. Genetic variants of GP1BA may be associated with venous thrombosis in Asian ancestry [28]. KLKB1 causes a dose-dependent enhancement of the anticoagulant effects of plasma thrombin generation (TG) and coagulation regulatory protein (TM) [29]. A decrease in KLKB1 increases the risk of atrial fibrillation [30]. However, KLKB1 may increase the risk of cerebral hemorrhagic transformation and angioedema [31]. In addition, the KLKB1 gene encodes a procoagulant basic protein, PK, that can regulate circulating cholesterol levels by binding to LDLR and inducing its lysosomal degradation. Blocking PK can stabilize LDLR, lower LDL cholesterol, and thus inhibit further development of atherosclerotic plaques [32]. TNXB haploinsufficiency or deficiency may provide some benefit to vascular events such as stroke by attenuating aging-related atherosclerosis and enhancing the body's adaptation to atherosclerotic plaques [33]. KNG1 not only plays a central role in coagulation and thrombosis but is also significantly associated with cryptogenic young stroke [34]. TNXB plays a role in inhibiting endothelial-to-mesenchymal transition (EndMT) and endothelial inflammation, thus ameliorating atherosclerosis by binding to TGF-beta and blocking its activity [35]. The third protein cluster had a total of seven proteins including COLEC11, F10, IGFALS, MMP16, MMP2, TIMP1, and TIMP2. An enrichment analysis of this protein Reactome pathway showed that it was mainly enriched in HSA-1592389: activation of matrix metalloproteinases (FDR =8.18E-07), HSA-381426: regulation of insulin-like growth factor (IGF)-binding proteins (IGFBPs) on insulin-like growth factor transport and uptake (FDR = 0.005). The WikiPathways enrichment analysis showed its enrichment in WP129: matrix metalloproteinases (FDR = 1.79E-07), WP2865: IL1 and megakaryocytes in obesity (FDR = 0.012), and similarly, the KEGG pathway enrichment analysis of this group of proteins did not enrich for closely related pathways. Among these proteins, COLEC11 belongs to the lectin pathway-related proteins, which have an essential impact on complement-mediated ischemic injury. In addition, the activity of the lectin pathway is reduced in asymptomatic patients with COVID-19 [36, 37]. Elevated thrombin levels in the brain further compromise the integrity of the blood–brain barrier (BBB) in patients with stroke, causing direct parenchymal damage. At the same time, systemic F10 inhibition improves neurological outcomes [38]. IGFALS is significantly increased in non-valvular atrial fibrillation expression [39], which may increase the risk of ischemic stroke. The downregulation of MMP16 inhibits the migration of vascular smooth muscle cells, which in turn affects the atherosclerotic development process [40, 41]. The secretion of MMP2, which promotes cell invasion and migration of vascular smooth muscle cells (VSMCs), influences the process of atherosclerosis [42]. Transient inhibition of MMP$\frac{2}{9}$ after stroke rescues the plasticity of the damaged cortex [43]. Inhibition of MMP-2 activity has neuroprotective effects and reduces edema and brain damage [44]. The expression level of MMP-2 was positively correlated with the size of CI and neurological deficit score in AMI patients with combined CI, the higher the expression level of MMP-2, the higher the risk of AMI with CI [45]. TIMP-1 levels are associated with an increased risk of death and primary disability after acute ischemic stroke, and serum TIMP-1 levels in patients with middle cerebral artery infarction are inversely correlated with survival rates [46, 47]. Increased activity as well as increased levels of MMP-2 in atherosclerotic plaques exert a prothrombotic effect by enhancing platelet activation, which increases the incidence of ischemic cerebrovascular events [48]. In the fourth protein cluster, there are four proteins, B2M, CALR, FCGRT, and LRG1. The KEGG pathway enrichment analysis shows that it is majorly enriched in hsa04612: Antigen processing and presentation (FDR = 0.0218). The Reactome pathway enrichment analysis shows that it is mainly enriched in HSA-983170: Antigen Presentation: Folding, assembly, and peptide loading of class I MHC (FDR = 0.0238). In contrast, there was no related pathway enriched by the WikiPathways enrichment analysis. In these proteins, elevated levels of Beta-2-Microglobulin (B2M) may cause neurological impairment resulting in cognitive deficits [49]. LRG1 is an important factor in pathogenic angiogenesis, a critical stage in the development of neurological diseases such as stroke [50, 51]. LRG1 significantly enhances apoptosis and autophagy during tMCAO, and a positive correlation was shown between LRG1 and severity in patients with cardiogenic embolic stroke [52, 53]. The fifth protein cluster contains a total of eight proteins, namely ADAMTS13, APOA1, APOC1, CP, GIG25, HP, LCAT, and PLTP. The KEGG pathway enrichment analysis shows that it is mainly enriched in hsa04979: cholesterol metabolism (FDR 1.03E-06). The Reactome pathway enrichment analysis shows that it is mainly enriched in HSA-174824: plasma lipoprotein assembly, remodeling, and clearance (FDR = 2.78E-05), HSA-8964058: HDL remodeling (FDR = 2. 78E-05), HSA-382551: transport of small molecules (FDR = 0.0019), HSA-2168880: clearance of heme from plasma (FDR = 0.0066), HSA-2168880: clearance of heme from plasma (FDR = 0.0066), HSA-8963898: plasma lipoprotein assembly (FDR = 0.0111), HSA-8964043: plasma lipoprotein clearance (FDR = 0.0268), and HSA-9029569: NR1H3 and NR1H2 regulate gene expression related to cholesterol transport and efflux (FDR = 0.0292). The WikiPathways enrichment analysis revealed its enrichment in WP430: cholesterol production inhibition by statins (FDR = 3.16E-07), WP5108: familial hyperlipidemia type 1 (FDR = 8.49E-06), WP5109: familial hyperlipidemia type 2 (FDR = 8. 49E-06), WP5110: familial hyperlipidemia type 3 (FDR = 8.49E-06), WP5112: familial hyperlipidemia type 5 (FDR = 8.49E-06), WP5111: familial hyperlipidemia type 4 (FDR = 1. 02E-05), WP3601: lipid particle composition (FDR = 0.00078), WP4522: metabolism of LDL, HDL and TG pathways, including disease (FDR = 0.0019), WP2878: PPAR-alpha pathway (FDR = 0. 0041), WP1533: vitamin B12 metabolism (FDR = 0.013), WP176: folate metabolism (FDR = 0.0208), WP3942: PPAR signaling pathway (FDR = 0.0208), and WP15: selenium micronutrient network 0.0287). Among these proteins, elevated APOA1 levels reduce the risk of ischemic stroke, may delay the progression of atherosclerotic lesions, and also promote lesion regression (54–57). CP can help regulate cellular iron homeostasis. Expression of CP is rapidly upregulated after permanent middle cerebral artery occlusion (pMCAO), while CP deficiency leads to dysregulation of iron homeostasis, increased oxidative damage, increased lesion size, and impaired functional recovery [58]. Glycosylated CP may play a key role in neuroprotection [59]. HP improves survival, motor function, and brain injury after cerebral ischemia by binding to HMGB1 and regulating macrophage/microglia polarization [60]. Furthermore, HP reduces the risk of ischemic stroke by scavenging free hemoglobin and protecting it from iron-induced oxidative damage, inflammatory responses, and consequent atherosclerosis [61]. LCAT may have an anti-atherogenic effect, and LCAT deficiency can cause lead to a severe reduction in HDL [62]. PLTP promotes phosphatidylserine externalization on the platelet plasma membrane and accelerates adenosine diphosphate (ADP) or collagen-induced platelet aggregation, and its altered expression may influence atherosclerosis [63, 64]. The sixth protein cluster involves seven proteins, namely AHSG, AMBP, APOC3, APOM, C9, ORM1, and TF. Reactome pathway enrichment analysis showed that it was mainly enriched in HSA-114608: platelet degranulation (FDR =0.0213). The KEGG pathway enrichment analysis as well as the WikiPathways enrichment analysis showed no significant enrichment of pathways. AHSG may enhance the inflammatory response of vascular endothelial cells, the formation of macrophage foam cells, and the proliferation of vascular smooth muscle cells and collagen production, leading to the development of atherosclerosis [65]. AMBP may be an atherosclerosis-promoting protein, but its downregulation during ischemia is associated with neuroprotective effects [66, 67]. APOC3 plays a key role in the progression of atherosclerosis by increasing the risk of ischemic stroke and counteracting the effects of ApoC3, which can substantially reduce the size of atherosclerotic lesions [68, 69]. APOM is involved in atherosclerosis, and its changes are associated with recovery from acute ischemic stroke [56, 70]. C9 levels may correlate with the degree of brain injury, and the complement system is strongly associated with the development of neuroinflammation [71]. Increased expression of ORM1 after cerebral ischemia exacerbates the disruption of the BBB after ischemic stroke [72], and may also contribute to thrombotic susceptibility through an immunothrombotic mechanism [73]. In addition to this, ORM 1 upregulation is also associated with neuroinflammation [74]. TF contributes to thrombosis and causes the destabilization of atherosclerotic plaques [75, 76]. In addition, transferrin expression is higher in men, increases with age, and is upregulated in response to SARS-CoV-2 infection [77]. The seventh protein cluster contained five proteins, CDK2, HIST1H2AE, MEGF8, SELL, and SPDYA, which were not enriched to the relevant pathways after pathway enrichment analysis. Among these proteins, activation of cell cycle protein-dependent kinases (CDKs) such as CDK2 may cause neuronal death after cerebral ischemia [78]. MEGF8 is involved in mediating BMP4 signaling and directing the development of trigeminal ganglion (TG) axons, and bone morphogenetic protein (BMP) signaling has emerged as an important regulator of sensory neuron development [79]. Increased levels of soluble L-selectin (SELL) may increase the risk of ischemic stroke [80]. In addition, some differential proteins were not in these seven clusters. Among these proteins, overexpression of C4A may cause changes in prefrontal neurological function, leading to cognitive decline [81]. Elevated levels of C4A may lead, for example, to Alzheimer's disease, with cognitive dysfunction [82]. C4B is significantly correlated with the ratio of total tau, which may affect the neuroprotective effects of APOE [83]. HPR and HP are closely linked and it may lead to higher activity [84]. Haptoglobin-related proteins bind to Hb and apolipoprotein-L, which not only link HPR to the cholesterol system, but the HPR/apo-L complex also has a specific trypanosomal lysis effect [85]. SERPINA3 can attenuate neuronal injury by interfering with granzyme B-mediated neuronal death after cerebral ischemia [86]. Presence of SERPINA3N/SERPINA3 aggregates in cortical oligodendrocytes in areas of brain injury [87]. We also used Cytoscape MCODE to screen the four most important protein clusters in the PPI network. The first cluster has seven proteins, including APOC3, PLTP, APOM, HP, APOA1, LCAT, and APOC1, which are mainly enriched in hsa04979: cholesterol metabolism (FDR = 2.83E-22), WP430: cholesterol production inhibition by statins (FDR = 3.64E-21), and hsa-174824: plasma lipoprotein assembly, remodeling, and clearance (FDR = 9.09E-20) pathways. The second cluster has four proteins, including TIMP2, TIMP1, MMP16, and MMP2, which are mainly enriched in WP129: Matrix metalloproteinase (FDR = 2.46E-19), HSA-1592389: activation of matrix metalloproteinase (FDR = 1.65E-18); and HSA-1474228: degradation of extracellular matrix (FDR = 5.44E-16). The third protein cluster has three proteins, including ORM1, AHSG, and AMBP, which are mainly enriched in HSA-114608: platelet degranulation (FDR = 9.36E-12), HSA-381426: regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins (IGFBPs) (FDR = 9.36E-12), and HSA-76002: platelet activation, signaling, and aggregation (FDR = 9.36E-12) pathways. The fourth protein cluster has three proteins, including C4A, CFB, and C4B, which are mainly enriched in the WP5090: complement system in neuronal development and plasticity (FDR = 4.75E-27), HSA-166658: complement cascade (FDR = 3.76E-26) and WP2806: complement system (FDR = 2.08E-24) passages. This is close to the four protein clusters obtained by STRING screening mentioned earlier, and their enrichment pathways are also consistent. The present study also has shortcomings. To reduce the effects of confounding factors such as age, sex, race, and comorbid diseases, we minimized these effects by including eligible ischemic stroke patients in the study and matching the selection of healthy controls accordingly, but there are still shortcomings due to the small sample size. Although these confounding factors can only be addressed in future studies, our findings are equally informative for future studies. ## 5. Conclusion In conclusion, by using the DIA technique to detect the serum protein expression profile before and after stroke rehabilitation treatment, 44 differentially expressed proteins were clarified, and 22 upregulated and downregulated proteins were found, respectively. Based on this, the GO and pathway enrichment analysis of DEPs were performed by using bioinformatics methods, and a DEPs PPI network consisting of 44 nodes and 81 edges was also constructed through the STRING website. We identified complement-related pathways, lipoprotein-related functions and effects, thrombosis and hemostasis, coronavirus disease (COVID-19), and inflammatory and immune pathways as the major pathways involved in neurological improvement after ischemic stroke rehabilitation. By the PRM validation, IGHA1, LRG1, IGHV3-64D, and CP may be biomarkers of neurological recovery after stroke. ## Data availability statement The original contributions presented in the study are publicly available. This data can be found here: ProteomeXchange Consortium, http://www.proteomexchange.org/, PXD036840. ## Ethics statement The studies involving human participants were reviewed and approved by Medical Ethics Committee of Xiangya Boai Rehabilitation Hospital. The patients/participants provided their written informed consent to participate in this study. ## Author contributions WH, NZ, and ST conceived and designed the study. WH and PL performed data analysis. WH and ST wrote the paper. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Brott T, Adams HP Jr, Olinger CP, Marler JR, Barsan WG, Biller J. Measurements of acute cerebral infarction: a clinical examination scale. Stroke. (1989) 20 864-70. DOI: 10.1161/01.STR.20.7.864 2. Martin-Schild S, Albright KC, Tanksley J, Pandav V, Jones EB, Grotta JC. Zero on the NIHSS does not equal the absence of stroke. Ann Emerg Med. 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--- title: Influence of altered serum and muscle concentrations of BDNF on electrophysiological properties of spinal motoneurons in wild-type and BDNF-knockout rats authors: - Norbert Grzelak - Piotr Krutki - Marcin Bączyk - Dominik Kaczmarek - Włodzimierz Mrówczyński journal: Scientific Reports year: 2023 pmcid: PMC10027728 doi: 10.1038/s41598-023-31703-8 license: CC BY 4.0 --- # Influence of altered serum and muscle concentrations of BDNF on electrophysiological properties of spinal motoneurons in wild-type and BDNF-knockout rats ## Abstract The purpose of this study was to determine whether altered serum and/or muscle concentrations of brain-derived neurotrophic factor (BDNF) can modify the electrophysiological properties of spinal motoneurons (MNs). This study was conducted in wild-type and Bdnf heterozygous knockout rats (HET, SD-BDNF). Rats were divided into four groups: control, knockout, control trained, and knockout trained. The latter two groups underwent moderate-intensity endurance training to increase BDNF levels in serum and/or hindlimb muscles. BDNF and other neurotrophic factors (NFs), including glial cell-derived neurotrophic factor (GDNF), neurotrophin-3 (NT-3), nerve growth factor (NGF), and neurotrophin-4 (NT-4) were assessed in serum and three hindlimb muscles: the tibialis anterior (TA), medial gastrocnemius (MG), and soleus (Sol). The concentrations of tropomyosin kinase receptor B (Trk-B), interleukin-15 (IL-15), and myoglobin (MYO/MB) were also evaluated in these muscles. The electrophysiological properties of lumbar MNs were studied in vivo using whole-cell current-clamp recordings. Bdnf knockout rats had reduced levels of all studied NFs in serum but not in hindlimb muscles. Interestingly, decreased serum NF levels did not influence the electrophysiological properties of spinal MNs. Additionally, endurance training did not change the serum concentrations of any of the NFs tested but significantly increased BDNF and GDNF levels in the TA and MG muscles in both trained groups. Furthermore, the excitability of fast MNs was reduced in both groups of trained rats. Thus, changes in muscle (but not serum) concentrations of BDNF and GDNF may be critical factors that modify the excitability of spinal MNs after intense physical activity. ## Introduction Spinal motoneurons (MNs) control the contractile properties of skeletal muscles and are often targets of adaptive processes resulting from alterations in motor activity. Electrophysiological studies in rats have shown that both decreased (induced by hypokinesis, spinal cord injury, or blocked action potential propagation) and increased (caused by endurance or strength training, muscle overload, or whole-body vibration) levels of locomotive behaviour modify the electrophysiological properties of MNs1–9. Adaptive changes in the electrophysiological properties of MNs are believed to be triggered by the activation of specific cellular proteins in response to altered motor demand. The molecular factors responsible for these MN transformations have not been identified, but the involvement of brain-derived neurotrophic factor (BDNF) is often suggested. BDNF is produced both by brain structures, such as the hippocampus and cerebral cortex, and skeletal muscles, and its levels can change in response to various forms of physical activity10–16. MNs express BDNF-responsive tropomyosin kinase receptors (Trks) such as Trk-B17–20. Moreover, according to Blum21 and Rose et al.22, BDNF can rapidly gate Na+ channels in the membranes of some types of neurons. Thus, BDNF may be a potential molecular player that induces changes in the electrophysiological properties of spinal MNs. Nevertheless, it is not clear whether MNs react to BDNF of central or peripheral origin. BDNF levels measured in serum and plasma often increase after endurance exercise in humans14,23–26 and rats27. Increased levels of post-training BDNF in the bloodstream may be due to its increased expression either in brain structures14,15 or skeletal muscles12,28. Gardiner29 proposed that activity-related changes in muscle BDNF concentrations are responsible for chronic changes in attributes of spinal MNs. Accordingly, BDNF binds to Trk-B receptors and alters the expression of ion channel genes in MNs, controlling their electrophysiological properties. This hypothesis is consistent with the findings of Sagot et al.30 and Rind et al.31, who reported that BDNF can be retrogradely and trans-synaptically transported from muscles to spinal MNs. In addition, Gonzales and Collins32 observed changes in the excitability of fast MNs in rats after exogenous application of BDNF to the MG muscle. The main goal of the present study was to determine whether altered serum and/or muscle concentrations of BDNF are related to changes in the electrophysiological properties of lumbar MNs in rats. For this purpose, four separate groups of animals were studied: [1] control rats (Bdnf+/+), [2] Bdnf knockout rats (HET, SD-BDNF) (Bdnf+/−), [3] trained control rats (5 weeks of endurance running exercises performed to induce increases in serum and/or muscle BDNF levels [Bdnf+/+T]), and [4] trained knockout rats that were subjected to the same training regimen (Bdnf+/−T). In each group, the passive, threshold, and rhythmic firing properties of MNs were investigated. Subsequently, the levels of myokines indicating the degree of muscle contractile activity [interleukin-15 (IL-15) and myoglobin (MYO/MB)]; BDNF and its receptor, Trk-B; and the most widespread NFs [glial cell-derived neurotrophic factor (GDNF), neurotrophin-3 (NT-3), nerve growth factor (NGF), and neurotrophin-4 (NT-4)] were measured in serum (NFs only) and three rat hindlimb muscles acting as ankle flexors or extensors: the tibialis anterior (TA), medial gastrocnemius (MG), and soleus (Sol). ## Body and muscle weights Significant differences were found between the mean body weights of Bdnf+/+ (524.7 ± 16.7 g) and Bdnf+/− (681.4 ± 74.4 g) rats measured on the day of the electrophysiological experiment (F1,33 = 56.84, $p \leq 0.0001$, post hoc test $$p \leq 0.0002$$). Animals in the Bdnf+/+ T and Bdnf+/− T groups weighed less than those in the Bdnf+/+ and Bdnf+/− groups by an average of 39.9 g (F1,33 = 14.76, $$p \leq 0.0005$$, post hoc test $$p \leq 0.33$$) and 92.8 g (F1,33 = 14.76, $$p \leq 0.0005$$, post hoc test $$p \leq 0.0051$$), respectively. There were also significant differences in the average weights of the TA muscles of the Bdnf+/+ and Bdnf+/− animals (0.9 ± 0.06 g vs. 1.1 ± 0.2 g, F1,33 = 14.90, $$p \leq 0.0005$$, post hoc test $$p \leq 0.0086$$), but not in those of the MG and Sol muscles. Endurance training did not cause considerable changes in the weights of any of the muscles tested in the Bdnf+/+ T or Bdnf+/− T animals ($p \leq 0.05$). However, endurance exercise did significantly increase the muscle-to-body weight ratio of the TA muscle from 0.0017 ± 0.0002 in the Bdnf+/+ group to 0.002 ± 0.0002 in the Bdnf+/+ T group (F1,33 = 24.34, $p \leq 0.0001$, post hoc test $$p \leq 0.0031$$), and from 0.0016 ± 0.0002 in the Bdnf+/− group to 0.0019 ± 0.0001 in the Bdnf+/− T group (F1,33 = 24.34, $p \leq 0.0001$, post hoc test $$p \leq 0.0154$$). Similar results were observed for the MG muscle, from 0.0019 ± 0.0002 in the Bdnf+/− group to 0.0023 ± 0.0003 in the Bdnf+/− T group (F1,33 = 16.24, $$p \leq 0.0003$$, post hoc test $$p \leq 0.00148$$). However, no changes in this parameter resulting from endurance exercise were found for the Sol muscle ($p \leq 0.05$). ## Concentrations of NFs in serum The serum of genetically modified rats (Bdnf + / −) had significantly lower concentrations of BDNF (by $20\%$), GDNF (by $22\%$), NT-3 (by $31\%$), NGF (by $37\%$), and NT-4 (by $29\%$) than the serum of Bdnf+/+ rats (Fig. 1A–E). Significantly lower concentrations of BDNF (by $15\%$), NT-3 (by $30\%$), NGF (by $26\%$), and NT-4 (by $28\%$) were also found in the trained group of genetically modified animals (Bdnf+/− T) compared to trained animals in the Bdnf+/+ T group (Fig. 1A–E). Surprisingly, 5 weeks of endurance training on a treadmill did not cause significant changes in the serum levels of any of the NFs tested in rats from both trained groups (Fig. 1A–E).Figure 1The concentrations of BDNF (A), GDNF (B), NT3 (C), NGF (D) and NT4 (E) in the serum of Bdnf+/+, Bdnf+/+ T, Bdnf+/−, and Bdnf+/− T rats. The bars indicate the mean values, box-plots ± $25\%$ of the dataset, and whiskers ± SD. Differences between the Bdnf+/+ and Bdnf+/− animals, as well as between the Bdnf+/+ T and Bdnf+/− T groups are indicated above the plots (two-way ANOVA with genotype and training as fixed factors with Tukey’s HSD post-hoc tests). ## Concentrations of NFs in hindlimb muscles In contrast to the results observed in serum, the levels of all tested NFs in the TA, MG, and Sol muscles (BDNF, GDNF, NT-3, NGF, and NT-4) of Bdnf+/+ and Bdnf+/− rats, as well as Bdnf+/− T and Bdnf+/− T rats, were not statistically different (Figs. 2, 3 and 4A–E). In addition, there were no differences in IL-15 or MYO/MB content across the three tested muscles of Bdnf+/+ and Bdnf+/− rats as well as Bdnf+/− and Bdnf+/− T rats (Figs. 2, 3 and 4G,H). A statistically significant difference in the concentration of Trk-B was found between the TA muscles of Bdnf+/+ and Bdnf+/− rats (Fig. 2F), but not between the MG or Sol muscles (Figs. 3 and 4F).Figure 2The concentrations of BDNF (A), GDNF (B), NT3 (C), NGF (D), NT4 (E), TrkB (F), IL-15 (G), MYO-MB (H) in tibialis anterior muscles of Bdnf+/+, Bdnf+/+ T, Bdnf+/−, and Bdnf+/− T rats. The bars indicate the mean values, box-plots ± $25\%$ of the dataset, and whiskers ± SD. Differences between the Bdnf+/+ and Bdnf+/− animals, as well as between the Bdnf+/+ T and Bdnf+/− T groups are indicated above the plots (two-way ANOVA with genotype and training as fixed factors with Tukey’s HSD post-hoc tests).Figure 3The concentrations of BDNF (A), GDNF (B), NT3 (C), NGF (D), NT4 (E), TrkB (F), IL-15 (G), MYO-MB (H) in medial gastrocnemius muscles of Bdnf+/+, Bdnf+/+ T, Bdnf+/−, and Bdnf+/− T rats. The bars indicate the mean values, box-plots ± $25\%$ of the dataset, and whiskers ± SD. Differences between the Bdnf+/+ and Bdnf+/− animals, as well as between the Bdnf+/+ T and Bdnf+/− T groups are indicated above the plots (two-way ANOVA with genotype and training as fixed factors with Tukey’s HSD post-hoc tests).Figure 4The concentrations of BDNF (A), GDNF (B), NT3 (C), NGF (D), NT4 (E), TrkB (F), IL-15 (G), MYO-MB (H) in soleus muscles of Bdnf+/+, Bdnf+/+ T, Bdnf+/−, and Bdnf+/− T rats. The bars indicate the mean values, box-plots ± $25\%$ of the dataset, and whiskers ± SD. Differences between the Bdnf+/+ and Bdnf+/− animals, as well as between the Bdnf+/+ T and Bdnf+/− T groups are indicated above the plots (two-way ANOVA with genotype and training as fixed factors with Tukey’s HSD post-hoc tests). On the other hand, five weeks of endurance training significantly increased the concentrations of BDNF, GDNF, Trk-B, and IL-15 in the MG muscles of rats in the Bdnf+/+ T group compared to the Bdnf+/+ group (Fig. 3A,B,F,G), as well as in the MG muscles of rats in the Bdnf+/− T group compared to the Bdnf+/− group (Fig. 3A,B,F,G). The levels of the other NFs (NT-3, NT-4, and NGF) and MYO/MB did not change in this muscle after endurance training (Fig. 3C–E,H). In the TA muscle, endurance training induced a significant increase in the concentrations of BDNG, GDNF, and MYO/MB in the Bdnf+/+ T group compared to the Bdnf+/+ group (Fig. 2A,B,H), and increased the level of MYO/MB in the Bdnf+/− T group compared to the Bdnf+/− group (Fig. 2H). Levels of the remaining NFs (NT-3, NGF, and NT-4), Trk-B, and IL-15 were not changed in the TA muscles of animals in the trained groups (Fig. 2C–G). Interestingly, no significant post-training changes were found in the Sol muscles of the Bdnf+/+ T and Bdnf+/− T groups compared to their respective controls (Fig. 4A,B,D–H), with the exception of a decrease in NT-3 in the Bdnf+/+ T group compared to the Bdnf+/+ group (Fig. 4C). ## Electrophysiological properties of fast and slow MNs Electrophysiological recordings from a total of 174 lumbar MNs were analysed: 42 in the Bdnf+/+ group (28 classified as fast and 14 as slow), 47 in the Bdnf+/− group (33 classified as fast and 14 as slow), 41 in the Bdnf+/+ T group (26 classified as fast and 15 as slow), and 44 in the Bdnf+/− T group (29 classified as fast and 15 as slow). There were no statistically significant differences in the electrophysiological properties of any fast- or slow-type MN ($p \leq 0.05$, Table 1) between rats of different genotypes (Bdnf+/+ and Bdnf+/−). However, 5 weeks of endurance training on a treadmill induced significant decreases in the RIN values of fast MNs from Bdnf+/+ T and Bdnf+/− T animals compared to their respective controls (Bdnf+/+ and Bdnf+/− groups) (F1,112 = 30.03, $p \leq 0.0001$, post hoc test $$p \leq 0.0081$$; F1,112 = 30.03, $p \leq 0.0001$, post hoc test $$p \leq 0.0066$$, respectively; Table 1). In addition, there were trends toward increased rheobase values (F1,112 = 8.27, $$p \leq 0.0048$$, post-hoc test $$p \leq 0.1367$$; F1,112 = 8.27, $$p \leq 0.0048$$, post-hoc test $$p \leq 0.2402$$), the minimum steady-state firing (SSF) current (F1,60 = 2.78, $$p \leq 0.1009$$, post-hoc test $$p \leq 0.8563$$; F1,60 = 2.78, $$p \leq 0.1009$$, post-hoc test $$p \leq 0.4003$$), and the maximum SSF current (F1,60 = 4.72, $$p \leq 0.0338$$, post-hoc test $$p \leq 0.7692$$; F1,60 = 4.72, $$p \leq 0.0338$$, post-hoc test $$p \leq 0.1565$$).Table 1The mean values (± SD) of passive, threshold, and rhythmic firing properties of fast and slow MNs from Bdnf+/+, Bdnf+/+ T, Bdnf+/−, and Bdnf+/− T rats. Passive and threshold properties of MNsRhythmic firing properties of MNsRMP (mV)APamp (mV)APhalf-width (ms)AHPpeak time (ms)AHPamp (mV)AHPhdt (ms)RIN (MΩ)Rheo (nA)VT (mV)Min SSF current (nA)Min SSS frequency (Hz)Max SSF current (nA)Max SSF frequency (Hz)f–I slopeFast MNs Bdnf+/+ ($$n = 28$$)−64.7 ± 8.873.7 ± 13.10.53 ± 0.18.0 ± 2.03.5 ± 0.812.1 ± 1.82.3 ± 0.68.2 ± 3.4−46.6 ± 9.912.6 ± 4.830.6 ± 8.926.4 ± 8.081.3 ± 33.83.8 ± 1.4 Bdnf+/+ T ($$n = 26$$)−63.4 ± 8.072.1 ± 12.80.53 ± 0.18.0 ± 1.73.3 ± 0.612.2 ± 1.31.7 ± 0.5##10.6 ± 4.5−45.5 ± 7.814.1 ± 5.132.8 ± 10.129.4 ± 9.580.4 ± 21.53.1 ± 0.9 Bdnf+/− ($$n = 33$$)−68.7 ± 6.174.5 ± 11.80.58 ± 0.17.9 ± 2.13.4 ± 0.712.0 ± 1.52.4 ± 0.88.4 ± 3.6−48.8 ± 9.212.3 ± 4.231.0 ± 7.225.7 ± 9.282.3 ± 23.23.9 ± 1.3Bdnf+/− T ($$n = 29$$)−64.5 ± 8.173.8 ± 9.40.55 ± 0.18.2 ± 1.53.3 ± 0.612.4 ± 1.31.7 ± 0.5###10.3 ± 4.4−47.2 ± 6.415.1 ± 6.330.9 ± 10.632.2 ± 8.286.5 ± 26.53.2 ± 1.1Slow MNs Bdnf+/+ ($$n = 14$$)−62.1 ± 6.471.5 ± 11.40.57 ± 0.18.8 ± 2.76.2 ± 1.821.3 ± 1.03.3 ± 0.52.6 ± 1.0−53.1 ± 6.53.9 ± 1.824.2 ± 8.313.4 ± 5.068.9 ± 31.95.0 ± 2.0 Bdnf+/+ T ($$n = 15$$)−59.0 ± 9.364.4 ± 6.80.49 ± 0.048.2 ± 2.25.6 ± 0.820.8 ± 0.73.3 ± 0.42.0 ± 0.9−52.3 ± 8.64.3 ± 1.930.0 ± 11.711.9 ± 4.561.8 ± 19.64.8 ± 2.3 Bdnf+/− ($$n = 14$$)−61.8 ± 6.868.6 ± 10.90.58 ± 0.18.5 ± 3.35.1 ± 1.421.0 ± 0.73.2 ± 0.52.6 ± 1.0−51.9 ± 7.75.4 ± 2.726.0 ± 10.415.9 ± 4.770.6 ± 15.85.0 ± 1.8 Bdnf+/− T ($$n = 15$$)−62.5 ± 5.967.7 ± 12.40.56 ± 0.16.9 ± 1.35.3 ± 0.920.9 ± 0.83.4 ± 0.62.4 ± 1.0−54.2 ± 6.04.1 ± 1.623.8 ± 4.512.0 ± 4.255.7 ± 10.44.5 ± 1.7Differences between Bdnf+/− and Bdnf+/− T rats are statistically significant at ##$p \leq 0.01$; ###$p \leq 0.001$ (two-way ANOVA with genotype and training as fixed factors and Tukey’s HSD post-hoc test).n number of MNs, RMP resting membrane potential, APamp action potential amplitude, APhalf-width action potential duration measured at the level of half-amplitude, AHPpeak time time to afterhyperpolarization (AHP) peak, AHPamp AHP amplitude, AHPhdt AHP half-decay time, RIN input resistance, Rheo rheobase, VT voltage threshold, min steady-state firing (SSF) current minimum current evoking SSF, min SSF frequency minimum frequency of the SSF, max SSF current maximum current evoking SSF, max SSF frequency maximum frequency of SSF, f–I slope slope of the f–I relationship. ## Discussion This study revealed several unexpected findings. We hypothesized that reduced serum BDNF levels would be the only factor to modify the properties of spinal MNs in knockout rats. However, reduced serum levels of all NFs tested were detected in rats with only one functional Bdnf allele (their levels were unchanged in muscles); therefore, a combined effect of all serum-reduced NFs on MNs was observed. However, lower serum NF levels had no effects on the passive, threshold, or rhythmic firing properties of fast or slow spinal MNs in rats. Numerous meta-analyses have shown that BDNF levels in serum and plasma can significantly increase after different forms of endurance activity33–35. However, moderate-intensity endurance training on a treadmill, which was used in our study, did not raise the levels of any NFs tested in serum, but significantly increased the concentrations of BDNF and GDNF in fast hindlimb muscles (TA and MG) of both wild-type and knockout rats. A major effect of the observed post-training increases in muscle BDNF and GDNF was a decrease in the excitability of fast MNs, which were already less excitable than the slow MNs36. Bdnf+/− rats have been previously used in numerous studies of psychiatric and neurodegenerative diseases associated with significant deficits in peripheral and central BDNF protein37–39. Therefore, these animals were selected as a model in our study to investigate changes in the electrophysiological properties of spinal MNs exposed to low concentrations of BDNF. Previous work has demonstrated that BDNF concentrations can be reduced by $73\%$ and $50\%$ in the serum and frontal cortex, respectively, of these animals compared to control rats37,38. Significantly lower ($20\%$) serum BDNF levels in Bdnf heterozygotes were also noted in our study. This finding coincided with lower levels of other NFs (GDNF, NT-3, NGF, and NT-4) in knockout compared to control rats. This result indicates that the absence of one Bdnf allele leads to reductions in the serum concentrations of several major NFs. Due to the existence of separate genes encoding different NFs40, we conclude that normal expression levels of Bdnf are necessary to maintain proper serum levels of the other NFs. Our results also indicate that the decreased serum levels of all NFs in Bdnf+/− rats were not due to their reduced expression in muscles. The levels of BDNF, GDNF, NT-3, NGF, and NT-4 (as well as those of IL-15 and MYO/MB) in the TA, MG, and Sol muscles were comparable between Bdnf+/+ and Bdnf+/− rats (Figs. 1, 2, 3 and 4). Since the membrane parameters and firing properties of fast and slow MNs in Bdnf+/− rats were not different from those in Bdnf+/+ rats (Table 1), the lower serum concentrations of BDNF, GDNF, NT-3, NGF, and NT-4, with their unchanged levels in hindlimb muscles, likely do not modify the electrophysiological properties of spinal MNs. Lower mean body weights were observed in both groups of trained rats (Bdnf+/+ T and Bdnf+/− T) relative to their respective controls ($7.6\%$ and $14.6\%$ decrease, respectively). The training regimen also impacted two fast hindlimb muscles (TA and MG) but did not affect the slow Sol muscle. The significant increases in the muscle-to-body weight ratio observed for the TA and MG muscles were accompanied by increased MYO/MB levels in the TA muscle, as well as elevated levels of IL-15 in the MG muscle, in both groups of trained rats (Figs. 2H, 3G). According to Underwood and Williams41 and Ordway and Garry42, elevated levels of MYO/MB in skeletal muscle occur in response to increased contractile activity, including progressive treadmill running43. In addition, Pedersen and Fabbraio44 and Lee and Jun45 reported that normal IL-15 expression levels in various muscles can be altered by endurance exercise, while Yang et al.46 showed an increase in MG IL-15 levels in obese rats after 8 weeks of treadmill training. We emphasize that the endurance regimen used in our study raised the levels of myokines in the TA and the MG muscles, but did not impact those in the Sol muscle in either the Bdnf+/+ T or Bdnf+/− T groups. This result suggests a stronger involvement of fast muscles in the increased motor activity that occurs during endurance training. Significant post-training increases in BDNF and GDNF concentrations were observed in the TA muscle of trained wild-type rats as well as in the MG muscles of both groups of trained rats irrespective of genotype. Significant increases in Trk-B were also observed in both groups of trained rats (Figs. 2, 3F). Notably, the changes in the TA and MG muscles coincided with training-evoked increases in myokine levels (MYO/MB in the TA muscle and IL-15 in the MG muscle). These results are consistent with a report by Matthews et al.12, who showed that BDNF is produced by human skeletal muscles in response to increased contractions but not released into the peripheral circulation. Moreover, Wehrwein et al.47 showed that levels of GDNF increase in the hindlimb muscles of rats after four weeks of treadmill walk training or two weeks of forced running wheel training. Thus, it is reasonable to speculate that post-training increases in muscle BDNF and GDNF concentrations may be involved in training-induced changes in MN electrophysiological properties. The endurance training regimen used in our study induced significant reductions in the input resistance of fast MNs in animals from the Bdnf+/+ T and Bdnf+/− T groups compared to their respective controls (Bdnf+/+ and Bdnf+/−). There were also trends toward increased rheobase values and the minimum and maximum currents required to generate rhythmic discharges in fast MNs after treadmill exercise (Table 1). It has been previously shown that MNs innervating fast muscle fibres have lower input resistance and higher rheobases than MNs innervating slow muscle fibres, leading them to be recruited later than slow MNs32,48. As a result, we conclude that our endurance training regimen resulted in reduced fast MN excitability in both wild-type and knockout rats. In turn, the fast MNs became even less likely to be recruited during motor activity, allowing the slow MNs to contribute even more to hindlimb muscle movements. To explain this unexpected result we suggest that fast MNs which were frequently recruited during the training could be transformed and were recognized as slow after the endurance training, while the least excitable fast MNs (with the lowest input resistance and the highest rheobase) were not recruited enough to provoke adaptations. This finding is partly in line with results reported by Beaumont and Gardiner4, who showed larger cell capacitances in fast MNs after endurance training, suggesting that MNs were larger. However, no effects on indices of excitability (rheobase, cell input resistance) were observed, and morphological studies in rats did not indicate changes in MN size after that the endurance training49,50. The aforementioned results, together with the observed training-induced increases in muscle BDNF and GDNF, suggest that these NFs may be retrogradely and trans-synaptically transported to MNs. They may also contribute to reduced fast MN excitability by altering ion conductance and the expression of ion channels as hypothesized by Gardiner29. The possibility that BDNF regulates MN excitability has been previously demonstrated by Gonzales and Collins32, who observed changes in the excitability of fast MNs in rats after 5 days of continuous BDNF administration to the gastrocnemius muscle. However, there is a substantial discrepancy between the general direction of changes in the MN excitability observed in our experiments (a decrease) and the aforementioned result (an increase). This difference may be due to multiple factors. First, different BDNF induction methods were used (5 weeks of endurance training vs. 5 days of continuous exogenous application). Second, different concentrations of BDNF in the MG muscle were attained; the mean post-training concentration in our study was 0.9–1.0 ng/ml, whereas relatively high doses (8.0 and 16.6 mg/ml) of intramuscular BDNF were applied in the previous study. Third, repetitive activation of spinal MNs through feedback reflex loops from contracting muscles during running exercise may be an additional influence. Slow motor units are believed to be highly involved whereas the least excitable fast motor units are less active or remain inactive, but an additional activity time during running exercises would have negligible influence on slow motor units, as their average daily time is in rat muscles significantly longer than a daily activity time of two subtypes of fast motor units (5.3–8.4 h per day vs. 23–72 min for fast resistant and 0.5–3 min for fast fatigable units, respectively)51. This phenomenon may obscure the full range of changes that occur in various types of MNs. In light of a report by Delezie et al.52, another possible explanation for our results is that BDNF is required for fibre-type specification in mouse skeletal muscle. Specifically, this previous report showed that BDNF overexpression promotes a fast muscle type gene program and elevates the number of muscle fibres with a glycolytic phenotype. This phenomenon could plausibly support our results, indicating that the observed decrease in fast MN excitability may be due to an increase in the number of glycolytic muscle fibres that these neurons innervate. However, it remains an open question whether such a transformation actually occurred in the muscles of any of the trained animals in our study. Furthermore, we note that muscle-derived GDNF is a synaptotrophin responsible for maintaining synaptic connections53. Moreover, activity-related changes observed in the expression of skeletal muscle GDNF suggest that it may be involved in the modulation of neuromuscular junction architecture47,54–57. For example, Gyorkos et al.58 demonstrated that voluntary high-intensity running (with and without resistance) significantly increased GDNF content and end-plate area in the fast plantaris muscle of rats. Thus, the observed increases in GDNF in both fast muscles (the TA and MG) after moderate endurance training may facilitate communication between MNs and their muscle fibres. We conclude that changes in the levels of specific skeletal muscle NFs evoked by moderately increased physical activity play an important role in spinal MNs’ plasticity, which is manifested by changes of certain electrophysiological properties of MNs, reflecting their excitability. In contrast, changes in the concentrations of circulating NFs do not induce any changes in MN properties. ## Animals This study was conducted on male Sprague–Dawley rats purchased from SAGE Labs (St. Louis, MO, USA). On average, the rats were 8 weeks old and weighed 298 ± 53 g upon arrival. Thirty animals were Bdnf-wild-type and 30 rats were heterozygous. Bdnf was knocked out using zinc finger nuclease technology (SD-BDNF). This technology uses artificial restriction enzymes generated by fusing a zinc finger DNA-binding domain to a DNA-cleavage domain. This design only targets unique genome sites and is unlikely to cause significant off-target effects, because the DNA binding motif specified by the zinc fingers directs the zinc-finger nuclease to a specific locus in the genome59,60. Therefore, other neurotrophins’ loci were not targeted in BDNF knockout rats. The animals were randomized to four groups, each consisting of 15 rats (Bdnf+/+, Bdnf+/−, Bdnf+/+ T and Bdnf+/− T). Due to diseases, four rats were excluded from the experiment, whereas ten animals that refused to participate in running exercises during the adaptation period were transferred to non-trained groups. As a result, the group sizes were as follows: Bdnf+/+: 19 rats, Bdnf+/−: 18 rats, Bdnf+/+ T: ten rats, and Bdnf + / − T: nine rats. Moreover, blood and muscle biomarkers could only be reliably measured in a proportion of samples, whereas stable electrophysiological recordings from individual MNs could not be obtained in some rats. The final group sampling taken for analysis was: 12 Bdnf+/+ rats, 11 Bdnf+/− rats, 9 Bdnf+/+ T rats, and 9 Bdnf+/− T rats. The animals were housed in standard laboratory cages (2 rats of the same genotype per cage) with water and standard laboratory food available ad libitum. The room in which the animals were housed had controlled environmental conditions (a reverse 12 h:12 h light/dark cycle, 55 ± $1\%$ humidity, and 22 ± 2 °C). The rats were acclimated to their environment for at least 7 days before starting the experimental procedures. Each rat was handled daily for approximately 15 min to reduce distress. ## Ethical approval All experiments were approved by the Local Ethical Committee in Poznań (approval number $\frac{58}{2018}$), and the research was conducted in strict compliance with the Polish Animal Protection Act and European Union regulations. This study was performed in accordance with ARRIVE guidelines. ## Endurance training protocol Endurance training in the Bdnf+/+ T and Bdnf+/− T groups was performed on an electric treadmill for small rodents (Exer-6M, Columbus Instruments). The same training protocol, consisting of one week of adaptation and 5 weeks of regular running exercises, was performed for both groups. Each training session took place between 9 and 10 a.m. and was controlled by the same observer. During the adaptation period, all animals were acclimated to running on a motorized treadmill (10–45 min twice per day at a speed of 10–13 m·min−1). During the training period, animals ran continuously (45 min per day, 5 days per week for 5 weeks), with gradually increasing speed as follows: 15 m·min−1 in the first week (average daily distance: 675 m), 17.7 m·min−1 in the second week (average daily distance: 797 m), 19.3 m·min−1 in the third week (average daily distance: 869 m), 21.5 m·min−1 in the fourth week (average daily distance: 963 m), and 24 m·min−1 in the final week (average daily distance: 1080 m). According to Lalanza et al.61, the treadmill endurance running regimen used in our study is classified as moderate-intensity exercise. ## Electrophysiological experiments Animals were deeply anaesthetized with intraperitoneal injections of sodium pentobarbital at an initial dose of 60 mg·kg−1 and supplemented with additional doses of 10 mg·kg−1·h−1 every hour. During surgery, the depth of anaesthesia was regulated based on a lack of pinna and withdrawal reflexes; during the recording sessions, the heart rate was monitored (300–360 beats·min−1). To paralyze the muscles and enable artificial ventilation during the recording sessions, pancuronium bromide (Pancuronium, Jelfa, Poland) was administered intravenously every 30 min (first dose: 0.4 mg·kg−1; supplementary doses: 0.2 mg·kg−1). From this point onwards, expired CO2 levels were monitored continuously (Capstar 100, CWE) and maintained at 3–$4\%$ by adjusting ventilation parameters (Small Animal Ventilator, SAR-830/AP, CWE). In order to minimize respiratory movements, a pneumothorax procedure was performed on the side of the recordings. The animals were euthanized with an overdose of sodium pentobarbital (180 mg·kg−1) at the end of the experiments. The surgical procedure consisted of the following steps: [1] insertion of catheters into the right saphenous and femoral veins for blood sampling and drug administration, respectively; [2] endotracheal intubation for artificial ventilation; [3] left-sided preparation of the tibial nerve branch for further electrical stimulation; and [4] laminectomy over the L4–L6 spinal cord segments for insertion of recording electrodes. The animals were then placed in a metal frame and the vertebral column was stabilized by steel clamps. The dissected nerves and the exposed area of the spinal cord were then covered with paraffin oil. The animals’ core and oil temperatures were maintained within physiological limits (37° ± 1 °C) using an automatic heating system (Model 507222F, Harvard Apparatus). Subsequently, the dura was removed and small holes were made in the pia to insert glass microelectrodes into the spinal cord. Bipolar silver ball electrodes connected to a square pulse stimulator (Model S88, GRASS Instrument Company) were used for antidromic stimulation of the tibial nerve (0.1-ms duration, amplitude up to 0.5 V, frequency of 3 Hz). Glass micropipettes with tips broken to 1.5–2.0 µm in diameter (10–15 Ohm) and filled with 2 M potassium citrate were used for intracellular recordings from single MNs located in lumbar spinal cord segments (L4–L5). Electrodes were inserted into the spinal grey matter using a step motor-driven manipulator with steps of 2–4 µm. MN recordings were acquired with an intracellular amplifier system (Axoclamp, model 2B, Axon Instruments) in bridge or discontinuous current clamp mode (current switch mode: 8 kHz) with capacitance maximally compensated and passed via a 16-bit analogue-to-digital converter (National Instruments, USB-6341) at a sampling rate of 10 kHz. A single MN was identified by antidromic stimulation of the tibial nerve, which innervates distal muscles of the hind limb (including the studied muscles: tibialis anterior, medial gastrocnemius and soleus). The antidromic nature of the recorded action potential was recognized on the basis of an all-or-none appearance and a stable, short-latency spike. Only stable recordings with resting membrane potentials of at least 50 mV and action potential amplitudes exceeding 55 mV with clear positive overshoot were considered. Twenty superimposed antidromic action potentials were automatically averaged by a custom laboratory computer program (BioLab) for further analysis. In the next step, 40 short pulses (100 ms) of hyperpolarization current (1 nA) were injected into each MN to measure input resistance (RIN). Intracellular depolarization current was later injected into individual MNs to induce an orthodromic action potential. From the averaged orthodromic spikes (obtained from 20 superimposed recordings), the following basic MN properties were calculated: resting membrane potential (RMP), action potential amplitude (APamp), action potential duration measured at the level of half-amplitude (APhalf-width), time to peak afterhyperpolarization (AHPpeak time), AHP amplitude (AHPamp), and AHP half-decay time (AHPhdt) (Fig. 5A). The rheobase value (Rheo) was calculated as the minimum amplitude of depolarization current required to induce a single spike within 50 ms, and the voltage threshold (VT) was determined from the rheobase trace as the point at which the first derivative of the voltage reached 10 mV·ms−162. Subsequently, 500-ms rectangular depolarizing currents of gradually increasing amplitudes (in steps from 0.1 to 2 nA) were applied intracellularly to individual MNs to induce rhythmic discharges. The minimum and maximum currents evoking SSF firing during the entire 500-ms recording window were identified and SSF frequencies were calculated from the averages of the last three inter-spike intervals (Fig. 5B). For each MN, a linear relationship between the SSF frequencies and the respective values of injected current was derived (f/I relationship) (Fig. 5C).Figure 5Examples of recordings obtained from a tibialis anterior MN from a Bdnf + / + rat. ( A) Action potential parameters: RMP: resting membrane potential; APamp: action potential amplitude; APhalf-width: action potential duration measured at the level of half-amplitude; AHPpeak time: time to afterhyperpolarization (AHP) peak; AHPamp: AHP amplitude; AHPhdt: AHP half-decay time. ( B) Discharge patterns generated by a MN during a gradually increasing injection of intracellular current. Note the lack of rhythmic firing until intracellular stimulation at 8.5 nA (minimum steady-state firing [SSF] frequency: 38.2 Hz) and the maximum SSF frequency at 16 nA (80.0 Hz) without further increases at stronger intracellular stimulation levels. SSF frequencies were calculated from the last three inter-spike intervals (indicated with circles). ( C) The frequency-current relationship (f/I) for the rhythmic firing in this MN, assessed based on the equation y = ax + b, where “a” determines the slope of the relationship. AHPhdt (see Fig. 5A) is a MN property, which was experimentally proved to be a reliable tool to divide rat spinal MNs into fast and slow types. According to the method presented by Gardiner et al.36 the MNs with the AHPhdt shorter than 20 ms were classified as fast, while those with AHPhdt equal or longer than 20 ms were classified as slow. Membrane and firing properties of MNs were further analyzed separately for each type. ## Biochemical analyses At the beginning of each electrophysiological experiment, a fresh blood sample of 0.8 ml was taken from the saphenous vein and stored in test tubes. Then, blood was centrifuged for 5 min at 5000 rpm at 4 °C in order to separate and obtain serum. The serum samples were stored at −80 °C until analyses. After the electrophysiological experiment, the right hindlimb TA, MG, and Sol muscles were dissected and any visible connective and fat tissues were removed. The prepared muscles were weighed and placed in cryogenic vials (NUNC/Thermo Fisher Scientific). The tissues were then frozen by immersion in liquid nitrogen and stored at −80 °C. The tendons from the TA and MG muscles were removed and cross-sections of the distal parts of these muscles were taken for homogenization. The Sol muscle was homogenized en bloc. Muscle samples were homogenized in phosphate-buffered saline (tissue to buffer ratio: 1:9) with EDTA-free Halt Protease Inhibitor Cocktail (100×; Thermo Fisher Scientific) to stop the protein lysis reaction. Homogenization was carried out using a dispersive homogenizer (VDI 12, VWR, Singapore) at 28,000–30,000 rpm in four 30-s cycles; cycles were separated by 1-min cooling breaks of ice water (4 °C). The homogenates were centrifuged (5000 rpm, 5 min, 4 °C) and supernatants were stored at −80 °C. Concentrations of BDNF (sensitivity: 0.035 ng·ml−1; cat. number: SRB-T-81493), GDNF (sensitivity: 0.05 ng·ml−1; cat. number: SRB-T-85857), NGF (sensitivity: 0.276 ng·ml−1; cat. number: SRB-T-85674), NT-3 (sensitivity: 0.184 ng/ml; cat. number: SRB-T-85583), and NT-4 (sensitivity: 12.887 pg·ml−1; cat. number: SRB-T-85586) in both serum and muscle tissue were measured by ELISA according to the manufacturer’s recommendations (Sunredbio, China). ELISA was also used to measure concentrations of Trk-B (sensitivity: 12.337 pg/ml; cat. number: 201-11-0426), MYO/MB (sensitivity: 4.157 ng/ml; cat. number: SRB-T-84588), and IL-15 (sensitivity: 1.026 ng·L−1; cat. number: SRB-T-83418) levels in muscles. Absorbance was read at 450 nm using a multi-mode microplate reader (Synergy 2 SIAFRT, BioTek, Winooski, VT, USA). ## Statistical analysis All statistical analyses were performed using Statistica 13 software (Statsoft, Cracow, Poland). Data were expressed as the mean ± standard deviation (SD) for MN parameters and concentrations of NFs and myokines. The data were analysed using a two-way analysis of variance (ANOVA) with genotype (Bdnf+/+ vs. Bdnf+/−) and training status (trained vs. untrained) as fixed factors. Tukey’s HSD post-hoc tests were performed to compare pairs of means. The levels of significance were set at $p \leq 0.05$, $p \leq 0.01$, and $p \leq 0.001.$ ## References 1. 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--- title: The therapeutic effects of attending a one-day outpatient service on patients with gestational diabetes and different pre-pregnancy body mass indices authors: - Yan-Min Cao - Min Ma - Wei Wang - Na-Na Cai journal: Frontiers in Public Health year: 2023 pmcid: PMC10027732 doi: 10.3389/fpubh.2022.1051582 license: CC BY 4.0 --- # The therapeutic effects of attending a one-day outpatient service on patients with gestational diabetes and different pre-pregnancy body mass indices ## Abstract ### Purpose This study investigated the effects of attending a one-day outpatient service on the outcomes of patients with gestational diabetes mellitus (GDM) and different pre-pregnancy body mass indices (BMIs). ### Methods The study recruited 311 pregnant women with GDM into a one-day outpatient service at The Fourth Hospital of Shijiazhuang from September 2019 to December 2021. They were randomly assigned to three groups, based on their pre-pregnancy BMI as follows: group A, BMI < 18.5 kg/m2; group B, 18.5 ≥ BMI > 25.0 kg/m2; group C, BMI ≥25 kg/m2. The following information was collected from all the participants: fasting blood glucose, hemoglobin A1c (HbA1C), insulin dose, gestational weight gain, weight gain after the one-day outpatient service, and perinatal outcomes. ### Results The three groups showed significant differences in fasting blood glucose and HbA1C, insulin treatment rate, and the incidence of pregnancy hypertension/preeclampsia and neonatal jaundice (all $P \leq 0.05$). The rate of excessive gestational weight gain in all of the groups also reflected significant differences ($P \leq 0.05$). Group A showed the lowest weight gain, while group C gained the most weight. There is no significant difference in the incidences of hypertension/preeclampsia, neonatal jaundice, or premature birth between patients with weight loss/no weight gain and those with positive weight gain. ### Conclusion One-day diabetes outpatient integrated management may effectively help to manage weight gain and blood glucose in patients with GDM and different pre-pregnancy BMIs. Dietary control after a GDM diagnosis may have helped to avoid weight gain entirely, as well as negative weight gain, but did not increase the risk of maternal and infant-related complications. ## Introduction Gestational diabetes mellitus (GDM) is the one of the most common complications during pregnancy. It is commonly defined as the very first recognition of diabetes or impaired glucose tolerance during pregnancy. However, there is no universally accepted protocol to diagnose GDM (1–3). According to the report of the International Diabetes Federation (IDF) in 2019, $16.7\%$ of live births were affected by hyperglycemia in pregnant women and $84\%$ of them had gestational diabetes [4]. GDM has gradually become a global health concern. The prevalence of GDM was the highest in Middle East and North Africa, followed by Southeast Asia, Western Pacific regions, and even in the developed areas, such as North America [5]. Approximately $10\%$ of pregnant women in Asia developed GDM, and the prevalence of GDM was much higher in low- and upper-middle areas [6]. GDM is associated with many other complications in pregnant women. Existing studies indicated that GDM was closely related to type 2 diabetes [7, 8]. Moreover, in 2011, the American Heart Association classified GDM as a risk factor for the development of cardiovascular disease in women [9]. GDM not only impairs the health of pregnant women but also potentially raises the risk of disease in the offspring. Several studies have shown that women who had been diagnosed with GDM were more likely to give birth to offspring with hypertension, meanwhile, high blood glucose levels during pregnancy were shown to alter the metabolism of neonates, leading to adiposity [10, 11]. Therefore, the effective management and treatment of patients with GDM are of great significance to maternal and infant health and prognosis. A meta-analysis showed a pooled estimated risk of GDM in pregnant women of $16.8\%$, with a risk of $10.7\%$ in the underweight/normal group, and $23\%$ in the overweight/obesity group, suggesting an elevated risk of GDM in pregnant women who are overweight/obese [12]. One-day outpatient management of diabetes enables patients with GDM to learn effective self-management strategies by providing them with information about diet control and exercise therapy, as well as one-to-one supervision and guidance using the WeChat application. By actively following a doctor's advice regarding diet control and exercise, patients can effectively manage their blood glucose levels and reduce adverse pregnancy outcomes. This study aimed to identify the differences in blood glucose control, perinatal outcomes, and weight change during pregnancy among participants with GDM who had different pre-pregnancy body mass indices (BMIs). Additionally, the study assessed the impact of attending a comprehensive one-day diabetes outpatient management program on gestational weight change and pregnancy-related outcomes. ## Study design This observational study included 311 pregnant women with GDM who participated in a one-day diabetes outpatient program at the authors' hospital from September 2019 to December 2021. All participants were of Chinese Han ethnicity. All participants provided a signed informed consent form for their inclusion in the study. Body mass index is an internationally used body-fat and health measurement tool and is calculated using the following formula: BMI = weight/height2, with weight in kg and height in meters. The participants were divided into three groups, based on their BMI prior to becoming pregnant as follows: group A represented the low weight group (BMI < 18.5 kg/m2 before pregnancy), group B reflected the normal weight group (18.5 ≤ BMI < 25.0 kg/m2 before pregnancy), and group C represented the overweight and obese group (BMI ≥25 kg/m2 before pregnancy). Age, height, family history of diabetes, gestational age, and other patient characteristics were not significantly different ($P \leq 0.05$), indicating comparability among the three BMI groups. After attending the one-day outpatient service, the pregnant women with GDM were classified according to positive, negative, or no weight gain. ## Inclusion and exclusion criteria The following criteria were based on guidelines issued by the American Diabetes Association [13]. All participants included in this study had been newly diagnosed with GDM using a 75 g oral glucose tolerance test (OGTT). The study's exclusion criteria were as follows: [1] fasting plasma glucose ≥7.0 mmol/L; [2] OGTT 2-h blood glucose ≥11.1 mmol/L; [3] typical symptoms of hyperglycemia or hyperglycemia crisis accompanied by an arbitrary blood glucose level ≥11.1 mmol/L; [4] hemoglobin A1c (HbA1c) ≥$6.5\%$, indicative of pre-pregnancy diabetes combined with pregnancy; [5] gemellary or multiple pregnancy. ## One-day outpatient service The one-day outpatient service is an umbrella program that includes the following services: [1] introducing a healthy diet and how to cook scientifically; [2] guiding patients on how to appropriately and individually design an exercise plan; [3] conducting introductory courses in the management of weight and blood glucose, and in addition, completing a quiz; [4] creating a friendly environment for instructors and patients in which to communicate with each other; [5] setting up a WeChat group to monitor and follow up the patients. The authors conducted the one-day outpatient training twice per week, each time with a small class of ~10 participants. An endocrinologist and a diabetes nurse were responsible for delivering this training. In the morning, participants underwent physical examinations and were given diabetes-friendly meals. The endocrinologist educated the participants about exercise and delivered introductory courses. Through these courses, the participants were able to learn about a GDM diagnosis and recognize its related risk factors. The courses enabled the current authors to help participants design more scientific and healthier diets and exercise plans, and they were also trained on how to monitor their blood glucose levels and other related simple symptoms on their own at home. The participants were reminded to conduct retesting once they had given birth. Counseling services were available to participants if necessary. In the afternoon, they watched childbirth and breastfeeding videos, and the study authors gave them one-on-one instructions. After finishing all the training sessions, the authors presented a questions-and-answers session. Furthermore, a quiz was conducted to test how much the participants had learned from the one-day outpatient service, and a questionnaire subsequently assisted in adjusting and improving the one-day outpatient service. During the entire day of training, participants completed multiple blood glucose tests before and after diets. Notably, WeChat groups were created at the end of the one-day program via which the authors regularly posted GDM-related information and reminded participants to regularly retest themselves. The authors also answered participants' questions via the application to help them gain a better understanding of their health condition. ## Statistical analysis The SPSS Statistics 26.0 software program was used to analyze the collected data. Continuous variables were described using mean ± standard deviation and ANOVA was used to compare the mean values among three groups. Categorical data were expressed as a percentage (%) and compared using chi-square (χ2) test. Given that the sample size was >30, and based on existing studies [14, 15], the statistical significance was set as $P \leq 0.05$, and all tests were two-sided. ## Demographic characteristics A total of 320 pregnant women were screened and 9 of them were excluded due to gemellary pregnancy. Finally, 311 patients were enrolled. The age, height, family history of diabetes, and gestational age were not significantly different among the three groups ($P \leq 0.05$) (Table 1). Table 1 \| Items \| A (n = 18) \| B (n = 183) \| C (n = 110) \| P \| \| --- \| --- \| --- \| --- \| --- \| \| Age (years) \| 30.33 ± 3.73 \| 30.43 ± 4.07 \| 31.01 ± 4.38 \| 0.49 \| \| Height (cm) \| 161.3 3± 4.55 \| 161.36 ± 4.71 \| 161.66 ± 4.85 \| 0.86 \| \| Pregnancy week at entry (weeks) \| 26.00 ± 1.19 \| 26.13 ± 1.34 \| 26.03 ± 1.30 \| 0.07 \| \| Family history of diabetes — no./total no. (%) \| 8/18 (44.4) \| 61/183 (33.3) \| 36/110 (32.7) \| 0.65 \| ## Comparison of blood glucose changes among the three groups after the intervention Fasting blood glucose and HbA1C were significantly different among the three groups at enrollment and before delivery ($P \leq 0.05$), where group A had the lowest and group C had the highest fasting blood glucose and HbA1C levels. No significant differences were observed in fasting blood glucose and HbA1C among the three groups before delivery ($P \leq 0.05$). Meanwhile, the insulin treatment rate was significantly different among the three groups ($P \leq 0.05$), where group A had the lowest and group C had the highest insulin treatment rate (Table 2). Table 2 \| Items \| Items.1 \| A (n = 18) \| B (n = 183) \| C (n = 110) \| P \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Fasting glucose (mmol/L) \| At entry \| 5.08 ± 0.38 \| 5.25 ± 0.46 \| 5.52 ± 0.43 \| < 0.001 \| \| \| Before delivery \| 4.66 ± 0.35 \| 4.77 ± 0.41 \| 4.93 ± 0.47 \| < 0.001 \| \| \| At entry to before delivery \| 0.43 ± 0.33 \| 0.49 ± 0.50 \| 0.60 ± 0.52 \| 0.11 \| \| HbA1C (%) \| At entry \| 5.09 ± 0.37 \| 5.18 ± 0.33 \| 5.24 ± 0.37 \| < 0.001 \| \| \| Before delivery \| 5.18 ± 0.40 \| 5.31 ± 0.34 \| 5.49 ± 0.44 \| < 0.001 \| \| \| At entry to before delivery \| −0.09 ± 0.21 \| −0.14 ± 0.26 \| −0.13 ± 0.27 \| 0.78 \| \| Insulin-treated women, no./total no. (%) \| Insulin-treated women, no./total no. (%) \| 0/18 (0.0) \| 10/183 (5.5) \| 15/110 (13.6) \| 0.02 \| ## Comparison of pregnancy outcomes among the three groups after the intervention The incidences of pregnancy, hypertension/preeclampsia, and neonatal jaundice were significantly different among the three groups ($P \leq 0.05$), where group A had the lowest and group C had the highest incidences. No significant differences were found in the incidence of premature membrane rupture, premature delivery, polyhydramnios/oligohydramnios, neonatal hypoglycemia, fetal distress, macrosomia, fetal growth restriction, or cesarean section rate among the three groups ($P \leq 0.05$) (Table 3). Table 3 \| Items \| A (n = 18) \| B (n = 183) \| C (n = 110) \| P \| \| --- \| --- \| --- \| --- \| --- \| \| Gestational hypertension/preeclampsia, no./total no. (%) \| 0/18 (0.0) \| 9/183 (4.9) \| 22/110 (20.0) \| < 0.001 \| \| Women with PMR, no./total no. (%) \| 5/18 (27.8) \| 34/183 (18.6) \| 20/110 (18.2) \| 0.62 \| \| Preterm birth, no. /total no. (%) \| 3/18 (16.7) \| 11/183 (6.0) \| 15/110 (13.6) \| 0.05 \| \| Women with hydramnios/oligohydramnios, no./total no. (%) \| 0/18 (0.0) \| 11/183 (6.0) \| 3/110 (2.7) \| 0.40 \| \| Newborn babies with jaundice, no./total no. (%) \| 0/18 (0.0) \| 42/183 (23.0) \| 38/110 (34.5) \| < 0.001 \| \| Newborn babies with hypoglycemia, no. /total no. (%) \| 1/18 (5.6) \| 6/183 (3.3) \| 1/110 (0.9) \| 0.19 \| \| Fetal distress, no. /total no. (%) \| 1/18 (5.6) \| 21/183 (11.5) \| 9/110 (8.2) \| 0.57 \| \| Macrosomia, no. /total no. (%) \| 0/18 (0.0) \| 9/183 (4.9) \| 9/110 (8.2) \| 0.26 \| \| Fetal growth restriction, no./total no. (%) \| 0/18 (0.0) \| 2/183 (1.1) \| 1/110 (0.9) \| 1.00 \| \| Cesarean delivery rate, no./total no. (%) \| 5/18 (27.8) \| 77/183 (42.1) \| 58/110 (52.7) \| 0.07 \| ## Comparison of body weight changes among the three groups after the intervention The overall weight change and excess weight-gain rate during pregnancy were significantly different among the three groups ($P \leq 0.05$), where group A had the lowest and group C had the highest overall weight change and excess weight-gain rate during pregnancy. No statistically significant differences were observed in negative weight-gain rate, underweight-gain rate, or normal weight-gain rate ($P \leq 0.05$). Body- weight changes were significantly different among the three groups after admission to a one-day outpatient clinic ($P \leq 0.05$), where group A had the lowest negative weight gain and a zero weight-gain rate, while group C had the highest, including three participants with negative weight gain. The positive weight-gain rate was the highest in group A and the lowest in group C (Table 4). Table 4 \| Items \| Items.1 \| A (n = 18) \| B (n = 183) \| C (n = 110) \| P \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Changes in total weight during pregnancy \| Negative rate of weight gain (%) \| 0/18 (0.0) \| 0/183 (0.0) \| 3/110 (2.7) \| 0.07 \| \| \| Underweight gain rate (%) \| 7/18 (38.9) \| 84/183 (45.9) \| 40/110 (36.4) \| 0.29 \| \| \| Normal rate of weight gain (%) \| 9/18 (50.0) \| 70/183 (38.3) \| 34/110 (30.9) \| 0.23 \| \| \| Excessive weight gain rate (%) \| 2/18 (11.1) \| 29/183 (15.8) \| 33/110 (30.0) \| 0.01 \| \| Body weight changes after admission to the one-day clinic \| Negative weight gain and no weight gain rate (%) \| 1/18 (5.6) \| 30/183 (16.4) \| 32/110 (29.1) \| 0.01 \| \| \| Positive rate of weight gain (%) \| 17/18 (94.4) \| 153/183 (83.6) \| 78/110 (70.9) \| \| ## The influence of weight gain on pregnancy outcomes after the one-day outpatient clinic The incidences of pregnancy, hypertension/preeclampsia, premature membrane rupture, premature childbirth, polyhydramnios/oligohydramnios, neonatal jaundice, neonatal hypoglycemia, fetal distress, macrosomia, fetal growth restriction, and cesarean section rate were not significantly different among the negative weight gain, no weight gain, and positive weight gain groups ($P \leq 0.05$) (Table 5). Table 5 \| Items \| Negative weight gain and no weight gain (n = 63) \| Positive growth (n = 248) \| P \| \| --- \| --- \| --- \| --- \| \| Gestational hypertension/preeclampsia, no./total no. (%) \| 4/63 (6.3) \| 27/248 (10.9) \| 0.28 \| \| Women with PMR, no./total no. (%) \| 7/63 (11.1) \| 52/248 (21.0) \| 0.08 \| \| Preterm birth, no./total no. (%) \| 8/63 (12.7) \| 21/248 (8.5) \| 0.33 \| \| Women with hydramnios/oligohydramnios, no./total no. (%) \| 3/63 (4.8) \| 11/248 (4.4) \| 1.0 \| \| Newborn babies with jaundice, no./total no. (%) \| 20/63 (31.7) \| 60/248 (24.2) \| 0.22 \| \| Newborn babies with hypoglycemia, no./total no. (%) \| 0/63 (0.0) \| 8/248 (3.2) \| 0.32 \| \| Fetal distress, no./total no. (%) \| 5/63 (7.9) \| 26/248 (10.5) \| 0.55 \| \| Macrosomia, no./total no. (%) \| 3/63 (4.8) \| 15/248 (6.0) \| 0.93 \| \| Fetal growth restriction, no./total no. (%) \| 1/63 (1.6) \| 2/248 (0.8) \| 0.49 \| \| Cesarean delivery rate, no./total no. (%) \| 27/63 (42.9) \| 113/248 (45.6) \| 0.7 \| ## Discussion Weight gain and obesity are critical risk factors for the development of diabetes. A cross-sectional study including 31 provinces and cities in Mainland China showed that the prevalence rates of diabetes and prediabetes in women of childbearing age in China between 2010 and 2012 were 1.4 and $12.9\%$, respectively. Meanwhile, incidences of being overweight (BMI 25.0–29.9 kg/m2) and obese (BMI ≥30 kg/m2) were 7.2 and $1.0\%$, respectively [16]. Being overweight or obese before becoming pregnant poses an independent risk for GDM development. A multivariate logistic regression study discovered that pre-pregnancy BMI was significantly associated with maternal hyperglycemia [17]. Similarly, another meta-analysis revealed that the incidence of GDM was significantly higher in obese women before pregnancy than in the normal BMI group, regardless of weight gain, and that the incidence of GDM increased by $0.92\%$ ($95\%$ confidence interval, 0.73–1.10) for every 1-unit increase in BMI [18]. Excessive weight gain during pregnancy increased the risk of infants presenting as large for gestational age (LGA), macrocephaly, cesarean section, hypertension during pregnancy, postpartum hemorrhage, and other adverse events. Even among women with a normal BMI before pregnancy, excessive gestational weight gain increased the risk of postpartum hemorrhage and LGA babies [19]. Therefore, pre-pregnancy weight and weight gain during pregnancy are critical for patients with GDM. The one-day outpatient service was implemented to manage patients' weight gain using a science-based diet and proper exercise, to empower patients to effectively control their blood glucose levels, and to reduce maternal and infant-related complications. This study observed the therapeutic effects of attending the one-day outpatient service on participants with GDM who had different pre-pregnancy BMIs. The study also assessed the impacts of different weight-gain levels during gestation on pregnancy-related outcomes. The study findings demonstrated that fasting blood glucose was lowest in the low weight group at enrollment and before delivery but was slightly higher in the normal weight group and highest in the overweight and obesity group. This difference was statistically significant among the three groups, indicating that pre-pregnancy BMI had a crucial impact on the blood glucose of pregnant women, with blood glucose increasing proportionately with an increase in BMI. By implementing the comprehensive management strategies during the one-day outpatient service, fasting blood glucose in all three groups was decreased, suggesting that the outpatient service had a vital impact on controlling blood glucose and protecting patients with GDM and different pre-pregnancy BMIs from pre-diabetes progressing to diabetes. According to the 2020 ADA guidelines for the diagnosis and treatment of diabetes during pregnancy, it is appropriate to set the HbA1C target to < $6\%$. Following a GDM diagnosis, HbA1C in the three groups was in the normal range; however, the low weight group showed the lowest HbA1C level compared with the normal weight and overweight and obesity groups. The data also showed that changes in blood glucose proportionally increased along with BMI. However, this study found that from enrollment to delivery, the average level of HbA1C increased in all three groups, with no significant difference in the degree of increase among the three groups, and HbA1C was within the normal range. In this case, however, HbA1C reflected only the average blood glucose levels of the previous 2–3 months. Additionally, insulin resistance in pregnant women with GDM was mild during early pregnancy but gradually increased at 24–28 weeks of pregnancy. Following a diagnosis, insulin resistance was steadily aggravated. Furthermore, the 2017 China Diabetes Society guidelines note that HbA1c was often underestimated and had limited value in GDM diagnosis because of increased red blood cell conversion in the second and third trimesters, and because of the effects of anemia during pregnancy. The insulin utilization rate was different among the three groups; the overweight and obesity group showed the highest insulin utilization rates. Among the three groups, the overweight and obesity group presented with the highest blood glucose levels. As a result of the comprehensive management presented in the one-day outpatient service, the blood glucose levels of these participants decreased compared with their initial levels; however, the overweight and obesity group still reported the highest levels of insulin utilization. These findings highlight that an increased BMI corresponds to greater perturbations in blood glucose and subsequently increases the likelihood that insulin will be used as a treatment for controlling blood glucose during pregnancy. Moreover, studies have shown that a pre-pregnancy BMI has a greater impact on insulin resistance than weight gain during pregnancy, with a higher BMI before pregnancy correlating with more severe insulin resistance [20]. Other studies have also demonstrated that insulin resistance indices were elevated at the time of a GDM diagnosis in patients who had been obese before conception, and the insulin resistance indices were positively correlated with BMI both before pregnancy and at GDM screening [21]. Therefore, it may be more challenging to control blood glucose during pregnancy in patients who had been overweight or obese before becoming pregnant due to more severe insulin resistance and a subsequent increase in the use of insulin. In this study, the overweight and obesity group had the highest incidence of gestational hypertension/preeclampsia and neonatal jaundice, indicating that being overweight or obese prior to becoming pregnant may increase the incidence of hypertension and neonatal jaundice in patients with GDM. No significant difference was observed in the rates of premature membrane rupture, premature delivery, polyhydramnios/oligohydramnios, neonatal hypoglycemia, fetal distress, macrosomia, fetal growth restriction, or cesarean section. These findings may have resulted from = improved blood glucose and weight control as a result of attending the one-day outpatient service. In addition, the study results showed that positive (but not excessive) weight gain and normal weight gain were predominant among the three groups, and the differences were not statistically significant. However, the overweight and obesity group had the highest rate of excessive weight gain, and the differences among the three groups were statistically significant. The highest negative weight-gain rate and the no weight-gain rate were observed in the overweight and obesity group, followed by the normal weight group, with the lowest rates found in the low-weight group. These data indicated that excessive weight gain in the overweight and obesity group occurred prior to enrollment, but the weight gain had been well-controlled after comprehensive diabetes outpatient management. Excessive weight gain might occur during pregnancy because of excessive weight gain that occurred prior to enrollment. Excessive weight gain during pregnancy is associated with an increased risk of medication use, hypertensive disorders, cesarean section, gestational age, and macrosomia, compared with normal or no excessive weight gain. Furthermore, low weight gain during pregnancy was shown to have a protective function against macrosomia and did not increase the risk of low birth weight. In pregnant women with GDM, less weight gain than what is recommended is beneficial, but the effective prevention of excessive weight gain is paramount [22]. After recruitment to the one-day outpatient service, some pregnant women evidenced no weight gain, as well as negative weight gain, but only three pregnant women showed negative weight gain during the entire pregnancy period, which was considered to have been related to a high pre-pregnancy BMI and excessive weight gain before a GDM diagnosis. After delivery of the outpatient service, no statistically significant difference was observed in the incidence of hypertension/preeclampsia, premature rupture of membranes, chorioamnionitis, premature delivery, polyhydramnios/oligohydramnios, neonatal jaundice, neonatal hypoglycemia, fetal distress, macrosomia, fetal growth restriction, and cesarean section rate among the participants with a negative, zero, and positive weight gain ($P \leq 0.05$). The results showed that no weight gain and negative weight gain had no significant adverse effects on maternal and infant outcomes. Existing findings have demonstrated a higher gestational weight gain during the third trimester (28–36 weeks) to be associated with LGA babies, higher insulin doses, and increased postpartum 2-h OGTT results [23]. Therefore, the one-day outpatient service had a substantial impact on weight control in pregnant women who had been diagnosed with GDM, as demonstrated by the controlled weight gain in late pregnancy and the reduced occurrence of related complications. Collectively, the current study results showed that the comprehensive one-day outpatient management of DM may control blood glucose and body weight in GDM patients with different pre-pregnancy BMIs. Overweight or obese patients reflected a higher risk of developing gestational hypertension/preeclampsia and neonatal jaundice. Furthermore, these patients had a higher use rate of insulin for controlling blood glucose. Some pregnant women showed negative weight gain, no weight gain, or insufficient weight gain, but these outcomes did not increase the risk of adverse pregnancy outcomes; this may have been due to inappropriate weight gain before or during early pregnancy. Therefore, women of childbearing age should control their weight to ensure they remain within a reasonable weight range before becoming pregnant to reduce the risk of GDM. The current study includes several limitations. First, all of the participants were recruited from the authors' hospital, thus limiting the patient representation. Second, all patient information was collected through the electronic medical records system of the authors' hospital, but, some important measurements were not included. Third, since this had been a cohort study, the authors lost some test information from participants in the mid-phase of the study. Nevertheless, despite these limitations, the authors can confidently assert that the one-day outpatient service presents a promising and effective strategy for improving the current management of women with GDM. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors. ## Ethics statement The studies involving human participants were reviewed and approved by The Fourth Hospital of Shijiazhuang Ethics Committee. The patients/participants provided their written informed consent to participate in this study. ## Author contributions Conception and design of the research, acquisition of data, and writing of the manuscript: Y-MC and MM. Analysis and interpretation of the data: MM and WW. Statistical analysis and critical revision of the manuscript for intellectual content: WW and N-NC. All authors have read and approved the final draft. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. McIntyre HD, Catalano P, Zhang C, Desoye G, Mathiesen ER, Damm P. Gestational diabetes mellitus. Nat Rev Dis Primers. (2019.0) 5 47. DOI: 10.1038/s41572-019-0098-8 2. Szmuilowicz ED, Josefson JL, Metzger BE. Gestational diabetes mellitus. Endocrinol Metab Clin North Am. (2019.0) 48 479-93. DOI: 10.1016/j.ecl.2019.05.001 3. O'sullivan Jb, Mahan Cm. 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--- title: Association Between Smoking and Pain, Functional Disability, Anxiety and Depression in Patients With Chronic Low Back Pain authors: - Qi-Hao Yang - Yong-Hui Zhang - Shu-Hao Du - Yu-Chen Wang - Xue-Qiang Wang journal: International Journal of Public Health year: 2023 pmcid: PMC10027735 doi: 10.3389/ijph.2023.1605583 license: CC BY 4.0 --- # Association Between Smoking and Pain, Functional Disability, Anxiety and Depression in Patients With Chronic Low Back Pain ## Abstract Objectives: Chronic low back pain (CLBP) accounts for a majority of the disability associated with LBP, which can produce long-term negative effects. This cross-sectional study aimed to investigate the association between smoking and pain, dysfunction and psychological status in patients with CLBP. Methods: The 54 patients with CLBP were recruited and divided into smoking and non-smoking groups. Their pain, dysfunction, anxiety, depression, fear and quality of life were evaluated. The amount of cigarettes smoked daily was recorded. Results: Significant differences in VAS, ODI, RMDQ and FABQ and the impact of LBP on life and work were found between smoking and non-smoking patients. In addition, a correlation was found between the daily cigarette smoking amount and VASmax, FABQtotal, SDS and FABQ-W. Moreover, a correlation was observed between the amount of cigarettes smoked daily and the degree of impact of low back pain on work. Conclusion: The study found that smoking affected the aggravation of symptoms in patients with CLBP, which indicated that patients with CLBP and people at risk of LBP should be aware of the harm caused by smoking. ## Introduction Low back pain (LBP) is a common musculoskeletal disorder experienced by adults of all ages [1]. In the US, more than $55\%$ of adults report back pain in the past year [2]. LBP is the primary cause of restricted activity, sickness absence, loss of work productivity and reduced quality of life worldwide, resulting in high healthcare costs for individuals, families, and society [3]. Chronic low back pain (CLBP) accounts for a majority of the dysfunction and expenses associated with LBP. The annual total direct cost per patient with CLBP in the US is $8,386 [4]. Studies have shown that exercise can relieve LBP (5–7). The risk factors for LBP are multifactorial, including hereditary factors, physical risk factors (such as gender, age and history of a back injury), psychological factors (such as long-term mental stress, anxiety and fear of activities that indicate bodily harm or pain) and unhealthy lifestyle (such as alcohol drinking and smoking) [8, 9]. Recently, the interrelationships between pain, cigarette and smoking have received considerable attention because of their prevalence, public health consequences and serious comorbidities [10]. More than two-thirds of Americans with chronic pain support lifelong nicotine use [11]. People with chronic pain likely smoke more than the general population [12]. Furthermore, recent estimates suggest that nearly $60\%$ of people who are addicted to tobacco meet the criteria for chronic pain [13]. Previous research has shown that smokers and those who have quit smoking tend to experience more widespread and severe pain than non-smokers [14]. Smoking has been linked to headaches, trunk pain and pain in the extremities [15, 16]. Despite the increased likelihood of generalized pain, studies have found a strong relationship between spinal pain and smoking [16]. Current and previous heavy smoking are associated with the amount and intensity of pain sites, that is, heavy smokers have a higher chance of having more pain sites and greater pain intensity than non-smokers [14]. However, research on the association between smoking and pain intensity, function, depression and fear in patients with CLBP remains limited, particularly in studies that have conducted detailed comparisons of disease activity and functional status in smoking and non-smoking patients with CLBP. This study aims to investigate the interrelationship between smoking and CLBP from multiple dimensions such as pain intensity, psychology and quality of life. In addition, this study will investigate whether the amount of cigarettes smoked daily affects the pain intensity, dysfunction, anxiety and depression degree of patients with CLBP. ## Study Design This cross-sectional study assessed pain, dysfunction, anxiety, depression, fear and quality of life in 54 patients with CLBP and compared the differences between smokers and non-smokers. We further examined the association between the amount of cigarettes smoked and pain, dysfunction and depression in patients with CLBP. Baseline data, including sex, age, height, weight, work status, physical activity, location of pain and duration of pain, were recorded. All patient assessment questionnaires were completed under the supervision of one researcher, and baseline data were recorded by another researcher. ## Participants G∗Power software was used to calculate sample size (one tail; α = 0.05; Power = 0.95; N2/N1 = 1), which is based on a previous study of differences in pain intensity measured by VAS between smokers and non-smokers [17]. The results showed that N1 and N2 were 26, and the actual power was 0.950. A total of 54 community residents in the main communities of Qingyuan Street, Yangpu District, Shanghai, were examined with an equal number of smokers and non-smokers. The inclusion criteria were: 1) age 18–65; 2) pain confined to the waist, buttocks and thighs, with or without leg pain; 3) pain intensity at worst 3 or higher on a visual analog scale; 4) CLBP for at least 3 months; 5) patients signed informed consent after receiving the purpose and method of this study. The exclusion criteria were: 1) with mental and cognitive diseases; 2) specific lumbago; 3) with neurological disorders, such as stroke and epilepsy. Standardized questionnaires were used to collect demographic information (e.g., age and gender), education, residential status, marital status, economic income, physical activity level and medical history. The comprehensive demographic variables are provided in Table 1. TABLE 1 \| Characteristic \| Smoking group (n = 27) \| Non-smoking group (n = 27) \| p Value \| \| --- \| --- \| --- \| --- \| \| Age, y \| 29.06 (22.11, 46.09) \| 30.09 (25.05, 37) \| 0.99 \| \| Sex-male, n (%) \| 18 (66.7) \| 16 (59.3) \| 0.78 \| \| Height, m \| 171 (166, 177.8) \| 167.2 (161.5, 171.7) \| 0.14 \| \| Weight, kg \| 71.8 (66.9,81.1) \| 68.3 (55.9,74.8) \| 0.13 \| \| BMI, kg/m2 \| 24.1 (22.6,27.4) \| 22.8 (20.4, 25.9) \| 0.18 \| \| Education levels \| \| \| 0.09 \| \| Junior middle school, n (%) \| 3 (11.1) \| 0 (0) \| \| \| High school, n (%) \| 2 (7.4) \| 1 (3.7) \| \| \| University, n (%) \| 18 (66.7) \| 12 (44.4) \| \| \| Postgraduate, n (%) \| 4 (14.8) \| 14 (51.9) \| \| \| Employment status \| \| \| 0.3 \| \| Employed part-time, n (%) \| 2 (7.4) \| 1 (3.7) \| \| \| Employed full-time, n (%) \| 13 (48.1) \| 16 (59.3) \| \| \| Student, n (%) \| 9 (33.3) \| 10 (37) \| \| \| Retired, n (%) \| 3 (11.1) \| 0 (0) \| \| \| Economy-income level \| \| \| 0.08 \| \| \| 7 (25.9) \| 4 (14.8) \| \| \| Less than 3,000 ¥ per month \| 2 (7.4) \| 6 (22.2) \| \| \| 3,000–5,000 ¥ per month \| 7 (25.9) \| 1 (3.7) \| \| \| 5,000–10000 ¥ per month \| 5 (18.5) \| 7 (25.9) \| \| \| More than 10,000 ¥ per month \| 6 (22.2) \| 9 (33.3) \| \| \| Marital status \| \| \| 0.85 \| \| Unmarried, n (%) \| 14 (51.9) \| 12 (44.4) \| \| \| Married, n (%) \| 11 (40.7) \| 13 (48.1) \| \| \| Divorced, n (%) \| 2 (7.4) \| 2 (7.4) \| \| \| Occupation time, h \| 8 (8, 10) \| 8 (8, 9) \| 0.74 \| \| Leisure time, h \| 7 (6, 8) \| 8 (7, 8) \| 0.17 \| \| Moderate-intensity physical activity per week \| \| \| 0.35 \| \| <75 min per week, n (%) \| 6 (22.2) \| 10 (37) \| \| \| 75–150 min per week, n (%) \| 6 (22.2) \| 7 (25.9) \| \| \| >150 min per week, n (%) \| 15 (55.6) \| 10 (37) \| \| \| Site of LBP \| \| \| 0.37 \| \| Left, n (%) \| 7 (25.9) \| 3 (11.1) \| \| \| Right, n (%) \| 4 (14.8) \| 8 (29.6) \| \| \| Middle, n (%) \| 9 (33.3) \| 8 (29.6) \| \| \| Both two sides, n (%) \| 7 (25.9) \| 8 (29.6) \| \| \| Low back pain Occurrence \| \| \| 0.77 \| \| Continuous, n (%) \| 8 (29.6) \| 9 (33.3) \| \| \| Interinittent, n (%) \| 19 (70.4) \| 18 (66.7) \| \| \| History of low back pain, y \| 4.42 (3.33, 7.58) \| 4.17 (3.58, 9.83) \| 0.92 \| \| Duration of first onset, d \| 7 (2, 60) \| 7 (2, 21) \| 0.43 \| \| Duration of low back pain per day, h \| 6 (2, 10) \| 6 (3, 12) \| 0.6 \| \| Nature of pain \| \| \| 0.14 \| \| Sore and Distended pain, n (%) \| 16 (59.3) \| 20 (74.1) \| \| \| Radiation pain, n (%) \| 2 (7.4) \| 4 (14.8) \| \| \| Needling pain, n (%) \| 2 (7.4) \| 0 (0) \| \| \| Other, n (%) \| 7 (25.9) \| 3 (11.1) \| \| \| Pain mode in 24 h \| \| \| 0.98 \| \| Gradually aggravated, n (%) \| 10 (37) \| 9 (33.3) \| \| \| Gradually relieved, n (%) \| 7 (25.9) \| 7 (25.9) \| \| \| No change, n (%) \| 6 (22.2) \| 6 (22.2) \| \| \| Other, n (%) \| 4 (14.8) \| 5 (18.5) \| \| ## Measures Smoking status was evaluated by self-report by asking “Do you currently smoke every day, occasionally, or never?” Former smokers were excluded from the study. The rest of the participants were divided into “non-smokers” and “daily smokers.” The investigation neither differentiated former smokers from those who never smoked, nor specified the minimum quitting time to be considered as a non-smoker. The daily amount of cigarettes smoked by smokers was recorded. Health status under CLBP was assessed on a self-reported basis. Pain intensity was measured based on the Visual Analogue Scale (VAS) [18]. The most severe pain intensity VASmax and the mildest pain intensity VASmin experienced by patients with CLBP were recorded. VAS is a commonly used pain scoring standard, and the pain intensity is divided into 10 points. 0 points indicate no pain, and 10 points indicate severe pain. Anxiety and depression levels were measured based on the Self-Rating Anxiety Scale (SAS) and Zung Self-rating Depression Scale (SDS) [19]. The higher their scores, the more severe the symptoms. The degree of physical dysfunction was measured by Oswestry Disability Index (ODI) and Roland‐Morris Disability Questionnaire (RMDQ) [20, 21]. The LBP disability assessment scale is an important tool in the evaluation and rehabilitation treatment system of LBP. The commonly used LBP dysfunction assessment scales in the world are ODI and RMDQ. The higher the scores, the more severe the dysfunction. The degree of fear was measured using the Fear-Avoidance Beliefs Questionnaire (FABQ), including FABQ-work (FABQ-W) and FABQ-physical activity (FABQ-PA) [22]. The higher the scores, the higher the degree of fear-avoidance beliefs. The quality of life was measured using the 36-Item Short Form Survey (SF-36). The unipedal stance test with eyes closed was also used to measure balance ability. ## Statistical Analyses All data were analysed using IBM SPSS Statistics Software (version 26.0). Demographic data from smoking and non-smoking groups were compared using the χ2 test and the Mann–Whitney U-test, expressed as frequencies and medians. Non-parametric tests were used to compare non-normal distributions and experiments with small samples, and p-values less than 0.05 were considered statistically significant. The differences in VAS, ODI, RMDQ, FABQ, SAS, SDS, and SF-36 between the smoking and non-smoking groups were examined by Mann–Whitney U-test. The correlation between the amount of cigarettes smoked daily and VASmax, VASmin, ODI, RMDQ, the frequency of LBP per month, FABQtotal, FABQ-W, FABQ-PA, SF-36, SAS and SDS was explored by linear correlation analysis. Linearly correlated variables, such as the amount of cigarettes smoked daily, were further analysed by multiple linear stepwise regressions. In addition, age, income, education level, history of LBP, frequency of LBP per month, work time, leisure time, duration of first pain and duration of LBP per day were included as confounding factors. Multiple linear regression was used to control for confounding factors and examine the association between the amount of cigarettes smoked daily and pain intensity, disability, mood and quality of life. Ordinal logistic regression analysis was used to explore the correlation between the amount of cigarettes smoked and the impact of LBP on work and life (none, mild, moderate and severe). ## Smoking vs. Non-Smoking As shown in Figure 1, patients in the CLBP smoking group had worse average values on several measures than the non-smokers, which included VASmax, RMDQ, ODI, FABQ-W, FABQtotal and frequency of LBP per month of patients. However, no significant difference in VASmin, SAS and SF-36 was found between the smoking and non-smoking groups. The lack of difference in FABQ-PA between the smoking and non-smoking groups was consistent with the finding that no significant difference in the duration of moderate physical activity was observed between the two groups. The difference in FABQ between the two groups was primarily manifested in the fear-avoidance about work. The two groups also showed significant differences in the performance of the unipedal stance test with eyes closed, with smokers standing on one leg for less time than the non-smoking group ($95\%$ CI, 6.00 to 49.00; $$p \leq 0.0061$$; Figure 2). The impact of LBP on life ($$p \leq 0.006$$) and work ($$p \leq 0.032$$) was mostly moderate and severe in the smoking group, whereas it was mostly no effect or mild in the non-smoking group (Table 2). FIGURE 1: Difference in pain intensity and frequency, disability, and negative emotion outcomes between the smoking group and non-smoking group (Shanghai, China. 2018–2019). The Box-Whisker plot for each variable included the interquartile range and maximum/minimum values. (A) Difference in ODI between the CLBP smoking group and non-smoking group. (B) Difference in RMDQ between the CLBP smoking group and non-smoking group. (C) Difference in VASmax between the CLBP smoking group and non-smoking group. (D) Difference in FABQtotal between the CLBP smoking group and non-smoking group. (E) Difference in FABQ-W between the CLBP smoking group and non-smoking group. (F) Difference in the frequency of LBP last month between the CLBP smoking group and non-smoking group. Abbreviations: ODI, Oswestry disability index; RMDQ, Roland‐Morris disability questionnaire; VAS, visual analogue scale; FABQ, fear-avoidance beliefs questionnaire; FABQ-W, FABQ work. FIGURE 2: Difference in unipedal stance test with eye closed between smoking group and non-smoking group (Shanghai, China. 2018–2019). The Box-Whisker plot for each variable included the interquartile range and maximum/minimum values. TABLE_PLACEHOLDER:TABLE 2 ## Association Between the Daily Cigarette Smoking Amount and CLBP A correlation was observed between the daily cigarette smoking amount and VASmax ($r = 0.651$, $p \leq 0.001$), FABQtotal ($r = 0.398$, $p \leq 0.040$), SDS ($r = 0.386$, $$p \leq 0.047$$) and FABQ-W ($r = 0.45$, $$p \leq 0.019$$), but no relationship was observed between the amount of cigarettes smoked daily and ODI, RMDQ, VASmin, SF-36, FABQ-PA, the frequency of LBP per month and SAS (Figure 3). Multiple linear stepwise regression analysis indicated that the amount of cigarettes smoked daily had a statistically significant effect on VASmax ($b = 0.18$, $t = 3.25$, $$p \leq 0.003$$), SDS ($b = 1.10$, $t = 2.87$, $$p \leq 0.008$$), FABQ-W ($b = 0.84$, $t = 2.25$, $$p \leq 0.02$$) and FABQtotal ($b = 1.10$, $t = 2.60$, $$p \leq 0.016$$). Considering that the partial regression coefficient values in multiple linear regression for different dependent variables are all positive, the increase in the amount of cigarettes smoked daily will lead to different degrees of increase in VASmax, FABQ-W, FABQtotal and SDS scores. The results of multivariate regression analysis indicated that after excluding the effect of other confounding factors, the amount of cigarettes smoked daily could independently be associated with pain, depressive symptoms and fear-avoidance belief in patients with CLBP. This result indicated that the increase in the daily cigarette smoking amount was positively correlated with the aggravation of pain intensity, depression and fear-avoidance belief in patients with CLBP. FIGURE 3: The correlation between the daily cigarette smoking amount and pain intensity, negative emotion outcomes (Shanghai, China. 2018–2019). (A) The correlation between the daily cigarette smoking amount and VASmax. (B) The correlation between the daily cigarette smoking amount and SDS. (C) The correlation between the daily cigarette smoking amount and FABQtotal. (D) The correlation between the daily cigarette smoking amount and FABQ-W. Abbreviations: SDS, Zung Self-rating depression scale; VAS, visual analogue scale; FABQ, fear-avoidance beliefs questionnaire; FABQ-W, FABQ work. The impact of LBP on work is an unavoidable status quo for patients with CLBP. Thus, the degree of work affected by CLBP is an important indicator of concern in this study. The effects of the amount of cigarettes smoked daily, moderate-intensity physical activity per week, age, leisure time, work time and economic income on this indicator were analysed using ordinal logistic regression with proportional odds assumption. Logistic regression analysis proved that the daily cigarette smoking amount was a significant predictor of the increasing impact of LBP on work ($$p \leq 0.019$$), which indicates its significant positive relationship with the impact level of LBP on work, that is, the higher the amount of cigarettes smoked daily, the greater the impact of LBP on the daily work. The OR value is 1.35 ($95\%$ CI, 1.05–1.73), which indicates that the amount of cigarettes smoked daily is an important factor leading to the aggravation of the impact of LBP on work. Amongst the factors related to the impact of LBP on life, the daily cigarette smoking amount did not show significance. ## Discussion This study examined the association between smoking and pain, dysfunction, depression, anxiety and quality of life in patients with CLBP. We found significant differences in VAS, ODI, RMDQ and FABQ between smokers and non-smokers, which indicated that the smoking group had higher pain intensity, degree of dysfunction and fear-avoidance caused by CLBP than the non-smoking group. In addition, a correlation was observed between the daily cigarette smoking amount and pain, depression and fear belief in patients with CLBP. The differences in pain and functional impairment between the smoking and non-smoking groups were statistically and clinically significant. VASmax of the smoking group was concentrated at 5–7, whereas the non-smoking group was concentrated at 4–6. Therefore, patients with CLBP in the smoking group had severe pain, whereas the other group had mostly moderate pain [23]. The clinical significance of the difference in ODI and RMDQ is significant. Patients in the smoking group likely have moderate disabilities, and they experienced more pain and difficulty sitting, lifting and standing [24]. Patients in the non-smoking group had a mostly minimal disability, and they can cope with most living activities. However, no correlation was found between the daily cigarette smoking amount and ODI and RMDQ, indicating that the impact of smoking on the function of patients with CLBP can occur under slight smoking intensity and cannot necessarily aggravate with the increase of the amount of cigarettes smoked. The extent of the damage may be associated with the initiation and duration of smoking or the daily cigarette smoking amount at a particular stage in the evolution of the disease. Regarding the association between smoking and pain, the results are consistent with previous studies [25]. Smoking has been confirmed as a potential cause of musculoskeletal pain, and it is closely related to back pain [16]. However, the mechanisms of LBP are only partly known. Smoking can increase the frequency of coughing, and coughing increases abdominal pressure, which intensifies the compression and stretch of the intervertebral disc on the nerve root, thereby blocking the venous return of the inflamed nerve root and increasing edema and sensitivity of the nerve to pain [26]. Smoking is also associated with osteoporosis, which may alter the microscopic structure of the spine by reducing bone mineral content [27]. It can impair fibrinolysis and increase fibrous deposition and scarring, leading to chronic infection and LBP [28]. Moreover, smoking can reduce vertebral blood flow and affect intervertebral metabolic balance, thereby accelerating the degenerative process and making the spine more vulnerable to mechanical deformation and trauma (29–31). And it also reduces arterial blood flow, leading to ischemia of compressed nerve roots and pain. Furthermore, smoking alters disc gene expression, reduces collagen genes and increases proteoglycan and metalloproteinase 1 tissue inhibitory activity [32]. The amount of cigarettes smoked daily was correlated with VASmax but not with VASmin. This result may indicate that the extent to which smoking affects chronic pain is related to its basic pain intensity. For slight pain conditions, the role of smoking as an influencing factor was not prominent. However, as the pain got worse, the association between smoking and pain also increased. Thus, this study may provide new insights into the mechanism of the association between smoking and pain. Later studies investigating the relationship and mechanism of smoking and pain can focus on further quantifying pain intensity and determining whether a non-linear correlation exists between smoking and pain intensity, that is, the greater the intensity of pain, the greater the association between cigarette smoking and pain intensity. The performance of the two groups on the one-leg standing test was consistent with their pain intensity differences. CLBP reduces the stability of the spinal area and increases the activation time of the gluteus medius muscle, which leads to differences in the ability to control lower limb balance [33]. Smoking may affect the ability of patients with CLBP to stand on a single leg by affecting their pain intensity. However, at present, no relevant research has been conducted to determine whether or not smoking directly affects the neural mechanism controlling muscle coordination, which may be the direction of future research. This study also found that after adjusting for age, income, education level, history of LBP, frequency of LBP last month and other confounders, the daily cigarette smoking amount remained positively correlated with the SDS, which indicated the relationship between the amount of cigarettes smoked daily and the aggravation of depressive symptoms in patients with CLBP. The current study found that depression was correlated with smoking [34]. Chronic pain reduces social connection, and smokers are less socially active and lonelier than non-smokers [35]. Vogt et al. [ 36] adjusted for income, education level, occupation, employment, life stress, childhood adversity, divorce and neurosis and found that smoking remained associated with depression. Research suggests that the smoking–depression association is bipolar. The presence of depressive symptoms increases the risk of developing nicotine dependence in smokers, thereby increasing the risk of depression [37]. The occurrence of smoking addiction and depressive symptoms has a common neurotransmitter pathway, and they may have a common genetic material basis [38]. Anxiety and depression have been associated with a bidirectional effect of promoting smoking [39], and negative emotions have been confirmed as a central mechanism for the correlation of pain with smoking [40, 41]. Pain-related anxiety was relevant to increasing pain intensity and positively related to heavy smoking and nicotine use to cope with aversive states [42]. Increasing pain-related anxiety and pain sensitivity were related to early initiation of smoking [43]. Whether or not smoking had a significant effect on the anxiety of patients with CLBP was not shown in this study probably because anxiety caused by the pain intensity of CLBP was not enough to cause the related effect of smoking, or the negative emotions caused by CLBP were more prone to depression. Future treatments should target whether CLBP-related anxiety may contribute to behavioural tendencies to relieve pain and negative effects through cigarette smoking. CLBP can be attributed to a biological–psychological–social phenomenon. Patient’s anatomical injury factors interact with psychosocial factors [44], such as fear-avoidance belief, which indicates that some patients with LBP have negative beliefs about pain that can lead to a catastrophic psychological reaction, causing the patient to fear activities that may aggravate pain or injury. Avoidance of such activities can reduce the likelihood of repeat pain or injury [45]. Fear-avoidance beliefs can be a predictor of outcome in patients with subacute LBP [46]. In this study, the daily cigarette smoking amount was found to be positively correlated with FABQtotal and FABQ-W, particularly to work, which in turn led to excessive fear of pain or injury and gradually extended to fear of physical movement. This finding is also consistent with the association between smoking and pain intensity and depression in this study. Depression is an important influencing factor of fear-avoidance beliefs [47]. Considering that depressed patients are often in a state of low emotional responsiveness and lack of enthusiasm and motivation to actively respond to symptoms, they tend to develop fear-avoidance beliefs about pain. Depression, including clinically diagnosed depression and patient-reported depressive symptoms, is common in patients with CLBP [48]. Pain patients with depressive symptoms tend to have more intense pain experiences and more severe physical damage. Smoking is closely related to depression [37], indicating that smoking may be related to fear beliefs by affecting depressive symptoms in patients with CLBP. Future research can focus on the interaction of smoking, depression and fear-avoidance beliefs in patients with CLBP. The daily cigarette smoking amount is related to the impact of LBP on work but not related to the impact of LBP on life probably because the working conditions of most people are relatively monotonous, which reduces the impact of other factors to a higher degree, thereby making the impact of smoking fully exposed. In daily life, individuals have more options to maintain low-intensity LBP for themselves, which highlight the weight of factors such as income, leisure time and daily activity in the impact of LBP on life and indirectly offset the effect of the amount of cigarettes smoked daily. This result indicates that future research should include more population in the study to explore the correlation between smoking and the impact of LBP on life under more undisturbed conditions. ## Conclusion This cross-sectional study investigated the association between smoking and pain, dysfunction and psychological status in patients with CLBP and analysed the relationship between the amount of cigarettes smoked daily and CLBP. The results of the study indicated that smoking was related to the aggravation of symptoms in patients with CLBP, which suggested that patients with CLBP and people at risk of LBP should be aware of the harm caused by smoking. Given the limitation of the sample size and cross-sectional study, this study cannot explain the corresponding causation between the amount of cigarettes smoked daily and the aggravation of CLBP. Future research should further expand the sample size and control economic conditions, medical level, occupation and other confounding factors. ## Ethics Statement The studies involving human participants were reviewed and approved by the Ethics Committee of Shanghai University of Sport. The patients/participants provided their written informed consent to participate in this study. ## Author Contributions X-QW: conceptualization, methodology, and funding acquisition. Q-HY: data curation, software, and writing-original draft preparation. Y-HZ: visualization and investigation. S-HD: supervision and validation. Y-CW: writing- reviewing and editing. ## Conflict of Interest The authors declare that they do not have any conflicts of interest. ## References 1. Maher C, Underwood M, Buchbinder R. Non-specific Low Back Pain. Lancet (2017) 389 736-47. DOI: 10.1016/S0140-6736(16)30970-9 2. Lawrence RC, Felson DT, Helmick CG, Arnold LM, Choi H, Deyo RA. 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--- title: Dietary administration with hydrolyzed silk sericin improves the intestinal health of diabetic rats authors: - Wenlin Zhou - Yujie Weng - Qian Liu - Chonglong Wang - Yu-Qing Zhang - Xing Zhang - Aihong Ye journal: Frontiers in Microbiology year: 2023 pmcid: PMC10027739 doi: 10.3389/fmicb.2023.1074892 license: CC BY 4.0 --- # Dietary administration with hydrolyzed silk sericin improves the intestinal health of diabetic rats ## Abstract Type II diabetes (T2D) is a global epidemic disease with an increased incidence and prevalence. Gut microbiota plays an important role in controlling T2D development. Dietary administration of prebiotics, probiotics, and drugs, including metformin, showed the regulatory impact on the change of gut microbiota, which is associated with the improvement of glucose tolerance. In this study, silk sericin was manufactured into hydrolyzed sericin peptide (HSP) powders as a dietary additive to investigate the effect on the gut microbiota of T2D model rats. The results indicated that the HSP-augmented dietary administration lowers the fast glucose level of diabetic rats, and HSP augmentation induces a change in the gut microbiota composition of T2D model rats toward the normal rats. Some key taxa, including Lactobacillus gasseri, were suggested to be involved in controlling T2D development. This finding provides new insight into developing sericin as functional food or therapeutic prebiotics against T2D in clinical practice. ## 1. Introduction Type II diabetes (T2D), accounting for approximately $90\%$ of all diabetes, is a globally prevalent disease associated with obesity and an unhealthy lifestyle. It is characterized by insulin deficiency, hyperglycemia, and metabolic disorder of many organs, resulting in a high risk of T2D mortality and morbidity (Zimmet et al., 2001). The estimated prevalence of diabetes in adults (aged 20–79 years) has tripled from 151 million in 2000 to 463 million in 2019, according to International Diabetes Federation reports (9th edition). The population with diabetes will rise by $10.2\%$ to 578 million by 2030 if no sufficient actions are implemented. However, T2D can be effectively managed by the support and adoption of healthy lifestyles in combination with as-required medication, such as insulin and glucose absorption inhibitors. In the past decade, it has been demonstrated that gut microbial dysbiosis contributes to the risk of developing obesity and diabetes (Tilg and Moschen, 2014; Wang and Jia, 2016). The gut microbiota is a complex ecosystem of microorganisms in the intestinal tract, which can affect host physiology and serves as the therapeutic route for antidiabetic medications (Wu et al., 2017). A cohort analysis of the gut microbiota of patients with T2D indicated significantly reduced proportions of the phylum Firmicutes and the class Clostridia compared with those of healthy controls (Larsen et al., 2010). Opportunistic pathogens such as Bacteroides, Clostridiales, Escherichia coli, and sulfate-reducing species Desulfovibrio were often enriched in patients with diabetes (Qin et al., 2012). Gut microbiota can be an active site of the antidiabetic drug metformin, which alters microbiota composition and improves glucose tolerance (Wu et al., 2017; Foretz et al., 2019). Metformin is reported to increase short-chain fatty acid (SCFA)-producing bacteria and Lactobacillus species and decrease Bacteroides fragilis (Forslund et al., 2015; de la Cuesta-Zuluaga et al., 2017; Bauer et al., 2018). The metabolic functions of microbiota and their interaction with the host metabolism can be reconstructed upon the composition alternation, e.g., bile acid homeostasis, glucagon-like peptide 1 (GLP1) secretion, and activation of the gut–brain–liver neuronal axis. Dietary administration of prebiotics, probiotics, and drugs appears to have a beneficial impact on insulin resistance in clinical or animal T2D models (Belizário and Napolitano, 2015; Salgaço et al., 2019; Rodrigues et al., 2021), although further evaluation is required in more individuals with diabetes (Bordalo Tonucci et al., 2017). Oral intake of probiotics or berberine has been reported to alter microbial bile acid metabolism and improve glycemic control, which is ascribed to the resultant changes in gut microbiota (Zhang et al., 2020). In any case, the manipulation of gut microbiota by dietary administration can be a readily adopted approach to control and ameliorate diabetes (Ghorbani et al., 2021). Sticky sericin coats the core fibroin and is removed as waste in silk processing (Aramwit et al., 2012). It is a mixture of macromolecule polypeptides with a molecular mass of 10–300 kDa and constitutes more than $25\%$ of the total cocoon weight (Cao and Zhang, 2016). Sericin shows various biological activities, including antioxidation, inhibition of tyrosinase, and protection against alcohol-induced liver and gastric injuries (Li et al., 2008; Cherdchom et al., 2021; Suzuki et al., 2022). Moreover, dietary sericin lowers the levels of triglyceride and cholesterol in rats fed with a high-fat diet (Seo et al., 2011). In this study, sericin diets were given to diabetic rats to assess the improvement of intestinal health through the alternation of gut microbiota. The result will be useful in the development of sericin as a functional food or a therapeutic agent against T2D. ## 2.1. Preparation of hydrolyzed sericin peptide powders Hydrolyzed sericin peptide (HSP) powders were prepared according to a modified method described previously (Zhang M. et al., 2019; Dong et al., 2020). In brief, the cocoon shells were weighted and soaked in $0.025\%$ (w/v) calcium hydroxide at a 1:90 bath ratio (w/v). The degumming was processed using a 30-min boiling treatment, which was conducted twice to improve sericin recovery. Then, the degumming solutions were condensed through rotary evaporation under the protection of negative pressure. The condensed solution was neutralized with sulfuric acid, and the resultant calcium sulfate precipitate was removed by centrifugation. The collected supernatant was finally dried by using a vacuum-freezing spray dryer (Figure 1A). Figure 1: Dietary administration with hydrolyzed sericin peptide (HSP) in T2D model rats. (A) Schematic of HSP processing. (B) T2D model preparation and HSP dietary administration. (C) Measurement of body weights and FBG levels. The measured values are presented as mean ± standard error. ## 2.2. Animals Male Sprague–Dawley rats weighing approximately 200 g were maintained in dim cyclic light (20–40 lux, 12-h light:12-h dark/light cycle) at 20–25 °C and 50–$80\%$ humidity, and the water and diets were provided ad libitum unless otherwise noted. The T2D model rats were intraperitoneally injected with streptozotocin (STZ) (Dong et al., 2020; Wei and Weng, 2022). The rats fasted overnight before the injection, and the injection dose was administered for 3 days, at a low dose of 35 mg/kg body weight per day. Normal diets were provided during the injection period, and a high-fat and high-sugar diet was consequently fed to the administrated rats for 5 days after injection. Five days after the first injection, tail vein blood was collected to measure the fasting blood sugar (FBG) level; rats with an FBG level higher than 11.1 mmol/L were considered T2D model rats (Figure 1B). The T2D model rats were randomly divided into four dietary groups ($$n = 4$$–7) for the experiment. All animal procedures were approved by the International Animal Welfare Committee and the Animal Experimental Operations and Ethics Committee of Soochow University (Animal License No. 201802A128). ## 2.3. Diets The rats of the normal group (Normal) and one T2D model rat group (Model) were fed with irradiated standard diets only as the positive and negative controls, respectively. The rats from two T2D model groups (HSP2.5 and HSP5.0) were fed with standard diets augmented with either $2.5\%$ (w/w) or $5.0\%$ (w/w) HSP powders, respectively. The T2D model rats were fed with standard diets supplemented with $0.5\%$ (w/w) metformin as the positive blood sugar control (Metformin). The feeding procedures were maintained for 7 weeks. ## 2.4. Measurement of body weight and FBG level The rats were weighed once per week. During the 7-week treatment, the FBG level was determined once a week using a glucose meter (Onetouch, LifeScan Inc.). The rats were fasted for 10 h before measurement, and blood was harvested using the tail-cutting method. ## 2.5. DNA isolation, library construction, and 16S rDNA amplicon sequencing Bacterial DNA was extracted from the fresh intestinal content using a QIAamp PowerFecal DNA Kit (Qiagen), following the manufacturer's protocols. The quality and quantity of bacterial DNA were verified using a NanoDrop spectrophotometer and through agarose gel electrophoresis. Bacterial 16S rDNA amplicon (V3 and V4) regions were amplified with barcoded primers. The amplicons were purified using AMPure XP beads (Agencourt) and amplified for another round of PCR. The final amplicon was quantified using a Qubit dsDNA Assay Kit (Thermo Fisher), and equimolar concentrations of libraries were pooled and sequenced on an Illumina MiSeq platform at OE Biotech (Shanghai, China). The raw sequence reads were deposited in the NCBI Sequence Read Archive database (www.ncbi.nlm.nih.gov/sra/), with the accession number PRJNA926516. ## 2.6. Data processing and analysis Paired-end reads were preprocessed using Trimmomatic software (version 0.35) to detect and filter ambiguous bases. The low-quality sequences with an average score below 20 were also cut off by sliding window trimming. The filtered reads were assembled using FLASH software (version 1.2.11) and denoised using QIIME software (version 1.8.0). The resulting clean reads were subjected to primer sequence removal and clustering to generate operational taxonomic units (OTUs) using Vsearch software (version 2.4.2) by a similarity cutoff of $97\%$. The representative read of each OTU was selected using the QIIME package. All representative reads were annotated and BLASTed against the Silva database (version 123) using the RDP classifier algorithm and a confidence threshold of $70\%$. The alpha diversities were measured using Chao1 richness, Simpson, and Shannon indexes. The OTU abundance table was used to analyze the beta diversity, including principal component analysis (PCA) and principal coordinate analysis (PCoA). The key communities of intestinal microbiota were determined using the linear discriminant analysis effect size (LEfSe) method. A p-value of < 0.05 and a linear discriminant analysis (LDA) score of ≥2.5 were considered statistically significant. ## 3.1. An HSP-augmented diet lowers the FBG level of T2D model rats An STZ injection was administered to induce T2D-like metabolic disease in SD rats. As expected, the STZ-injected rats exhibited glucose intolerance with an FBG level of >15 mmol/L 1 week after the first injection. These T2D model rats were grouped and subjected to different dietary processes (Figure 1C). Administration of the standard diet (Model) resulted in severe body weight loss and an extremely high FBG level (20 mmol/L) in T2D model rats, whereas this phenotypic change of T2D model rats could be effectively alleviated by dietary HSP augmentation (HSP 2.5 and HSP5.0). Especially, the FBG level lowered to below 10 mmol/L after 1 week of HSP-augmented feeding in both groups, albeit with a slight rebound at week 7. The analysis of variance (ANOVA) of the FBG observation after the first week indicated that HSP augmentation was superior to metformin administration (Supplementary Table 1, $$P \leq 0.0098$$), and Duncan's multiple range test showed that HSP at a concentration of $2.5\%$ or $5.0\%$ did not have a significant difference in controlling the FBG level of T2D model rats. In comparison with the Model group, the HSP 2.5 and HSP 5.0 groups, as well as the Metformin group, showed a slight increase in body weight. These results suggest that HSP augmentation potently alleviates high FBG levels in T2D model rats. The hypoglycemic effect of dietary HSP administration has also been reported in T2D model rats (Dong et al., 2020). Oral HSPs have been proven to regulate the gene expression involved in gluconeogenesis, lipid metabolism, and inflammation. We suspected the gut microbiota plays an essential role in bringing about these functions of HSP. ## 3.2. HSP augmentation induces a change in the gut bacterial composition in T2D model rats To examine the effects of HSP augmentation on the intestinal microbiota of T2D model rats, the fecal samples were collected at the experimental endpoint (week 7) for sequencing of the 16S rRNA gene (Supplementary Table 2). As a result, an average of 76,762 valid reads (ranging from 70,331 to 81,207) was obtained for 28 samples. These quantified sequences were clustered into a total of 5,662 unique OTUs, and only 707 OTUs were shared in the five groups (Supplementary Figure 1, Supplementary Table 3), which suggested a difference in bacterial abundance among the groups. The composition overview at the phylum level showed that Bacteroidetes, Firmicutes, Proteobacteria, and Cyanobacteria are the major communities (>$84\%$), and the Model group showed the highest profiling variation (Figure 2A), although the ANOSIM of Bray–Curtis distances did not show a significant difference ($R = 0.083$, $$P \leq 0.106$$) among the groups (Supplementary Figure 2). ANOVA (Supplementary Table 4) suggested a significant increase ($$p \leq 0.030$$) in the phylum Firmicutes in the Model group ($38.5\%$) compared with the Normal group ($25.3\%$) and groups administered other diets (28.8–$30.6\%$), which contributed to the highest Firmicutes/Bacteroidetes (F/B) ratio in the Model group (1.29). These results suggest there were changes in gut microbiota in the T2D model rats. Thus, the alpha diversity metric was analyzed in all groups by measuring Chao1, Simpson, and Shannon indexes (Figure 2B). The results revealed that the bacterial species richness and diversity were significantly increased in the Model group compared with the Normal group. However, HSP administration either at 2.5 or $5\%$ resulted in reductions in species richness and diversity of gut microbiota in T2D model rats, which was comparable to or better than the effects of metformin exposure. The PCA plot also suggested group differences between the Normal group, Model group, and HSP or metformin groups (Figure 2C). The Model group was clearly separated from the other groups, and the HSP or metformin dietary administration groups were close to the Normal group. Figure 2: Analyses of alpha and beta diversities among groups. (A) Phylum structure of gut microbiota. The evolutionary tree was built with the Bray–Curtis distance using the UPGMA method. (B) Simpson, Shannon, and Chao indexes. (C) PCA. The error bars of each sample indicate the standard deviations at PC1 and PC2, respectively. (D) PCoA. The sample distributions are shadowed with different colors for each group. The overall microbial structure among the groups was further analyzed on the beta diversity metric using PCoA at the OTU level (Figure 2D), which suggested that HSP or metformin dietary administration can reshape the microbial structure of the Model group to a structure similar to that of the Normal group. A high microbiological variation (large standard deviation) was also observed among the T2D model rats. Nevertheless, these results demonstrate that HSP dietary administration contributes to a substantial shift in the gut microbiota of T2D model rats. ## 3.3. Identification of key taxa associated with an HSP-augmented diet in T2D model rats Linear discriminant analysis (LDA) effect size (LEfSe) was employed to identify key bacterial taxa associated with an HSP-augmented diet using OTUs. The statistical differences in microbial communities were compared at different taxonomic levels, and taxa cladograms presented enrichment at a cutoff LDA score of >3 (Figure 3A, Supplementary Figure 3, Supplementary Table 5). Firmicutes was the dominant phylum in T2D model rats, which was consistent with the outcome of the highest F/B ratio in this group. The gut microbiota of the Normal group showed 15 predominant family cladograms out of 25 identified families; however, only the observed Limnochordaceae family belongs to the phylum Firmicutes. A similar observation was made at the genus level, in which three of 16 and five of six genera belong to the phylum Firmicutes for Normal and Model groups, respectively. Overall, these two groups showed a distinct distribution of enriched cladograms. Nevertheless, many enriched cladograms for the Normal group were also observed in the HSP 2.5, HSP 5.0, and Metformin groups (Figure 3A). This observation was consistent with the PCA analysis of microbial structure among the groups. It suggested that HSP dietary augmentation can change the composition of intestinal microbiota in T2D model rats as metformin performs. Figure 3: Taxa associated with an HSP-augmented diet. (A) LEfSe analysis. The analysis was performed using P=0.05 and a logarithmic LDA score of 3.0. (B) Relative abundances of the top 10 most significant OTUs. Relative abundances are presented as log10(100 × value + 1), and the P-values were adopted from ANOVA. Duncan's multiple range test is denoted by “a”, “b”, and ‘c' (P = 0.05). ANOVA was performed to identify specific microbes associated with T2D disease and control using the relative abundance of OTUs. A total of 112 OTUs showed statistical differences ($p \leq 0.05$) among the five groups (Supplementary Table 6). They account for 35.1 ± $2.59\%$ of the total abundance in the T2D Model group, 25.6 ± $7.01\%$ in the Normal group, and 25.0–$27.4\%$ in the HSP- or Metformin groups. The top 10 most significantly changed species are presented in Figure 3B. Mycoplasma sp. ( OTU160), Staphylococcus sp. ( OTU170), Muribaculaceae sp. ( OTU27), Lachnospiraceae sp. ( OTU67), Rikenellaceae sp. ( OTU84), and Klebsiella aerogenes (OTU22) had significantly higher abundances in normal rats than in the T2D model rats, whereas Lactobacillus gasseri (OTU), Lactobacillus acidophilus (OTU), and Aerococcus sp. ( OTU) showed a reverse fashion between both groups. This distinct performance was elucidated through Pearson coefficient analysis (Figure 4), while the metformin and HSP administration could change the Model abundance toward those in the Normal group (Figures 3, 4). These abundance-increased OTUs in the Normal group are pathogens or opportunistic pathogens, which have often been reported to be increased in individuals with T2D (Redel et al., 2013; Zhang Z. et al., 2019; Deng et al., 2020). Such an aberrant appearance was most likely due to a huge increase in lactic acid bacteria (e.g., L. gasseri and L. acidophilus) and Aerococcus sp. Surprisingly, our results did not show the enrichment of some known SCFA-producing bacteria, such as Clostridia, in either the Metformin- or HSP-administered groups. Figure 4: Pearson coefficient analysis among groups using the relative abundances of the top 10 significant OTUs. ## 4. Discussion Our study demonstrated the effect of lowering FBG levels through HSP administration, which even outperformed metformin (Figure 1C). Both HSP and metformin differentially affected the diversities and compositions of gut microbiota in T2D model rats. Diversity and richness were significantly increased in T2D model rats conpared with normal rats. Of interest, HSP administration reduced these alpha diversity indexes, which is often observed in drug administration in T2D animals (Lee et al., 2018; Yang et al., 2020). Additionally, HSP and metformin had differential effects on the composition of gut microbiota, according to beta diversity analysis (Figures 2C, D). HSP administration was more effective at restoring the diversity of normal microbiota than metformin in T2D model rats, whereas the dosage effect of HSP (either 2.5 or $5\%$) was not observed. Additionally, our results showed that HSP changes the F/B ratio in T2D model rats. It has been proposed that the F/B ratio is linked to T2D dysfunction and obesity, and SCFA metabolism is positively related to the F/B ratio (Fernandes et al., 2014; Wang and Jia, 2016). Our results contradicted the previous observation that individuals with T2D have a lower F/B ratio than individuals without diabetes (Demirci et al., 2020), which has remained controversial due to the experimental subjects (Schwiertz et al., 2010). As Firmicutes and Bacteroidetes were the most abundant phylogenetic categories, it was suspected that only a portion of bacterial species associated with SCFA metabolism contribute to T2D development. This change could not be directly assessed by the F/B ratio, while different F/B ratios could reflect the changes in gut microbiota. LEfSe analysis and ANOVA identified L. gasseri as an enriched species in T2D model rats (Figure 3). An increase in the abundance of L. gasseri has been reported in the T2D group of European women and correlates positively with fasting glucose and glycosylated hemoglobin HbA1c (Karlsson et al., 2013). Lactobacilli, including L. gasseri, are generally recognized as probiotics that can prevent obesity, improve glucose tolerance, and attenuate inflammation (Shirouchi et al., 2016; Niibo et al., 2019; Youn et al., 2021). Host species, enterotypes, different compounds, and interval of dietary administration probably caused the discrepancy among different cases. Nevertheless, Lactobacilli have been identified as a potential infectious factor in immunocompetent individuals (Pararajasingam and Uwagwu, 2017; Ramos-Coria et al., 2021). It would not be surprising to observe an increase in L. gasseri in T2D model rats with immune dysfunction. It should also noted that the 16S rDNA sequencing used in this study was limited to interpreting relevant function at a species level. The STZ induced diabetic model might be the same reason in human T2D. However, this study demonstrated that HSP administration was an effective way of controlling FBG levels in T2D model rats, and the beneficial effects were associated with changes in gut microbiota. Our results suggested a possible use of HSP dietary administration to build microbiota of T2D patients in clinical practice. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors. ## Ethics statement All animal experiments were conducted in accordance with the relevant regulations required by the International Animal Welfare Committee and the Animal Experiment Operation and Ethics Committee of Soochow University. The Institutional Review Board Statement and approval number for studies involving humans or animals. Approval Code: 201911A063, Approval Date: 5 November 2019. ## Author contributions AY, XZ, CW, and Y-QZ designed the research and analyzed the data. WZ, YW, and QL conducted the research. WZ, XZ, and CW wrote the manuscript. AY, XZ, and WZ had primary responsibility for the final content. All authors read and approved the final manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Celastrol directly binds with VAMP7 and RAB7 to inhibit autophagy and induce apoptosis in preadipocytes authors: - Chenshu Liu - Na Li - Meixiu Peng - Kan Huang - Dongxiao Fan - Zhengde Zhao - Xiuyi Huang - Yunchong Liu - Sifan Chen - Zilun Li journal: Frontiers in Pharmacology year: 2023 pmcid: PMC10027750 doi: 10.3389/fphar.2023.1094584 license: CC BY 4.0 --- # Celastrol directly binds with VAMP7 and RAB7 to inhibit autophagy and induce apoptosis in preadipocytes ## Abstract Obesity is one of the most prevalent chronic metabolic diseases, and induction of apoptosis in preadipocytes and adipocytes is a potential strategy to treat obesity. Celastrol represents one of the most robust anti-obesity phytochemicals so far, yet its direct binding target remains elusive. Here, we determined that celastrol could induce apoptosis in preadipocytes via mitochondrial mediated pathway. Further study clarified that celastrol inhibited the fusion of autophagosome and lysosome to prohibit autophagy, leading to cell apoptosis. By conducting virtual screening and genetic manipulation, we verified that overexpression of VAMP7 and RAB7 could block the effects of celastrol on inhibiting autophagy and inducing apoptosis. The Surface Plasmon Resonance study confirmed the direct binding of celastrol with VAMP7 and RAB7. The functional study illustrated the inhibition of RAB7 GTPase activity after celastrol treatment. Moreover, celastrol induced comparable apoptosis in murine epididymal adipose tissue, human preadipocytes and adipocytes, but not in human hepatocytes. An inhibitory effect on differentiation of human primary visceral preadipocytes was also observed. In conclusion, celastrol exhibited inhibitory effect of autophagy via direct binding with VAMP7 and RAB7, leading to an increase in preadipocytes apoptosis. These results advance our understanding in the potential application of celastrol in treating obesity. ## 1 Introduction Obesity is one of the most prevalent chronic diseases among the world, and threatens human health with tremendous social-economics cost (Ng et al., 2014). More than 1.9 billion adults are overweight, and over 650 million are obese worldwide (https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight). In obesity, adipose tissue exhibits hypertrophy and pathological adipogenesis, which lead to insulin resistance and chronic inflammation (Piche et al., 2020). In obesity, the proliferation and differentiation of preadipocytes to adipocytes are overrepresented, while the mature adipocytes enlarge their volume to store the increased triacylglycerols (Ali et al., 2013). Hence, decreasing fat mass with activation of lipolysis, inhibition of adipogenesis or apoptosis of preadipocytes and adipocytes, are considered as potential strategies to treat obesity. In perspective of apoptosis in preadipocytes and adipocytes, several phytochemicals were proven to induce apoptosis of preadipocytes, which decreased the fat mass accumulation (Hsu and Yen, 2006; Yang et al., 2007; Chen et al., 2012; Zhang and Huang, 2012; Lone and Yun, 2017; Wu et al., 2019), suggesting them as promising compounds in treating obesity. Celastrol is a natural friedelane pentacyclic triterpenoid, which can be extracted from some celastraceae plants such as Tripterygium wilfordii and Celastrus orbiculatus. ( Xu et al., 2021). It is also one of the most robust anti-obesity phytochemicals that has been reported so far, yet its direct target in this regard remains unknown. Liu et al. had reported that up to $45\%$ weight loss was observed in obese mice treated with celastrol (Liu et al., 2015), which is even more potent than $35\%$–$40\%$ weight loss in mice after bariatric surgery (Liou et al., 2013; Mokadem et al., 2014; Ryan et al., 2014). Despite the strong anti-obesity effect of celastrol, identification of its direct target remains challenging. To date, only adenylyl cyclase-associated protein 1 (Zhu et al., 2021) and nuclear receptor subfamily four group A member 1 (Hu et al., 2017) were reported to be able to directly bind with celastrol, yet neither was verified as the direct target of its anti-obesity effect. Hence, identification of the target would significantly advance its mechanistic investigation and clinical translation. Autophagy plays a pivotal role in preadipocytes differentiation and fat accumulation. Autophagy is an essential mechanism for cells to maintain physiological homeostasis, including turnover of the protein and nutrients, and elimination of the potential hazards (Doherty and Baehrecke, 2018). Studies showed that damage of autophagic flux would inhibit preadipocytes differentiation and subsequently induce apoptosis, indicating a vital role of autophagy in adipogenesis (Baerga et al., 2009; Singh et al., 2009; Zhang et al., 2009). In our study, we demonstrated that celastrol could induce apoptosis of preadipocytes and mature adipocytes. With autophagic flux assay, gene manipulation and small molecule-protein binding assay, we found that celastrol could directly bind with vesicular transport related proteins, namely, VAMP7 and RAB7, inhibit the fusion of autophagosome and lysosome, leading to impaired autophagic flux and subsequent induction of cell apoptosis in preadipocytes. This study determined a direct effect of celastrol on preadipocytes and uncovered its direct binding target, advancing the potential application of celastrol in treating obesity. ## 2.1 Cell culture The cell lines of murine 3T3-L1 preadipocytes and human hepatocytes HL-7702 presented in this study were obtained from the National Collection of Authenticated Cell Cultures of China (Shanghai, China). The studies involving human primary visceral preadipocytes and adipocytes of human participants were reviewed and approved by the Institutional Review Board of The First Affiliated Hospital of Sun Yat-sen University (Guangzhou, China). The patients/participants provided their written informed consent to participate in this study. Cells were cultured in normal cell culture incubator. Murine 3T3-L1 preadipocytes and human hepatocytes HL-7702 were cultured in Dulbecco’s modified Eagle’s medium (DMEM, Gibco, United States) supplemented with $10\%$ fetal bovine serum (FBS, Gibco, United States), 100 U/mL of penicillin and 100 μg/mL of streptomycin. The human primary visceral preadipocytes and adipocytes were cultured in DMEM/F12 medium without phenol red (Gibco, United States), supplemented with $10\%$ FBS, 100 U/mL of penicillin and 100 μg/mL of streptomycin. For adipocytes differentiation, the classic protocol of preadipocytes differentiation was followed (He et al., 2021). In brief, 3T3-L1 preadipocytes were cultured 2 days in DMEM medium (0.5 mmol/L IBMX, 1 μmol/L dexamethasone, 10 μg/mL insulin, and $10\%$ FBS), and then 2 days in DMEM medium (5 μg/mL insulin and $10\%$ FBS), once reached full confluence. Afterwards, the cells were maintained in DMEM medium ($10\%$ FBS) for 4 days till full differentiation. DMEM/F12 medium without phenol red was applied for human visceral preadipocytes in this differentiation protocol. ## 2.2 Animals Four-week-old male C57BL/6 mice were housed under normal specific pathogen free (SPF) conditions with unrestricted access to food and water and were fed with $60\%$ high fat diet for 24 weeks to induce diet-induced obesity. After induction, mice were randomly divided into five groups, 1) chow diet + vehicle, 2) high fat diet + vehicle, 3) high fat diet + celastrol, 4) pair-feeding + vehicle and 5) pair-feeding + celastrol ($$n = 4$$). For 2 weeks intervention of celastrol (100 μg/kg/day, i. p.), mice in two pair-feeding groups only received equal amount diet comparing with high fat diet + celastrol treatment group. After 6 h fasting, all mice were sacrificed with anesthesia to harvest their epididymal adipose tissue. All animal studies were approved by the Institutional Animal Care and Use Committees of the First Affiliated Hospital of Sun Yat-sen University. ## 2.3 Cell treatment For RFP-GFP-LC3 adeno-associated virus (Hanbio, China) infection, 3T3-L1 preadipocytes in 12-well plate were transfected according to the manufacturers’ instructions, celastrol were given 48 h after transfection, and harvested after 24 h for the following experiments. For siRNA and plasmids transfection, Lipofectamine 2000 (Invitrogen, United States) was applied according to the manufacturers’ instructions. Preadipocytes in 12-well plate were transfected with each RAB7, VAMP7 or VTI1B plasmids for 48 h for overexpression, and then subjected to celastrol for additional 24 h. Ten nM of the RAB5C siRNA was applied for each well in 12-well plate, added together with celastrol, and subjected to the following experiments after 24 h. All control wells received the corresponding blank plasmids or scramble siRNA. ## 2.4 Apoptosis assay Flow cytometry was applied for cell apoptosis assay. In brief, preadipocytes after treatment were harvested, washed twice with cold PBS and resuspended with buffer to reach 1 × 105 cells/mL. Suspension was further incubated with dyes from Annexin V, FITC Apoptosis Detection Kit (Dojindo, China). The percentages of distribution of normal (Annexin V−/PI−), apoptotic (Annexin V+/PI− and Annexin V +/PI+) and necrotic cells (Annexin V−/PI+) were calculated. ## 2.5 Mitochondrial membrane potential assay Mitochondrial membrane potential assay was applied using a JC-10 based commercial kit (Biosharp, China). JC-10 exhibits potential-dependent aggregate status in normal mitochondria membrane (red), and monomer status in abnormal mitochondria membrane (green). 3T3-L1 preadipocytes in 12-well plate were administrated with celastrol for 24 h and then subjected to JC-10 dye loading solution at 37°C in a $5\%$ CO2 incubator for 20 min, avoid light. The plates were further observed with fluorescence microscope (Leica DMi8, United States). ## 2.6 Terminal deoxynucleotidyl transferase dUTP nick end-labelling (TUNEL) staining For in vitro TUNEL assay, preadipocytes were treated with celastrol for 24 h and dyed by One Step TUNEL Apoptosis Assay kit (Beyotime Institute of Biotechnology, China) (Liu et al., 2019). Preadipocytes were fixed with $4\%$ paraformaldehyde at room temperature for 30 min. Untreated cells were pre-incubated with DNase I recombinant (5 μg/mL) for 10 min at room temperature to serve as a positive control. Preadipocytes were further incubated with TUNEL reaction mixture for 60 min at 37°C in dark. The TUNEL-positive nuclei (green) was observed under a fluorescence microscope (Leica DMI8, United States). For in vivo TUNEL assay, In situ Cell Death Detection Kit (Roche, Switzerland) was applied for epididymal adipose tissue. The histological sections were incubated with TUNEL reaction mixture for 60 min at 37°C in the dark, incubated with Converter-POD antibody (1:500) for 30 min at 37°C, followed by DAB substrate incubation for 10 min at room temperature, and then mounted with PBS/glycerol. The number of TUNEL-positive nuclei (brown) was calculated from six random fields of each sections under a light microscope (Zeiss Axio Observer Z1, German). ## 2.7 LysoTracker red staining Preadipocytes were treated with celastrol, 40 μM chloroquine or 200 nM bafilomycin A1 for 24 h and stained with LysoTracker Red fluorescence probes (Solarbio, China). After compounds treatment, cells were incubated with DMEM complete media containing LysoTracker Red dye (25 nM) and Hoechst 33258 Staining Dye for 20 min at 37°C in dark. Cells were further observed under a fluorescence microscope (Leica DMI8, United States). ## 2.8 Immunofluorescence staining Preadipocytes infected with RFP-GFP-LC3 adeno-associated virus were treated with 1 μM celastrol, 40 μM chloroquine or 200 nM bafilomycin A1 for 24 h. Cells were fixed with $4\%$ paraformaldehyde and blocked with $5\%$ BSA, then subjected to primary antibody LAMP1 (Cell Signaling Technology, United States, Cat #9091, RRID:AB_2687579, 1:100 dilution) overnight, followed by incubation with Alexa Fluor 647-conjugated goat anti-rabbit IgG antibody (Abcam, United States, Cat # ab150079, RRID:AB_2722623, 1:500 dilution). Finally, the autophagosomes were observed under a fluorescence microscope (Leica DMI8, United States). Yellow against red puncta ratio was determined by the exact puncta numbers in five random fields of one slice obtained from YFP and RHOD channels separately. The Pearson correlation coefficient (PCC) for the colocalization of RFP-LC3 and Alexa Fluor 647-LAMP1 was calculated with the Coloc2 module of ImageJ (National Institutes of Health, United States, RRID:SCR_003070) (Elimam et al., 2019). ## 2.9 Electron microscopy 3T3-L1 preadipocytes were treated with 1 and 2 μM celastrol or 40 μM chloroquine for 12 h and were fixed with $2.5\%$ glutaraldehyde in sodium phosphate buffer (pH 7.4) for 30 min at room temperature. The samples were then dehydrated in a series of aqueous alcohol solutions, and finally $100\%$ alcohol and embedded in epoxy resin. Ultrathin sections cut in a Leica ultramicrotome (Leica UC7, United States) were stained with lead citrate and uranyl acetate and observed using a HT7800 electron microscope (HITACHI, Japan). ## 2.10 Oil Red O staining Oil Red O staining was performed as described (Huang et al., 2021). During differentiation, 3T3-L1 preadipocytes were treated with 200 nM–1,000 nM celastrol constantly. After full differentiation, cells were stained with Oil red O dye (Sigma, United States) at room temperature for 15 min, followed by de-staining with $60\%$ isopropyl alcohol for 5 s. Oil red O staining was obtained with a light microscope, and the statistics was calculated by ImageJ software (National Institutes of Health, United States, RRID:SCR_003070). ## 2.11 Real-time quantitative PCR (RT-qPCR) The 3T3-L1 preadipocytes were treated with 2 and 4 μM celastrol for 16 h, then subjected to AG RNAex Pro Reagent (Accurate Biotechnology, China) for total RNA extraction and further reverse transcription. RT-PCR was performed using SYBR staining (Accurate Biotechnology, China) in a LightCycle480 II thermal cycler (Roche, Switzerland). Relative gene expression was normalized against Actin, with control group value set to 1. The primer sequences are Bax-forward primer: AGGATGCGTCCACCAAGAAGCT, -reverse primer: TCCGTGTCCACGTCAGCAATCA; Bcl2-forward primer: CCTGTGGATGACTGAGTACCTG, -reverse primer: AGCCAGGAGAAATCAAACAGAGG; Chop-forward primer: GGAGGTCCTGTCCTCAGATGAA, -reverse primer: GCTCCTCTGTCAGCCAAGCTAG; P62-forward primer: GCTCTTCGGAAGTCAGCAAACC, -reverse primer: GCAGTTTCCCGACTCCATCTGT; Lc3b-forward primer: GTCCTGGACAAGACCAAGTTCC, -reverse primer: CCATTCACCAGGAGGAAGAAGG; Becn1-forward primer: CAGCCTCTGAAACTGGACACGA, -reverse primer: CTCTCCTGAGTTAGCCTCTTCC; Hif1a-forward primer: CCTGCACTGAATCAAGAGGTTGC, -reverse primer: CCATCAGAAGGACTTGCTGGCT; Hif2a-forward primer: GGACAGCAAGACTTTCCTGAGC, -reverse primer: GGTAGAACTCATAGGCAGAGCG; Bnip3-forward primer: GCTCCAAGAGTTCTCACTGTGAC, -reverse primer: GTTTTTCTCGCCAAAGCTGTGGC; Vim-forward primer: CGGAAAGTGGAATCCTTGCAGG, -reverse primer: AGCAGTGAGGTCAGGCTTGGAA; Col3a-forward primer: GACCAAAAGGTGATGCTGGACAG, -reverse primer: CAAGACCTCGTGCTCCAGTTAG; Actin-forward primer: CATTGCTGACAGGATGCAGAAGG, -reverse primer: TGCTGGAAGGTGGACAGTGAGG. ## 2.12 Western blotting For whole cell protein extraction, preadipocytes and fat tissue were lysed in RIPA buffer (50 mM Tris-HCl, pH 7.4, 150 mM NaCl, $1\%$ NP-40, $0.1\%$ SDS) with protease inhibitors and phosphatase inhibitors. For cytoplasm protein extraction, cells were prepared in RSB buffer (10 mmol/L Tris (pH 7.4), 10 mmol/L NaCl, 3 mmol/L MgCl2, $0.5\%$ NP40) with protease inhibitors and phosphatase inhibitors. The bicinchoninic acid (BCA) (ComWin Biotech, China) was used to measured protein concentration. For RAB7 GTPase activity assay, 3T3-L1 preadipocytes were treated using Rab7 Pull-Down Activation Assay Kit (NewEast Biosciences, United States). After 1 and 2 μM celastrol treatment for 16 h, whole cell proteins from preadipocytes were harvested using lysis buffer from the kit, and a half extracts from the control group was incubated with GDP for 30 min at 30°C to serve as negative control. The extracts were further incubated with protein A/G Agarose beads conjugating anti-Rab7-GTP antibody for 1 h at 4°C. The beads were washed and resuspended with SDS-PAGE loading buffer. Equal amounts of protein were subjected to SDS-PAGE and transferred to a PVDF membrane, then the membrane was incubated in $5\%$ milk in Tris-buffered saline for 1 h at room temperature, followed by primary antibodies of Cytochrome C (Cell Signaling Technology, United States, Cat # 11940, RRID:AB_2637071, 1:1,000 dilution), cleaved-Caspase3 (Cell Signaling Technology, United States, Cat #9664, RRID:AB_2070042, 1:1,000 dilution), P62 (Sigma, United States, Cat #P0067, RRID:AB_1841064, 1:1,000 dilution), LC3 I/II (Sigma, United States, Cat #L7543, RRID:AB_796155, 1:1,000 dilution), LAMP1 (Cell Signaling Technology, United States, Cat #9091, RRID:AB_2687579, 1:1,000 dilution), RAB7 (Cell Signaling Technology, United States, Cat #9367, RRID:AB_1904103, 1:1,000 dilution), β-actin (Cell Signaling Technology, United States, Cat #4970, RRID:AB_2223172, 1:1,000 dilution), GAPDH (Cell Signaling Technology, United States, Cat #5174, RRID:AB_10622025, 1:1,000 dilution) and Flag (Cell Signaling Technology, United States, Cat #14793, RRID:AB_2572291, 1:1,000 dilution) overnight at 4°C, and a secondary antibody (1:5,000 dilution) conjugated with horseradish peroxidase (Cell Signaling Technology, United States, Cat #7074, RRID:AB_2099233) for 1 h at room temperature. Membranes were developed with chemiluminescent ECL reagents (Millipore, United States). The relative expression of target protein to the control was determined by ImageJ software (National Institutes of Health, United States, RRID:SCR_003070). ## 2.13 Virtual docking The crystal structure of the apo form of human VAMP7 (PDB: 2VX8), RAB5C (PDB: 1Z0D), RAB7 (PDB: 1VG1), VTI1B (PDB: 2V8D), VAMP8 (PDB: 3ZYM), SNP29 (PDB: 4WY4), PLEKHM1 (PDB: 5DPT) and SNARE-complex (PDB: 3RK2) were applied for molecular docking. The AutoDockTools-1.5.7 was used for virtual docking of abovementioned proteins and celastrol (Rogério et al., 2022). ## 2.14 Surface Plasmon Resonance (SPR) The SPR assays were performed to analyze the interactions between the compounds and VAMP7, RAB7 proteins (Sino Biological, China) by using a Biacore T100 machine with Sensor Chip CM5 (GE Healthcare, United States) at 25°C. Two proteins were immobilized onto CM5 chips, and sensorgrams were recorded by injecting various concentrations of compounds. The binding kinetics (Kd) was analyzed with the software BIA evaluation Version 4.1 (GE Healthcare, United States). ## 2.15 Statistical analysis All data were shown as mean ± SEM. All results shown were representative of at least three independent biological replicates of experiments. Data were analyzed with SPSS version 24.0 software (IBM Corp, United States, RRID:SCR_002865). One-way analysis of variance (ANOVA) was performed for the comparison of multiple groups. Bonferroni post-hoc testing was used following ANOVA for analyzing all pairwise comparisons between groups. $p \leq 0.05$ was considered a statistically significant difference. ## 3.1 Celastrol induced 3T3-L1 preadipocytes apoptosis via mitochondrial mediated pathway Induction of preadipocytes apoptosis represents a potential anti-obesity treatment strategy, and our previous study of resveratrol and other studies showed several phytochemicals with this effect (Hsu and Yen, 2006; Yang et al., 2007; Chen et al., 2012; Zhang and Huang, 2012; Lone and Yun, 2017; Wu et al., 2019). In consideration that celastrol was reported as one of the most robust anti-obesity phytochemicals (Liu et al., 2015), we compared the induced apoptosis effects of these compounds with celastrol. As shown in Figure 1A, a rather lower concentration of celastrol illustrated the strongest efficacy on preadipocytes apoptosis compared to other compounds after 24 h treatment. Furthermore, 3T3-L1 preadipocytes were subjected to 1, 2 and 4 μM celastrol for 24 h, and the pathomorphological alteration of apoptosis of preadipocytes as verified with light microscope (Figure 1B), condensed nuclei (Figure 1C) and fracture of genome DNA (Figure 1D) were observed. A concentration dependent effect of celastrol on preadipocytes apoptosis was also observed after 16 h treatment (Figure 1E). FIGURE 1: Celastrol induced 3T3-L1 preadipocytes apoptosis via mitochondrial mediated pathway (A), Flow cytometry of apoptosis assay of 3T3-L1 preadipocytes after 24 h treatment of celastrol (0, 0.5, 1 and 2 μM), curcumin (0, 10, 20 and 40 μM), honokiol (0, 10, 20 and 40 μM), quercetin (0, 12.5, 25 and 50 μM), resveratrol (0, 20, 40 and 80 μM) or xanthohumol (0, 5, 10 and 20 μM) (n = 7–10) (B–D), 3T3-L1 preadipocytes after 24 h treatment of 0, 1, 2 and 4 μM celastrol were subjected to light microscope imaging (B), Hoechst 33258 staining (C), Terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL) staining (D) (n = 4) (E), 3T3-L1 preadipocytes after 16 h treatment of 0, 1, 2 and 4 μM celastrol were subjected to flow cytometry analysis (n = 7) (F), 3T3-L1 preadipocytes after 24 h treatment of 1 μM celastrol were subjected to JC-10 dye staining (n = 4) (G), Western blotting of Cytochrome C (Cyt C) was conducted with cytoplasm extracts of preadipocytes after 1, 2 μM celastrol and 100 μM resveratrol treatment for 24 h (n = 3), resveratrol treatment was applied as a positive control (H), Western blotting of cleaved-Caspase 3 was developed in preadipocytes after 1, 2 and 4 μM celastrol treatment for 24 h (n = 3) (I–J), 3T3-L1 adipocytes were treated with 0, 1, 2 and 4 μM celastrol for 24 h, and subjected to flow cytometry analysis (I) (n = 6) and Western blotting of cleaved-Caspase3 (J) (n = 3). Error bars represent SEM; p < 0.05; p < 0.01; p < 0.001. Veh, vehicle; Cas 3, Caspase 3. To determine the mechanism underlying celastrol-induced apoptosis, we first observed enhanced green fluorescence after celastrol treatment with the JC-10 fluorescence probe, showing that mitochondria membrane potential was impaired after celastrol treatment (Figure 1F). Next, Western blotting illustrated a concentration dependent release of Cytochrome C in cytoplasm extracts (Figure 1G), and accumulation of cleaved-Caspase 3 in whole cell extracts after celastrol treatment (Figure 1H). These results indicated that celastrol activated the intrinsic apoptosis via mitochondrial mediated pathway in preadipocytes. Lastly, the effect of celastrol was also investigated in 3T3-L1 mature adipocytes. As shown in flow cytometry (Figure 1I) and Western blotting (Figure 1J), 24 h celastrol treatment exhibited similar apoptotic effects on mature adipocytes. ## 3.2 Celastrol induced 3T3-L1 preadipocytes apoptosis through inhibition of autophagy In consideration that autophagy (Gordy and He, 2012), hypoxia (Sendoel and Hengartner, 2014) and fibrogenesis (Mehal and Imaeda, 2010) pathways were reported intensively interplay with apoptosis pathway to maintain cell viability, we hypothesized that celastrol might induce apoptosis of preadipocytes through regulation of these pathways. We first applied Real-time qPCR to search the potential pathway. As shown in Figure 2A, after celastrol treatment, the pro-apoptotic genes: Bax and Chop were significantly upregulated and the anti-apoptotic gene Bcl2 was significantly downregulated. The autophagy-related genes: P62 and Lc3b showed significantly upregulation, while Becn1 showed a mere alteration. However, no significant changes were observed in the hypoxia-related genes: Hif1a, Hif2a and Bnip3. The fibrogenesis-related genes: Vim and Col3a showed downregulation. Therefore, we believed celastrol induced apoptosis of preadipocytes through regulation of autophagy. Furthermore, the inhibition of autophagic flux was also verified with accumulation of P62 and LC3 II after celastrol treatment, using Western blotting (Figure 2B). Autophagy involves three main steps: 1) the formation of double membrane-bound vesicles called autophagosomes, 2) the fusion of autophagosomes and lysosomes, and 3) the acidification of autophagolysosomes (Mauvezin et al., 2015). The abrogation of each step would lead to the halt of the whole autophagic flux. To further verify the exact autophagy step in which celastrol regulates, rapamycin, an activator of autophagy initiation and autophagosome formation, was firstly applied in addition to celastrol. If celastrol inhibits the formation of autophagosome, it is plausible that rapamycin can block the inhibitory effect of celastrol on autophagy. Conversely, we observed increased accumulation of P62 and LC3 II after this treatment, indicating that celastrol probably targets the downstream of autophagic flux (Figure 2C). Furthermore, chloroquine, an inhibitor only targeting step 2 (the fusion of autophagosomes and lysosomes) (Mauthe et al., 2018), and bafilomycin A1, an inhibitor targeting step 2 and step 3 (the acidification of autophagolysosomes) (Mauvezin et al., 2015), were applied in addition to celastrol. We observed that only celastrol + bafilomycin A1 group, but not celastrol + chloroquine group, showed increased accumulation of P62 and LC3II comparing to the celastrol group (Figure 2C). Therefore, we deduced that celastrol might have a similar effect with the chloroquine and only inhibit the fusion of autophagosomes and lysosomes. FIGURE 2: Celastrol induced 3T3-L1 preadipocytes apoptosis through inhibition of autophagy (A), Real-time qPCR of genes were studied in 3T3-L1 preadipocytes after 0, 2 and 4 μM celastrol treatment for 16 h (n = 6) (B), Western blotting of P62 and LC3 I/II were developed in 3T3-L1 preadipocytes after 0, 1 and 2 μM celastrol treatment for 24 h (n = 3) (C), Western blotting of P62 and LC3 I/II were developed in preadipocytes after 2 μM celastrol treatment for 24 h with or without 15 μM rapamycin, 40 μM chloroquine or 200 nM bafilomycin A1 (n = 3) (D), Autophagosome degradation was observed with RFP-GFP-LC3 adeno-associated virus, after 24 h treatment of 1 μM celastrol, 40 μM chloroquine or 15 μM rapamycin, respectively (n = 7). Red fluorescence represented normal autophagosome degradation, while yellow represented halt of degradation (E–F), 3T3-L1 preadipocytes were treated with 24 h of celastrol, 15 μM rapamycin, 40 μM chloroquine, 200 nM bafilomycin A1, celastrol + 15 μM rapamycin, celastrol + 40 μM chloroquine or celastrol + 200 nM bafilomycin A1, respectively, and subjected to flow cytometry analysis (E) (n = 7) and Western blotting of cleaved-Caspase3 (F) (n = 3). Protein expression was calculated relative to β-actin and depicted at the top of each blot. Error bars represent SEM; p < 0.05; p < 0.01; p < 0.001. Veh, vehicle; Cela, celastrol; 1C, 1 μM celastrol; 2C, 2 μM celastrol; BafA1, bafilomycin A1; CQ, chloroquine; Rapa, rapamycin; Cas 3, Caspase 3. To further verify celastrol’s effect on autophagy, RFP-GFP-LC3 adeno-associated virus was applied to monitor autophagosome degradation. The number of autophagic vacuoles and vesicles containing RFP-GFP-LC3 was markedly increased after celastrol treatment, indicating the impairment of autophagosome degradation. Similar phenomenon was observed after chloroquine treatment, while rapamycin treatment exhibited normal red fluorescence (Figure 2D). Last, the combination effects of celastrol and these three compounds on apoptosis were studied. As observed by flow cytometry (Figure 2E) and cleavage of Caspase3 (Figure 2F), celastrol + chloroquine group and celastrol + rapamycin group exhibited comparable apoptotic effect again celastrol group, whereas celastrol + bafilomycin A1 group showed higher apoptotic effect. We speculated that celastrol might share similar mechanism with chloroquine, targeting the inhibition of autophagosomes and lysosomes fusion, since their combination did not show superimposed effect. ## 3.3 Celastrol inhibited the fusion of autophagosome and lysosome To further validate our hypothesis that celastrol mainly inhibits the autophagosome and lysosome fusion, we first tested the lysosome acidification after intervention. As shown in Figure 3A, no decrease of red puncta was observed in celastrol and chloroquine group, indicating no effect on lysosome acidification. Bafilomycin A1, however, exhibited significant decrease of red puncta, indicating an abrogation of lysosome acidification. Furthermore, the colocalization of autophagosome marked with RFP-GFP-LC3 and lysosome marked with its membrane protein LAMP1 was studied. Decreased colocalization was observed in celastrol, chloroquine and bafilomycin A1 groups (Figure 3B), whereas LAMP1 protein was not altered after celastrol treatment (Figure 3C). The accumulation of autophagosomes after celastrol and chloroquine treatment was also directly observed via electron microscopy (Figure 3D). These data indicated the inhibition of autophagosome and lysosome fusion after celastrol treatment. Considering the vital role of autophagy in adipogenesis (Baerga et al., 2009; Zhang et al., 2009), we also observed a significant inhibition of differentiation of human primary visceral preadipocytes with low doses of celastrol (200 nM–800 nM) (Supplementary Figure S1). Moreover, the inhibition of autophagic flux upon celastrol treatment was also confirmed on 3T3-L1 mature adipocytes as determined by the Western blotting of P62 and LC3 II (Figure 3E). FIGURE 3: Celastrol inhibited the fusion of autophagosome and lysosome (A–B), 3T3-L1 preadipocytes were treated with 24 h of 1 μM celastrol, 40 μM chloroquine or 200 nM bafilomycin A1, and subjected to LysoTracker red staining (A) (n = 3) and immunofluorescence staining of LAMP1 and LC3 (B). The representative images were shown on the left, and the Pearson correlation coefficient (PCC) for the colocalization of RFP-LC3 and Alexa Fluor 647-LAMP1 were presented on the upper right (n = 3) (C), 3T3-L1 preadipocytes were treated with 1 and 2 μM celastrol for 24 h and subjected to Western blotting of LAMP1 (n = 3) (D), 3T3-L1 preadipocytes were treated with 1, 2 μM celastrol and 40 μM chloroquine for 12 h and subjected to electron microscopy (n = 5) (E), 3T3-L1 adipocytes were treated with 1 and 2 μM celastrol for 24 h and subjected to Western blotting of P62 and LC3 I/II (n = 3). Protein expression was calculated relative to β-actin or GAPDH and depicted at the top of each blot. Error bars represent SEM. Veh, vehicle; Cela, celastrol; CQ, chloroquine; BafA1, bafilomycin A1; AP, autophagosome. ## 3.4 Celastrol bond with VAMP7 and RAB7 to inhibit autophagy and subsequently induce apoptosis In light of the abovementioned results, we hypothesized that celastrol might directly bind with certain proteins during the fusion of autophagosome and lysosome, and subsequently inhibit the autophagic flux. To determine the direct binding protein of celastrol, the components mediating autophagosome and lysosome fusion were applied for virtual docking with celastrol (Supplementary Figure S2). Notably, VAMP7, RAB5C, RAB7 and VTI1B were the top four candidate proteins with the strongest binding potential with celastrol. Therefore, the genic manipulation studies of VAMP7, RAB5C, RAB7 and VTI1B were applied. Importantly, overexpression of VAMP7 or RAB7 significantly inhibited celastrol induced apoptosis in 3T3-L1 preadipocytes (Figures 4A,B, Supplementary Figure S2), whereas no effect was observed with overexpression of VTI1B (Figures 4A, C, Supplementary Figure S2) or knockdown of RAB5C (Supplementary Figure S2) in the combination with celastrol. Next, these manipulations on celastrol induced autophagic flux inhibition was also investigated. As shown in Figure 4D, overexpression of VAMP7 or RAB7 significantly reversed the accumulation of P62 and LC3II after celastrol treatment in 3T3-L1 preadipocytes, while overexpression of VTI1B showed no effect. Likewise, knockdown of RAB5C exhibited no effect on autophagy after celastrol treatment (Supplementary Figure S2). Taken together, these data suggested that VAMP7 and RAB7, not VTI1B or RAB5C, intervened the effect of celastrol on autophagy and apoptosis, which might be the direct target of celastrol. FIGURE 4: Celastrol bond with Vamp7 and Rab7 to inhibit autophagy and subsequently induce apoptosis (A–D), 3T3-L1 preadipocytes were transfected with blank, Vamp7, Rab7 and Vti1b plasmids for 48 h, and then treated with 0, 1 and 2 μM celastrol for 24 h and subjected to phase contrast light microscope imaging (A) (n = 4), flow cytometry analysis (B, C) (n = 4), and Western blotting of P62 and LC3 I/II (D) (n = 4) (E–H), Surface Plasmon Resonance studies of VAMP7 with celastrol (E) and hesperidin (F), RAB7 with celastrol (G) and hesperidin (H) were shown. (I) RAB7-GTP pull-down assay was performed in preadipocytes after 1 and 2 μM celastrol treatment for 16 h and shown by Western blotting of RAB7-GTP and total RAB7 (n = 3). Protein expression was calculated relative to β-actin and depicted at the top of each blot. Error bars represent SEM; ns, no significance; *p < 0.001. Veh, vehicle; CON, control; Cela, C, celastrol; 1C, 1 μM celastrol; 2C, 2 μM celastrol; NC, negative control. To further verify the direct binding target, we purified VAMP7 and RAB7 (Supplementary Figure S2) and performed Surface Plasmon Resonance study. The results showed a direct binding of celastrol with VAMP7 (Kd = 3.24 μM, Figure 4E), as well as celastrol with RAB7 (Kd = 7.58 μM, Figure 4G). Meanwhile, hesperidin, another phytochemical, showed no binding with VAMP7 (Figure 4F) or RAB7 (Figure 4H). We further studied RAB7 GTPase activity with a RAB7-GTP pull-down assay. As the initial switch of these interactions and the following membrane fusion, RAB7 GTPase activity was significantly reduced after celastrol treatment, as indicated by the decrease of RAB7-GTP against total RAB7 (Figure 4I). These data confirmed that VAMP7 and RAB7 were the direct binding targets of celastrol, and celastrol inhibited the GTPase activity of RAB7 via direct binding. ## 3.5 Celastrol induced apoptosis and inhibited autophagy in murine epididymal adipose tissue and human primary visceral preadipocytes. To validate celastrol’s effect in murine fat tissue, we further performed in vivo study using diet-induced obese mice treated with celastrol. Given that Liu et al. firstly reported up to $79\%$ food intake reduction after celastrol administration in mice (Liu et al., 2015), we applied the pair-feeding group given equal amount diet (about $21\%$) per day, as a control to exclude the potential side-effects due to food reduction. Diet-induced obese mice were randomly divided into five groups including 1) chow diet + vehicle (CD + Veh), 2) high fat diet + vehicle (HFD + Veh), 3) high fat diet + celastrol (HFD + Cela), 4) pair-feeding + vehicle (PF + Veh) and 5) pair-feeding + celastrol (PF + Cela). After 2 weeks intervention of celastrol, significant decrease of body weight was observed in HFD + Cela group versus HFD + Veh group, whereas pair feeding groups showed similar decrease of body weight comparing with HFD + Cela group. No significant body weight change was observed in PF + Veh group and PF + Cela group (Figure 5A). We further harvested murine epididymal adipose tissue from these five groups for apoptosis and autophagy study. The TUNEL staining of epididymal adipose tissue showed a slight increase of apoptosis in celastrol treatment groups comparing with the corresponding vehicle groups (Figure 5B). Using Western blotting of P62 and LC3 I/II, we found that significant accumulation of both P62 and LC3 II in celastrol treatment groups comparing with the corresponding vehicle groups (Figure 5C). These in vivo findings were in consistence with our in vitro results, showing direct inhibitory effect on autophagy and pro-apoptosis effect of celastrol on visceral fat tissue, apart from its anorexia effect. FIGURE 5: Celastrol induced apoptosis and inhibited autophagy in murine epididymal adipose tissue and human primary visceral preadipocytes (A–C), 4-week-old male C57BL/6 mice were fed with 60% high fat diet for 24 weeks to induce diet-induced obesity. Mice were randomly divided into five groups, 1) chow diet + vehicle, 2) high fat diet + vehicle, 3) high fat diet + celastrol, 4) pair-feeding + vehicle and 5) pair-feeding + celastrol (n = 4). For 2 weeks intervention of celastrol, mice in pair-feeding group only received equal amount diet comparing with celastrol treatment group to mimic the anorexia effect of celastrol. Body weight change were shown in (A). The murine epididymal adipose tissue was further harvested and subjected to TUNEL staining (B), and Western blotting of P62 and LC3 I/II (C), the representative images were shown on the left, quantification of P62 and LC3 I/II were shown on the right) (D–F), Human primary visceral preadipocytes were treated with 0, 1 and 2 μM celastrol and subjected to light microscope imaging and Hoechst 33258 staining (D), flow cytometry analysis (E) (n = 4) and Western blotting of P62 and LC3 I/II (F) (n = 3) (G–H), Human primary visceral adipocytes were treated with 0, 1 and 2 μM celastrol for 24 h and subjected to flow cytometry analysis (G) (n = 6) and Western blotting of cleaved-Caspase3 (H) (n = 3) (I–J), Human hepatocytes HL-7702 were treated with 0, 1, 2 and 4 μM celastrol for 24 h and subjected to flow cytometry analysis (I) (n = 6) and Western blotting of P62, LC3 I/II and cleaved-Caspase3 (J) (n = 3). Protein expression was calculated relative to β-actin or GAPDH and depicted at the top of each blot. Error bars represent SEM; p < 0.05; **p < 0.001. Veh, vehicle; Cela, celastrol; Cas 3, Caspase 3; CD + Veh, chow diet + vehicle; HFD + Veh, high fat diet + vehicle; HFD + Cela, high fat diet + celastrol; PF + Veh, pair-feeding + vehicle; PF + Cela, pair-feeding + celastrol. The induced apoptosis of celastrol might present a potential treatment strategy for obesity, however, efficacy of this compound in human remained unknown. Therefore, a direct induction of apoptosis of celastrol on human primary preadipocytes was observed as determined by morphological alteration of cells (Figure 5D). Furthermore, flow cytometry exhibited a dose-dependent apoptotic effect of celastrol (Figure 5E). Additionally, the accumulation of P62 and LC3 II was observed after celastrol treatment, confirming an inhibition of autophagic flux (Figure 5F). The induced apoptosis of celastrol was observed in human primary visceral mature adipocytes after 24 h treatment, as indicated by the flow cytometry (Figure 5G) and Western blotting of cleaved-Caspase3 (Figure 5H). Finally, to verify its potential safety in human, we applied the same dosage of celastrol on human hepatocytes HL-7702 for 24 h. No induction of apoptosis (Figure 5I) and inhibition of autophagy were observed (Figure 5J), suggesting a potential selectivity of celastrol on regulation of apoptosis and autophagy in human primary cells. ## 4 Discussion Celastrol was reported as one of the most robust anti-obesity phytochemicals, yet its direct target remained unclear. In this study, we identified VAMP7 and RAB7 as the direct binding targets of celastrol, which mediate the regulatory effects of celastrol on cellular apoptosis and autophagy in preadipocytes. These findings clarified the direct effect of celastrol on preadipocytes and its underlying mechanism, which would broaden our understanding of the anti-obesity effect of celastrol. In our study, celastrol demonstrated an effect of inhibiting the fusion of autophagosomes and lysosomes in preadipocytes, and we further found that VAMP7 and RAB7 were the direct targets of celastrol mediating its regulation on autophagy. During autophagosomes and lysosomes fusion, the soluble N-ethylmaleimide-sensitive fusion protein-attachment protein receptor (SNARE) complexes and Rab-GTPases participated in the trafficking between autophagosome and lysosomes (Dingjan et al., 2018; Langemeyer et al., 2018). VAMP7 was reported as one of the key components of the SNARE complex. Previous studies illustrated that overexpression of VAMP7 could increase autophagolysosomes (Fader et al., 2009). Meanwhile, RAB7 is a key member of Rab-GTPases and is required for autophagic pathway. For the initiation of autophagosome and lysosome fusion and degradation, RAB5C is the main endosomal GTPase, which is replaced by RAB7 during maturation of endosomes and lysosomes (Langemeyer et al., 2018). Studies in yeast showed that the RAB7-like Ypt7p mediated the anchoring of HOPS to the membrane (Hickey et al., 2009), which subsequently recruited and retained the VAMP7-like Vam7p (Ungermann et al., 2000). In light of these studies, our study showed overexpression of VAMP7 or RAB7 could reverse the inhibitory effect of celastrol on autophagy, which subsequently block the apoptotic effect. The Surface Plasmon Resonance study further confirmed the direct binding of celastrol with VAMP7 and RAB7. Further functional study illustrated the inhibition of RAB7 GTPase activity after celastrol treatment. Taken together, we proposed that celastrol directly bond with VAMP7 and RAB7 to inhibit autophagy. Despite that autophagy was first observed under starvation and nutrients depleted status, recent studies illustrated an important role of autophagy in regulation of obesity. The crosstalk between autophagy and apoptosis is vital for cell hemostasis (Gordy and He, 2012). Cells utilize autophagy for recycling essential metabolites, such as lipids and amino acids for fueling the bioenergetic machinery (Doherty and Baehrecke, 2018). Therefore, when autophagy was blocked, apoptosis was induced with mitochondrial outer membrane permeabilization and subsequent a serial of caspases activation (Boya et al., 2005; González-Polo et al., 2005). Moreover, the inhibition of the fusion of autophagosomes and lysosomes could result in accumulation of autophagosomes, which would further sequestrate the essential nutrients required for metabolism. The data in our study revealed that celastrol inhibited the fusion of autophagosomes and lysosomes, and subsequently induced apoptosis via mitochondrial mediated pathway in preadipocytes. Moreover, the autophagy was documented closely connected with preadipocyte differentiation process. The inhibition of autophagy, by knockout of autophagy-related gene 5 (atg5) and atg7, would inhibit the adipogenesis both in vitro and in vivo (Baerga et al., 2009; Zhang et al., 2009). In line with these findings, we also observed a significant inhibition of preadipocyte differentiation and decrease of lipid accumulation after low concentration celastrol treatment, potentially due to the inhibition of autophagy. In consistence with our in vitro study, we observed comparable pro-apoptotic effect of celastrol in both murine epididymal adipose tissue and human mature adipocytes. Apart from our study, we must point out that excessive induction of preadipocytes apoptosis might abrogate the homeostasis of adipocyte metabolism, the appropriate dosage of celastrol in clinical translation should be further studied. In conclusion, celastrol inhibits the fusion of autophagosomes and lysosomes via a direct binding with VAMP7 and RAB7, leading to accumulation of autophagosomes. Abrogation of autophagy by celastrol further induced apoptosis in preadipocytes and adipocytes, thus reducing excessively fat mass accumulation. These effects suggest a potential strategy of using celastrol for treating obesity. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by the Institutional Review Board of The First Affiliated Hospital of Sun Yat-sen University. The patients/participants provided their written informed consent to participate in this study. ## Author contributions Methodology, Investigation, Visualization, Formal analysis, Writing—Original Draft Preparation, CL; Methodology, Visualization, Formal analysis, Writing—Original Draft Preparation, NL; Methodology, Investigation, Visualization, Formal analysis, MP; Investigation, Visualization, KH, DF, ZZ, XH, and YL; Conceptualization, Writing—Review and Editing, Supervision, SC and ZL. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Amelioration of alcohol-induced acute liver injury in C57BL/6 mice by a mixture of TCM phytochemicals and probiotics with antioxidative and anti-inflammatory effects authors: - Zhiguo Li - Xuexun Fang - Xin Hu - Congcong Li - Youzhong Wan - Dahai Yu journal: Frontiers in Nutrition year: 2023 pmcid: PMC10027757 doi: 10.3389/fnut.2023.1144589 license: CC BY 4.0 --- # Amelioration of alcohol-induced acute liver injury in C57BL/6 mice by a mixture of TCM phytochemicals and probiotics with antioxidative and anti-inflammatory effects ## Abstract ### Background There are many causes of acute liver injury (ALI), such as alcohol, drugs, infection, and toxic materials, which have caused major health problems around the world. Among these causes, alcohol consumption induced liver injury is a common alcoholic liver disease, which can further lead to liver failure even liver cancer. A number of traditional Chinese medicine (TCM) and TCM derived compounds have been used in treating the liver-associated diseases and combination use of probiotics with TCM phytochemicals has attracted interests for enhanced biological effects. ### Methods This study investigated the hepatoprotective effect of TCM-probiotics complex (TCMPC) and its underlying mechanism for the treatment of ALI in mice. The TCMPC is composed of TCM phytochemicals puerarin, curcumin, ginsenosides, and 5 lactobacteria strains. We first established a mouse model of alcohol-induced ALI, then the therapeutic effects of TCMPC on alcohol-induced ALI were monitored. A series of measurements have been performed on antioxidation, anti-inflammation, and lipid metabolism regulation. ### Results The results showed that TCMPC can reduce the level of liver injury biomarkers and regulate oxidative stress. Histopathological results indicated that TCMPC could ameliorate ALI in mice. In addition, it can also significantly reduce the production of inflammatory cytokines caused by ALI. ### Conclusion Our research has proved the therapeutic effect of TCMPC on alcohol-induced ALI. The potential mechanism of hepatoprotective effects of TCMPC may be related to its antioxidative and anti-inflammatory effects. Our research might provide a new way for liver disease treatment. ## Introduction Alcohol abuse has been recognized as a major cause of liver injury, including acute and chronic liver injury [1]. In the region with the highest alcohol consumption (Europe), there are about 287,000 premature deaths related to liver disease each year, of which about $40\%$ are caused by alcohol [2]. Alcohol-induced acute liver injury (ALI) is a common alcoholic liver disease, which refers to sudden liver damage caused by heavy drinking, and is one of the common causes of liver failure and even liver cancer [3]. Acetaldehyde is the major toxic metabolite of ethanol. One of the targets of acetaldehyde is mitochondria, and mitochondrial damage induces over accumulation of reactive oxygen species (ROS) and the reduction of antioxidant activities [4]. Under physiological conditions, ROS are efficiently eliminated by antioxidant defense systems including superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) [5]. However, the hyper-levels of ROS, acting as a second messenger, enhance the transportation and activation of nuclear factor kappa B (NF-κB), a reduction/oxidation (redox)-sensitive factor, from the cytoplasm into the nucleus [6]. The activation of NF-κB inflammatory pathway accelerates the release of inflammatory cytokines including tumor necrosis factor-α (TNF-α), interleukin-1-β (IL-1β), and interleukin-6 (IL-6), exacerbating hepatic inflammation and systemic injury [7, 8]. So the inhibition of NF-κB activation and cytokine synthesis is a potential mechanism for the treatment of alcohol-induced liver injury. Increasing studies have demonstrated a two-way communication between the gut and liver [9]. Alcohol can cause intestinal flora change, intestinal epithelial cell barrier dysfunction, intestinal bacterial metabolite translocation, and endotoxemia, suggesting that intestinal flora plays an important role in alcohol-induced liver damage [10]. Inhibiting oxidative stress, preventing inflammation, and restoring intestinal homeostasis are promising therapeutic approaches to treat ALI. Common ALI treatments include antioxidant drugs, anti-fibrosis drugs, anti-inflammatory drugs, glucocorticoids, and cell membrane protective agents [11]. Some of these drugs may also have adverse effects on patients such as diarrhea, allergy, gastric irritation, and kidney damage. In order to avoid the metabolic burden of organs, these drugs are not suitable for long-term use, and long-term use may produce drug resistance, which is not conducive to or even aggravate the condition of alcoholic liver disease [12]. At present, some emerging ALI treatment strategies include microecological therapy based on intestinal-liver axis, hepatocyte regeneration technology, and targeted pathogenic molecular therapy [13]. Increasing studies have shown that the intestinal-liver axis is related to the occurrence and development of alcoholic liver disease. The intestinal-liver axis refers to the bidirectional relationship between the gut with its microbiota and the liver, which is generally established by the portal vein and regulated by diet, genetics, and environmental factors [14]. Bile acids produced in the liver regulate microbiota composition and gut barrier function, and gut products regulate bile acid synthesis and glucose and lipid metabolism in the liver [15]. The intestinal barrier can limit the systemic spread of microorganisms and toxins, while allowing nutrients to enter the portal circulation and reach the liver. Alcohol has been shown to alter gut microbiome composition and impair gut integrity and barrier function in addition to its direct toxicity to hepatocytes. Alcohol can cause changes in the expression of tight junction proteins in the intestine, such as zonula occludens-1 (ZO-1), thereby increasing intestinal permeability [16]. Disruption of the gut microbiota and increased intestinal permeability can lead to the influx of endotoxins such as lipopolysaccharide (LPS) into the portal circulation, which activate toll-like receptor 4 (TLR4) to promote inflammation in alcoholic liver disease [17]. Traditional Chinese medicine (TCM) has been used for the treatment of diseases, and it is still regarded as an important source of therapeutic drugs [18]. Many studies have now demonstrated the therapeutic effects of TCM in cancer, cardiovascular disease, inflammation, and liver disease (19–22). Some low-toxic Chinese herbs, such as Salvia miltiorrhiza, licorice, Radix Puerariae, and Panax ginseng C. A. Meyer, contain a variety of active ingredients such as terpenoids, flavonoids, alkaloids, phenols, polysaccharides, and other active ingredients, which can play a strong role in anti-inflammation and antioxidation, improve the immune function of the body, and regulate intestinal flora (23–26). Puerarin is a bioactive flavone isolated from Puerariae Lobatae Radix (gegen), an herbal TCM used for many pathological conditions; Curcumin is the main ingredient of Curcuma longa L., commonly known as turmeric (jianghuang), a plant of high medicinal values and widely used especially in Southeast Asia; Ginsenosides are saponins, which are the major pharmacologically active components of Panax Ginseng (renshen). Total ginsenosides are isolated and purified from ginseng roots, and are a mixture of ginsenosides such as Rg1, Re, Rb1, Rc, and Rd. These TCM phytochemicals have shown many biological effects such as antioxidant, anti-inflammatory, antidiabetic, and anticancer. Puerarin is widely used as a dietary supplement and has shown good effects in the treatment of obesity, diabetes, cardiovascular, cerebrovascular diseases, and inflammatory diseases [27]. Curcumin has a variety of biopharmacological effects, including antioxidation, anticancer, liver protection, immune regulation, and hypoglycemia in vitro or in vivo [28]. The pharmacological studies of ginsenosides have focused on their anticancer, antioxidant, and anti-inflammatory activities [29]. Zhao et al. found that Rg1 has a positive role in the treatment of CCI4-induced ALI mouse model, which may be related to NF-κB /NLRP3 inflammasome signaling pathway [30]. In traditional Chinese medicinal practice, a number of herbs with different properties are mixed for optimal therapeutic effects. Based on the intestinal-liver relationship, modulation of the gut microbiota, including probiotics, fecal microbiota transplantation, and antibiotics, has been investigated in the treatment of alcoholic liver disease with varying degrees of success. Probiotics are biologically active bacteria that also have a variety of beneficial effects, such as regulating immune responses, maintaining intestinal barrier homeostasis, promoting nutrient absorption, and improving intestinal flora imbalance [31, 32]. Previous studies have demonstrated the therapeutic effects of probiotics on cancer, hypertension, diabetes, and alcoholic/non-alcoholic fatty liver disease (33–35). Li et al. showed that a mixture of Lactobacillus plantarum KLDS1.0344 and Lactobacillus acidophilus KLDS1.0901 can improve intestinal epithelial cell permeability and reduce serum LPS levels, thereby inhibiting alcohol-induced liver inflammation [12]. Using a mouse model, Christoph et al. found that A. muciniphila can promote gut barrier integrity and improve experimental alcoholic liver disease [36]. TCM-probiotics complex (TCMPC) is composed of TCM phytochemicals and a variety of probiotics. Here, we used puerarin, curcumin, total ginsenosides, and 5 lactobacteria strains to formulate TCMPC to treat ALI in a mouse model. The TCMPC preparation has the advantage of both probiotics and TCM compounds. As TCM compounds may benefit the growth of probiotics, and probiotics can also promote the absorption and utilization of TCM compounds [37]. This study focused on the liver damaging response and hepatic pathology alteration caused by alcohol-induced ALI. Further, anti-inflammatory and antioxidant effects of TCMPC preparation on the treatment of alcohol-induced ALI were monitored. ## Preparation of TCMPC TCMPC is the mixture of probiotics and TCM phytochemicals. The ingredients of TCMPC are shown in Table 1. Puerarin (purity ≥$98\%$ by HPLC), curcumin (purity ≥$98\%$ by HPLC), and total ginsenosides (purity ≥$80\%$ by UV spectrometry) were purchased from Shanghai Yuanye Biological Technology Co., Ltd (Shanghai, China). The above drug quality inspection data are shown in Supplementary Figures S1–S3. Lactobacillus animalis-BA12, Lactobacillus bulgaricus-LB42, Lactobacillus paracasei-LC86, Lactobacillus casei-LC89, and Lactobacillus plantarum-LP90 were all purchased from Wecare Probiotics Co., Ltd (Suzhou, China). Table 1 \| TCM compounds \| Active ingredient /Formula \| Chemical structures \| Solubility (In water) \| TCM content (w/w) \| Probiotics strain \| Probiotics content (CFU/g) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Puerarin \| C21H20O9 \| \| Soluble (Heating) \| 40 \| Lactobacillus animalis-BA12 \| 2 × 1012 \| \| Curcumin \| C21H20O6 \| \| Insoluble \| 20 \| Lactobacillus bulgaricus-LB42 \| 2 × 1012 \| \| Total ginsenosides \| Rg1 (C42H72O14) \| \| Soluble \| 1 \| Lactobacillus paracasei-LC86 \| 2 × 1012 \| \| Total ginsenosides \| Re (C48H82O18) \| \| Soluble \| 1 \| Lactobacillus casei-LC89 \| 2 × 1012 \| \| Total ginsenosides \| Rb1 (C54H92O23) \| \| Soluble \| 1 \| Lactobacillus plantarum-LP90 \| 2 × 1012 \| \| Total ginsenosides \| … \| … \| Soluble \| 1 \| \| \| ## Animal models and treatment The methodology for establishing the mice model with alcohol-induced ALI was modified according to previous studies [38]. The mice were randomly divided into six groups ($$n = 10$$/group): control group, model group, positive control group, and TCMPC treatment group that conclude three group: high dose group (800 mg/kg), medium dose group (400 mg/kg), and low dose group (200 mg/kg). The mice in the control group were treated by intragastric administration of normal saline at 0.1 ml/10 g twice a day at 9:00 am and 4:00 pm for 14 days. The mice in the other groups were gavaged with the white spirit (56°, Beijing Shunxin Agricultural Co. Ltd., China) at 9:00 am once every day and the dosage of it is 13 g/kg. And after 7 days, every day at 4:00 pm, the mice in the model group were gavaged with normal saline, the mice in the positive control group were intragastric administrated with silymarin (Sil) (Tianjin Tasly Sants Pharmaceutical Co. Ltd., China), a clinical used liver protectant, which can reduce oxidative stress in patients with alcoholic liver disease and prevent the lipid peroxidation. The mice in other treatment group were given TCMPC at the dose of 200 mg/kg, 400 mg/kg and 800 mg/kg, respectively. Before administration, TCMPC was dissolved in $1.5\%$ (w/v) sodium carboxymethyl cellulose solution. The above specific operation flow is shown in Figure 1A. The body weight of mice was monitored every day for 14 days. Following the last administration, all mice were fasted overnight before taking blood from their eyeballs, and all the mice were euthanized by CO2. After dissection, the various organs of the mice were rapidly removed and frozen at −80°C for further analysis. At the same time, part of the liver tissue was collected and put into formalin buffer. Animal experiment and grouping. Figure 1: The hepatoprotective effects by TCMPC in mice with alcohol-induced ALI. Schematic diagram of the administration of alcohol-induced ALI mice in the control group, model group, and treatment groups after 1 week of adaptive feeding (A), The effects of TCMPC on AST levels of serum, liver, and spleen, respectively, in alcohol-treated mice (B), The effects of TCMPC on ALT levels of serum, liver, and spleen, respectively (C), The effects of TCMPC on ALDH levels of serum, liver, and spleen, respectively (D), and The effects of TCMPC on ADH levels of serum, liver, and spleen, respectively (E). Data are expressed as mean ± SD, n = 10. #p < 0.05, ##p < 0.01, and ###p < 0.001, versus control group. p < 0.05, p < 0.01, and p < 0.001, versus model group. AST, aspartate aminotransferase; ALT, alanine aminotransferase; ALDH, aldehyde dehydrogenase; ADH, antidiuretic hormone. ## Biochemical indicators detection The mouse blood was collected and allowed to coagulate naturally at room temperature for 20–30 min, and then centrifuged at 4°C for 20 min (3,000 rpm) to obtain mouse serum. Take an appropriate amount of organ tissue and put it in normal saline, grind it on ice, and then centrifuge it at 4°C for 10 min (3,500 rpm) to obtain tissue fluid. The levels of aspartate aminotransferase (AST; CK-E90386M), alanine aminotransferase (ALT; CK-E90314M), antidiuretic hormone (ADH; CK-E92648M), aldehyde dehydrogenase (ALDH; CK-E92649M), nitric oxide (NO; CK-E20293M), reactive oxygen species (ROS; CK-E91516M), superoxide dismutase (SOD; CK-E20348M), catalase (CAT; CK-E92636M), glutathione peroxidase (GSH-Px; CK-E92669M), and malondialdehyde in (MDA; CK-E20347M), high-density lipoprotein (HDL; CK-E91912M), triglyceride (TG; CK-E91830M), and total cholesterol (TC; CK-E91839M) were detected using enzyme-linked immunosorbent assay (ELISA) kits purchased from the Shanghai Yuanye Biological Technology Co., Ltd (Shanghai, China) according to the operating instructions. ## Histopathological analysis Part of the liver was excised and fixed in $10\%$ (v/v) neutral formalin buffer. Afterward, the fixed tissues were dehydrated with gradient ethanol ($70\%$, $80\%$, $90\%$, $95\%$, and $100\%$), and then samples were transparent twice in xylene and embedded in paraffin. The paraffin samples were sliced into 5 μm thickness, and then stained with hematoxylin and eosin (H & E). After dehydration, the pathological sections were observed under light microscope (200×, Olympus, Japan) and photographed. ## Statistical analysis All statistical analyzes were performed using SPSS 23.0 software (IBM Corporation, Armonk, NY, United States). Data were presented as means ± standard error of the mean (S.E.M.). Differences were tested by one-way analysis of variance (ANOVA). p values of <0.05 were considered statistically significant. ## The TCMPC exhibited hepatoprotective effects in mice with alcohol-induced ALI AST and ALT are two hepatic-specific enzymes that reflect the degree of acute liver damage and they are frequently elevated after excessive alcohol intake [39]. When liver cells are damaged, cell membrane permeability increases and AST and ALT are released into the blood, increasing serum transaminase content, which is the essential enzyme in metabolic processes [40]. In serum, the activities of AST and ALT of mice in the control group were 27.93 U/mL and 10.41 U/ml, those in the model group were 33.18 U/mL and 15.37 U/mL, and those in the TCMPC high dose group were 27.02 U/mL and 10.39 U/mL. Compared with the control group, the activities of AST and ALT in the model group was significantly higher, while TCMPC treatment significantly decreased the activities of AST and ALT ($p \leq 0.05$; Figures 1B,C). Similar results were also observed in liver and spleen tissue, where the activities of AST, ALT were increased by alcohol treatment, whereas TCMPC treatment prevented these increases ($p \leq 0.05$; Figures 1B,C). Overall, TCMPC significantly reduced the activities of AST and ALT in all cases at different dosages (200, 400, and 800 mg/kg). Excessive alcohol consumption can result in over accumulation of acetaldehyde in the liver, which promotes the formation of protein adducts through reactions with various macromolecules in the body, leading to functional impairment of key proteins. ADH and ALDH are two key enzymes responsible for ethanol/acetaldehyde metabolism, and they are involved in the susceptibility to alcoholism and alcohol-related liver damage and diseases [41]. Significant decrease ($p \leq 0.05$; Figures 1D,E) of the levels of ALDH and ADH was observed in model group compared with that of control group. The levels of ALDH and ADH were 4.91 ng/mg and 2.7 ng/mg in liver of mice in the control group and 3.32 ng/mg and 1.83 ng/mg in the model group. Comparatively, TCMPC treatments significantly ameliorated the changes in the levels of ALDH and ADH in alcohol-injured mice (Figures 1D,E) to 4.09 ng/mg and 2.0 ng/mg in TCMPC high dose group. ## TCMPC enhanced antioxidant capacity in alcohol-induced ALI mice Oxidative stress is one of the most important processes in the pathogenesis of ALI. ROS is a natural by-product of normal metabolism of oxygen, but during environmental stress, the level of ROS increases sharply, which may cause serious damage to cell structure known as oxidative stress [42]. The interaction between NO and ROS will form reactive nitrogen species (RNS), and the excessive accumulation of RNS will also cause cell and tissue damage [43]. MDA is a product of lipid peroxidation, and its overexpression indicates oxidative stress in the body [44]. The ROS, MDA, and NO levels of serum, liver, and spleen were significantly increased ($p \leq 0.05$; Figures 2A–C) in model group compared to the control group. The levels of ROS, MDA, and NO in serum of mice in the control group were 66.52 U/mL, 3.85 nmol/mL, and 6.54 μmol/mL; 79.93 U/mL, 5.17 nmol/mL, and 8.24 μmol/mL in the model group; 75.23 U/mL, 4.13 nmol/mL, and 6.49 μmol/mL in the TCMPC high dose group, respectively. In general, administration of TCMPC reduced the levels of these indicators and showed a very significant difference ($p \leq 0.05$). Figure 2: Antioxidative effects of TCMPC on alcohol-induced ALI mice. The levels of ROS (A), MDA (B), NO (C), SOD (D), GSH-Px (E), and CAT (F) in serum, liver, and spleen were detected by ELISA kit. Data are expressed as mean ± SD, n = 10. #p < 0.05, ##p < 0.01, and ###p < 0.001, versus control group. p < 0.05, p < 0.01, and p < 0.001, versus model group. Enzymatic antioxidant system is essential for cellular response in order to deal with oxidative stress under physiological condition. Antioxidant enzyme such as CAT, SOD, and GSH-Px are affected and used as indexes to evaluate the level of oxidative stress, as SOD can convert superoxide into H2O2, and CAT can convert H2O2 into H2O [45, 46]. Alcohol significantly destroys the antioxidant defense enzyme activity in the body. The activities of SOD, GSH-Px, and CAT in serum of mice in the control group were 38.93 U/mL, 65.19 U/mL, and 9.59 U/mL; 29.79 U/mL, 47.33 U/mL, and 7.94 U/mL in the model group; 37.09 U/mL, 62.35 U/mL, and 8.26 U/mL in the TCMPC high dose group. Similar results were also observed in liver and spleen (Figures 2D–F). Our results showed that the enzyme activities of SOD, GSH-Px, and CAT in the model group were significantly decreased compared with the control group, and administration of TCMPC prevented the reduction in the SOD, GSH-Px, and CAT levels in the serum, liver, and spleen of mice with alcohol-induced ALI. And TCMPC displayed even better antioxidative effects than Sil against alcohol-induced ALI in mice in most cases. ## Regulation of lipid metabolism and inflammatory cytokines in mice with alcohol-induced ALI Lipid metabolism, especially the levels of TG, TC, and HDL, can serve as a metric indicating the extent of liver injury [47]. Alcohol elevated TG and TC levels and lowered HDL levels in the liver of the mice ($p \leq 0.05$; Figures 3A–C). Compared with model group mice, TCMPC reduced the levels of TG and TC by $44.8\%$ ($p \leq 0.01$; Figure 3A) and $53.1\%$ ($p \leq 0.05$; Figure 3B), and enhanced the levels of HDL by $37.9\%$ ($p \leq 0.001$; Figure 3C). The regulation efficiency of hepatic levels of TG, TC, and HDL by TCMPC were comparable with those of Sil. Figure 3: Effect of TCMPC on TG (A), TC (B), and HDL (C) levels in mice with alcohol-induced ALI. Data are expressed as mean ± SD, n = 10. #p < 0.05, ##p < 0.01, and ###p < 0.001, versus control group. p < 0.05, p < 0.01, and p < 0.001, versus model group. Alcohol also causes the accumulation of inflammatory factors in the liver of mice, leading to the occurrence of hepatitis. For example, NF-κB is a key inflammatory response mediator and can regulates multiple aspects of innate and adaptive immune function [48]. The spleen is an important immune organ, and changes in the levels of inflammatory cytokines in the spleen can effectively reflect the inflammatory state of the body [49]. NF-κB levels in control and model group were 424.31 pg./mg and 564.93 pg./mg, respectively; TNF-α were 299.46 pg./mg and 465.7 pg./mg; IFN-α were 15.62 pg./mg and 28.85 pg./mg; IFN-β were 155.98 pg./mg and 226.09 pg./mg; IFN-γ were 97.56 pg./mg and 122.66 pg./mg. The levels of NF-κB, TNF-α, IFN-α, IFN-β, and IFN-γ in high dose group mice were 410.82 pg./mg, 402.06 pg./mg, 18.47 pg./mg, 153.94 pg./mg, and 106.91 pg./mg, respectively. Our results showed that the levels of NF-κB, TNF-α, IFN-α, IFN-β, and IFN-γ in spleen were significantly increased ($p \leq 0.05$; Figure 4) in the model group compared to those of the control group. And TCMPC treatment reduced the levels of these inflammatory factors, showing an anti-inflammatory effect (Figure 4). Figure 4: The effects of TCMPC on inflammatory cytokines levels in alcohol-induced ALI mice. The levels of NF-κB (A), TNF-α (B), IFN-γ (C), IFN-α (D), and IFN-β (E) in spleen were detected by ELISA kit. Data are expressed as mean ± SD, n = 10. #p < 0.05, ##p < 0.01, and ###p < 0.001, versus control group. p < 0.05, p < 0.01, and p < 0.001, versus model group. ## Effect of TCMPC on the histopathological changes of liver tissue In mice with alcohol-induced ALI Light microscope observation showed that hepatic lobules were intact in normal control group, hepatocytes structure and the morphology were normal; the liver cells were arranged radially and the cells were closely arranged with hepatocyte outline, nucleus clarity, and no necrosis (Figure 5). The hepatic lobule structure was damaged in the model group, as the boundaries were hazy, the arrangement of hepatic cells was disordered, hepatocytes were significantly swollen, and the nucleus was shriveled. There were diffuse fat vacuoles of different sizes in the cytoplasm and extensive infiltration of inflammatory cells. Compared with the model group, the histopathological changes of Sil and TCMPC treatment group of liver injury was significantly alleviated, as the inflammatory cells infiltration and necrosis in the liver of the mice were decreased, slight swelling of some hepatocytes and most of the liver cell structure becomes tight and few inflammatory infiltration areas can be found. Figure 5: Liver pathology by H&E staining. Representative H&E-stained liver tissue sections are shown at 200x magnification. ## Discussion The main cause of liver injury is excessive drinking, including acute and chronic liver injury. Alcohol-induced ALI is a life-threatening disease and has become a public health problem worldwide [50]. The metabolic mechanism of alcohol is very complex. Acetaldehyde, as the main toxic metabolite of ethanol, can directly damage mitochondria. At present, more and more evidence showed that there is a close relationship between intestinal microflora and liver injury. Studies have shown that acute exposure to high concentration of ethanol will lead to intestinal microflora imbalance and mucosal damage, which, in turn, increase intestinal permeability and lead to the transfer of endotoxin from the intestinal tract to the liver [51]. At the same time, ALI usually causes inflammation and oxidative stress in the body. In recent years, based on anti-inflammatory, antioxidant, and regulation of intestinal microflora, more and more methods of ALI prevention and adjuvant therapy have been proposed [52, 53]. Compared with other drugs, TCM has many advantages, including low toxicity, fewer side effects, and less risk of drug resistance [54]. It has been reported that Radix Puerariae extract can treat alcoholic liver disease by improving alcohol-induced intestinal barrier dysfunction, and Panax ginseng C. A. Meyer extract can treat non-alcoholic fatty liver disease by regulating intestinal flora [26, 55]. Rhubarb extract pretreatment can improve alcohol-induced ALI in mice by improving intestinal homeostasis and restoring intestinal barrier function, especially by increasing the relative abundance of Akkermansia muciniphila in cecal contents, which is considered to be a potential probiotic [56]. Yang et al. found that puerarin can treat ALI in LPS/D-Gal-induced by increasing the expression of E-box-binding homeobox 2 and inhibiting the activation of NF-κB signal pathway [57]. Zhong et al. have shown that curcumin can inhibit oxidative stress-related inflammation through PI3K/AKT and NF-κB related signals, and reduce LPS-induced septicemia and liver injury in mice [58]. It has been reported that ginsenoside Rb1 plays a hepatoprotective role in acetaminophen-induced ALI model in mice by regulating the inflammatory response mediated by MAPK and PI3K/Akt signaling pathways [59]. A variety of human diseases, including metabolic syndrome, cancer, inflammatory diseases, and infections, are associated with changes in intestinal microbiome. The interaction between intestinal microbiome, intestinal barrier, and liver seems to play a key role in the pathogenesis of alcoholic liver disease, so it is necessary to further explore the intestinal-liver axis in ALI. It is reported that Lactobacillus rhamnosus GG culture supernatant can improve the intestinal integrity and liver injury of alcohol-induced ALI mice [60]. Bifidobacterium longum R0175 and Bifidobacterium adolescentis CGMCC 15058 can reduce ALI induced by D-galactosamine, enhance intestinal barrier, and improve intestinal microflora in rats [61]. Of course, the improvement of probiotics in ALI needs to be further studied in clinical studies. In this study, we used TCMPC, a mixture of several probiotics and TCM, to treat alcohol-induced ALI mice in order to provide a new method for the treatment of ALI. Excessive drinking usually leads to the increase of AST and ALT [39]. ADH and ALDH are two key enzymes in ethanol metabolism, and their lack of function makes it impossible for ethanol to be metabolized normally, which eventually leads to liver damage [41]. TCMPC treatment can significantly reduce the levels of AST and ALT in serum, liver and spleen, increase the levels of ALDH and ADH, and reduce alcohol-induced ALI in mice, this is consistent with the results of Xiao et al [62]. ALI induces oxidative stress, which increases the level of strong oxidants such as ROS, destroys cellular macromolecules, and leads to hepatocyte damage [63]. Antioxidant enzymes such as CAT, SOD, and GSH-Px can be used as indicators to evaluate the level of oxidative stress, and the level of MDA can also be used as an index to evaluate oxidative stress, which is a product of lipid oxidation [46]. The levels of ROS, MDA, and NO in serum, liver, and spleen in the model group were significantly higher than those in the control group, and TCMPC could significantly decrease the above-mentioned indexes. Compared with the control group, the activities of SOD, GSH-Px, and CAT in the model group decreased significantly, but TCMPC could prevent this phenomenon to a certain extent. The above results suggest that TCMPC has a protective effect on ALI in mice, which may be achieved by inhibiting oxidative stress and enhancing antioxidant capacity. The levels of TG, TC and HDL can also be used as indicators of the degree of liver injury. Previous studies have found that alcohol can increase the levels of TG and TC and decrease the level of HDL in the liver of mice [64]. Compared with the model group, the serum TG of TCMPC treatment group decreased by $44.8\%$, TC decreased by $53.1\%$, and HDL increased by $37.9\%$. Abnormal cytokine metabolism is another major feature of alcohol-induced ALI. Alcohol leads to the accumulation of inflammatory factors, which leads to the occurrence of hepatitis [12]. The levels of pro-inflammatory cytokines NF-κB, TNF-α, IFN-α, IFN-β, and IFN-γ in the model group were significantly higher than those in the control group, which was consistent with previous studies [65]. TCMPC can significantly reduce the level of the above inflammatory factors. In addition, alcohol causes liver damage, such as disordered arrangement of hepatocytes, nuclear atrophy, and infiltration of inflammatory cells. Compared with the model group, the liver injury, inflammatory cell infiltration, and cell necrosis in TCMPC treatment group were significantly alleviated. ## Conclusion In summary, this study shows the therapeutic effect of TCMPC on alcohol-induced ALI, and the potential mechanism may be through anti-inflammation and antioxidation in mice. Compared with the mice in the model group, after oral administration of TCMPC, the levels of ROS, MDA, and NO in ALI mice were significantly decreased, and the levels of CAT, SOD, and GSH-Px were significantly increased, indicating that TCMPC has a strong antioxidant capacity. At the same time, compared with the mice in the model group, pro-inflammatory cytokines such as NF-κB, TNF-α, IFN-α, IFN-β, and IFN-γ were significantly decreased in ALI mice after oral administration of TCMPC, which showed the anti-inflammatory ability of TCMPC. Although TCMPC displayed even better amelioration effects than Sil against alcohol-induced ALI in mice in the majority of cases, it is hard to conclude which one shows better effects based on our present data. Our research provides a new scheme for the treatment of liver disease. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The animal study was reviewed and approved by Jilin University. ## Author contributions ZL: writing – original draft, methodology, and formal analysis. XF: writing – original draft, investigation, and supervision. XH: resources and writing – review and editing. CL: writing – original draft, methodology, and data curation. YW: project administration, writing – review and editing, and supervision. DY: conceptualization, writing – review and editing, and funding acquisition. All authors contributed to the article and approved the submitted version. ## Funding This work was supported by Jilin Province Science and Technology Development Project [nos. YDZJ202101ZYTS080, 20200708076YY], and Jilin Provincial Department of Education [no. 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--- title: 'BMI, socioeconomic status, and bone mineral density in U.S. adults: Mediation analysis in the NHANES' authors: - Yun Zhang - Caixia Tan - Wenfu Tan journal: Frontiers in Nutrition year: 2023 pmcid: PMC10027781 doi: 10.3389/fnut.2023.1132234 license: CC BY 4.0 --- # BMI, socioeconomic status, and bone mineral density in U.S. adults: Mediation analysis in the NHANES ## Abstract ### Introduction The mechanism by which socioeconomic status (SES) affects bone mineral density (BMD) remains unknown, and body mass index (BMI) may be a potential mediator. The purpose of this study was to investigate whether BMI mediates the relationship between SES [education level and poverty income ratio (PIR)] and lumbar BMD and the proportion it mediates. ### Methods This study included a total of 11,075 adults from the National Health and Nutrition Examination Survey (NHANES). Lumbar BMD was measured at the lumbar spine by dual-energy X-ray absorptiometry (DXA). Multivariate linear regression and smoothing curve fitting were used to investigate the relationship between SES and lumbar BMD. Mediator analysis was used to investigate the proportion of BMI mediating the association between SES and BMD. ### Results In the fully adjusted model, there was a positive correlation between SES and BMD (education level: β = 0.025, $95\%$ CI: 0.005, 0.045; PIR: β = 0.007, $95\%$ CI: 0.002, 0.011). Mediation analysis showed that BMI mediated the relationship between PIR, education level, and lumbar BMD with a range of mediation proportions from 13.33 to $18.20\%$. ### Conclusion BMI partially mediated the positive association between SES and BMD, and this association may be largely mediated by factors other than BMI. ## 1. Introduction Osteoporosis is a bone disease characterized by impaired bone strength that puts individuals at increased risk of fractures in the spine and joint areas [1, 2]. As the global population ages, osteoporosis imposes a heavy socioeconomic and public health burden [3]. The annual cost of osteoporosis fracture prevention and treatment in the United States is expected to exceed $50 billion 20 years from now [4, 5]. Investigation of risk factors for osteoporosis is an important tool for maintaining bone mass and reducing fracture risk [6]. In addition to common laboratory and screening indicators (e.g., blood lipids, body composition, etc.) [ 7, 8], sociological factors are receiving increasing attention in bone metabolism [9]. Wang and Dixon used multiple linear regression to investigate and find a significant positive association between education level, poverty income ratio (PIR), and BMD in menopausal women [10]. A recent cross-sectional study in adult men again validated this association and highlighted the importance of socioeconomic status (SES) in the management of osteoporosis [11]. However, the mechanisms behind the association between SES and BMD are complex and unclarified. Available evidence suggests that this association may arise primarily from the indirect effects of potential mediators, and exploring the main mediators is important for targeting groups with unequal SES for the prevention and management of osteoporosis [12, 13]. Individuals with low SES are often associated with problems such as inadequate energy intake [14] and lack of essential nutrients [15], which may lead to an unhealthy body mass index (BMI) or waist circumference. On the other hand, BMI has long been considered to be strongly associated with SES as a protective factor against bone loss [16, 17]. Given these associations, BMI is considered to be a potentially important factor in mediating the relationship between SES and BMD. Therefore, a cross-sectional study based on the four cycles of National Health and Nutrition Examination Survey (NHANES) 2011–2018 was carried out, to investigate the mediating role of BMI in the association between SES and lumbar BMD. ## 2.1. Study population and data source The NHANES is a comprehensive, national survey that collects health and nutrition information from non-institutionalized civilian residents in the United States [18, 19]. The National Center for Health Statistics (NCHS) Research Ethics Review Board authorized the study protocol. At the time of recruiting, all subjects provided written consent. According to inclusion exclusion criteria, excluded 20,434 participants without SES data or BMD data, 7,228 participants age less than 20 years and 419 samples with cancer or malignancy. The study eventually included 11,075 participants (Figure 1). FIGURE 1: Flow chart of participants selection. NHANES, National Health and Nutrition Examination Survey; BMD, bone mineral density; BMI, body mass index; PIR, poverty income ratio. ## 2.2. Study variables The exposure variable is SES, which consists of PIR and educational attainment. PIR is a continuous variable, which is the rate of self-reported household income, based on household or family size, household age composition and year. Educational level is a categorical variable and is divided into three groups less than high school, high school, and more than high school. BMI was calculated according to international standards: weight divided by height squared. Outliers will receive reasonable verification to ensure the credibility of the data. For BMI classification according to WHO standards (underweight <18.5, normal 18.5–24.9, overweight 25–29.9, obese ≥30 kg/m2). Lumbar BMD was measured as the primary outcome of this study by dual-energy X-ray absorptiometry. Age, gender, race, diabetes status, smoke status, high blood pressure status, total calcium, serum phosphorus, blood urea nitrogen, activities status, direct HDL cholesterol, serum creatinine, and total cholesterol were all covariates in this study. The interpretation, measurement and calculation of all variables can be found on the official NHANES website.1 ## 2.3. Statistical analysis All analyses were performed with R (version 4.2) and Empowerstats (version 4.1). The Chi-square test and t-test were used to assess the demographic characteristics of the participants by BMI subgroups. Multivariate logistic regression analyses were used to investigate the association between SES, BMI and lumbar BMD (20–22). The potential mediated effect of BMI on the association between SES and lumbar BMD was estimated by parallel mediator analysis. The parallel mediation model uses individual indicators as mediators. The direct effect (DE) is the effect of SES on lumbar BMD without mediators. Indirect effects (IE) are the consequences of SES on lumbar BMD that are mediated by mediators. The fraction of mediators was estimated by dividing IE by TE (total effect). ## 3.1. Baseline characteristics Table 1 shows the weighted characteristics of the participants stratified by BMI. A total of 5,717 male and 5,358 female adults participated, of whom 190 were underweight ($1.72\%$), 3,235 were normal ($29.21\%$), 3,465 were overweight ($31.29\%$), and 4,185 were obese ($37.77\%$). All variables except smoking status differed significantly ($P \leq 0.05$) at baseline characteristics according to BMI category. Underweight and obese participants tended to have lower income and education and lower lumbar BMD compared to normal weight and overweight participants. TABLE 1 \| Outcome \| BMI (kg/m2) categorical \| BMI (kg/m2) categorical.1 \| BMI (kg/m2) categorical.2 \| BMI (kg/m2) categorical.3 \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Underweight <18.5 (N = 190) \| Normal 18.5–24.9 (N = 3,235) \| Overweight 25.0–29.9 (N = 3,465) \| Obese ≥30.0 (N = 4,185) \| \| \| Age (years) \| 30.873 ± 12.208 \| 36.430 ± 11.981 \| 40.156 ± 11.254 \| 40.382 ± 11.266 \| <0.001 \| \| Gender (%) \| \| \| \| \| <0.001 \| \| Male \| 41.352 \| 47.037 \| 60.234 \| 51.013 \| \| \| Female \| 58.648 \| 52.963 \| 39.766 \| 48.987 \| \| \| Race (%) \| \| \| \| \| <0.001 \| \| Non-Hispanic White \| 57.877 \| 63.817 \| 60.201 \| 57.377 \| \| \| Non-Hispanic Black \| 15.167 \| 9.699 \| 10.492 \| 15.500 \| \| \| Mexican American \| 3.713 \| 6.092 \| 11.468 \| 13.044 \| \| \| Other race \| 23.243 \| 20.392 \| 17.839 \| 14.079 \| \| \| PIR \| 2.071 ± 1.402 \| 2.970 ± 1.703 \| 3.068 ± 1.675 \| 2.825 ± 1.635 \| <0.001 \| \| Education level (%) \| \| \| \| \| <0.001 \| \| Less than high school \| 13.520 \| 11.734 \| 14.293 \| 14.017 \| \| \| High school \| 26.111 \| 19.317 \| 21.358 \| 24.438 \| \| \| More than high school \| 60.369 \| 68.949 \| 64.349 \| 61.545 \| \| \| Moderate activities (%) \| \| \| \| \| <0.001 \| \| Yes \| 35.733 \| 29.998 \| 30.966 \| 29.813 \| \| \| No \| 64.267 \| 70.002 \| 69.034 \| 70.187 \| \| \| Diabetes status (%) \| \| \| \| \| <0.001 \| \| Yes \| 1.280 \| 2.172 \| 4.247 \| 9.543 \| \| \| No \| 98.720 \| 97.828 \| 95.753 \| 90.457 \| \| \| High blood pressure status (%) \| \| \| \| \| <0.001 \| \| Yes \| 6.458 \| 10.681 \| 20.258 \| 33.173 \| \| \| No \| 93.542 \| 89.319 \| 79.742 \| 66.827 \| \| \| Smoking status (%) \| \| \| \| \| 0.359 \| \| Ever \| 39.620 \| 38.337 \| 41.193 \| 41.637 \| \| \| Never \| 60.380 \| 61.663 \| 58.807 \| 58.363 \| \| \| Total calcium (mmol/L) \| 2.361 ± 0.080 \| 2.352 ± 0.081 \| 2.346 ± 0.084 \| 2.331 ± 0.084 \| <0.001 \| \| Total cholesterol (mmol/L) \| 4.457 ± 0.787 \| 4.771 ± 0.983 \| 5.070 ± 1.034 \| 5.045 ± 1.041 \| <0.001 \| \| Direct HDL cholesterol (mmol/L) \| 1.572 ± 0.410 \| 1.556 ± 0.439 \| 1.340 ± 0.374 \| 1.218 ± 0.332 \| <0.001 \| \| Serum phosphorus (mmol/L) \| 1.260 ± 0.178 \| 1.223 ± 0.176 \| 1.192 ± 0.178 \| 1.183 ± 0.180 \| <0.001 \| \| Blood urea nitrogen (mmol/L) \| 4.174 ± 1.841 \| 4.459 ± 1.461 \| 4.709 ± 1.493 \| 4.576 ± 1.578 \| <0.001 \| \| Creatinine (mmol/L) \| 70.300 ± 35.257 \| 74.160 ± 22.315 \| 77.813 ± 20.627 \| 75.777 ± 27.316 \| <0.001 \| \| Lumbar BMD (g/cm2) \| 0.983 ± 0.125 \| 1.044 ± 0.146 \| 1.044 ± 0.146 \| 1.036 ± 0.154 \| <0.001 \| ## 3.2. Association between SES and BMI with lumbar BMD Table 2 shows the results of multivariate logistic regression analysis with a positive association between SES and lumbar BMD. There was a significant positive linear association between PIR and lumbar BMD, with an increase in lumbar BMD of 0.007 g/cm2 per unit increase in PIR (β = 0.007, $95\%$ CI: 0.002, 0.011). This association also existed between education level and lumbar BMD, participants with more than high school education having a 0.025 g/cm2 higher lumbar BMD than those with less than high school education (β = 0.025, $95\%$ CI: 0.005, 0.045). And participants with high school education having a 0.019 g/cm2 higher lumbar BMD than those with less than high school education (β = 0.019, $95\%$ CI: 0.003, 0.035). TABLE 2 \| Subgroups \| Model 1 [β (95% CI)] \| Model 2 [β (95% CI)] \| Model 3 [β (95% CI)] \| \| --- \| --- \| --- \| --- \| \| PIR \| 0.006 (0.001, 0.011) \| 0.005 (0.001, 0.009) \| 0.007 (0.002, 0.011) \| \| Education level \| Education level \| Education level \| Education level \| \| Less than high school \| Reference \| Reference \| Reference \| \| High school \| 0.017 (0.007, 0.027) \| 0.019 (0.006, 0.032) \| 0.019 (0.003, 0.035) \| \| More than high school \| 0.030 (0.021, 0.039) \| 0.027 (0.009, 0.045) \| 0.025 (0.005, 0.045) \| \| Body mass index (kg/m2) \| 0.001 (0.000, 0.001) \| 0.001 (0.000, 0.001) \| 0.001 (0.001, 0.002) \| \| Categories \| Categories \| Categories \| Categories \| \| Underweight (<18.5) \| Reference \| Reference \| Reference \| \| Normal (18.5–24.9) \| 0.006 (0.003, 0.009) \| 0.009 (0.006, 0.012) \| 0.012 (0.007, 0.017) \| \| Overweight (25.0–29.9) \| –0.001 (–0.004, 0.002) \| 0.000 (–0.003, 0.004) \| 0.002 (–0.004, 0.008) \| \| Obese (≥30) \| 0.003 (0.003, 0.004) \| 0.003 (0.002, 0.004) \| 0.003 (0.001, 0.004) \| \| P for trend \| 0.094 \| 0.225 \| 0.609 \| \| Subgroup analysis stratified by gender \| Subgroup analysis stratified by gender \| Subgroup analysis stratified by gender \| Subgroup analysis stratified by gender \| \| Males \| 0.001 (0.000, 0.002) \| 0.001 (0.001, 0.002) \| 0.002 (0.001, 0.004) \| \| Females \| 0.001 (0.000, 0.001) \| 0.001 (0.000, 0.001) \| 0.001 (–0.000, 0.002) \| The results of the multiple logistic regression analysis showed a positive relationship between BMI and lumbar BMD, and this association remained significant and stable in all models (Table 2). For every 1 kg/m2 increase in BMI, lumbar BMD increased by 0.001 g/cm2 (β = 0.001, $95\%$ CI: 0.001, 0.002). In contrast, when BMI was transformed into a categorical variable for analysis, this relationship became reversed and insignificant in overweight participants. When subgroup analysis was performed by gender, the relationship between BMI and lumbar BMD showed a positive association in both male and female participants. Considering that the results were not significant in the sensitivity analysis, smoothed curve fitting was further utilized to confirm the non-linear relationship between BMI and lumbar spine BMD. The results showed a non-linear positive relationship between BMI and lumbar BMD with saturated values (Figure 2). FIGURE 2: The association between body mass index and lumbar bone mineral density. (A) Each black point represents a sample. (B) The solid red line represents the smooth curve fit between variables. Blue bands represent the 95% of confidence interval from the fit. ## 3.3. Mediation analysis The mediation analysis investigated whether and to what extent BMI mediated the association between SES and lumbar BMD. Table 3 shows the total effect, which is the effect of SES on lumbar BMD; the direct effect, which is the effect of PIR, education level on lumbar BMD, not mediated by BMI; and the indirect effect, which is the effect of PIR, education level on lumbar BMD, mediated by BMI. In general, the direct effect greatly exceeded the indirect effect, although the statistical significance of the latter was significant. The proportion of BMI mediating the effect of PIR and education level on lumbar BMD was 18.20 and $13.33\%$, respectively (Figure 3). ## 4. Discussion In the present study, the results of the multiple regression analysis suggest that US adults with higher SES are associated with higher lumbar BMD. More importantly, this study found that BMI mediated the positive association between SES and lumbar BMD, although the proportion of mediation was less than $20\%$. This suggests that the association between SES and BMD may be primarily due to factors other than BMI, such as genetics, dietary intake, and levels of systemic inflammation. For most causes of morbidity and mortality, SES is of great importance and impact (23–25). Therefore, the study of SES as a risk factor for bone health is essential to reduce the public health burden. The association between SES and BMD has been of interest to researchers for 30 years, but results have been inconsistent due to differences in populations and study methods [26]. Fehily et al. in 1992 investigated factors that may have influenced BMD during the development of over 500 14 years-olds and showed that males in manual occupations may have higher BMD [27]. Results from the Louisiana Osteoporosis Study suggest a positive association between SES and BMD in the total population, with males with lower education and females with lower income being the most susceptible to relatively lower BMD [28]. A meta-analysis including eight epidemiological studies showed that most population-based studies support the idea that participants with higher income levels and education are more likely to have higher BMD [29]. This finding was also validated in this cross-sectional study, which included 11,075 representative US participants. However, the reasons behind the positive association between SES and BMD are complex and unexplained. Based on the available evidence, the negative effects of SES on BMD are thought to possibly stem from unhealthy lifestyles, including factors such as food insecurity [30], lack of essential nutrients [31, 32], and exposure to harmful substances [33]. Health outcomes of an unhealthy lifestyle, such as underweight [34] and visceral fat accumulation [35, 36], may further negatively affect bone metabolism. In the past, obesity and being overweight have been considered a protective factor. A positive association between BMI and BMD was found in several studies as early as 20 years ago [37, 38]. Researchers concluded that BMI reduced the risk of bone loss and fracture in gender-specific populations and groups of menopausal women [39, 40]. The results of multivariate logistic regression and subgroup analyses also indicated a positive association between BMI and BMD, which was maintained significantly in both men and women. The mechanisms underlying the positive association between obesity and BMD have long been described. The main include: [1] the mechanical overload generated in the presence of obesity leads to bone deformation, which triggers a series of transduction signals that stimulate increased bone mass through increased osteoblast activity; [2] increased osteogenic differentiation and osteoblast maturation of mesenchymal stem cells through adipocyte production of sex steroids; and [3] adipose tissue is a substrate for sex hormone synthesis and secretes adipokines and cytokines, which play a role in bone metabolism. Given these mechanisms, obesity is thought to be a potentially important mediator of the association between SES and BMD, and the results of the mediation analysis support this hypothesis. Exploring the main mediators of the relationship between SES and BMD is important for the prevention and management of osteoporosis [11]. The data suggest that the effects of SES on BMD are broad and complex and may affect bone metabolism in a variety of ways, including through diet, inflammation, and physical activity patterns (32, 41–43). However, a significant proportion of these can have a large effect on body size, with changes in both diet and physical activity patterns leading to corresponding changes in BMI, which can further influence bone metabolic processes [44]. The results of mediating effects analysis suggest that BMI is indeed a mediator of the relationship between SES and BMD, but the proportion of mediators for both PIR and education level is below $20\%$, implying that there may be other major mediators. Dietary intake factors may be worth investigating. Lim et al. investigated calcium intake among adults in six regions of Korea, and the authors found significant regional differences in calcium intake. Furthermore, participants with lower SES had inadequate calcium intake and low diet quality, and inadequate calcium and energy intake may have a negative impact on bone metabolism [45]. In addition, inflammation levels may also be an important factor in the association between SES and BMD [46]. It has been shown that lower SES is associated with increased psychosocial stress and elevated blood inflammation levels, and higher levels of systemic inflammation have been shown to be negatively associated with BMD in menopausal women [47]. In addition, higher dietary inflammatory potential has also been suggested as a risk factor for bone health, and a meta-analysis that included more than 100,000 participants suggested that a diet high in pro-inflammatory components may increase the risk of osteoporosis and fracture [48]. Our study has some limitations. First, due to the design of the cross-sectional study, the current study were unable to determine the causal relationship between SES and lumbar BMD. In addition, self-reported SES may lead to data bias and affect the accuracy of conclusions [49, 50]. Despite these shortcomings, our study has several advantages. This study includes data from a large and representative cross-sectional survey. More importantly, this study confirms the association between SES and lumbar BMD and extends these studies for the first time to the potential mediation effects of BMI. ## 5. Conclusion According to the findings of this study, BMI partially mediates the positive relationship between SES and BMD. Further investigation is needed to determine whether there are higher mediating variables than BMI in this association, such as dietary intake and inflammation levels. ## Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: https://wwwn.cdc.gov/nchs/nhanes/ContinuousNhanes. ## Ethics statement The studies involving human participants were reviewed and approved by the NCHS Ethics Review Board. The patients/participants provided their written informed consent to participate in this study. ## Author contributions YZ and WT designed the research and revised the manuscript. YZ and CT collected and analyzed the data and drafted the manuscript. All authors contributed to the manuscript and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Gut microbiota is associated with response to 131I therapy in patients with papillary thyroid carcinoma authors: - Lei Zheng - Linjing Zhang - Li Tang - Dingde Huang - Deng Pan - Wei Guo - Song He - Yong Huang - Yu Chen - Xu Xiao - Bo Tang - Jing Chen journal: European Journal of Nuclear Medicine and Molecular Imaging year: 2022 pmcid: PMC10027784 doi: 10.1007/s00259-022-06072-5 license: CC BY 4.0 --- # Gut microbiota is associated with response to 131I therapy in patients with papillary thyroid carcinoma ## Abstract ### Purpose Radioactive iodine (131I) therapy is a conventional post-surgery treatment widely used for papillary thyroid carcinoma (PTC). Since 131I is orally administered, we hypothesize that it may affect gut microbiome. This study aims to investigate alterations of intestinal microbiome caused by 131I therapy in PTC patients and explore its association with response to 131I therapy. ### Methods Fecal samples of 60 PTC patients pre- and post-131I therapy were collected to characterize the 131I therapy-induced gut microbiota alterations using 16S rRNA gene sequencing. According to the inclusion criteria, sequence data of 40 out of the 60 patients, divided into excellent response (ER) group and non-excellent response (NER) group, were recruited to investigate the possible connection between gut microbiota and response to 131I therapy. Multivariate binary logistic regression was employed to construct a predictive model for response to 131I therapy. ### Results Microbial richness, diversity, and composition were tremendously altered by 131I therapy. A significant decline of Firmicutes to Bacteroides (F/B) ratio was observed post-131I therapy. 131I therapy also led to changes of gut microbiome-related metabolic pathways. Discrepancies in β diversity were found between ER and NER groups both pre- and post-131I therapy. Furthermore, a predictive model for response to 131I therapy with a p value of 0.003 and an overall percentage correct of $80.0\%$ was established, with three variables including lymph node metastasis, relative abundance of g_Bifidobacterium and g_Dorea. Among them, g_Dorea was identified to be an in independent predictor of response to 131I therapy ($$p \leq 0.04$$). ### Conclusion For the first time, the present study demonstrates the gut microbial dysbiosis caused by 131I therapy in post-surgery PTC patients and reveals a previously undefined role of gut microbiome as predictor for 131I ablation response. G_Dorea and g_Bifidobacterium may be potential targets for clinical intervention to improve response to 131I in post-operative PTC patients. ### Trial registration ChiCTR2100048000. Registered 28 June 2021. ## Introduction Thyroid carcinoma (TC) is the commonest endocrine malignancy, the incidence of which is still increasing worldwide. Differentiated thyroid cancer (DTC) takes up over $90\%$ of all TC, among which, papillary thyroid carcinoma (PTC) is the foremost histopathologic type, taking up more than $85\%$ of TC [1]. Radioactive iodine (131I) therapy is the mainstay of treatment for PTC after surgery [2], which has been used for nearly 80 years and still plays a central role in the management of PTC today [3]. However, response to 131I therapy varies among patients. Distinct responses to 131I determine different subsequent clinical strategies and prognosis. According to the 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer [4], responses to 131I ablation can be categorized as excellent response (ER) or non-excellent response (NER). PTC patients with a negative imaging, negative thyroglobulin antibody (TgAb) and either suppressed thyroglobulin (Tg) < 0.2 ng/ml or thyroid stimulating hormone (TSH) stimulated Tg (sTg) < 1 ng/ml are evaluated as ER. Those who do not achieve these standards are defined as NER. ER to 131I indicates clinical cure of PTC, whereas patients with NER may require another course of 131I therapy. Hence, the need for a reliable tool predicting therapeutic response to 131I ahead of 131I therapy is vital and urgent, but has not been fulfilled yet. 131I is an orally administered radioactive nuclide used for internal-radiation therapy to treat PTC. After application, 131I stays and accumulates in the gastro-intestinal tract, thus is very likely to affect gut microbiome. Gut microbiota and its vital role in various diseases have drawn more and more attention. Accumulating evidences have indicated that gut microbiota plays a part in the pathophysiology of cancer [5]. Recently, a close connection between gut microbiome and thyroid carcinoma has been implicated [6]. Another study finds out that gut microbiome is tremendously altered in thyroid carcinoma patients compared to control subjects [7]. Thyroid function is influenced by gut microbiota [8]. Moreover, gut microbiota is also reported to be associated with radiation sensitivity and radiation-related toxicities [9–11]. Evidences from animal models have also shown that gut microbiome composition may predict radiation injury [9]. However, the alterations of gut microbiota after 131I therapy and the significance have not been elucidated yet. Whether difference in gut microbiome is related to distinct responses to 131I remains elusive. Can one or several gut genera be used as predictors of 131I response in PTC patients before the therapy is still unknown. Given the paucity of gut microbiome studies in post operative PTC patients receiving 131I therapy, we reported the present prospective study. ## Study participants This study was performed in agreement with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the First Affiliated Hospital of Army Medical University (Third Military Medical University), China (Date Oct. 16th, 2020/No. KY2020214). The authors affirmed that all participants provided informed consents for clinical data and bio-sample use and publication of their basic characteristics and 16s rRNA sequence results of their stools. All patients included in this study were from the southwest region of China (including Chongqing, Sichuan, and Guizhou), where the climate and residents’ eating habits were similar. 99mTcO4− thyroid imaging was negative before the 131I therapy on every single participant. Forty-eight hours after taking 131I orally at a dose of 150 mCi (5.55 GBq), a very small part of thyroid tissue was displayed in all of the patients by post radioactive iodine therapy whole-body scanning (RxWBS), with no significant difference in the amount of residual thyroid tissue among the patients. Serum TSH concentration of each participant before 131I therapy was over 30 mIU/L. ## Self-controlled study cohort, recruitment of subjects, procedures of 131I therapy, and sampling The inclusion criteria for TC patients were as follows: [1] patients were diagnosed as PTC; [2] patients had undergone complete thyroid resection procedure; [3] patients were scheduled to receive 131I therapy for the first time; [4] patients were willing to participate in this study and signed the informed consent forms; [5] patients promised to voluntarily accept and comply with this experimental protocol. The exclusion criteria were as follows: [1] patients with known history of any other cancer besides PTC; [2] patients who had undergone prior 131I therapy or other radiotherapy; [3] patients with notable gastrointestinal disorder; [4] patients with known history of gastrointestinal surgery; [5] patients with long-term use of any probiotics or antibiotics, non-steroid anti-inflammatory drugs or proton pump inhibitors; [6] patients with an age < 18 years. Finally, a total of 60 subjects from the nuclear medicine department of the First Affiliated Hospital of the Army Medical University (Third Military Medical University), China, between October 2020 and March 2021, who fulfilled the inclusion criteria and provided the fecal and blood samples, were included in the self-controlled study to explore microbiota changes induced by 131I therapy. Baseline characteristics and clinical parameters are listed in Table 1. Patients were given iodine-free diet for 4 weeks and underwent 3 weeks of Euthyrox withdrawal before each sampling. Two sequential peripheral blood and fecal samples were collected from patients at time points 1–2 days before and 5 months after 131I administration, respectively. Since the patients recruited in this study are either high-risk PTC, or intermediate-risk PTC with aggressive histology or with unexplained elevated sTg levels (sTg > 10 ng/ml) or with both, 131I was administered orally at a dose of 150 mCi (5.55 GBq) for adjuvant therapy. All samples were aliquoted and stored at − 80 °C for further use. TSH and sTg levels in the blood samples were assayed. DNA extraction and 16S rRNA gene sequencing were performed using the fecal samples. Table 1Demographic and clinical characteristics of participants in self-controlled studyBaseline characteristicsParticipants ($$n = 60$$)Age, years: mean ± SD40.02 ± 10.23Gender: N (%)Male: 19 ($31.7\%$)Female: 41 ($68.3\%$)Risk level: N(%)Low: 0 ($0.0\%$)Medium: 49 ($81.7\%$)High: 11 ($18.3\%$)Pathologic stage post-surgery: N (%)I: 54 ($90.0\%$)II: 4 ($6.7\%$)III: 2 ($3.3\%$)IV: 0 ($0.0\%$)Maximum tumor diameter, cm: mean ± SD1.38 ± 0.82Local invasion by pathology: N (%)Without: 43 ($71.7\%$)With: 17 ($28.3\%$)Lymph node metastasis by RxWBSa: N (%)Without: 49 ($81.7\%$)With: 11 ($18.3\%$)Distant metastasis by RxWBS: N (%)Without: 59 ($98.3\%$)With: 1 ($1.7\%$)sTgb or TgAbc level: sTg < 1 ng/ml: N (%)25 ($41.7\%$) sTg 1–10 ng/ml: N (%)22 ($36.7\%$) sTg > 10 ng/ml or rising TgAb level: N (%)13 ($21.6\%$)aRxWBS, post radioactive iodine therapy whole-body scanningbsTg, TSH-stimulated thyroglobulincTgAb, thyroglobulin antibody ## Patient demographics and clinical parameters of 131I ER and NER patients According to the definition of ER demonstrated previously, before 131I ablation, 20 out of the 60 patients already fulfilled all other criteria of ER (no metastases observed, sTg < 1 ng/ml and negative TgAb) except for very small amounts of thyroid remnant imaging as shown by RxWBS, indicating that after 131I ablation, it was very likely that these 20 patients would be classified as ER according to both our experiences and literatures [12]. As a matter of fact, after 131I ablation, they were actually evaluated as ER in this study. Thus, in order to reduce false-positive rate, these 20 patients were excluded from this part of study. The rest 40 participants were divided into 131I ER group (24 cases) and NER group (16 cases) based on their assessment post 131I ablation according to the guidelines. Gut microbiota of the two groups both 1–2 days pre-treatment of 131I (Pre-) and 5 months post-treatment (Post-) were analyzed and compared. ## DNA extraction and 16S rRNA sequencing analysis Fecal DNA extraction was performed using CTAB method. The V3–V4 region of the bacterial 16S rRNA gene was amplified by Phusion® High-Fidelity PCR Master Mix (New England Biolabs, New England) using specific primers (515F-806R) with a barcode. PCR product purification was carried out with Qiagen Gel Extraction Kit (Qiagen, Germany). TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) was used to generate sequencing libraries. The libraries were sequenced using the Illumina NovaSeq 6000 platform (Novogene Company, China). We took advantage of Microbial Ecology 2 (QIIME2, version 2020. 2) [13] platform to process the sequencing data in a conda environment. In brief, the V3–V4 primers of paired-end fastq format sequence files were trimmed using cutadapt 3.1 [14]. Next, trimmed fastq files were imported into QIIME2. After the DADA2 denoising, a feature table listing sequence number of all samples and features was analyzed. Samples with less than 8000 sampling depth were excluded. All samples were normalized to the same depth. After rarefaction, alpha diversity and beta diversity were calculated respectively. Then, the results of the principal coordinate analysis (PcoA analysis) was calculated. Taxonomic composition of the samples were also displayed. The discrepantly abundant bacterial taxa between two groups were analyzed and displayed by linear discriminant analysis effect size (LEfSe) [15]. ## Establishment of a response-prediction model for 131I therapy Preliminary covariates were discriminatory gut taxa between ER and NER pre-131I therapy and some possibly associated clinical factors, including relative abundance of Tannerellacea, g_Parabacteroides, g_Dorea, g_Bifidobacterium, f_Bifidobacteriaceae, o_Bifidobacteriales, f_Erysipelotrichaceae, c_Erysipelotrichia, o_Erysipelotrichales, age, gender, risk level, pathologic stage, maximum tumor diameter, local invasion, lymph node metastasis, distant metastasis, sTg or TgAb level, and Firmicutes/Bacteroidetes ratio. Variable screening was carried out by determination method based on characteristic root. In short, 5 variables, including f_Tannerellaceae, f_Bifidobacteriaceae, o_Bifido-bacteriales, c_Erysipelot-richi, and o_Erysipelotrichales, with multicollinearity were eliminated from the model. It was validated that there was no multicollinearity among the left variables post screening by both the characteristic root determination method and calculation of the ranks of matrixes. The left variables, including age, gender, risk level, pathologic stage, maximum tumor diameter, local invasion, lymph node metastasis, distant metastasis, sTg or TgAb level, Firmicutes/Bacteroidetes ratio, relative abundance of g_Parabacteroides, g_Dorea, g_Bifidobacterium, and f_Erysipelotrichaceae, were analyzed by multivariate analysis using binary logistic regression with backward stepwise to establish the predictive model. After 12 rounds of conditional backward stepwise removal of covariates with non-significant predictive effect, ultimately 3 variables, consisting of lymph node metastasis, relative abundance of g_Bifidobacterium and g_Dorea, were screened out as the optimal set of characteristic variables to predict patients’ response to 131I therapy. The statistical software used was IBM SPSS Statistics 26.0. ## Bioinformatics and statistical analysis Alpha (α) diversity was evaluated by a set of indexes, including Shannon, Simpson, ACE, and CHAO1, while beta (β) diversity was assessed using Bray–Curtis distance-based non-metric multidimensional scaling (NMDS) analysis, supervised partial least squares-discriminant analysis (PLS-DA), PCoA based on unweighted unifrac distance matrix or based on Jaccard index. Taxa bar plot and LEfSe analysis were performed to distinguish discrepant abundant genera between the two groups. The predicted metabolic functional differences of gut microbiota between the two groups were compared using picrust2 to identify differentially involved Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The normality of distribution was determined using Shapiro–Wilk test. Homogeneity of variance was tested by F test. Comparison of baseline characters was performed by χ2 or Student’s t-test. Paired t-test was used in the statistical analysis of self-controlled study. Significance between two groups was determined by Student’s t-test or Wilcoxon-Rank test. P values < 0.05 were considered significant. ## Study cohorts and clinical parameters of self-controlled study Sixty post-operative PTC patients, who were planning for 131I therapy, were recruited in the self-controlled study to explore microbiota changes due to 131I therapy. Baseline characters are listed in Table 1. ## Microbial richness, diversity, and composition alteration after 131I therapy From the 60 self-controlled fecal samples, 7,540,436 high-quality sequences were used (average 59,845 per sample). The total number of ASV was 9583 at $99\%$ similarity level. The Good’s coverage of each group was over $99\%$ (Fig. 1a). In addition, alpha Rarefaction curve in each group is nearly smooth with a sufficient amount of sequencing data (Fig. 1b), indicating that the sequence depth represented the majority of gut bacteria in the samples. Fig. 1Microbial richness and diversity alterations post-131I therapy. Pre-131I: 1–2 days before 131I administration; Post-131I: 5 months after.131I administration. a Goods_coverage indexes of two groups. b Alpha rarefaction of each group indicating sequencing depth. c Ace and Chao1 indexes of the taxonomic α diversities. Ace: paired t-test, $$p \leq 8.6$$e-05. Chao1: paired t-test, $$p \leq 0.00016.$$ d PCoA plot, NMDS, and PLS-DA indexes of the β diversity (paired t-test, PCoA unweighted index: $r = 0.2037$, $$p \leq 0.001$$; PCoA Jaccard index: $r = 0.2813$, $$p \leq 0.001$$; NMDS: stress = 0.1366; PLS-DA: R2X = 0.128, R2Y = 0.733, Q2Y = 0.417) The taxonomic α diversity indicated by ACE (Abundance-based Coverage Estimator) richness ($$p \leq 8.6$$e − 05) and Chao1 ($$p \leq 0.00016$$) indexes displayed significantly lower community richness and diversity in the fecal microbiota after 131I therapy (Fig. 1c). Shifts in β diversity (composition and structure) from pre-131I therapy to post-131I therapy were also observed by PCoA based on unweighted unifrac distance matrix and Jaccard index, NMDS analysis, and PLS-DA (Fig. 1d). All results indicated that the samples within each group were clustered together, while the samples between groups were separated, illustrating prominent differences in bacterial structure pre- and post-131I therapy (PCoA unweighted index: $r = 0.2037$, $$p \leq 0.001$$; PCoA Jaccard index: $r = 0.2813$, $$p \leq 0.001$$; NMDS: stress = 0.1366; PLS-DA: R2X = 0.128, R2Y = 0.733, Q2Y = 0.417). Taxa bar plot showed the bacterial composition of each group in Phylum (Fig. 2a). The most pronounced differences were decrease of Firmicutes and increase of Bacteroidetes, consequently leading to a significant decline in the Firmicutes to Bacteroides (F/B) ratio ($$p \leq 0.0002$$) after treatment (Fig. 2b), suggesting dysbiosis following 131I therapy. LEfSe analysis illustrated remarkably different microbes between the two groups with a LDA score over 3.5 (Fig. 2c), with notable increments in relative abundance of f_Bacteroidaceae and f_Prevotellaceae post-131I therapy, and reverse trend in f_Ruminococcaceae. Genus-level distribution alterations of fecal microbiota was demonstrated by pie plot (Fig. 2d), showing an overall pattern of an increase in “pathogenic microbiota,” for example, Bacteroides, Prevotella_9, and a decrease in “beneficial microbiota,” including Roseburia and Blautia. Fig. 2Microbial composition alterations post-131I therapy. a Taxa bar plot illustrating the bacterial composition of each group in Phylum. b Firmicutes to Bacteroides (F/B) ratio of the two groups. Paired t-test. $$p \leq 0.0002.$$ c Linear discriminant analysis effect size (LEfSe) analysis indicating differentially abundant bacterial taxa with a LDA score over 3.5 between the two groups. d Pie plot showing genus-level distribution of fecal microbiota ## Predicted functional changes of microbiome induced by 131I therapy We further investigated the predicted functional alterations in gut microbiota owing to 131I therapy. Analysis of KEGG pathways showed that compared with microbiome before 131I therapy, after 131I therapy, pathways involved in lipopolysaccharide biosynthesis, apoptosis, alanine, aspartate, and glutamate metabolism, etc., were markedly altered (Fig. 3).Fig. 3Significantly altered KEGG pathways of gut microbiome post-131I therapy ## Study cohorts and clinical parameters of 131I ER and NER patients After identifying the impact of 131I therapy on gut microbiota, we then explored whether microbiome dysbiosis would be correlated with therapeutic response to 131I therapy. After aforementioned exclusion of 20 patients, 40 participants were included and classified into 131I ER group or NER group. Patients’ demographic features and clinical parameters are shown in Table 2.Table 2Demographic and clinical characteristics of participants in the curative effect-controlled studyBaseline characteristicsParticipants ($$n = 40$$)Comparison between groups (p value)Excellentresponse (ER)Non-excellent response (NER)Case: N (%)24 ($60\%$)16 ($40\%$)Age, years: mean ± SD38.67 ± 9.735.63 ± 9.00.324Gender: N (%)Male: 8 ($33.3\%$)Female: 16 ($66.7\%$)Male: 8 ($50\%$)Female: 8 ($50\%$)0.234Risk level: N (%)Medium: 20 ($83.3\%$)High: 4 ($16.7\%$)Medium: 14 ($87.5\%$)High: 2 ($12.5\%$)0.718Pathologic stage post-surgery: N (%)I: 22 (91.6)II: 1 ($4.2\%$)III: 1 ($4.2\%$)I: 16 ($100\%$)II: 0 ($0\%$)III: 0 ($0\%$)1.000Maximum tumor diameter, cm: mean ± SD1.42 ± 0.951.48 ± 0.800.852Local invasion by pathology: N (%)Without: 18 ($75\%$)With: 6 ($25\%$)13 ($81.3\%$)3 ($18.7\%$)0.717Lymph node metastasis by RxWBSa: N (%)Without:15 ($62.5\%$)With: 9 ($37.5\%$)14 ($87.5\%$)2 ($12.5\%$)0.148Distant metastasis by RxWBS: N (%)Without: 23 ($95.8\%$)With: 1 ($4.2\%$)16 ($100\%$)0 ($0\%$)1.000sTgb or TgAbc level:sTg < 1 ng/ml: N (%)sTg 1–10 ng/ml: N (%)sTg > 10 ng/ml or rising TgAb level: N (%)4 ($16.7\%$)15 ($62.5\%$)5 ($20.8\%$)1 ($6.3\%$)7 ($43.7\%$)8 ($50\%$)0.186aRxWBS, post radioactive iodine therapy whole-body scanningbsTg, TSH-stimulated thyroglobulincTgAb, thyroglobulin antibody ## Gut microbiota richness, diversity, and composition differences between 131I ER and NER group pre- and post-131I therapy At 1–2 days prior to 131I radiotherapy, from the 40 participants’ samples, 2,268,985 high-quality sequences were used (average 56,725 per sample). The total number of ASV was 4958 at $99\%$ similarity level. The Good’s coverage (over $99\%$) (Fig. 4a) and the observed species rarefaction curve (Fig. 4b) were used to characterize good sequencing depths. The microbiota of the two groups showed no significant difference in α diversity (data not shown). β diversity analysis represented by PLS-DA-plot illustrated that the microbiome samples were clustered by group (Fig. 4c; R2X = 0.16, R2Y = 0.655, Q2Y = − 0.179). Different bacterial composition of each group in Phylum was shown in taxa bar plot (Fig. 4d). A prominent decrease of F/B ratio was observed in the NER group as compared to ER group (Fig. 4e; $$p \leq 0.0118$$). LEfSe analysis illustrated substantially differently abundant bacterial genera, including Parabacteroides, Dorea, and Bifidobacterium and microbial family, such as Erysipelotrichaceae with a LDA score over 2.0 between the two groups (Fig. 4f).Fig. 4Significant differences in microbial richness, diversity, and composition between excellent response (ER) and non-excellent response (NER) groups 1–2 days before 131I therapy. ER: excellent response to 131I therapy; NER: non-excellent response to 131I therapy. a Goods_coverage indexes of two groups. b Alpha rarefaction of each group indicating sequencing depth. c PLS-DA-plot of the two groups. d Taxa bar plot indicating the bacterial composition of each group in Phylum. e Firmicutes to Bacteroides (F/B) ratio of the two groups (t-test, $$p \leq 0.0002$$). f LEfSe analysis showing significantly different gut flora with a LDA score over 2 between the two groups From the data of specimens collected at 5 months post-131I therapy, 2,253,269 high-quality sequences were used (average 60,832 per sample). The total number of ASV was 2,896 at $99\%$ similarity level. The Good’s coverage of each group was over $99\%$ (Fig. 5a). Rarefaction curve indicated good sequencing depths (Fig. 5b). No significant α diversity was noted between ER and NER groups. However, in line with the result of pre-131I therapy, a notable clustering effect of β diversity by response status post-131I was also revealed by PLS-DA-plot (Fig. 5c; R2X = 0.341, R2Y = 0.518, Q2Y = − 0.083). Taxa bar plot showed elevation of Firmicutes and reduction of Bacteroidetes composition in the NER group (Fig. 5d), but no significant statistic difference was found in F/B ratio (Fig. 5e). LEfSe analysis illustrated significantly different bacterial taxa with a LDA score over 2.0 between ER and NER groups (Fig. 5f), including f_Erysipelotrichaceae, f_Lachnospiraceae, etc. Fig. 5Significant differences in microbial richness, diversity, and composition between excellent response (ER) and non-excellent response (NER) groups 5 months after 131I therapy. a Goods_coverage indexes of two groups. b Alpha rarefaction of each group indicating sequencing depth. c PLS-DA-plot of the two groups. d Taxa bar plot indicating the bacterial composition of each group in Phylum. e Firmicutes to Bacteroides (F/B) ratio of the two groups (t-test). f LEfSe analysis showing significantly different gut flora with a LDA score over 2 between the two groups ## Establishment of a predictive model consisting of gut microbiome and clinical data for response to 131I therapy Based on the results above, we speculated that the gut microbiome signatures of ER or NER pre-131I therapy, combined with some clinical data possibly affecting 131I response, might construct a predictive model for response to 131I therapy in post-surgery patients with PTC. After screening and elimination of variables with multicollinearity, a multivariate analysis using binary logistic regression analysis was performed to analyze the remaining 14 variables to establish a predictive model. Finally, lymph node metastasis and relative abundance of g_Bifidobacterium and g_Dorea were finally chosen as the optimal set to establish the predictive model, with a p value of 0.003 and an overall percentage correct of $80.0\%$ (Table 3), suggesting that this model performed well on predicting response to 131I therapy. High abundance of g_Bifidobacterium and g_Dorea and no lymph node metastasis predicted the patient to be ER, and vice versa. Of note, with a p value of 0.04, g_Dorea was an independent predictor of 131I therapy response. Table 3Summary of predictive model for response to 131I therapy by multivariate analysis using binary logistic regressionPercentage correctChi-squarepVariablesBWaldpModel$80.0\%$13.7950.003Lymph node metastasis-1.6582.8320.092g_Bifidobacterium33.2292.7740.096g_Dorea161.7794.2060.040 ## Discussion Previous literatures have shown variable results in gut microbiome alterations induced by irradiation exposure [11, 16–18]. Nevertheless, the gut microbiota changes in the setting of 131I therapy have not been elucidated yet. Coinciding with some other findings in gut microbiota changes post radiation [19], our data illustrated that 131I therapy also resulted in deterioration of patients’ microbiome alpha diversity and alteration of microbial composition, with an enrichment of “harmful microbiota” and a reduction in “beneficial microbiota” on the whole. The impact of 131I therapy on gut microbiota was huge and durable, at least lasting until the end of our observation, 5 months post 131I administration. As shown by accumulating literatures, gut microbiota dysbiosis is involved in the development of a wide range of diseases, including diarrhea, allergy, cancer, aging, and diabetes [20]; thus, the dysbiosis after 131I therapy described in the present study has great influence on intestinal homeostasis and PTC patients’ health. Among the changes of gut microbiome, alteration in F/B ratio is worth noting. In a healthy host, the majority of gut microbiome is typically dominated by four major phyla: Bacteroidetes, Firmicutes, Actinobacteria, and Proteobacteria [21]. Firmicutes and Bacteroidetes take up over $90\%$ of the relative abundance of the gut microbiota, and their relationship plays a critical role in the maintenance of gut homeostasis. Aberrant ratio between the relative abundance of Firmicutes and Bacteroidetes (F/B ratio) has been found in a series of physiological and pathological conditions, including aging [22], tumor [23], obesity [24], type I diabetes [25], intestinal inflammation. In the present study, besides reduced F/B ratio, our data showed a prominent decline of Firmicutes post-131I therapy, which was in line with Firmicutes change after pelvic radiotherapy [17], indicating a comparable dysbiosis of gut microbiota after 131I therapy. Of note, F/B ratio was also found to be declined in NER group compared to ER group, indicating a possible connection between response to 131I therapy and microbial dysbiosis. For the sake of understanding metabolic profile alterations of gut microbiota after 131I therapy, KEGG analysis of stool samples was performed. Some of the increases in metabolites after 131I therapy, such as bile acids and alanine, are in accordance with a previous reported characteristic elevation in radiation-induced acute intestinal symptoms in cervical cancer patients [26], indicating that metabolism of gut microbiome might share some common features post radiation, which warrants further study. Numerous previous literatures have shown that gut microbiota exhibits value in predicting diseases [27, 28] and treatment response [29, 30]. In order to explore potential predictors of response to 131I therapy, gut microbiome differences were compared between 131I ER and NER groups both before and after the therapy. Different gut microbiota structures were identified as shown in LefSe analysis in Figs. 4f and 5f. Of note, in agreement with a previous study investigating gut microbiota signatures of distinct responses to neoadjuvant chemoradiotherapy [16], g_Dorea, a butyric acid-producing flora, regarded as an important component of functional microbiota in a healthy GI tract [31], was found to be significantly less abundant in 131I NER group pre-therapy in this study as well. Moreover, g_Dorea is later identified to be an independent predictor of response to 131I therapy, further suggesting its importance and predictive value. It is of interest that Erysipelotrichaceae, which belongs to the Firmicutes phylum, with multiple interactions with host immune response, gut inflammation, and lipid metabolism, etc. [ 32], was markedly decreased in NER patients in comparison with ER patients consistently before and after 131I therapy. Lachnospiraceae family, which is positively associated with protection against radiation-induced intestinal damages [33], was found to be remarkably increased in the NER group post-131I therapy, which awaits more study. The comparison of gut microbiota between groups with distinct responses to 131I may provide additional information on how the gut microbiome interacts with radiotherapy responses. Moreover, a predictive model for response to 131I therapy of PTC patients was established in this study. Three variables, including lymph node metastasis, relative abundance of g_Bifidobacterium, and g_Dorea, for the first time, are identified to compose a model capable of predicting response to 131I therapy before the treatment initiation, which will be beneficial for customizing optimal therapeutic approaches for each PTC patient. The strategies to increase relative abundance of g_Dorea and g_Bifidobacterium in the gut microbiota may be promising in improving the response to 131I therapy in post-operative PTC patients. As stated before, g_Dorea, which produces butyric acid, is identified to be an independent predictor of response to 131I therapy in this study. Intriguingly, short-chain fatty acids (SCFAs), including butyric acid, are able to upregulate sodium/iodide (Na+/I−) symporter (NIS) expression through epigenetic modification, thus promote I− uptake and beneficially affect response to 131I therapy[34], which may be one of the underlying mechanisms and needs more investigation. To sum up, the present study demonstrates gut microbiota changes after 131I therapy in post-surgery PTC patients. Differences in gut flora are also found between 131I ER and NER groups both pre- and post-131I therapy, suggesting microbiome may be associated with therapeutic responses. Hence, a predictive model is established to provide a noninvasive tool for predicting response to 131I therapy prior to treatment initiation. These conclusions are based on analysis of data obtained from a small sample pool, and reported in the pilot study herein. The long-term systemic effects of alterations in gut microbiome after 131I therapy still need rigorous evaluation. With the aim to restore a healthy intestinal microbial ecosystem, which is beneficial for the response to 131I therapy, personalized modulation of gut microbiota of PTC patients during the therapy, including dietary changes, administration of antibiotics or probiotics, and fecal microorganism transfers, is promising. Since advanced PTC patients with distant metastasis often have unfavorable prognosis, future studies on gut microbiota with larger cohort of these patients and employing whole genome metagenomics approaches should be carried out. 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--- title: 'Physical activity, obesity, and quality of life among rural Australian cancer survivors: a cross-sectional study' authors: - Michael J. Leach - Georgina Barber - Stephanie Monacella - Philip Jamieson - Thi Trinh - Ngan Vo - Ulla Schmidt - Anny Byrne - Eli Ristevski journal: Supportive Care in Cancer year: 2023 pmcid: PMC10027785 doi: 10.1007/s00520-023-07691-w license: CC BY 4.0 --- # Physical activity, obesity, and quality of life among rural Australian cancer survivors: a cross-sectional study ## Abstract ### Purpose We aimed to describe physical activity (PA), obesity, and quality of life (QoL) among rural Australian cancer survivors, assess whether total and item-specific QoL are associated with sufficient PA and obesity, and assess whether PA and obesity interact with respect to QoL. ### Methods In a cross-sectional study, convenience sampling was used to recruit adult cancer survivors via a chemotherapy day unit and allied health professionals at a rural hospital in Baw Baw Shire, Australia. Exclusion criteria were acute malnutrition and end-of-life care. PA and QoL were measured using Godin-Shephard and 7-item Functional Assessment of Cancer Therapy (FACT-G7) questionnaires, respectively. Factors associated with total and item-specific QoL were assessed via linear and logistic regression, respectively. ### Results Among 103 rural cancer survivors, the median age was 66 years, $35\%$ were sufficiently physically active, and $41\%$ presented with obesity. Mean/median total QoL scores were 17 on the FACT-G7 scale (0–28; higher scores indicate better QoL). Sufficient PA was associated with better QoL (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\upbeta }$$\end{document}β^=2.29; $95\%$ confidence interval [CI] = 0.26, 4.33) and more energy (odds ratio [OR] = 4.00, $95\%$ CI = 1.48, 10.78) while obesity was associated with worse QoL (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\upbeta }$$\end{document}β^=-2.09; $95\%$ CI = -4.17, -0.01) and more pain (OR = 3.88, $95\%$ CI = 1.29, 11.68). The PA-obesity interaction was non-significant (p-value = 0.83). ### Conclusions This is the first known study conducted among rural survivors of any cancer to find sufficient PA and obesity are associated with better and worse QoL, respectively. PA, weight management, and QoL—including energy and pain—should be considered when targeting and tailoring supportive care interventions for rural cancer survivors. ## Introduction Cancer survivors may experience a range of disease- and treatment-related effects, including pain, fatigue, insomnia, and peripheral neuropathy [1], as well as more chronic comorbidities than those without a cancer diagnosis [2]. As a consequence, cancer survivors are predisposed to relatively poor quality of life (QoL) [2]. Furthermore, a cancer survivor’s residential location may influence their quality and quantity of life [3–6]. Studies from the United States (US), Canada, Australia, New Zealand, and European countries report that non-metropolitan (henceforth termed ‘rural’) cancer survivors have worse QoL [3, 4, 7] and lower survival [6] than their metropolitan counterparts. Factors related to QoL could be modified to improve the lives of cancer survivors, particularly in rural areas where patient outcomes and the level of unmet needs are known to be worse [3–5, 7]. Such potentially modifiable factors may include physical activity and obesity. In countries such as the US and Australia, people residing in rural areas are significantly more likely to experience obesity and to be insufficiently physically active than their metropolitan counterparts [8, 9]. Internationally, sufficient physical activity has been shown to be associated with better QoL among cancer survivors diagnosed with particular tumour types, including bladder [10], breast [11–13], endometrial [14], lung [15], non-Hodgkinson’s lymphoma [16], and ovarian [17] cancers. Conversely, obesity has been shown to be associated with worse QoL among survivors of any tumour type in a nationally representative US sample [18] as well as particular tumour types, including breast [11, 19, 20], colorectal [19], endometrial [14], lymphoma [21], melanoma [19], prostate [19], and uterine [19] cancers. All but one of these studies [13] were conducted in metropolitan, state/province-wide or nation-wide samples [3–5, 7, 10–12, 14–17, 19–21]. The cross-sectional study by Vallance et al. [ 13] reported a positive association between sufficient physical activity and better QoL in a sample of rural Canadian breast cancer survivors. No known studies have assessed the associations of physical activity and obesity with total QoL and/or specific QoL components among rural survivors of any, as opposed to a specific, tumour type. As obesity is considered to be a lifestyle disease [22], obesity and physical activity may interact or confound one another in relation to their associations with cancer-related QoL. Three known cross-sectional studies set in the US [19], Canada [14] and Australia [11], respectively, reported that physical activity and obesity did not interact in relation to their associations with cancer-related QoL. As these three studies were conducted among state-wide or nation-wide samples, it is unclear if such an interaction exists for rural or metropolitan cancer survivors considered separately. Given these gaps in the literature, we aimed to investigate physical activity, obesity, and QoL among rural Australian survivors of any cancer by answering four research questions: [1] What are the levels of sufficient physical activity, obesity, and QoL among rural Australian cancer survivors? [ 2] Is QoL associated with each of sufficient physical activity and obesity among rural Australian cancer survivors? [ 3] Which specific components of QoL, if any, are associated with each of sufficient physical activity and obesity among rural Australian cancer survivors? [ 4] Do physical activity and obesity interact in relation to QoL among rural Australian cancer survivors? ## Setting This study is set in Baw Baw Shire—a local government area located within West Gippsland in the state of Victoria, Australia. In 2021, Baw Baw Shire had an estimated resident population of 57,626 people and a median age of 41 years [23]—slightly older than the metropolitan population of Victoria [24]. Baw Baw Shire’s suburbs and postcodes are all classified as non-metropolitan by the Modified Monash Model (MMM), with the shire’s two most populous suburbs (Warragul and Drouin) having an MMM score of 4 (medium rural town) [25, 26]. Rates of potentially modifiable risk factors such as obesity, inadequate fruit intake, harmful alcohol use, smoking, and insufficient physical activity are higher in Baw Baw Shire than state- or nation-wide [27]. In Baw Baw Shire, chemotherapy is offered one day a week at the local hospital’s chemotherapy day unit (CDU), which has the capacity to treat 18 patients per week on average. On clinic days, this CDU is resourced by a visiting medical oncologist and a visiting haematologist. The nearest radiotherapy center is 54 km away by road. ## Design and sampling A cross-sectional study was conducted using baseline (pre-intervention) data collected as part of a prospective cohort study investigating a nutrition and physical activity health coaching intervention: the I.CAN program [28]. Participants were primarily recruited via convenience sampling of cancer patients who, over the period August 2017-December 2021, attended the CDU at a rural hospital in Baw Baw Shire. The I.CAN program was offered to all cancer patients at the particular CDU as part of routine care, subject to pre-defined inclusion and exclusion criteria. The following inclusion criteria were applied: aged 18 years or over and diagnosed with any type and stage of cancer. The latter inclusion criterion aligns with the National Coalition for Cancer Survivorship’s broad definition of cancer survivorship: “from the time of diagnosis and for the balance of life.” [ 29] Those experiencing acute malnutrition or receiving end-of-life care were excluded from the present study due to urgent, complex needs. In addition to the recruitment of participants through the CDU, some eligible participants were recruited over the same period through referrals from allied health professionals practicing in the same hospital. ## Data collection Quantitative data on participants’ characteristics were collected via paper-based forms prior to manual entry into a secure, password-protected Microsoft Access database (Microsoft Corp., Redmond, WA, USA). ## Measures In the present study, I.CAN data on participants’ QoL, demographics, cancer type, treatment status, physical activity, and body mass index (BMI) were used. The outcome of interest, QoL, was assessed using the 7-Item Functional Assessment of Cancer Therapy – General (FACT-G7) instrument Version 4 [30, 31], which has been shown to be valid and reliable in cancer populations. We chose the FACT-G7 over the broader FACT-G [32] because it is relatively brief and, thus, may help to reduce patient and clinician burden while increasing the rate of response to all items. The FACT-G7 instrument comprises the following seven items describing components of a cancer patient’s QoL over the past week:I have a lack of energyI have nauseaI am able to enjoy lifeI have painI am sleeping wellI worry that my condition will get worseI am content with the quality of my life right now [31]. For each item, respondents choose one response from a five-point Likert scale: ‘Not at all’ (scored 0), ‘A little bit’ (scored 1), ‘Somewhat’ (scored 2), ‘Quite a bit’ (scored 3), and ‘Very much’ (scored 4). In the present study, the total FACT-G7 score was computed in accordance with the developers’ scoring guidelines [31]. This gave a total FACT-G7 score in the range 0–28 (inclusive), where a higher number indicates better QoL. The total FACT-G7 score and item-specific FACT-G7 scores were treated as continuous variables. Additionally, each of the seven FACT-G7 items was assessed as a separate binary variable with categories of ‘more’ (scores of 3 or 4) and ‘less’ (scores of 0, 1 or 2). In order to obtain larger reference groups and, thus, more stable effect estimates in (binary) logistic regression, item-specific FACT-G7 scores were reversed as needed when creating binary outcome variables. In terms of demographic and clinical variables of interest, age at baseline was treated as both a continuous variable and a polytomous variable with three categories: < 65, 65–74, and ≥ 75 years. Binary variables were created for gender (male or female), Aboriginal and/or Torres Strait Islander origin (yes or no), and country of birth (Australia or country other than Australia). Cancer type was dichotomised into categories of ‘breast’ and ‘not breast’ due, firstly, to the high proportion of participants with breast cancer and, secondly, to the fact that the only known rural study investigating the association between physical activity and cancer-related QoL was conducted among breast cancer survivors [13]. Given some relevant past studies were conducted among cancer survivors defined from the time of treatment completion rather than diagnosis (e.g. an Australian study of breast cancer survivors [11]), treatment status in our study was defined as a binary variable with the following categories: current or completed/ceased. Regarding lifestyle factors, physical activity was measured using the Godin-Shephard Leisure-Time Physical Activity Questionnaire [33]—a validated instrument that is commonly used in oncology research [34]. This questionnaire includes four questions about the frequency of strenuous, moderate, and mild exercise during a typical week [33]. In the present study, participants’ physical activity scores were considered both as a continuous variable as well as a binary variable with the following categories: sufficiently active (score ≥ 14) and insufficiently active (score < 14) [35]. BMI in kg/m2 was calculated from participants’ height and weight. BMI was treated as a continuous variable, a polytomous variable with the World Health Organization’s (WHO’s) six nutritional status categories, and a binary obesity variable with WHO-defined categories of ‘yes’ (BMI ≥ 30.0 kg/m2) and ‘no’ (BMI < 30.0 kg/m2) [36]. ## Statistical analyses Polytomous and binary variables were described in terms of the frequency and percentage. The Shapiro–Wilk test was used to check whether continuous variables were approximately normally distributed. The distributions of the total FACT-G7 score, Godin-Shephard score, and BMI were further assessed via boxplots visualising the five-number summary (minimum, lower quartile, median, upper quartile, and maximum). The interquartile range (IQR) was calculated by subtracting the lower quartile from the upper quartile. The normally distributed total FACT-G7 score was summarised using the mean (standard deviation [SD]). As the remaining continuous variables were non-normally distributed, they were summarised using the median (IQR). As varying definitions of cancer survivorship exist [8, 11, 37], the difference in FACT-G7 scores between treatment status groups was assessed using the two-samples t-test while differences in sufficient physical activity and obesity between treatment status groups were assessed using Pearson’s chi-squared tests. Associations between the primary outcome of interest, the total FACT-G7 score, and all categorical explanatory variables (except the polytomous BMI variable) were assessed via univariable and multivariable linear regression. The multivariable linear regression model contained all categorical variables except the polytomous BMI variable, giving seven independent/exposure variables in total. This permitted the assessment of the two independent variables of interest, sufficient physical activity and obesity, as well as the effects of potential confounding factors. Fitting the univariable and multivariable linear regression models involved calculating regression coefficients (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\upbeta }$$\end{document}β^s) as well as corresponding $95\%$ confidence intervals (CIs) and p-values. After fitting the multivariable linear regression model, the statistical significance of the interaction between sufficient physical activity and obesity was also tested in the multivariable setting. If this interaction term was not statistically significant, then it was excluded from the final multivariable model. Associations between the secondary outcomes of interest—the binary, item-specific FACT-G7 variables—and each of obesity and sufficient physical activity were assessed via univariable and multivariable logistic regression. Due to limited numbers of outcome events, obesity and physical activity were only adjusted for one another and age group in the multivariable logistic regression models. Across all analyses, a p-value less than 0.05 denoted a statistically significant result at the $5\%$ level of significance. A complete-case approach to handling missing data was followed and all analyses were conducted in Stata v15.0 (StataCorp, College Station, TX, USA). ## Ethical considerations Ethics approval was obtained from West Gippsland Healthcare Group Research Ethics Committee (ID: ICAN), Latrobe Regional Hospital Human Research Ethics Committee (ID: 2020-14) and Monash University Human Research Ethics Committee (ID: 11890). Prior to providing informed consent in writing, all participants were provided with written and verbal information about the I.CAN study. ## Study population Of the 112 eligible adult cancer survivors, 103 ($92\%$) had complete baseline data on all variables of interest and were included for analysis. The nine excluded participants were five individuals with missing FACT-G7 data and four individuals with missing BMI data. The demographic, medical, physical activity, and anthropometric characteristics of all included participants are described in Table 1. Continuous age in years was non-normally distributed (p-value < 0.05) with a negative skew (data not shown). The median (IQR) age of participants was 66 [19] years (Table 1).Table 1Rural cancer survivors’ demographic, medical, physical activity, and anthropometric characteristics in relation to quality of life ($$n = 103$$) FACT-G7 scoreUnivariable effectMultivariable effectCharacteristicn†%†Mean (SD)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\upbeta }$$\end{document}β^ ($95\%$ CI)p-value\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\upbeta }$$\end{document}β^ ($95\%$ CI)p-valueDemographic Age in years (Median, IQR)65.719.4––––– Age group < 65 years$4947.6\%$16.84 (4.33)0–0– 65–74 years$3836.9\%$16.39 (5.41)–0.44 (–2.47, 1.59)0.667–0.01 (–2.05, 2.02)0.990 ≥ 75 years$1615.5\%$19.38 (4.19)2.54 (–0.17, 5.25)0.0662.68 (–0.19, 5.55)0.067 Gender Female$7572.8\%$17.32 (4.55)0–0– Male$2827.2\%$16.39 (5.45)–0.93 (–3.04, 1.18)0.386–0.98 (–3.41, 1.44)0.424 Aboriginal and/or Torres Strait Islander origin No$103100\%$17.07 (4.80)–––– Yes$00\%$––––– Country of birth Australia$8986.4\%$17.09 (4.63)0–0- Country other than Australia$1413.6\%$16.93 (5.99)–0.16 (–2.91, 2.59)0.908–1.65 (–4.42, 1.11)0.239Medical Cancer type Other‡$5553.4\%$16.85 (5.00)0–0– Breast$4846.6\%$17.31 (4.60)0.46 (–1.43, 2.35)0.6311.08 (–1.09, 3.24)0.329 Treatment status Current$5250.5\%$17.13 (5.03)0–0– Completed/ceased$5149.5\%$17.00 (4.61)–0.13 (–2.02, 1.75)0.888–1.11 (–2.97, 0.75)0.240Physical activity Sufficient physical activity§ No$6765.0\%$16.04 (4.81)0–0– Yes$3635.0\%$18.97 (4.21)2.93 (1.04, 4.82)0.0032.29 (0.26, 4.33)0.028Anthropometry BMI group¶ Underweight (BMI < 18.5 kg/m2)$21.9\%$16.00 (4.24)–––– Normal weight (18.5 kg/m2 ≤ BMI < 25.0 kg/m2)$2524.3\%$17.68 (4.76)–––– Pre-obesity (25.0 kg/m2 ≤ BMI < 30.0 kg/m2)$3433.0\%$18.59 (5.18)–––– Obesity class I (30.0 kg/m2 ≤ BMI < 35.0 kg/m2)$2524.3\%$15.92 (4.06)–––– Obesity class II (35.0 kg/m2 ≤ BMI < 40.0 kg/m2)$1110.7\%$14.45 (4.99)–––– Obesity class III (40.0 kg/m2 ≤ BMI)$65.8\%$15.83 (3.25)–––– Obesity¶ No (BMI < 30 kg/m2)$6159.2\%$18.13 (4.94)0–0– Yes (BMI ≥ 30 kg/m2)$4240.8\%$15.52 (4.17)–2.61 (–4.46, -0.76)0.006–2.09 (–4.17, –0.01)0.049n frequency, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\widehat{\upbeta }$$\end{document}β^ estimated linear regression coefficient, CI confidence interval, FACT-G7 Functional Assessment of Cancer Therapy – General (7-item version), IQR interquartile range, BMI body mass index, kg kilograms, m meters†Unless otherwise stated‡ ‘Other’ (i.e. non-breast) cancers include central nervous system, colorectal, genitourinary, gynaecological, haematological, lung, and upper gastrointestinal cancers§Classified as a score ≥ 14 on the Godin-Shephard physical activity scale [35]¶Classified using the World Health Organization’s definition [36] ## Level of quality of life The total FACT-G7 score was approximately normally distributed (p-value = 0.20; Fig. 1). The mean (SD) total FACT-G7 score was 17.07 (4.80) (Table 2) on the scale of 0–28 while the corresponding median (IQR) score was 17.00 (8.00). Among the seven individual FACT-G7 items, the lowest mean score (1.68 on the scale of 0–4, SD = 1.09) was observed for ‘I do not have a lack of energy’ whereas the highest mean score (3.39 on the scale of 0–4, SD = 0.91) was observed for ‘I do not have nausea’ (Table 2).Fig. 1Boxplot for participants’ total FACT-G7 (quality of life) score ($$n = 103$$)Table 2Descriptive statistics for individual FACT-G7 items and the total FACT-G7 score ($$n = 103$$)FACT-G7 ItemsMean (SD)1. I do not have a lack of energy†1.68 (1.09)2. I do not have nausea†3.39 (0.91)3. I am able to enjoy life†2.74 (0.96)4. I do not have pain†2.52 (1.24)5. I am sleeping well†2.18 (1.35)6. I do not worry that my condition will get worse†2.29 (1.45)7. I am content with the quality of my life right now†2.26 (1.16)Total FACT-G7 Score‡17.07 (4.80)FACT-G7 Functional Assessment of Cancer Therapy-General (7-item version), SD standard deviation†Item-specific score in the range 0–4, where 0 = Not at all, 1 = A little bit, 2 = Somewhat, 3 = Quite a bit, and 4 = Very much‡Total score in the range 0–28. For each individual participant, this total score is the sum of the 7 item-specific scores Table 1 shows the mean (SD) total FACT-G7 score for each category of participants’ demographic, medical, physical activity, and anthropometric characteristics. The mean (SD) FACT-G7 score did not significantly differ (p-value = 0.89) between the 51 participants who had completed/ceased treatment (17.00 [4.61]) and the 52 participant who were currently receiving treatment (17.13 [5.03]). ## Level of physical activity The Godin-Shephard physical activity score was non-normally distributed ($p \leq 0.05$) with a positive skew (Fig. 2). The median (IQR) Godin-Shephard physical activity score was 6.00 (15.00). Overall, $35\%$ of participants had sufficient physical activity (Table 1). Regarding stratification by treatment status, $43\%$ of the 51 participants who had completed/ceased treatment and $27\%$ of the 52 participants currently receiving treatment were sufficiently physically active. This difference was not statistically significant (p-value = 0.08).Fig. 2Boxplot for participants’ Godin-Shephard score ($$n = 103$$), with a dashed reference line at 14 denoting the threshold for sufficient physical activity ## Level of BMI BMI was non-normally distributed ($p \leq 0.05$) with a positive skew (Fig. 3). The median (IQR) BMI was 28.00 (7.80) kg/m2. Forty-one per cent of the sample was classified into the obesity category (Table 1). Regarding stratification by treatment status, $37\%$ of the 51 participants who had completed/ceased treatment and $44\%$ of the 52 participants currently receiving treatment were people with obesity—a non-significant difference ($$p \leq 0.48$$).Fig. 3Boxplot for participants’ body mass index ($$n = 103$$), with a dashed reference line at 30 kg/m2 denoting the threshold for obesity ## Correlates of QoL Table 1 shows the univariable and multivariable linear regression results for factors associated with QoL. The two independent variables of primary interest—sufficient physical activity and obesity—were both found to be significantly associated with QoL. More specifically, independent of all other variables in the multivariable model, sufficient physical activity was associated with significantly better QoL while obesity was associated with significantly worse QoL. As the physical activity-obesity interaction was not statistically significant (p-value = 0.83), this interaction term was not included in the multivariable model. All other factors adjusted for in the multivariable model—age, gender, country of birth, cancer type, and treatment status—were not associated with QoL. Due to small numbers of outcome events, only five of the seven item-specific FACT-7 scores could be collapsed into binary variables and used as outcomes in logistic regression models. The five binary outcome variables were ‘more energy’, ‘more pain’, ‘less worry’, ‘more sleep’ and ‘more content’. The adjusted odds of having more energy were significantly increased for those participants with sufficient physical activity, while the adjusted odds of experiencing more pain were significantly increased for those with obesity (Table 3).Table 3Associations between individual FACT-G7 items and each of sufficient physical activity and obesity ($$n = 103$$)aOR ($95\%$ CI)†CharacteristicMore energyMore painLess worryMore sleepMore contentSufficient physical activity‡ No1.001.001.001.001.00 Yes4.00 (1.48–10.78)0.82 (0.28–2.36)0.98 (0.40–2.37)2.18 (0.89–5.30)1.84 (0.77–4.42)Obesity§ No (BMI < 30 kg/m2)1.001.001.001.001.00 Yes (BMI ≥ 30 kg/m2)0.67 (0.22–2.02)3.88 (1.29–11.68)0.78 (0.32–1.91)1.28 (0.51–3.17)0.67 (0.28–1.61)n frequency, aOR adjusted odds ratio (in multivariable logistic regression model), CI confidence interval, BMI body mass index, kg kilograms, m meters†In multivariable models, each of the variables ‘Sufficient physical activity’ and ‘Obesity’ were adjusted for one another as well as age‡Classified as a score ≥ 14 on the Godin-Shephard physical activity scale [35]§Classified using the World Health Organization’s definition [36] ## Discussion Our study found the average level of total QoL in our sample of rural cancer survivors was 17 on the FACT-G7 scale—slightly higher than this instrument’s established cut-off value for low QoL (a score ≤ 16) [38]. While the average level of QoL in our sample was not low, the median FACT-G7 score was also 17. This means that nearly half of the sample would be considered to have low QoL. These low QoL scores were driven by the FACT-G7 item about lacking energy—a common problem that reportedly affects 10–$27\%$ of cancer survivors [39]. As no known studies set in rural areas have reported a mean or median FACT-G7 score, our study may provide a benchmark for any future rural studies conducted in samples with similar participant characteristics. In our study, $41\%$ of the sample presented with obesity while $35\%$ had sufficient physical activity during a typical week. On the one hand, our study’s level of obesity is eight percentage points higher than the corresponding level of obesity in a study of rural South Australian cancer survivors: $33\%$ [8]. On the other hand, the observed level of sufficient physical activity is five percentage points lower than the corresponding percentage of rural South Australian cancer survivors who had sufficient physical activity: $40\%$ [8]. In terms of international comparisons, our study’s median BMI of 28 kg/m2 is similar to the mean BMI of 27 kg/m2 among rural Canadian breast cancer survivors [13]. However, the present study’s levels of sufficient physical activity post treatment ($43\%$) and during treatment ($27\%$) exceeded the corresponding percentages for rural Canadian breast cancer survivors ($35\%$ and $14\%$, respectively) [13]. Our study also found that sufficient physical activity was associated with significantly better QoL while obesity was associated with significantly worse QoL. The former result was driven by a significant association between sufficient physical activity and more energy while the latter result was driven by a significant association between obesity and more pain. The observed sufficient physical activity-QoL association aligns with past international studies conducted among survivors of particular tumour types [10–17], including a state-wide cross-sectional study of Western Australian breast cancer survivors [11] and a Canadian cross-sectional study of rural breast cancer survivors [13]. The obesity-QoL association in our sample is also supported in the broader literature [14, 18, 19, 21], as is the association between obesity and more pain [40]. The observed association between sufficient physical activity and more energy points towards a potential bidirectional association: those with less energy may feel less inclined to exercise while those who exercise may subsequently feel more energetic. This association could be investigated in future cohort studies. Ours is the first known study to find that total QoL and specific QoL items are associated with sufficient physical activity and obesity among rural cancer survivors. Pertinent supportive care interventions should be considered for rural cancer survivors experiencing physical inactivity, obesity and/or poor QoL, although any local barriers to supportive care screening and referrals among oncology staff (e.g. time constraints and scope of practice concerns [41]) would first need to be addressed. The present study also found that, with respect to their associations with QoL, obesity did not interact with physical activity. This result aligns with those of past studies [11, 14, 19] while providing a novel finding for rural cancer survivors. The results reported here should be interpreted in light of four key limitations. Firstly, as a cross-sectional study design was used, causation cannot be inferred from the statistically significant associations. For instance, it is unclear whether sufficient physical activity likely led to better QoL or better QoL likely led to sufficient physical activity. Secondly, unmeasured factors—most notably comorbid conditions [37]—could have confounded the observed associations between total/item-specific QoL and each of physical activity and obesity. Thirdly, the Godin-Shephard Leisure-Time Physical Activity Questionnaire [33] measures the frequency and intensity of physical activity but does not measure resistance activities, which are also recommended in clinical guidelines [42]. Fourthly, using a convenience approach to sampling eligible participants from one particular Australian setting has limited the generalisability of our findings. Despite these limitations, key strengths of the present study include the use of validated measures of QoL [30] and physical activity [34], use of the standard international definition of obesity [36], and minimal missing data. ## Conclusion In our sample of 103 rural Australian cancer survivors, approximately one-third were sufficiently physically active, $41\%$ presented with obesity, and QoL on the FACT-G7 scale was, on average, 17—slightly higher than the cut-off of 16 for low QoL [38]. This is the first known study conducted in rural survivors of any cancer to find that sufficient physical activity and obesity are associated with better and worse QoL, respectively, and that these two factors do not interact with one another. This is also the first known study to find that, among rural cancer survivors, sufficient physical and obesity are associated with individual QoL items: more energy and more pain, respectively. 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--- title: The association between health costs and physical inactivity; analysis from the Physical Activity at Work study in Thailand authors: - Katika Akksilp - Wanrudee Isaranuwatchai - Yot Teerawattananon - Cynthia Chen journal: Frontiers in Public Health year: 2023 pmcid: PMC10027789 doi: 10.3389/fpubh.2023.1037699 license: CC BY 4.0 --- # The association between health costs and physical inactivity; analysis from the Physical Activity at Work study in Thailand ## Abstract ### Introduction Physical inactivity increases the risks of several common yet serious non-communicable diseases, costing a tremendous amount of health expenditure globally. This study aimed to estimate the association between health costs and physical inactivity in Thailand. ### Methods Data from the Physical Activity at Work cluster randomized controlled trial participants with valid objective physical activity data were extracted. Health costs were collected using the Health and Welfare Survey and the Work Productivity and Activity Impairment Questionnaire and were categorized into past-month outpatient illness, past-year inpatient illness, and past-week presenteeism and absenteeism. Time spent in moderate-to-vigorous physical activity was used to determine the activity level according to the current guideline (i.e., ≥150 minutes moderate-intensity or ≥75 minutes vigorous-intensity equivalent physical activity per week). The primary analysis evaluated the association between direct cost (treatment and travel costs) and societal cost (direct cost plus absenteeism due to the illness) of past-month outpatient illness and physical inactivity using a two-part model. ### Results In total, 277 participants with a mean age of 38.7 were included. Average direct and societal cost due to past-month outpatient illness were 146 THB (3.99 USD) (SD = 647 THB) and 457 THB (12.5 USD) (SD = 1390 THB), respectively. Compared to active participants, direct and societal cost of past-month outpatient illness were 153 THB (4.18 USD) ($95\%$CI: –54.7 to 360 THB) and 426 THB (11.7 USD) ($95\%$CI: 23.3 to 829 THB) higher in physically inactive individuals, respectively, adjusted for covariates. The additional societal cost of past-month outpatient illness was $145\%$ higher in physically inactive participants compared to active participants. On the other hand, there was no significant association in direct and societal cost of past-year inpatient illness nor past-week indirect costs between physically active and non-active participants. ### Discussion Results were similar to recent findings in different countries. However, the findings should be generalized with caution due to the small sample size and potential bias from reverse causation. Future research is crucial for clarifying the health costs of physical inactivity in Thailand and other countries. ## 1. Introduction Physical inactivity is a significant risk factor for developing several non-communicable diseases (NCDs), such as metabolic syndromes and cardiovascular diseases (1–4). In 2013, the total cost attributable to physical inactivity was $67.5 billion globally, calculated from five major NCDs: coronary heart disease, stroke, type 2 diabetes, breast cancer, and colon cancer [5]. In addition, the lack of physical activity (PA) has recently been found to increase the risks of other NCDs as well as depression and falls [1, 6]. These have become common serious issues with increasing prevalence (7–9). Strategies to increase PA have been researched and implemented in several countries at all income levels. However, little success has been observed due to the lack of awareness of the intermediate impact at the individual level, e.g., short-term health and economic consequences from physical inactivity [10]. To direct policymakers' focus on this crucial matter, many studies have recently focused on economic burdens related to physical inactivity (11–14). Various methodological approaches were implemented to estimate health expenditures due to physical inactivity [15]. Moreover, it is well established that the economic burden depends significantly on regional cultural differences [16]. Therefore, more studies have been conducted to generate better evidence in low- and middle-income countries, where the prevalence of physical inactivity has been increasing rapidly, especially in the urban population [3, 10]. In Thailand, there have been many attempts to increase PA in all age groups, including the National Step Challenge in 2020. This is a nationwide program under the “Thailand Physical Activity Strategy 2018–2030”, a roadmap led by the Ministry of Public Health, Thailand [17]. Self-report PA data from Thai studies showed that around three-quarters of the adult population is physically active, as defined by the current guideline [18, 19]. On the other hand, a recent report from the Thai National Step Challenge data, translated from built-in smartphone accelerators, depicted a low level of PA with an average of 3,200 steps per day despite being encouraged by the intervention [20]. There has been no explicit estimation of health costs due to physical inactivity in Thailand. Most Thai citizens are covered by either the universal coverage scheme, the government health insurance scheme or the social security scheme [21]. This universal health coverage initiative results in fewer patients spending out-of-pocket when using health services. However, the incidence of NCDs has been rising from 15.8 to $17.8\%$ of the population between 2009 and 2019 [22]. There is an urgent need for evidence to enhance awareness of policymakers and the general population regarding the intermediate economic impact of physical inactivity on Thai society. This analysis aims to evaluate the association between health costs and physical inactivity using secondary data from a cluster-randomized trial in Thai office workers. ## 2.1. Samples This study used data from the Physical Activity at Work (PAW) cluster-randomized controlled trial. The Ethical Review Committee for Research in Human Subjects, Ministry of Public Health (ECMOPH) (Protocol Number: 004-2563) approved the study in accordance with the Declaration of Helsinki. The trial was registered with the Thai Clinical Trials Registry (TCTR) (TCTR20200604007) [23]. More details on the trial can be found in a published trial protocol [24]. Participants were recruited between July and September 2020 with the inclusion criteria of: [1] was employed during the study period, [2] aged at least 18 years old, [3] had no physical mobility limitations, [4] worked at least 3 days a week, and [5] owned a smartphone compatible with Fitbit® application. Participants with plans to take leaves of absence for more than 2 weeks and those who were pregnant during the trial were also excluded. In total, 282 office workers in the Ministry of Public Health, Thailand, participated in the study and were allocated by cluster to either the intervention or control group. At the 6-month, 28 participants dropped out from the study resulting in 254 remaining for the follow-up data collection. ## 2.2.1. Physical activity From the PAW data collection, participants were objectively measured PA and sedentary levels by wearing the ActiGraph™ wGT3X-BT tri-axial accelerometer (ActiGraph™, Pensacola, Florida, USA) for 10 days on the waist. The accelerometer was tested as a valid tool for measuring step counts and “a reliable tool for measuring PA in adults under free-living conditions” in recent studies [25, 26]. An eligible ActiGraph™ data analysis criteria of wearing the device at least 10 h each day for 3 workdays was implemented to drop invalid data. The research team downloaded the pre-initialized 60 Hz count data from the device using ActiLife 6 software to analyze on RStudio Version 1.4.1103 with the R package “Physical Activity”. Choi's algorithm [27] was used to distinguish wear from non-wear. Freedson's cut-points [28] were used to categorize PA levels as sedentary [<151 counts per minute (CPM)], light-intensity (151–2,689 CPM), moderate-intensity (2,690–6167 CPM), or vigorous-intensity (>6,167 CPM). Finally, time spent in moderate- and vigorous-intensity PA (MVPA) was calculated as: [(daily average of time spent in moderate-intensity PA) + 2 x (daily average of time spent in vigorous-intensity PA)] × 7. Individuals were physically inactive if they had MVPA of <150 min based on the current guideline [29]. ## 2.2.2. Health costs Economic cost of physical inactivity was estimated from the societal perspective. The Health and Welfare Survey from the National Statistical Office (NSO), a nationwide survey initiated since 1974 for health and welfare reports in Thailand [30, 31], was used as part of the questionnaire to collect health-related expenditures, including out-of-pocket payment of treatment (direct medical cost), travel fees (direct non-medical costs), and absent days (indirect cost) of the past-month outpatient illness and past-year inpatient illness. In spite of the frequent use of the survey data in previous studies (30, 32–34), there has been no validation study of the tool. We use the questionnaire, nevertheless, as it has been used for health and welfare-related research as well as situation analyses in Thailand so that we can compare results effectively with other studies. Regarding indirect costs, an average wage of 20,000 THB (547 USD) per month acquired from the Human Resource office, which was equivalent to 909 THB (24.9 USD) per day or 130 THB (3.56 USD) per hour, was used for the population. We calculated the cost due to absenteeism by multiplying absent days with the daily wage. Eventually, direct and indirect costs were summed up to represent the societal cost. Participants were also asked if their current health insurance scheme covered each payment. If participants were fully covered, the average cost of outpatient care (165 THB; 4.51 USD) and inpatient care (2,944 THB; 80.5 USD), reported by the NSO in 2019 [22], were used to calculate the service fee. Baseline interviews were done between August and September 2020, and follow-up interviews between February and March 2021. All costs are presented in year 2022 dollars. Moreover, in the sensitivity analysis, we estimated the additional indirect cost due to physical inactivity, calculated as the past-week absenteeism and presenteeism from the Work Productivity and Activity Impairment Questionnaire: General Health V2.0 (WPAI-GH). For absenteeism cost, we asked the absent hours due to health problems (i) and the hours actually worked (ii) during the previous week to calculate the score: (i)/((i) + (ii)). Next, we multiplied the absenteeism score by the average weekly wage (4,546 THB; 126.8 USD). For presenteeism cost, the degree health problems affected work productivity over the last seven days were asked using a rating scale from 0 to 10; 0 for no effect of health problem on work and 10 for being wholly prevented from working. The scale was then used to calculate the cost of past-week presenteeism by the formula; (WPAI-GH presenteeism scale/10) × (number of hours actually worked per week) × (the hourly wage). ## 2.2.3. Covariates Questionnaire interviews and physical examinations were done to collect participants' demographics and biomarker data. Covariates included sex, age (continuous, in years), education (highest at bachelor's degree or above bachelor's degree), and obesity (body mass index; BMI ≥ 25). ## 2.3. Statistical analysis We summarized baseline characteristics by physically active or inactive groups using the mean and standard deviation for continuous variables and the frequencies and percentages for categorical variables. Between-group comparisons of baseline characteristics were done using t-test and chi-square test for continuous and categorical variables, respectively. A two-part model was used to deal with the zero-inflated and the skewness nature of the health cost data (35–37). A probit model first predicted the probability of having health costs. Then, of those with positive health costs, the generalized linear model with a log link and gamma family was used to explore the relationship between health costs and physical inactivity. In the primary analysis, the model used direct and societal cost of the past-month outpatient illness as the outcome, physically active or inactive as a binary exposure, and adjusted for the covariates. The sensitivity analyses were done for: (i) changing the outcome to direct and societal cost of the past-year inpatient illness; (ii) changing the outcome to the past-week health-related absenteeism and presenteeism from the WPAI-GH questionnaire; and (iii) estimating direct and societal cost using 6-month follow-up data as the outcome, with additional adjustment for baseline costs. We used Stata software version 14.2 for all statistical data analyses, and the significance level was set at $5\%$. ## 3. Results The primary analysis involved 277 participants from the PAW study. Participants' mean age was 38.7 years (SD = 10.3), where $81\%$ were women, $23\%$ were obese, and $48\%$ were physically active at baseline. Physically inactive participants appeared to be female (91.7 vs. $69.9\%$, $p \leq 0.001$) and older (39.9 vs. 37.3 years, $$p \leq 0.036$$). Overall, the average direct and societal cost due to past-month outpatient illness were 146 THB (3.99 USD) (SD = 647 THB) and 457 THB (12.5 USD) (SD = 1,390 THB), respectively (Table 1). Table 1 \| Unnamed: 0 \| Total \| Activea \| Inactivea \| P-value \| \| --- \| --- \| --- \| --- \| --- \| \| \| N = 277 \| N = 133 \| N = 144 \| \| \| Age, year \| 38.7 (10.3) \| 37.3 (8.97) \| 39.9 (11.3) \| 0.036 \| \| Gender, female \| 225 (81.2%) \| 93 (69.9%) \| 132 (91.7%) \| < 0.001 \| \| Education, above bachelor's degree \| 98 (35.4%) \| 48 (36.1%) \| 50 (34.7%) \| 0.812 \| \| Body mass index (BMI), kg/m2 \| 24.4 (5.17) \| 24.9 (5.40) \| 24.0 (4.94) \| 0.128 \| \| Obese (BMI ≥ 25 kg/m2) \| 64 (23.1%) \| 32 (24.1%) \| 32 (22.2%) \| 0.717 \| \| Physical activity \| Physical activity \| Physical activity \| Physical activity \| Physical activity \| \| Moderate physical activity/week, min \| 24.9 (17.5) \| 38.1 (16.8) \| 12.8 (4.82) \| < 0.001 \| \| Vigorous physical activity/week, min \| 0.652 (1.64) \| 1.19 (2.20) \| 0.152 (0.417) \| < 0.001 \| \| Health cost (THB) \| Health cost (THB) \| Health cost (THB) \| Health cost (THB) \| Health cost (THB) \| \| Direct costb: Past-month outpatient illness \| 146 (647) \| 85.6 (464) \| 201 (777) \| 0.014 \| \| Societal costc: Past-month outpatient illness \| 457 (1,390) \| 294 (1,080) \| 608 (1,620) \| 0.063 \| \| Direct costb: Past-year inpatient illness \| 1,300 (12,500) \| 261 (1,840) \| 2,260 (17,300) \| 0.158 \| \| Societal costc: Past-year inpatient illness \| 2,110 (18,300) \| 563 (3,770) \| 3,530 (25,000) \| 0.155 \| Compared to physically active participants, additional direct and societal cost of past-month outpatient illness due to physical inactivity were 153 THB (4.18 USD) ($95\%$CI: −54.7 to 360 THB) and 426 THB (11.7 USD) ($95\%$CI: 23.3–829 THB), respectively (Figure 1; Tables 2, 3). The additional societal cost of past-month outpatient illness in inactive participants could be calculated as a $145\%$ increase compared to physically active participants. Nevertheless, while physically inactive individuals had 2,000 THB and 2,970 THB higher direct and societal cost, respectively, in the past-year inpatient illness, this finding was not statistically significant (Figure 1). Figure 1: Additional health costs in physically inactive participants compared to activea participants. aActive refers to physically active participants according to the current guideline (≥150 min moderate-intensity or ≥75 min vigorous-intensity equivalent physical activity per week). bDirect cost included treatment and travel costs. cSocietal cost included treatment, travel costs, and absenteeism due to the illness. dPart 2 of the two-part model; adjusted for age, sex, obesity, and education. ePart 2 of the two-part model; unadjusted because only 14 participants reported having past-year inpatient illnesses. $p \leq 0.05.$ TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3 Regarding indirect costs due to the past-week health-related presenteeism and absenteeism from the WPAI-GH questionnaire, the average cost was 982 THB (26.9 USD) (SD = 1,290 THB). Around $85.9\%$ of this was from the presenteeism cost. The additional indirect cost due to physical inactivity was 271 THB (7.41 USD) ($95\%$CI: −51.1 to 593 THB), adjusted for covariates (Supplementary Tables S1, S2). In the adjusted analyses for both the societal cost of the past-month outpatient illness and the additional cost due to past-week health-related presenteeism and absenteeism due to physical inactivity, having obesity (BMI ≥ 25 kg/m2) was significantly associated with additional health costs (Table 3; Supplementary Table S3). The sensitivity analysis showed that 89 participants were physically inactive at both baseline and follow-up time points, 26 participants were physically active at baseline but became inactive at follow-up, 36 participants were physically inactive at baseline but became active at follow-up, and 94 participants were physically active at both time points. We found no evidence in different health costs of past-month outpatient illness at follow-up among different PA change categories (Supplementary Table S4; Supplementary Figure S1). ## 4. Discussion This study was the first study in Thailand to research the associations between health costs and physical inactivity using the PAW cluster-randomized controlled trial data. The primary analysis showed evidence of a $145\%$ increase in societal cost among those with physical inactivity compared with active participants. The result aligned with previous research in other countries [38, 39] that being physically active was associated with a lower health cost. Using mean annual health expenditure as the outcome variable, Carlson et al. found $30\%$ higher health expenditure in physically inactive US adults compared to active adults, and Brown et al. found $26\%$ higher health expenditure in sedentary than in moderately active Australian women [40, 41]. However, comparing findings between studies is challenging because of the different methods of assessment and estimation of the association between cost outcomes and physical inactivity, including the general context of study settings [15, 40]. Most studies reported health expenditures that included the payer's perspective and neglected direct non-medical and indirect costs (15, 42–44). While it is true that these studies extracted extensive variables from linked databases, the challenge remained to extrapolate societal cost. In our study, keeping in mind the small sample size, we analyzed both the direct costs and the societal cost of different components of health costs, inspiring further evidence generation for informing policy and practice in the future. Indirect cost is a crucial component in health economic studies and frequently constitutes a significant amount of health costs in various diseases (45–47). Different methods have been used to evaluate indirect costs, including different data collection tools and parameters such as disease categories and gross domestic product (GDP) per capita, resulting in high variance and obstacles to comparing studies [48, 49]. We used the validated WPAI-GH questionnaire [50] and estimated costs due to the past-week of health-related absenteeism and presenteeism aligned with the commonly used technique, i.e., the human capital approach. Compared to the cost of the past-month outpatient illness, the past-week indirect cost was higher, with a considerable proportion from presenteeism, which was found in other studies (46, 49, 51–53). However, estimating indirect cost have always been a controversial issue and might result in overestimation. Hence, standardization of the methodology in future evaluation and reporting is needed (54–57). Reverse causality is a potential issue in this study because the primary analyses used cross-sectional data where both PA and health costs data were collected at the same time (baseline), unlike some studies, which incorporated a lag of cost data collection succeeding the PA data extraction [40]. Nevertheless, the PAW study inclusion criteria addressed the issue by recruiting participants without physical mobility limitations [24]. In addition, we analyzed associations between health costs of the past-month outpatient illness at the follow-up time point and the changes in PA level from baseline to follow-up. However, this sensitivity analysis found no significant association (Supplementary Table S4). A strength of our analysis other than the previously mentioned corporation of different cost components was that, while other studies use self-report or prevalence estimates of physical inactivity [15], we used objectively measured PA levels from a standardized tool (ActiGraphTM) with validation processes. However, this study has some limitations; firstly, only 277 observations were included in the primary analysis. In order to detect the reported between-group difference, with a standard deviation of 645, a minimum sample size of 495 participants per group is required. Compared to most studies evaluating associations between health costs and physical inactivity, our sample size was relatively small. Moreover, PAW participants were office workers from the Ministry of Public Health, Thailand, who were presumably more health conscious, limiting generalizability. Secondly, we used self-report data from the PAW study to estimate health costs, including out-of-pocket payment for services, travel fees, and days absent due to the illness, which may result in more information bias compared to most studies using health expenditure data from national databases [15]. Thirdly, due to the way questions were asked in the Health and Welfare Survey, it was not possible to combine different cost components as they could refer to different illnesses (e.g., the societal cost of past-month outpatient illness and past-year inpatient illness). This hindered reporting annual health costs, thus, preventing comparison among studies from different contexts. More sophisticated cost data will be required for future studies to advance health economic research. ## 5. Conclusions There was evidence of a positive association between the societal cost of the past-month outpatient illness and physical inactivity. The change in PA level might not be large enough to detect the change in health costs within the next follow-up period. These additional analyses of the PAW trial provided important evidence for public health communication and future policy advocacy. Future PA studies, including experimental as well as observational designs, should incorporate the economic cost of health data collection to comprehensively evaluate the associations between the change in health costs and the change in PA levels. ## Data availability statement During the study, only de-identified data were used, and the data were only accessible to the research team. The research team will have exclusive rights to the de-identified data for 24 months after the trial is completed. After that, the data and full protocol will be publicly accessible on the HITAP website. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethical Review Committee for Research in Human Subjects, Ministry of Public Health, Thailand. The patients/participants provided their written informed consent to participate in this study. ## Author contributions KA was Principal Investigator (PI) of the trial. KA, YT, WI, and CC drafted the manuscript together. WI and CC provided statistical expertise. YT, WI, and CC provided expertise on economic analyses. All authors contributed to the study design, reviewed the manuscript draft, have read, and approved the final version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1037699/full#supplementary-material ## References 1. Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. 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--- title: 'Imaging urolithiasis: complications and interventions in children' authors: - Magdalena Maria Woźniak - Joanna Mitek-Palusińska journal: Pediatric Radiology year: 2022 pmcid: PMC10027801 doi: 10.1007/s00247-022-05558-6 license: CC BY 4.0 --- # Imaging urolithiasis: complications and interventions in children ## Abstract Urolithiasis affects people in all age groups, but over the last decades there has been an increasing incidence in children. Typical symptoms include abdominal or flank pain with haematuria; in acute cases dysuria, fever or vomiting also occur. Ultrasound is considered the modality of choice in paediatric urolithiasis because it can be used to identify most clinically relevant stones. Complementary imaging modalities such as conventional radiographs or non-contrast computed tomography should be limited to specific clinical situations. Management of kidney stones includes dietary, pharmacological and urological interventions, depending on stone size, location or type, and the child’s condition. With a very high incidence of underlying metabolic abnormalities and significant recurrence rates in paediatric urolithiasis, thorough metabolic evaluation and follow-up examination studies are of utmost importance. ## Introduction Urolithiasis, which refers to accumulation of stones along the urinary tract, is a common condition affecting people in all age groups. Over the last decade or two, the incidence of urinary stone disease in children has increased significantly, with the highest rates in adolescent girls [1, 2]. The mean annual rate reported by Ward et al. [ 2] was 59.5 cases per 100,000 U.S. children, still infrequent compared to $\frac{217}{100}$,000 in adult women and $\frac{299}{100}$,000 in adult men [3]. The most common symptoms of urolithiasis include abdominal or flank pain and macro- or microhaematuria; nausea, vomiting or fever might coexist. Because of indeterminate symptoms, urolithiasis might be initially misdiagnosed, especially in newborns and infants, who often present to the emergency department with only irritability. Many children with kidney stones remain asymptomatic and are diagnosed incidentally on imaging examinations. Underlying metabolic abnormalities are one of the most important risk factors of paediatric urolithiasis, identifiable in more than $50\%$ of affected children. The most common metabolic aberrations are hypercalciuria (52–$64\%$), hyperoxaluria, hypocitraturia and cystinuria [1, 4, 5]. Other risk factors include urinary tract infections (UTIs), urinary tract malformations and diversions, low fluid intake and high sodium intake. As with the adult population, most paediatric kidney stones are composed of calcium oxalate and calcium phosphate. Other stones, including struvite or cystine, are less common. Urinary tract infections, as well as urinary tract malformations predisposing to UTI, including horseshoe kidney and duplex collecting system, can increase the risk of developing struvite stones [1, 5, 6]. Recurrence rates in paediatric patients are high, especially among children with metabolic abnormalities; recent studies showed $15\%$ to $50\%$ recurrence rates during the 3 years after treatment, indicating a necessity for follow-up examinations [1, 4, 7, 8]. Given the high incidence of metabolic aberrations combined with high recurrence rates in children, metabolic studies (including screening for acidosis, serum electrolytes, blood urea nitrogen, creatinine, serum phosphorous, magnesium, calcium and uric acid levels) should be performed in every case of kidney stones confirmed on diagnostic imaging [6]. The aim of this review is to provide an overview of imaging modalities and interventional treatment methods used in paediatric urolithiasis and to highlight specificity of diagnostics and treatment in these children. ## Ultrasound (US) Many imaging modalities can be useful in diagnosing urolithiasis, including US; conventional radiographs of the kidney, ureter and bladder (KUB); non-contrast CT; and magnetic resonance (MR) urography [9]. Unlike in the adult population, where non-contrast CT is considered the gold standard in the diagnosis of urolithiasis, US, being a non-ionizing, easily accessible and effective procedure, is recommended as the initial diagnostic method in children [1, 6, 10]. Although sensitivity and specificity of US depend on the physician as well as the machine and patient position, they have been reported to be as high as 67–$90\%$ and 95–$100\%$, respectively [1, 11]. Ultrasound is used to visualise presence, size and location of stones, along with potential complications of urolithiasis. Typical US findings of urolithiasis include a hyperechogenic structure in the renal collecting system with or without posterior shadowing, depending on the size of the calculus. Pelvicalyceal dilatation, an indirect sign of obstruction, might also be seen (Fig. 1) [9, 12]. In chronic cases of severe obstruction, thinning of the renal cortex can occur. Fig. 1Drug-induced urolithiasis (post-diuretic) in a premature 35-week-old boy (born at 26 weeks of gestational age). a Sagittal US of the left kidney shows a large, calcified structure in the renal hilum (arrow), corresponding to a stone. b Follow-up US colour Doppler examination after 5 weeks. Transverse view of the kidney shows the stone to have dissolved, with formation of a calculus in the proximal part of the ureter visualised by virtue of the twinkling artifact (arrow). Obstruction of the ureter resulted in pelvicalyceal dilatation with echogenic concrement in the renal pelvis (asterisk). Case courtesy of Prof. Philippe Petit, Marseille, France Ultrasound should cover the urinary tract from the top of the kidneys to the bottom of the bladder. It ought to be performed in a well-hydrated, calm child, in both supine and prone positions, including curved and linear transducers. When cystolithiasis is suspected, the lateral decubitus position may be useful to confirm the nature and possible movement of an echoic structure in the bladder. When measuring identified calculi, it is suggested that the width of the acoustic shadow behind the stone, rather than the hyperechogenic line is measured, given the possible overestimation of the stone’s true size [12]. US efficacy can be improved by using colour Doppler, which may produce the “twinkling artifact,” defined as the appearance of alternating colours behind a reflective object. Twinkling artifact, reported to be seen in more than $80\%$ of urinary stones, can help to confirm or exclude presence of a stone, especially in cases of uncertainty caused by the proximity of the echogenic renal sinus or its possible location near the ureterovesical junction (Fig. 2). To obtain best results, increase the pulse repetition frequency to suppress background colour signal and aim the beam at various angles to improve identification and visualisation of the artifact [13]. The focal zone should be located below the stone; moving it at or above the stone may weaken the artifact [14].Fig. 2Colour Doppler axial image of the bladder of a 17-year-old girl who presented with right lower quadrant pain and haematuria shows a stone in the distal part of the right ureter. Twinkling artifact facilitates differentiation of the stone from other echogenic structures Other possible US findings in children with urolithiasis include weakening of the ureteric jet and elevation in renal resistive index resulting from obstruction, which might precede pelvicalyceal dilatation [15, 16]. A few possible mimics of kidney stones might arise on US, one of the most important being medullary nephrocalcinosis, referring to accumulation of calcium deposits in medullary pyramids. This condition may be differentiated from nephrolithiasis by the specific localisation of the echogenic foci outside the collecting system; early changes involving only the apices of the medullary pyramids do not result in acoustic shadowing. Other causes of hyperechogenic medullary pyramids or their apices include papillary necrosis, medullary sponge kidneys or renal infection. In neonates, Tamm-Horsfall protein accumulation resulting in transient pyramidal echogenicity must be considered. An echogenic, highly vascular benign tumour — angiomyolipoma — is another possible sonographic mimic of a kidney stone. It can be differentiated by its intraparenchymal or exophytic localization; acoustic shadowing is occasionally seen. Echogenic renal foci can also represent calcifications in renal vessels or in the cortex. It is important not to confuse a ureteric stent left in place following urological procedures for pathological echogenic foci. In cases of unclear US appearance of echogenic renal foci, the use of other imaging modalities such as non-contrast CT should be considered [17]. ## Kidney, ureter, bladder (KUB) radiography Kidney, ureter and bladder (KUB) radiography applied alone has low estimated sensitivity and specificity (57–$69\%$ and 76–$82\%$, respectively) [10, 12]. Calculi may be obscured by bowel content due to inadequate bowel preparation, obesity or extrarenal calcifications. Moreover, not all stones are radiopaque. Visibility of a stone on a KUB radiograph depends on its composition — calcium-containing calculi are radiopaque; struvite or cystine are sometimes opaque; uric acid, medication or matrix stones are radiolucent and impossible to see on radiography [10, 12]. Exact location or signs of pelvicalyceal dilatation are undetectable on KUB radiographs; thus, a KUB radiograph is recommended only as a method additional to US [9, 12, 18]. Radiographic identification of stones missed on US is rare, and US alone is sufficient in most cases. Therefore, KUB radiography in combination with US should be reserved for specific clinical situations because the risk of ionising radiation exposure should be always balanced by possible benefits, especially in paediatric patients [1]. As reported by Marzuillo et al. [ 19], many children with symptoms and metabolic risk factors of urolithiasis are negative on US, KUB radiography and even CT; thus, the authors suggested repeat US examination 1–2 years later in children suspected of having urolithiasis with negative US and KUB radiography evaluations to detect possible calculi. ## Non-contrast computed tomography Non-contrast CT is considered the gold standard for the diagnosis of urolithiasis in adults [18, 20]. It has the potential to visualise almost all types of calculi, even those located in the ureters; to define the exact localisation, size and shape of stones; and to reveal signs of pelvicalyceal dilatation or possible differential diagnoses [9]. Given all these advantages and its very high sensitivity (97–$100\%$) and specificity (96–$100\%$) in diagnosing urolithiasis, non-contrast CT would be considered the perfect method if not for its ionising radiation burden [10, 12]. The potential risk of developing radiation-associated malignancies contributed to creating similarly effective ultra-low-dose non-contrast CT protocols with reported mean effective radiation dose 0.8–2.5 mSv (with that of KUB radiography about 0.01–0.11 mSv), which have been implemented for detecting urolithiasis in both children and adults [9, 10, 21–23]. Nevertheless, the excessive prevalence of kidney stones and high recurrence rates make it questionable whether to perform CT and put children at risk of frequent radiation exposure in every case of possible suggestive symptoms [19]. Children are particularly at risk of developing radiation-associated pathologies because of their longer life expectancy and possible cumulative effects, thus use of non-contrast CT in children should be well-justified. The lack of intra-abdominal fat compared to adults is another significant limitation to the use of CT in young children; this can cause difficulties in evaluating the ureters unless upstream obstruction is present. Furthermore, given the necessity of remaining still during CT, younger children might require sedation [6, 18]. Non-contrast CT, compared to US, has higher sensitivity in detection of small calculi, especially those localised in the ureters [19, 20, 24, 25]. In a study comparing US to non-contrast CT for diagnosing renal stones, Fowler et al. [ 26] reported an US specificity of $90\%$ but sensitivity of only $24\%$, though it is important to highlight that the majority of missed calculi were ≤ 3.0 mm in size. According to multiple studies, such small stones are not clinically significant, do not alter the management and tend to pass spontaneously [20, 24–26]. Thus, even though non-contrast CT is more sensitive in identifying microcalculi, US reveals most clinically relevant stones and given its safety, remains the method of choice in the paediatric population [6, 24]. Use of non-contrast CT in children suspected of having urolithiasis should be limited to certain clinical situations, such as when the child has major colic symptoms with nondiagnostic US and KUB radiography or when other imaging modalities are insufficient for guiding surgical intervention. If necessary, non-contrast CT should always be used with an ultra-low-dose protocol to minimise radiation exposure [10, 18, 19, 24, 25]. ## Other modalities Magnetic resonance (MR) urography is a safe and sensitive (82–$100\%$) method of imaging urolithiasis, visualising stones as signal voids and revealing potential pelvicalyceal dilatation without ionising radiation exposure. MR urography provides detailed anatomical information about the kidneys and collecting system, including three-dimensional (3-D) visualisation, allowing for identification of any urinary tract malformation. Furthermore, MR urography enables evaluation of renal function by assessing parenchymal contrast agent uptake and urine excretion. Despite strong advantages, MR urography accounts for only about $2\%$ of imaging studies in children with suspected kidney stone disease because of its high cost, sedation requirements, long image acquisition times and limited availability. It is sometimes recommended as a complementary method when US is negative [9, 10, 12, 27, 28]. Functional imaging studies such as radionuclide scan with dimercaptosuccinic acid (DMSA) or single photon-emission CT (SPECT-CT) with DMSA can be used in select children, especially those with urolithiasis complicated by UTI and a high possibility of renal scarring. DMSA SPECT-CT can play a role in cases of complex renal calculi, allowing identification of anatomical details of the urinary tract and the number, location and size of stones, as well as providing information about renal parenchymal function. A recent study by Robinson et al. [ 1] suggested an association between urolithiasis and long-term renal scarring, leading to abnormal DMSA scan results in about $60\%$ of children tested. Thus, the authors recommended considering DMSA scan in children with significant kidney stones or signs of pelvicalyceal systems obstruction. In recent years, there has been continual progress in the application of artificial intelligence (AI) in diagnostics and management of urolithiasis. Future implementation of AI could lead to improved decision-making and procedural outcome prediction, increased patient safety and more personalised management [29]. ## Complications Numerous possible complications arise from urolithiasis, particularly in children with delayed diagnosis and treatment. Khan et al. [ 30], in their study of children with acute renal colic, reported acute renal failure as the most common complication, observed in as many as $33\%$ of the children. Chronic renal failure may also occur, especially in children with bilateral or recurrent calculi [30, 31]. As a cause of obstruction and pelvicalyceal dilatation, kidney stones are associated with higher risk of urinary tract infections, including recurrent UTIs, chronic pyelonephritis, pyonephrosis (Fig. 3) and sepsis [32–34]. Identifying any signs of infection, such as echogenic debris or gas shadows in the collecting system, thickening of renal pelvic wall, focal areas of abnormal parenchymal echogenicity or areas of reduced vascularity, is crucial. Perinephric fat stranding or formation of parenchymal or perinephric abscess might be seen. Life-threatening forms of UTI such as xanthogranulomatous pyelonephritis with enlargement of the kidney and distortion of its outline or emphysematous pyelonephritis with gas in the collecting system and renal parenchyma combined with parenchymal destruction and areas of necrosis or abscess are uncommon [35, 36]. Single cases of nephrobronchial, psoas muscle or cutaneous fistula formation resulting from stone-associated xanthogranulomatous pyelonephritis have been described [37, 38]. Taşkınlar et al. [ 39] reported a unique case of spontaneous rupture of the renal pelvis with perirenal urinoma formation caused by a calculus in a previously healthy 18-month-old girl. Fig. 3Pyonephrosis in a 13-year-old boy. Coronal contrast-enhanced CT shows a large multicystic lesion with destruction of the left kidney parenchyma (star) corresponding to left-side pyonephrosis as a complication of severe obstruction of the pelvicalyceal system ## Management Appropriate management of symptomatic urolithiasis in paediatric patients depends on a few factors, specifically size, location and composition of calculi. The child’s general health, comorbidities and complications, kidney function, anatomical variations of the urinary tract, and the local availability of treatment and experience of physicians must all be considered [40, 41]. Most children with uncomplicated urolithiasis and small stones that are likely to pass spontaneously (< 4–5 mm) do not require urological intervention. Conservative treatment includes adequate hydration and increased fluid intake, pain control and medical expulsive therapy to relax ureteric smooth muscle and facilitate stone passage [18, 41]. Given the common underlying metabolic abnormalities and very high risk of kidney stone recurrence, children require a thorough metabolic evaluation and follow-up examinations. Change in diet, or long-term pharmacological treatment might be required. Interventional treatment of urolithiasis is generally advised in cases of severe obstruction and surgical decompression (e.g., acute renal failure, infection, obstructed solitary functioning kidney) or unsuccessful pharmacological therapy [41]. Indications and surgical techniques in paediatric patients are similar to those in adults, with the need for general anaesthesia in children being the most important difference. Minimally invasive urological procedures are considered safe and effective; thus, open surgery should be limited to select children with large stones, congenital abnormalities or complications [18, 40]. Extracorporeal shock wave lithotripsy is said to be the least invasive and safest interventional treatment method for proximal stones in children. In this procedure, shock waves are used to fragment the stones into small enough pieces to pass through the ureter (Fig. 4). Additional ureteric stenting is rarely needed [40, 42]. Extracorporeal shock wave lithotripsy is recommended for upper urinary tract stones of diameter ≤ 1.5–2 cm because its effectiveness is inversely proportional to stone size and decreases with lower calyx or ureteric calculi location. Stone-free rates after the procedure are high, ranging from $57\%$ to $92\%$ [18, 40, 42]. Although extracorporeal shock wave lithotripsy is generally considered safe, it can be complicated by haematuria, infection or “steinstrasse” (German for “stone street”), wherein a column of stone fragments forms and blocks the ureter (Fig. 5), with uncertain long-term consequences in children [40, 41].Fig. 4Extracorporeal shock wave lithotripsy in a 15-year-old boy. a Transverse US of the right kidney immediately before the procedure shows a large calculus in the pelvicalyceal system. b Postoperative transverse image shows fragmentation of the stone into smaller pieces likely to pass spontaneously through the ureter. Case courtesy of Prof. Philippe Petit, Marseille, FranceFig. 5“Steinstrasse” in a 12-year-old boy. Anteroposterior kidney, ureter and bladder radiograph shows steinstrasse, or a column of stone fragments in the distal part of the right ureter (arrow) as a complication of extracorporeal shock wave lithotripsy. Note stone fragments in the kidney and right ureter (asterisk). Case courtesy of Prof. Philippe Petit, Marseille, France Ureteroscopy involves placing a ureteroscope through the bladder to aid the performance of lithotripsy or removal of calculi. It is recommended primarily in children with small ureteric stones, and has high estimated stone-free rates of 93–$100\%$ [40, 42]. Up to $7\%$ of patients have ureteroscopy complications such us haematuria, infection, ureteric stricture or ureteric perforation [42]. However, as suggested by Nerli et al. [ 43], because of the recent development of miniaturised and more durable ureteroscopes, ureteroscopy can be considered safe and the procedure of choice for ureteric and select renal pelvic calculi, even in the youngest children. Follow-up US examination is recommended 2–4 weeks after ureteroscopy [42]. A more invasive procedure, percutaneous nephrostomy, which involves inserting a tract between the skin and renal collecting system, might be indicated in complex cases of urolithiasis to relieve obstruction when transurethral access is impossible (Fig. 6), in cases of acute renal failure affecting a single functioning kidney or both kidneys (Fig. 7), or to provide drainage in children with pyonephrosis or abscess formation. In acutely obstructed and infected kidneys, it is recommended that both percutaneous nephrostomy and transurethral retrograde double J stenting are performed to enable urine drainage into the bladder and to protect the ureters (Fig. 8) [44]. A created passageway from the skin to the renal collecting system can be used to insert urologic devices to crush and remove calculi. The procedure, called percutaneous nephrolithotomy, is reserved for children with large stone burden, significant obstruction, complications or after unsuccessful shock wave lithotripsy. Percutaneous nephrolithotomy is the most effective of the urological interventions for large and complex upper urinary tract stones, with stone-free rate of about $90\%$; however, severe complications occur in > $10\%$ of patients [18, 41, 42]. Possible complications of the procedure include severe bleeding, collecting system perforation, sepsis or other organ injury [18]. Increasing experience and development of smaller urological equipment have resulted in implementation of mini-, ultra-mini- and micro-percutaneous nephrolithotomy techniques to reduce the risk of complications [45].Fig. 6Double nephrostomy performed for bilateral stenosis post ureteral reimplantation in a male neonate. Anteroposterior radiograph with contrast agent administered through nephrostomy tubes (arrows) confirms the correct localisation of the catheters in the pelvicalyceal systems and shows dilatation of the urinary tract with enlarged ureters (stars). Case courtesy of Pr. Philippe Petit, Marseille, FranceFig. 7Severe congenital stenotic megaureters causing acute renal failure in a male neonate (different patient from Fig. 6). He underwent a double nephrostomy. Anteroposterior radiograph with contrast agent administered through nephrostomy tubes (arrows) shows severe dilatation of the urinary tracts. Case courtesy of Prof. Philippe Petit, Marseille, FranceFig. 8Percutaneous nephrostomy for ureter stenosis of a transplanted kidney in a 17-year-old boy. He underwent stenting of the ureter with a double J catheter to enable urine drainage to the bladder. Anteroposterior radiograph with contrast agent administration through the nephrostomy tube (arrow) confirms connection of the pelvicalyceal system to the bladder (star) through the double J catheter (arrowhead). 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--- title: 'multiSyncPy: A Python package for assessing multivariate coordination dynamics' authors: - Dan Hudson - Travis J. Wiltshire - Martin Atzmueller journal: Behavior Research Methods year: 2022 pmcid: PMC10027834 doi: 10.3758/s13428-022-01855-y license: CC BY 4.0 --- # multiSyncPy: A Python package for assessing multivariate coordination dynamics ## Abstract In order to support the burgeoning field of research into intra- and interpersonal synchrony, we present an open-source software package: multiSyncPy. Multivariate synchrony goes beyond the bivariate case and can be useful for quantifying how groups, teams, and families coordinate their behaviors, or estimating the degree to which multiple modalities from an individual become synchronized. Our package includes state-of-the-art multivariate methods including symbolic entropy, multidimensional recurrence quantification analysis, coherence (with an additional sum-normalized modification), the cluster-phase ‘Rho’ metric, and a statistical test based on the Kuramoto order parameter. We also include functions for two surrogation techniques to compare the observed coordination dynamics with chance levels and a windowing function to examine time-varying coordination for most of the measures. Taken together, our collation and presentation of these methods make the study of interpersonal synchronization and coordination dynamics applicable to larger, more complex and often more ecologically valid study designs. In this work, we summarize the relevant theoretical background and present illustrative practical examples, lessons learned, as well as guidance for the usage of our package – using synthetic as well as empirical data. Furthermore, we provide a discussion of our work and software and outline interesting further directions and perspectives. multiSyncPy is freely available under the LGPL license at: https://github.com/cslab-hub/multiSyncPy, and also available at the Python package index. ## Introduction Across physical, biological, and social systems, interacting components of complex systems coordinate and, at times, synchronize their behavior. When two or more system components are aligned temporally and spatially, then their behavior is thought to be synchronized. Synchronization is a well-known natural phenomenon, and a seemingly universal property exhibited by complex systems (Amazeen, 2018; Kelso, 1995; Lee, 2005; Pikovsky et al., 2001; Xuan et al., 2018; Xuan & Filkov, 2013) appearing in the temporal alignment, for example, in the cycles of subpopulations of cells (Banfalvi, 2017), in the oscillations of pendulums and brainwaves (Dikker et al., 2017; Lai et al., 2006), light pulses in groups of fireflies (Strogatz & Stewart, 1993), primate interaction behaviors (Yu & Tomonaga, 2015), physical networks such as power grids (Motter et al., 2013), and in a variety of physiological and behavioral modalities of interacting humans (Feldman, 2007; Hoehl et al., 2020; Palumbo et al., 2017; Wiltshire, Philipsen, et al., 2020a). Despite its pervasiveness, there is still much uncertainty about how synchronization originates in systems, its functional role in different contexts and different modalities, what forms it takes in groups larger than two (or between more than two variables), and how it changes over time (Duranton & Gaunet, 2016; Hoehl et al., 2020; Knoblich et al., 2011; Launay et al., 2016; Mayo & Gordon, 2020; Nowak et al., 2017; Timmons et al., 2015; Wiltshire, Steffensen, et al., 2020b). In order to advance the scientific exploration of multivariate coordination dynamics, an area of inquiry that “describes, explains and predicts how patterns of coordination form, adapt, persist and change in living things” (p. 1537, Kelso, 2009), this paper, and the corresponding Python package, focuses on metrics that are used to measure multivariate synchrony. Coordination, in the broadest sense, is a behavior exhibited by dynamical systems that is sometimes recognized as a superordinate construct characterizing the ways in which components and processes of complex dynamical systems covary over time (Butner et al., 2014; Richardson et al., 2013; Turvey, 1990; Wiltshire, Philipsen, et al., 2020a), in which phenomena like synchronization, coupling, alignment, entrainment, behavior matching, and so on are forms of coordination (Butler, 2011). While some work aims to differentiate these forms of coordination, in our work, we use these terms somewhat interchangeably with the consistent aspect being that they are all concerned with the temporal covariation of multivariate time series. While there has been much work on the bivariate and dyadic synchronization (Abney et al., 2015; Altmann et al., 2020; Cohen et al., 2021; Delaherche et al., 2012; Likens & Wiltshire, 2020; Louwerse et al., 2012; Palumbo et al., 2017; Paulick et al., 2018; Ramseyer & Tschacher, 2011; Tschacher & Meier, 2020; Wiltshire, Steffensen, et al., 2020b), there are relatively fewer cases examining multivariate synchrony (e.g., Butner et al., 2014; Dias et al., 2019; Reinero et al., 2020; Wallot & Leonardi, 2018; Zhang et al., 2019), which can be useful for understanding how for example groups, teams, and families coordinate their behaviors, or how multiple modalities from an individual can become synchronized. While the relatively simpler bivariate case only considers dyadic (pairwise) relationships, multivariate synchrony, in general, extends this beyond dyads such that, e.g., triads or even larger groups can be analyzed. Past work on interpersonal coordination comprises a diverse body of work with investigations in general social interactions (Abney et al., 2015; Chanel et al., 2013; Louwerse et al., 2012), parent–child interactions (Abney et al., 2017; Crowell et al., 2017; Feldman, 2007; Nguyen et al., 2020), child–child interactions (Altmann, 2011), romantic partners (Butler & Barnard, 2019; Gottman, 2014; Randall et al., 2013; Timmons et al., 2015), families (Butner et al., 2018), strangers and friends (Bizzego et al., 2020; Galati et al., 2020), mental health-related interactions (Butner et al., 2017; Ramseyer & Tschacher, 2011; Soma et al., 2019; Wiltshire, Philipsen, et al., 2020), teamwork (Dias et al., 2019; Likens et al., 2014; Palumbo et al., 2017; Reinero et al., 2020; Wiltshire et al., 2019), performance groups (Keller et al., 2014; Setzler & Goldstone, 2020), and even inter-species interactions (Wanser et al., 2021). In addition, there is evidence of synchronization phenomena in many modalities including non-verbal behaviors and movements (Ramseyer, 2019; Schoenherr et al., 2019), acoustic properties of speech (Fischer et al., 2017; Imel et al., 2014; Wieder & Wiltshire, 2020), alignment and matching in language (Duran et al., 2019; Fusaroli & Tylén, 2016; Lord et al., 2015; Niederhoffer & Pennebaker, 2002), physiological signals from the autonomic nervous system (Kleinbub, 2017; Kleinbub et al., 2020; Konvalinka et al., 2011), patterns of neural activation (Dikker et al., 2017; Hoehl et al., 2020; Koban et al., 2019), and between multiple modalities (Amon et al., 2019; Gorman et al., 2016). Not only this, but a growing number of options exist for measuring signals to examine interpersonal coordination, ranging from the traditional method of hand coding video frames (Bernieri & Rosenthal, 1991), motion capture systems (Romero et al., 2017), video and audio and speech processing (Cao et al., 2021; Kleinbub & Ramseyer, 2020; Paxton & Dale, 2013; Pouw et al., 2020; Vilela Barbosa et al., 2012; Weusthoff et al., 2018), physiological sensors (Guastello & Peressini, 2017; Palumbo et al., 2017), neuroimaging devices (Dumas et al., 2010, 2011; Reindl et al., 2018), and sociometric sensors (Kozlowski, 2015; Montanari et al., 2018; Parker et al., 2018). To be able to measure coordination in a variety of modalities and across groups or teams of sizes three or greater, different methods are needed. It has been toward that aim that we developed this package and in which we include a measure based on Symbolic Entropy, Multidimensional Recurrence Quantification Analysis (mdRQA), Coherence (and a related but newly proposed ‘Sum-Normalized cross-spectral density (CSD)’), the Cluster-Phase ‘Rho’ metric, and a statistical test based on the Kuramoto order parameter. That being said, while many of the metrics we present have been utilized for examining the coordination of behavioral and physiological signals in these inherently social contexts, the methods are also applicable to other pertinent research contexts such as human–computer interaction (Novick & Gris, 2014) that look at coordination properties of a variety of multivariate signal streams. In this paper, we present multiSyncPy, a Python package for computing a variety of synchrony metrics on multivariate time series. Our aim in developing this package is to make these methods more accessible and to encourage their systematic use to enrich our understanding of coordination dynamics beyond the dyad (Amon et al., 2019). In the next section, we discuss related past work, focusing on the background of the metrics included in our Python package while highlighting important considerations for their use in research. After that, we present the contents of the package and show how to use it with some example code. Following this, we demonstrate the use of our multivariate synchrony metrics on a series of synthetic datasets and two real-world empirical datasets, showcasing the results obtained from a variety of situations. Importantly, we aim to explore the performance of the multivariate synchrony metrics included in multiSyncPy on a variety of datasets and types as there are always a number of decisions to be made. Thus, we also provide several lessons learned from this initial investigation. In particular, using synthetic data from autoregressive processes with additional correlated noise, we find that some metrics respond more strongly than others to unstructured noise that is duplicated across all variables. Synthetic data from Kuramoto models is used to show that the metrics are capable of distinguishing different degrees of coupling; it also provides evidence of convergent validity between different multivariate synchrony metrics as well as an aggregated version of a well-established bivariate synchrony metric. Two empirical datasets provide realistic examples of how multiSyncPy can be used, specifically when investigating synchronization in body movement data from groups with more than two members. Finally, in our paper, we conclude with a summary, discuss our work in its broader scientific context, and highlight some important future directions. ## Symbolic entropy One approach to the investigation of synchronization in multi-component systems is investigating the level of temporal regularity of state sequences. Since complex systems have many components (Amazeen, 2018; Favela, 2020), they exhibit a large number of possible states and can cycle through varied combinations of these behavioral states. If the components of the system are synchronized, then the system will exhibit a smaller number of different states and it will do so with increased repetitiveness. One approach in this vein is what we will refer to as ‘symbolic entropy’. This information-theoretic approach, following the work of Stevens and colleagues on neurodynamic and physiological synchronization in teams (Dias et al., 2019; Likens et al., 2014; Stevens, 2012; Stevens et al., 2012; Stevens & Galloway, 2014), aims to characterize the state of the system at each point in time as a discrete value, and examines the entropy of these discrete states over some window of time. Low values of entropy are considered indicative of behavior synchronized around some shared, regular and ordered pattern. This method can readily apply to nominal data, but additionally, to obtain discrete states from continuous measurements, each variable can be individually mapped to a value of either ‘low’, ‘medium,’ or ‘high’ at each time step (or some other discretization procedure). Each conjunction of low, medium, and high values in the variables in the system then becomes an element in a symbol set that characterizes the overall collective system state for a given observation, where for example, a symbol for a three-person team could be one that captures the high, high, low pattern and so on (Dias et al., 2019; Stevens et al., 2019). Each method for measuring synchronization has its advantages and disadvantages, which often relate to the type of data being analyzed. In the case of symbolic entropy, one consideration is that the number of possible system states increases exponentially with the number of components in the system, potentially to the point that entropy becomes hard to estimate without an extremely long time series to analyze. Since entropy is based on an estimation of the probability of the different states occurring, with many states, a long time series may be required in order for all possible states of the system to be observed enough times to reliably estimate their probabilities. Additionally, because entropy is affected by the number of possible states, symbolic entropy scores should be compared between time series with the same number of variables. An advantage of the entropy-based method is that it makes no assumptions about the temporal signature so it can apply to cyclical or non-cyclical signals. And, even if it is difficult to get a reliable estimate of the state probabilities, it is still suitable for relative synchrony comparisons between, for example, experimental conditions assuming the lengths of time series are approximately equal. Another consideration is that our implementation of symbolic entropy is based on categorizing values into ‘low’, ‘medium’, and ‘high’ based on tercile boundaries. Components that are synchronized at a high frequency might pass through the tercile boundaries (and so have increased entropy) compared to unrelated components that each have a low frequency. Note that other methods do exist for generating symbolic states from continuous time series (Cysarz et al., 2013; Qumar et al., 2013). Finally, it is important to remember that low entropy is not the same as synchronization, and this could reflect other phenomena, such as a period of rest in which the components of the system stay at ‘low’ measurement values for some amount of time. Some options like surrogate testing, which will be discussed later, might help to distinguish synchrony from other phenomena leading to low entropy. ## mdRQA Similar to symbolic entropy, multidimensional recurrence quantification analysis (mdRQA) uses the temporal regularity and recurrence of system states and sequences of states as a proxy for synchronization (Wallot & Leonardi, 2018). The regularity of the system is described using a binary recurrence matrix that indicates which points in time are similar to which other points in time (Coco et al., 2020). For a given state to count as recurrent, the similarity of two states is typically determined using the Euclidean distance, and then a radius threshold is applied to provide a binary classification: states are either ‘recurrent’, meaning that they are sufficiently similar, or not. The results of these binary classifications form a square matrix, where the row index and the column index both specify times (the times being compared). Multivariate time series data series can either be entered ‘raw’ into mdRQA or following time-delayed embedding to reconstruct higher-dimensional dynamics of the system (Takens, 1981; Wallot & Leonardi, 2018). Recent methods for selecting appropriate multidimensional delay and embedding dimension parameters have also been developed (Wallot & Mønster, 2018). Once a recurrence matrix has been created, mdRQA proceeds by computing metrics that summarize the recurrence observed in the system. These are based on the diagonals of the recurrence matrix, which represent the initial time series compared to itself at some particular time delay. For example, the main diagonal represents a comparison of the system to itself without any time delay, and so is always populated entirely with ones – indicating that the system is recurrent, or similar to itself, all the way along. Diagonals close to the main diagonal represent a comparison of the system to itself with a short delay, while diagonals further from the main diagonal represent a comparison with a longer delay. Four main metrics are commonly used to summarize the recurrence matrix. All of these metrics consider only cells that are off the main diagonal, which is an assumption of the following descriptions. The proportion of recurrence (%REC) is simply the number of recurrent cells divided by the total number of cells. The proportion of determinism (%DET) only considers sequences of diagonally recurrent cells that are longer than a specified length. It is the number of recurrent cells left after applying this criterion, divided by the number of recurrent cells. The average length of a diagonal sequence (ADL) of recurrent cells provides a third metric, and the length of the longest diagonal sequence provides the final metric (maxL). One of the benefits of recurrence-based analysis is that it is considered to handle nonlinearity and nonstationarity well, since it does not explicitly model the variables or their interactions as some particular set of functions (Wallot & Leonardi, 2018; Webber & Marwan, 2015; Webber & Zbilut, 2005). This may make mdRQA more desirable than other metrics in contexts where nonlinearity and nonstationarity are expected. If mdRQA is chosen, there are some additional considerations that do not apply to other metrics for synchrony. First, there is the ‘radius’ parameter, which cannot typically be decided analytically, and so an appropriate value must be determined empirically by iteratively running the analysis with different values until a reasonable amount of recurrence is returned, typically between 1 and $5\%$ recurrence (for further discussion and insights on setting the radius and other mdRQA parameters see Wallot & Leonardi, 2018; Wallot & Mønster, 2018). Because an unweighted Euclidean distance is used to compare time steps, normalization is also an important part of data processing, to make sure that all variables vary across a similar scale. A final point to note is that, like with symbolic entropy, the recurrence in a system is not exactly the same as synchrony and the difference might be noticeable in the results when looking at periods of relatively low activity (which could have high recurrence though little synchronization). ## Coherence Another method used to measure group synchrony is averaging of spectral coherence scores. This is based on examining the power spectrum of each variable through spectral decomposition and comparing the power at each frequency between signals (White, 1984). Mathematical details and sensitivity analysis for this measure can be found in (Winterhalder et al., 2006). Ultimately, the metric ranges from 0 to 1 indicating how well one signal can be approximated by a linear function of the other signal. As described so far, the spectral coherence metric only operates with two signals. However, recent work by Reinero et al. [ 2021] has used a multivariate version of coherence in the context of comparing synchrony between individuals in EEG recordings across multiple frequency bands (Reinero et al., 2021). Their method provided aggregated scores across a team by simply averaging across frequencies and across participants. This process is what we use in our software package to offer a multivariate synchrony metric based on coherence. There are two important assumptions to the spectral coherence measure of synchrony: that the signals are related by a linear rather than nonlinear function, and that the signals are stationary (White & Boashash, 1990). In contexts where these assumptions are violated, this may mean that the performance of the synchrony metric is impaired. Another note about this metric is that it relies on cross-spectral density, which may be difficult to reliably compute with lower sampling frequencies or shorter time series. There is one additional and important consideration for the averaged coherence metric. This method uses an average across frequencies of the coherence, which is a normalized value (ranging between 0 and 1). This means that information about the relative amplitude at different frequencies is ignored, which may be undesirable for some types of signals. For example, this issue often becomes noticeable when a recording includes Gaussian noise, since Gaussian noise impacts the spectral content at all frequencies, whilst the meaningful content of the ‘true’ signal may only be contained in a limited range of frequencies. If this is the case, then the Gaussian noise may dominate over the meaningful content when averaging across frequencies, especially when using a high sampling rate which allows for many frequency components to be computed. If possible, the reliability of the metric could be improved if filters are applied to remove noise such as a bandpass filter that removes noise occurring at irrelevant frequencies. If filtering is not possible, noise may have a serious impact on the results. To mitigate this issue, we propose an additional metric that is closely related to the averaged spectral coherence. Observing that the coherence value at each frequency component is a normalized version of the cross-spectral density (CSD) at that component, we propose to use the cross-spectral density to define an additional metric, but postpone normalization until after aggregating the values across frequencies. As a consequence, information contained in the cross-spectral density regarding amplitude can be used to moderate the impact that each frequency component has on the final output. Our proposal is to use the sum across frequencies of the squared cross-spectral density, and normalize by the sum across frequencies of the auto-spectral density of the first signal multiplied by the auto-spectral density of the second signal. This then produces a value between 0 and 1 for a pair of variables. For two variables, the calculation is as follows: where each sum is across n frequency components. Repeating the process across all pairs of variables and then averaging leads to a final multivariate metric. Hereafter, we shall refer to this additional metric as “sum-normalized CSD”. This metric may be preferable when there is substantial noise that cannot be filtered out, for example, because it is in a frequency range of interest. A final point to note about both the aggregated coherence and sum-normalized CSD is that they are based on estimating the spectral density at different frequencies. As with any time-frequency analysis, the number of frequencies that can be analyzed varies according to the length of the input time series, making it difficult to compare results from multivariate time series of different lengths. ## Rho The remaining two synchrony metrics are based on the concept of the phase of a periodic signal, which describes how far the signal is along its cycle of behavior, at any given moment in time (Haken et al., 1985). Signals can be compared based on how similar their phases are over time using, for example, the relative phase measure (Lamb & Stöckl, 2014), which is the difference in phase between signals. Richardson et al. [ 2012] developed a ‘cluster-phase’ method, which looks at an aggregate relative phase across multiple signals, and then computes how closely the phases of each individual signal are to the aggregate-level phase. Their method is able to provide an overall measure of synchronization across an entire set of signals, and also makes it possible to obtain a synchrony estimate at each point in time. Before these analyses can be completed, it is necessary to extract phase information from the raw signals. There are various ways of doing this, which take a time series of amplitude measurements as an input and return a time series of phase values as an output. Two of the most common methods are [1] to perform the Hilbert transform and then calculate angles from the resulting complex numbers, and [2] to perform wavelet analysis (Issartel et al., 2015). This is a necessary step in data preparation that the analyst must decide. One potential issue for the cluster-phase rho metric is that it may be more difficult to extract reliable phase information from quasi-periodic signals (see Hurtado et al., 2004 for some strategies to mitigate this). Extracting meaningful phase information is a precondition for obtaining meaningful results from the ‘rho’ metric. ## Kuramoto weak null test The next metric operates on a collection of several multivariate recordings, rather than a single recording, and provides an estimation of the statistical significance of the synchrony observed in the collection, based on a null hypothesis that the observed levels of synchrony are due to chance. The method is based on the relative phases of the multiple variables in each recording, which are summarized using the Kuramoto order parameter. This ‘order parameter’ is based on Kuramoto’s mathematical model of coupled oscillators, and represents a key value for describing the behavior in the model (Kuramoto, 1975; O’Keeffe et al., 2017). The values observed for the order parameter across the sample can then be analyzed with reference to the values that would be expected due to chance, leading to a statistical test for significant levels of synchrony in the sample. The Kuramoto order parameter is based on the idea that if a system is composed of oscillators that are coupled to each other with equal strength, then the oscillators will experience some attraction to the average phase (the average across all oscillators in the system). There are some free parameters for the model that will determine what proportion of the oscillators in the system will synchronize. First, there is the coupling strength, which represents how strongly each oscillator influences the others. Second, each oscillator has its own natural frequency, and an oscillator’s preference for its natural frequency may pull it away from a shared/common frequency of synchronization. This mathematical model of course relies upon some simplifying assumptions, specifically that the oscillators follow sinusoidal patterns of amplitude and that they are all equally coupled to each other, which may not be true in all real-world examples. Nevertheless, it is a highly influential model of synchronization and is often used to model real-world data. Frank and Richardson [2010] constructed null hypotheses for the Kuramoto order parameter values observed across a sample of recordings. These hypotheses predict a probability distribution of what will be observed when sampling oscillators that are not coupled to one another. We focus on the ‘weak null hypothesis’ and associated test for significance, which does not assume that autocorrelation is absent in the variables, since this is more general than the alternative ‘strong null’, which does make that assumption. The ‘weak null’ was deemed more appropriate because it relies on fewer assumptions and so is applicable to a wider range of scenarios (Frank & Richardson, 2010). Before running the test, we recommend visually inspecting the distribution of phase values that were extracted during data preparation from the variables in the time series of the sample, because it is important to check the assumption that each variable has an approximately uniform distribution of phase values. An uneven distribution of extracted phase values can lead to highly overestimated significance. ## Overview and examples of the multiSyncPy package A number of packages in different programming languages exist to calculate synchronization measures. Gouhier and Guichard [2014] presented one such package, named ‘synchrony’, for the R statistical programming language, offering Kendall’s W, Loreau and de Mazancourt’s φ and two ‘nonlinear’ metrics based on similarities between phase as determined after applying a preliminary peak-picking step, as synchrony metrics to use on multivariate time series. Wallot and Leonardi [2018] created functions for the R programming language for performing multidimensional recurrence quantification analysis (‘mdRQA’) on multivariate time series, and the same functionality was also presented by Wallot et al. [ 2016] in the context of MATLAB instead of R. For the Python programming language, there is the syncPy package from Varni et al. [ 2015] for analyzing synchrony in dyadic or multiparty contexts, which mostly provides metrics designed for bivariate time series, although some methods work for multivariate data, specifically: Granger causality, Omega complexity, the S-Estimator, and partial coherence. The scientific analysis package Scipy (Virtanen et al., 2020) contains digital signal processing methods such as spectral coherence, which can be used to quantify synchronization, although these are limited to bivariate data. For the analysis of neuronal activity, Mulansky and Kreuz [2016] introduce PySpike, which provides various methods for computing spike chain synchrony. Functions for quantifying synchrony are also available for MATLAB, such as in the HERMES toolbox of Niso et al. [ 2013], which primarily focuses on bivariate synchrony, but does include the multivariate ‘synchronization likelihood’ metric. Recently, Baboukani et al. [ 2019] created a MATLAB package that provides the ability to calculate four different multivariate synchronization metrics. These tools are published openly, but they are tied to MATLAB, which requires a paid license. Our Python package is unique in that it is the only package we know that is completely free to use that focuses exclusively on multivariate synchrony, and the only one to offer our particular combination of metrics, the value of which is demonstrated by the fact that these metrics have been used in recent empirical investigations of synchronization. As previously stated and reviewed, six types of multivariate synchrony analysis are included in our package: symbolic entropy, multidimensional recurrence quantification analysis (mdRQA), coherence, the cluster-phase ‘rho’ metric, and a statistical test based on the Kuramoto order parameter (based on Frank and Richardson’s weak null hypothesis), plus our proposed ‘sum-normalized CSD’. In addition to the different multivariate synchronization methods mentioned above, multiSyncPy also offers functions to generate surrogate datasets from samples containing several multivariate time series. Surrogate data is often used in synchronization-focused research as a way to determine whether or not the observed dynamics are different than chance levels (Moulder et al., 2018; Schreiber & Schmitz, 1996; Strang et al., 2014; Theiler et al., 1992). This is useful because in many cases it is not possible to deduce the likelihood of observed synchrony scores without an appropriate null hypothesis value to compare to. We offer two ways to create surrogate data: segment shuffling and variable swapping. Segment shuffling first involves dividing each variable in a time series into windows, and shuffling the windows independently for each variable. This aims to preserve most of the structure of the signals while removing temporal relationships arising from synchronization. The second method is to swap variables across time series, by separating out the variables and then rearranging into new time series, so that temporal relationships between observations are lost, but each signal and its temporal relationships with itself are retained. For example, if there are three time series consisting of variables (X,Y,Z), (U,V,W) and (R,S,T), then after swapping variables the surrogate time series might be (X,U,R), (Y,V,S) and (Z,W,T). This requires that the time series have the same number of variables and time steps, but is desirable because it preserves the full structure of the signals. Prior to giving our demonstration of how to compute the different metrics multiSyncPy offers using Python, we give an overview of the workflow and then details of the synthetic data we generated. As a scientist prepared to analyze data from an experiment, in which they wish to establish synchronization within a collection of multivariate recordings, the typical workflow is shown below in Fig. 1A. First, the scientist would prepare the sample of time series that they want to analyze by doing any necessary pre-processing, for example by removing outliers or applying a bandpass filter to remove noise from the recordings, and by preparing corresponding phase time series to be used with the phase-based metrics. If the variables in a multivariate time series are of different lengths (e.g., R-R intervals from a group), then time normalization may be necessary. Testing the Kuramoto order parameter can be done directly on the prepared time series, while surrogate data should be prepared in order to test the other metrics. When using surrogate data, metrics should be calculated on both the prepared sample and the surrogate sample, and the resultant values can then be compared via a t test. In the case that two experimental conditions are being compared, it may be preferable to simplify the workflow by not creating surrogate data or performing the test on the Kuramoto order parameter, and instead comparing the computed synchrony metrics between the two conditions. Fig. 1Example workflows for analysis with multiSyncPy Another workflow could be more practitioner-oriented, suited for those studying groups or teams and aiming to provide immediate feedback on their coordination. This workflow is shown in Fig. 1B. The data should be prepared with any necessary pre-processing, for example by removing outliers or applying filtering, and by preparing corresponding phase time series. A window size should be chosen within which to calculate the various metrics. The metrics can then be computed and their progression over time can be visualized as a time series plot, which can then be presented by the practitioner for interpretation and discussion. The multiSyncPy package includes a windowing function that allows the user to estimate changes in synchrony over time for most of the measures. In addition to offering the synchrony metrics and surrogation methods mentioned previously, multiSyncPy also provides the ability to generate two types of synthetic time series to support users’ initial exploration of the synchrony metrics. In fact, we make use of this synthetic data generation to showcase our code, and we also use it in the subsequent section to test out the results given by each metric, by systematically varying the properties of the synthetic data. The first kind of synthetic data is generated using a stochastic autoregressive function (Gouhier & Guichard, 2014). At each time step, this computes the new value based on a weighted sum of the previous two values and some additional Gaussian noise. Iterating the process enough times will lead to a time series of any desired length. This is used to produce multiple univariate times series of the same length, which can then be stacked alongside one another and treated as the different variables of a multivariate time series. It is important to note that, due to the stochastic nature of the autoregressive function used, and the fact that the univariate time series are created independently, there is no coupling or other interaction between the variables that would lead to synchrony. The different variables do however exhibit a similar autocorrelation since they come from the same class of process. This synthetic data therefore serves as our ‘null scenario’ in which the amount of synchrony computed should not be above chance level. The second process used to generate synthetic data is a Kuramoto model (Kuramoto, 1975), which models the behavior of coupled oscillators over time. Each oscillator has its own natural frequency at which it cycles when there is no influence from the others, and the model overall has a coupling strength parameter which reflects how strongly each oscillator influences the others. Our implementation also adds a small amount of Gaussian noise to make the data more naturalistic. Keeping the natural frequencies of the oscillators the same whilst increasing the coupling strength should lead to higher levels of synchrony. We investigate the extent to which the synchrony metrics match to this expectation. The outputs of the Kuramoto model are naturally multivariate, with a variable corresponding to each simulated oscillator. The model specifies how to update the phase of each oscillator at each time step, from the phases at the previous time step. Iterating this procedure leads to a time series of any desired length. In contrast to the autoregressive data, which produces variables independently with no above chance-level synchrony expected, the data from the Kuramoto model is expected to exhibit synchrony as the coupling strength is increased. Synthetic data from the Kuramoto model therefore constitutes a verifiable example of multivariate synchrony, which should be reflected in the values computed for our synchrony metrics. ## Computing the synchronization metrics First of all, before computing the synchronization metrics, we import multiSyncPy and related packages into Python, as below. Next, for the purposes of illustration, we generate some synthetic data on which to compute synchrony metrics, in this case using the Kuramoto model. The function ‘kuramoto_data’ requires specification of multiple parameters, which correspond to the parameters of the Kuramoto model (described in more detail in the next section). The parameter K is the coupling strength. The initial phases for the oscillators are provided as a numpy array, and so too are the natural frequencies in the parameter ‘omegas’. The alpha value modulates the contribution of Gaussian noise to the signals. The standard deviation of the Gaussian noise is the square root of parameter ‘d_t’, which is the length in seconds of the period between time steps, and the noise is multiplied by alpha before being added. The length is the number of time steps to generate. Below is the code to generate some example data. The function returns a numpy array of shape (number_oscillators, sequence_length). Note that the numpy array is the data structure used across our package to represent multivariate time series. If we had chosen to use autoregressive synthetic data instead of data from a Kuramoto model to showcase our code, the synthetic data would be generated using the following code. The length must be specified, along with ‘phi_1’ and ‘phi_2’, respectively, the weighting of the values one and two time steps ago in the autoregressive process, ‘epsilon’ which is the standard deviation of Gaussian noise added at each time step, and an optional bias term ‘c’. These parameters correspond to the parameters of the autoregressive process described in more detail in the next section. This returns a univariate time series of the desired length. To construct a multivariate time series, multiple univariate time series would be generated and stacked together. With some data on which to compute the metrics, now it is possible to calculate the symbolic entropy. This requires only a simple function call: The symbolic entropy across the entire time series is returned as a single number. For mdRQA, our package includes a function to create the recurrence matrix for a multivariate time series. The user must specify a ‘radius’, which is used as a threshold to decide when two time points are sufficiently similar to be considered recurrent. If an appropriate value is not known a priori, then typically the radius is established by iteratively running the mdRQA analysis and adjusting the radius until the percentage of recurrence is between 1 and 5 (see Wallot & Leonardi, 2018). By default, normalization is applied so that each variable has a mean of 0 and variance of 1 before computing Euclidean distances and deciding which points count as recurrent. In addition, users can also optionally provide an ‘embedding dimension’ and a corresponding delay parameter to use when constructing the recurrence matrix, if they want to use embedding in the mdRQA. Using embedding is not obligatory however, and its value and validity when applied to multivariate time series is still open to discussion. Wallot and Leonardi [2018] provide a more detailed explanation of embedding in recurrence quantification analysis and a discussion about its use with multivariate time series. With the recurrence matrix available, the mdRQA metrics can be computed easily: The ‘rqa_metrics’ function returns four values in a tuple: proportion of recurrence (%REC), proportion of determinism (%DET), mean diagonal length (ADL), and max diagonal length (maxL). Each of these are a single number. The next metric is the aggregated coherence score, as used in Reinero et al. [ 2021]. Once again, this is a simple function call on a numpy array containing our multivariate data. The coherence is returned as a single number which is the aggregation of the pairwise coherence values. The code is as follows: It is worth noting that the aggregated coherence score can be affected by the presence of Gaussian noise, and if this is likely, then it may be valuable to also compute the ‘sum-normalized CSD’ metric proposed in this paper, as follows: The remaining metrics are slightly different because they are based upon analyzing the phase time series of the different variables. This means that it is necessary to convert the raw amplitude values in the time series into phase values. Because there are multiple valid ways to do so, such as extracting them from the Hilbert transform or using wavelet analysis, it is left open to the user to decide which method to use. The example code below shows how to use functions from the numpy (van der Walt et al., 2011) and scipy (Virtanen et al., 2020) packages to obtain phase time series via the Hilbert transform (including a refinement to normalize the data to have a mean value of zero, which facilitates the phase calculation): Given estimation of the phase angle time series, it is now possible to compute the cluster-phase ‘rho’ metric described by Richardson et al. [ 2012], and here it can be called using the ‘rho’ function as follows: The cluster-phase ‘rho’ metric is different from those showcased above, in that it provides both a time-varying synchrony estimate as well as an overall estimate for the entire time series. For this reason, the rho function returns two objects: [1] a numpy array of length equal to the length of the input time series, which is a continuous estimate of synchrony at each moment, and [2] the overall score as a single value. By default our ‘rho’ function returns a continuous and time-varying estimate of synchronization. In contrast, we also provide a windowing function that allows users to do the same in order examine the development of synchrony over time using the other metrics. In other words, by default a majority of these metrics return values that summarize the entire time series, but our windowing function allows one to conveniently estimate the change in coordination over time. The user simply provides the time series data, the function used to compute a specific metric, the number of time steps to use as a window, and the number of time steps to use as a step size between successive windows. The outputs are provided in a numpy array with the first dimension representing windows, and the other dimensions being determined by the synchrony metric in question. For example, to calculate the symbolic entropy in windows of size 100 with an offset of 100 and thus, no overlap, the code is as follows: The final synchrony metric included in our package is a statistical test on the Kuramoto order parameter, to determine how likely a sample of data is according to the weak null hypothesis of Frank and Richardson [2010]. Unlike the methods described previously, the statistical test operates over a sample of data, and so multiple time series are required. The Kuramoto order parameter values of multiple different time series, for example coming from multiple runs of an experiment, are put into an aggregate comparison to the values expected under a null hypothesis that synchronization is not occurring. Therefore, the function to compute this metric requires as input a list of numpy arrays which each have the shape (number_variables, length_of_time_series). The lengths of the multivariate time series can vary (as long as each variable in a particular multivariate time series is the same length), but they must all have the same number of variables since this affects the predicted values under the null hypothesis. Moreover, it is important to remember that the inputs should be the phase time series rather than the raw amplitude values. To demonstrate this function, we generate a sample of multivariate time series from Kuramoto models and convert them into phase time series. Once the synthetic data are available, performing the test can be done using the ‘kuramoto_weak_null’ function as follows: This function returns the p value, t-statistic, and degrees of freedom for the sample provided. Lastly, multiSyncPy provides two functions for generating surrogate data from a sample of time series, which for example can then be used to calculate baseline results. The first cuts each variable in each time series of the sample into windows, and reorders the windows. The code to construct this type of surrogate data from a sample is as follows. A list containing numpy arrays of shape (number_variables, number_time_steps) is required, as is the desired length of a window. The second method for constructing surrogate data works by swapping variables between time series, leaving the variables the same individually, but combined into new time series (with other randomly selected variables). The code to create this type of surrogate data is as follows. The sample must be a numpy array of shape (number_time_series, number_variables, number_time_steps). The function returns a surrogate sample with the same shape. ## Simulated and empirical demonstrations Now that we have demonstrated how to compute the various metrics and take advantage of the functions of multiSyncPy, we next showcase the use of our package using the aforementioned two types of synthetic data as well as two existing empirical datasets. Doing so allows us to compare the methods for quantifying multivariate synchrony, and provide lessons learned from their application to different types of data. First, explorations of two types of synthetic data are presented. We used a stochastic autoregressive function, following the example of Gouhier and Guichard [2014], which produces variables that have temporal structure, but no above chance-level synchrony. Such variables can be stacked together to create a multivariate time series. Adding correlated noise to the variables in one of these synthetic time series gives a simplified illustration of the effects of unstructured noise on the synchrony metrics. Our other synthetic data comes from a stochastic Kuramoto model, which mathematically models a group of interacting oscillators, and in which the strength of coupling between oscillators can be systematically varied. With this data, it is possible to test our prediction that multivariate synchrony increases with the strength of the coupling parameter. In addition to synthetic data, we use two datasets from real experiments. The ELEA corpus of recordings (Sanchez-Cortes et al., 2012) includes video recordings of small groups performing a team task, along with transcripts, questionnaires and annotations. We focus on the video recordings and use OpenPose (Cao et al., 2021) to extract information about the body posture of participants and how it changes over time. The synchronization of body movement is analyzed using various synchrony metrics as a case study. We also use data from another experiment investigating interactions in small groups (Gervais et al., 2013), which has previously been used to study the dyadic synchronization of body movement in triads (Dale et al., 2020), but not multivariate synchronization. ## Synthetic data results As explained in the previous sections, the multiSyncPy package provides the ability to compute a range of metrics which assume varying forms of underlying coordination (e.g., matching of states, matching phase, etc.). However, so far it is not clear what the relative merits of the different methods are because most studies examining multivariate coordination employ only a single technique. Using synthetic data, we can compare the performance of these multivariate synchrony metrics by systematically varying simple parameters used to generate this data. Examples of our two types of synthetic time series (stochastic autoregressive process and Kuramoto oscillators) are visualized in Fig. 2.Fig. 2Synthetic data time series examples ## Autoregressive data with correlated noise The first type of synthetic data we examine comes from a stochastic autoregressive function. The data generated by this process has temporal structure, as each value in the time series is a product of the previous two (plus Gaussian noise), however the way that the values develop over time is unpredictable and a consequence of the Gaussian noise added at each time step. The process is described by the following equation, where Xt is the value of the process X at time t, β0, β1, β2 are fixed parameters and εt is Gaussian noise added at time t.1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_t={\beta}_0+{\beta}_1{X}_{t-1}+{\beta}_2{X}_{t-2}+{\varepsilon}_t$$\end{document}Xt=β0+β1Xt-1+β2Xt-2+εt The advantage of this is that two different time series generated in this manner should have temporal structure, but no above chance-level synchrony with one another. We generate five time series of length 1000 from the autoregressive function, and stack them alongside one another to act as the variables of a multivariate time series. Following the example of Gouhier and Guichard [2014], an important addition to our method is that we also add a certain amount of correlated noise to the multivariate time series. Gouhier and Guichard contend that correlated noise, added to the signals in this way, might lead to the ‘false’ detection of synchrony occurring despite the fact that the underlying autoregressive processes are unrelated. According to this line of reasoning, how a metric is impacted by the presence of correlated noise is an important characteristic of how the metric performs. We select a number from a Gaussian distribution at each time step, and then add this same number to each variable in the multivariate data. The relative contribution of the correlated noise to the final data is a parameter that we vary through the course of our experiments, i.e., we treat it as an independent variable. The variables in the synthetic data are each normalized to have mean 0 and variance 1, and the Gaussian distribution used to generate correlated noise has a mean of 0, but a variable standard deviation. To perform our investigations, we gradually increase the standard deviation of the correlated noise. It begins at zero, and increases in steps of 0.1 up to 1.0. At each value for the standard deviation of the correlated noise, we use the aforementioned procedure to create a sample of 500 time series, each containing five variables and 1000 time steps, and then compute the synchrony metrics. See Fig. 2 for example time series that might appear in a sample. To help us evaluate the observed values for each metric, we create surrogate data for each sample after adding correlated noise and re-calculate the metrics on these, providing ‘surrogate baseline’ results. The observed values without surrogation are compared to the surrogate baseline and we report Cohen's d and the percentage change to determine how robust the metrics are to changes in parameters of the synthetic data. We attempted both strategies mentioned in the previous section for constructing surrogate data, namely [1] shuffling windows of data, and [2] swapping variables between time series so that the variables have the same structure but appear in different time series. For the approach of shuffling windows, each variable was cut up into windows with length $\frac{1}{10}$ of the total time steps and reordered. However, this produced unexpectedly large discrepancies between the main results and the surrogate results even when there was no correlated noise, while in this circumstance there is no synchrony expected between variables in the original time series or the surrogate baseline (since the variables come from unconnected autoregressive processes), and so the outputs should not differ noticeably. For this reason, we report the results using the second type of surrogate data, in which variables are swapped. For this type of surrogate data, the results were as expected when no correlated noise had been added. The results are presented in Figs. 3 and 4, which respectively show the (absolute value of) Cohen’s d and the average percentage increase in each metric, compared to the surrogate baseline where variables had been swapped between time series after adding correlated noise. Fig. 3Absolute values for Cohen’s d when comparing synchrony scores on autoregressive data to surrogate dataFig. 4Change in synchrony scores on autoregressive data compared to surrogate with increasing levels of noise Using the outputs of this investigation, we are able to compare how synchrony metrics perform as the standard deviation of the correlated noise is increased, and highlight some important differences. All of the metrics are evidently affected once the standard deviation of the correlated noise becomes sufficiently high. As shown in Fig. 4, the coherence metric appears to be affected soonest, increasing quickly over the corresponding baseline values. Coherence and percentage of recurrence exhibit dramatic rises as a response to the correlated noise, ending up with such high values compared to the baselines that it is impractical to plot fully in the figure. Coherence reaches a plateau of approximately $500\%$ average change in value when scores reach the maximum value of 1.0 while baseline values remain essentially unchanged. Our proposed sum-normalized CSD metric takes longer to rise over the baseline, compared to the closely related coherence metric, although eventually this also becomes strongly affected by the correlated noise. The reason for this difference is that the noise tends to affect all the frequency components of the signals, even when it is added in a small degree. The coherence score is much more influenced by the number of frequency components at which there is high cross-spectral density, whilst the sum-normalized CSD is also sensitive to the amplitude of the frequency components and therefore is less affected when there is only a small degree of synchronization (due to correlated noise) in many of the frequency components. Relative to the other metrics, rho appears to stay close to zero for the longest, and has one of the lowest increases over the baselines when the correlated noise has its highest standard deviation. Unlike with coherence, the rho metric did not reach values close to its maximum of 1.0. One reason rho is less likely to be impacted by correlated noise is that this metric is based upon phase, which quantifies progression through some structured pattern; since the added noise does not have temporal structure, it may have a limited impact on the extracted phase, and therefore affect rho less than it affects the other metrics. When interpreting the percentage change results, it is important to note that, while the surrogate baseline values for coherence and rho remained largely consistent while changing the correlated noise and then shuffling variables to create surrogate data, the opposite was true for recurrence. The recurrence of the first baseline was on average $13.6\%$ without correlated noise, but fell to $1.1\%$ when the standard deviation of the noise was 1.0. This may help to explain the larger over-the-baseline increases observed in the recurrence. Symbolic entropy appears to have remained close to zero for longer than recurrence and coherence while increasing the standard deviation of the noise, but not for as long as rho. At no point was the change over the baselines particularly large for symbolic entropy. When interpreting the symbolic entropy, it is worth noting that there is a theoretical minimum of roughly 1.1 and a theoretical maximum value of 5.5 when using five variables. This provides some limitation on how much the observed entropy can increase over a baseline, which is not the case for the other metrics. Before moving on, a note of caution is that these results give a limited impression of how the metrics will perform in real scenarios. The autoregressive data, especially when we use a high parameter value for noise, adds correlated random numbers at each time step, which may be quite different from what would be expected with real sources of noise. Real sources might occur infrequently rather than having a consistent impact over all time points or have a more distinctive spectrum. Nevertheless, these investigations with unstructured but correlated noise can help to give an initial characterization of the different metrics in multiSyncPy. ## Kuramoto model data Next, we quantify the impact of increasing the coupling strength of oscillators in a Kuramoto model on the synchrony metrics. We assume that data generated from models where the coupling is higher will have higher levels of synchrony on average, and the following results show how well our metrics reflect this assumed trend. For each variable i in the model (which we base on the equations of Acebrón et al., 2005), the update rule for the phase of the variable is given below. θi is the phase/angle of the variable and ωi is the natural frequency of the variable. K is the coupling strength parameter, which is shared across the system. ψ is the average phase. If each variable is represented as a point along the circumference of the unit circle, then r is the distance of the centroid from the origin. The final term, ξi(t) denotes the random noise added at time t.2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\overset{\acute{\mkern6mu}}{\theta}}_i={\omega}_i+ Krsin\left(\psi -{\theta}_i\right)+{\xi}_{i(t)}$$\end{document}θ´i=ωi+Krsinψ-θi+ξi(t) We systematically increase the coupling strength parameter K, from 0.0 to 2.0 in steps of 0.2 and investigate the impact on the synchrony metrics. For each setting of K, we create a sample of 500 Kuramoto models and generate a multivariate time series with five variables and 1000 time steps. For each model in the sample, we choose a different set of natural frequencies for the oscillators sampled from an exponential distribution. This is important for the sake of our surrogate baseline where variables are swapped across time series, remaining the same but ending up in different time series, since high levels of synchrony could be observed in the surrogate data simply because the same natural frequencies are repeated across variables in the sample. Figures 5 and 6 show how the synchrony scores vary as the coupling strength is increased, compared to a surrogate baseline made by swapping the very same variables across time series in the sample. Fig. 5Absolute values for Cohen’s d when comparing synchrony scores on Kuramoto data to surrogate dataFig. 6Change in synchrony scores on Kuramoto data compared to surrogate at increasing levels of coupling strength Using these results to compare the metrics, we find that all metrics increase as the coupling in the Kuramoto models is strengthened. Figure 5 shows that the estimated effect increases similarly for all metrics, although Fig. 6 shows that the magnitude of the change over the baseline can differ quite considerably. In Fig. 6, the potential effect of Gaussian noise on the coherence metric is exposed, with the coherence scores barely rising above the baseline, even with strong coupling between oscillators. This is due to the fact that coherence at frequencies where the amplitude is high are treated equally with coherence at low-amplitude frequencies, when averaging across frequency components. In our Kuramoto data, each signal is a combination of a sinusoidal progression and a small amount of added Gaussian noise. Synchronization of the main sinusoidal component occurs at a single frequency, while the unsynchronized Gaussian noise (being white noise) affects the signal at all frequencies. Due to the normalization that occurs when calculating coherence at each frequency component (which removes information about amplitude), the presence of white noise at many frequencies ends up being dominant in the final averaged coherence score. The sum-normalized CSD metric we propose is better able to handle this issue, by adjusting the way that normalization is applied to the cross-spectral density. This is demonstrated by the fact that it rises quickly over the baseline as the coupling strength between oscillators is increased. Note that despite this issue with coherence, Fig. 5 indicates that an effect is observed when comparing the coherence scores to the baseline, meaning that the metric may still be useful in analyzing experimental data. Rho seemingly exhibits an increase over the baseline more quickly than some of the other metrics when coupling strength becomes larger, suggesting it does detect synchrony well in this context. The percentage of recurrence also seems to be responsive to coupling strength when looking at the percentage increase over the baseline, however the estimated effect size increases slightly more slowly than with the other metrics. Symbolic entropy showed lower increases over the surrogate baseline for smaller coupling strengths, but still demonstrated increases when the coupling strength was higher. Overall, the metrics fit the assumption that increased synchrony should be detected when the coupling between oscillators is increased. Using this type of synthetic data from Kuramoto models, it is possible to investigate various types of validity for our metrics. First, we can gain an insight into convergent validity by calculating the correlation between the metrics, since they are all expected to measure some form of multivariate synchrony. We do this across all Kuramoto time series generated according to the above procedure. With the same data, we also consider whether the multivariate metrics have concurrent validity with a metric that reflects the related concept of dyadic synchrony. We chose cross-correlation as a standard dyadic metric (Schoenherr et al., 2019) with a lag of 0, and averaged across all pairs within a time series to obtain a single value per time series. Pearson correlation coefficients are presented in Table 1.Table 1Pearson correlation coefficient between multivariate synchronization metricsRhoCoherenceSymbolic entropySum-normalizedCSDMean dyadic correlation% Recurrence.74.82–.91.67.88Rho.69–.90.92.88Coherence–.76.68.80Symbolic entropy–.83–.93Sum-normalized CSD.85 The absolute values of the correlation coefficient are all above.66, which Schoenherr et al. [ 2019] classified as ‘high’ correlation when comparing dyadic synchrony metrics, suggesting that there is substantial convergent validity between the different measures. Coherence has slightly lower scores than the others, perhaps due to the effect that Gaussian noise has on the metric when using this type of synthetic data (as described earlier). Nevertheless, these correlations are generally high and give support for convergent validity amongst the metrics. Additionally, it is possible to examine how well each multivariate metric is suited to predicting the known coupling parameter of the Kuramoto models, providing an insight into criterion validity. Figure 6 shows that across all metrics, there is a nearly perfect correlation (\|r[9]\| >.95) between the coupling parameter and the average synchrony score of the time series generated using that coupling parameter. This provides strong evidence of criterion validity, in that all metrics increase when synchrony in the form of coupling is increased. It is worth noting that this synthetic data may not resemble all of the important aspects of empirical signals as the Kuramoto data we generated assumes that the signals all come from periodic oscillators with equal and constant coupling. ## Empirical data results To complement the investigation of synthetic data, which allows us to vary key parameters of the data and examine the consequences, we also examine data from empirical studies, which have more complex and realistic properties, but at the cost that we do not know what parameters are driving it. We present two case studies using openly available data from the ELEA corpus (Sanchez-Cortes et al., 2012) and a study of triadic interaction (Gervais et al., 2013; Dale et al., 2020). Using these real-world data sets, we are able to showcase how multiSyncPy is used to perform novel scientific work, while also presenting new results from an analysis of bodily synchrony in two different tasks. In fact, our results on the ELEA corpus are the first to our knowledge to investigate bodily synchrony in this data. Through the case studies, we are able to provide ‘lessons learned’ from initial usage of the package. This makes it possible to describe the difficulties that real-world data, as opposed to synthetic data, can present. It also serves to highlight that it can be difficult to detect group-level synchrony, including in a case study where dyadic synchrony had been observed previously. ## ELEA corpus The ELEA corpus (Sanchez-Cortes et al., 2012) contains video recordings and transcripts from groups of three or four people performing a disaster survival task where the group must rank the importance of a list of items given a disaster scenario. The corpus also includes details of the rankings that individuals came up with individually and the group rankings they agreed upon, plus personality questionnaires, and ratings of dominance and leadership provided by experts. We use the videos included in the corpus to investigate postural synchrony within the teams. First, key points of the participants’ bodies were extracted for each frame of video using OpenPose (Cao et al., 2021). Then, we select a subset of key points to represent posture. Specifically, we use key points 0, 1, 2, 5, 15, 16, 17 and 18 from model ‘BODY_25’, which correspond to the nose, neck, right and left shoulders, right and left eyes, and right and left ears. These were chosen on the basis that they reflect postural information while being visible in the ELEA video recordings, which show participants from above the waist. Calculating the Euclidean distance for each key point across frames and then averaging across key points gives a single postural movement signal for each participant; the three or four participants together make a multivariate time series. This method is commonly used for dimensionality reduction of motion capture data and is sometimes referred to as an interpoint distance time series (Davis et al., 2016) or displacement time series (Borjon et al., 2018). Additionally, there are occasional jumps in the positions of key points estimated with OpenPose, for example where the participant moved out of view of the camera, or another person appears briefly, which leads to sudden large changes in the Euclidean distances. These points were identified using outlier detection (if they possessed a z-score greater than 5) and replaced using linear interpolation. This affected only $0.3\%$ of data. Because the recordings are different lengths, we take 14,000 frames from the middle of each recording, to produce a dataset with greater consistency and with which it is easier to swap variables across recordings to produce a surrogate baseline. Because we are interested in the videos, we use the ELEA-AV sub-corpus, which includes all the sessions which were video-recorded, amounting to 26 different teams. From these, the 20 recordings with four members in a team were selected for use, so that each multivariate time series would contain the same number of variables. Windows containing 300 time steps of data (equating to 10 s of recording) with high and low levels of synchronization are displayed below in Fig. 7 (each letter/line refers to postural movements from different group members).Fig. 7Example movement data extracted from ELEA from two meetings each with four participants, with participants indicated by line style and color Our first goal in investigating the data is to identify whether synchrony occurs at above the level expected by chance. Our first option in this regard is to apply the statistical test of Frank and Richardson on the Kuramoto order parameter. This method compares the observed data to a null hypothesis stating how a sample of values is expected to be distributed. This works for the Kuramoto order parameter. For the other metrics included in the package, the expected distribution of values in a sample is not known, and so we use surrogate data to gain an understanding of whether observed synchrony is above chance level. In general, the test of the Kuramoto order parameter offers one way to examine levels of synchrony within a sample of recordings. However, this is predicated on the data being suitable for such analyses. The test is based upon the Kuramoto model of oscillating components, in which the components follow sinusoidal progressions and have a uniform distribution of phase values. As can be seen from Fig. 7, the ELEA data is characterized by short spikes or peaks in movement occurring with a background of no motion otherwise. Using this data, the phase values extracted by the Hilbert transform have a skewed rather than uniform distribution (due to the periods of stillness between spikes which have no progression). To demonstrate the point, we examine what happens if one does perform the test of the Kuramoto order parameter on the movement data. For the 20 recordings of four variables, t[19] = 51.7, $p \leq .001$, $95\%$ CI [.49,.61], Cohen’s $d = 10.4$, demonstrating a strong effect. However, we also find that when shuffling variables between time series to create a surrogate baseline in which synchronization is not expected, we achieve very similar results: t[19] = 46.5, $p \leq .001$, CI = [.45,.56], Cohen’s $d = 10.4.$ This acts as a word of caution and a ‘lesson learned’ from our empirical case study, confirming that the test of the Kuramoto order parameter must be used on data from which appropriate phase information can be extracted, otherwise the results may be highly inflated. The distribution of phase values can be inspected to see if it is (roughly) uniform before performing the test. These findings also suggest that there is value in having access to multiple methods for analyzing synchrony, which is what multiSyncPy offers, since one method may overcome the limitations of another method and provide a way to identify inconsistent results. Further investigations to determine how both the type of data and the extracted phase information impact the test of the Kuramoto order parameter, using synthetic data and alternative methods for extracting phase, is presented in our Appendix I. Our next focus is the performance of the remaining synchrony metrics compared to a surrogate baseline. We take the same data and compute the percentage of recurrence (using a radius of 0.4 to determine recurrent points), cluster-phase rho, coherence, sum-normalized CSD and symbolic entropy. Then, the variables are shuffled across recordings to produce a surrogate baseline and the metrics are re-computed for comparison. The mean values of the different metrics are presented in Table 2, along with standard deviations, the results of a Welch’s independent samples t test, and estimated effect sizes. All of the metrics show some differences that are consistent with the hypothesis that synchrony is greater in the real compared to the surrogate data, and t tests suggest significance across all metrics except recurrence (as shown in Table 2) after applying a Bonferroni adjustment for the fact that we are running five tests, which lowers the significance level to.01. For all metrics, the differences appear quite small in terms of absolute difference, although the effect sizes for rho, coherence, symbolic entropy and sum-normalized CSD are typically considered large. These results give some indication that global multivariate synchrony may have occurred during the team task. Table 2Synchrony metrics on ELEA recordingsMetricMSDM (surrogate)SD (surrogate)dftpCohen’s dRecurrence (%)5.002.423.590.8123.32.48.0210.74Rho0.570.030.520.0126.47.46<.0011.52Coherence0.030.010.020.0019.83.92<.0011.06Symbolic entropy4.300.104.380.0119.2– 3.31.004– 0.93Sum-normalized CSD0.030.020.010.0019.43.72.0011.02 The relative distribution of the synchrony metrics across ELEA meetings is shown in Fig. 8, giving an indication of variability in the data. Because the range and units of each measure varies, each has its own scale determined by the theoretical minimum and maximum. Recurrence values are generally close to $5\%$, which is to be expected since the radius used to decide recurrence was chosen specifically to produce around $5\%$ recurrence. Coherence and sum-normalized CSD have low values relative to the theoretical maximum, although this is highly influenced by the fact that the time series have 14,000 time steps, allowing for the extraction of many frequency components, while it would not be expected that synchronization occurs at all of these frequencies. Symbolic entropy scores tend to be close to the theoretical maximum, but still demonstrate variability. Fig. 8Distribution of scores for each metric, with scales determined by theoretical minimum and maximum We also analyzed the data dividing each meeting into smaller, non-overlapping windows, following the example of (Dale et al., 2020). We used 300 frames for a window, corresponding to 10 s. Since OpenPose is unable to accurately extract key point locations in every frame, which may impact the reliability of the results, we select windows where OpenPose reports a confidence of $100\%$ when extracting key points in all frames. The number of windows obtained from a meeting can vary, according to the length of the meeting and the proportion of frames where OpenPose reports less-than-perfect confidence. The number of windows from a meeting varies between 9 and 79, with a median of 48.5. We then identify the maximum values of synchrony across all windows for a meeting, and use this score to summarize the entire meeting (Dale et al., 2020). This is motivated by the notion that synchrony may not occur consistently across a group meeting and instead has time-varying properties (Likens & Wiltshire, 2020; Mayo & Gordon, 2020; Wiltshire, Steffensen, et al., 2020b). With this method it should be possible to detect the presence of synchrony in general by looking at the maximum across time segments of a meeting (or observed period of interaction). Like with our previous analyses, we also create a surrogate baseline by swapping variables across windows, and then extracting maxima per meeting. The number of meetings and the number of windows per meeting is kept the same in the surrogate baseline. There are a few additional things to note about how the different metrics are calculated on this data. First, extracting the maximum recurrence across windows in a meeting led to particularly high values, being above $30\%$ in some cases. This implies that the radius used previously to decide when points are recurrent might be too large, and so we reduce the radius for this piece of analysis from 0.4 to 0.3 (see (Wallot & Leonardi, 2018) for guidance on setting the radius parameter for recurrence quantification analyses). Second, the highest point of synchronization is reflected by the lowest entropy, and so the minimum value rather than the maximum is used for that metric. Because the sequences consist of only 300 time steps, we reduced the window length to 75 (from the scipy default of 256) when using Welch’s method to determine power spectral density within the coherence calculation. This allows for multiple overlapping windows to be used in the calculation, which improves the estimation of spectral density and leads to usable results that are reported here. The results for the actual observations and the surrogate baseline are shown in Table 3. The first two columns show the mean value across meetings, with the standard deviation in parentheses. The next columns show the results from Welch’s independent samples t tests, and the estimated effect size. Table 3Synchrony metrics based on maximum value from windows per ELEA recordingMetricMSDM (surrogate)SD (surrogate)dftpCohen’s dRecurrence (%)6.703.106.432.9637.90.28.7800.09Rho0.830.050.820.0537.80.87.3910.28Coherence0.280.040.260.0335.61.02.3160.32Symbolic entropy3.000.083.000.1234.4– 0.33.744– 0.11Sum-normalized CSD0.310.070.280.0534.61.83.0760.56 Consistent with our previous results, we find that the synchrony values show an increase over the surrogate baseline, except for entropy which changes negligibly, giving us some added confidence that synchrony is still present after excluding frames where OpenPose had low confidence. However, using independent-samples t tests with a Bonferroni adjustment to reduce the significance level to.01 does not suggest that there is statistical significance to any of these differences. All of the metrics showed increased synchrony scores compared to the baseline, but the increases are small and not statistically significant. Figure 9 shows the relative distribution of the synchrony metrics for this data, i.e., using the value reflecting highest synchronization from the windows for each ELEA meeting. Compared to the 14,000 time step data used previously, the variability observed is greater with these windows of 300 time steps. Fig. 9Distribution of scores for highest synchrony across windows per ELEA meeting, with scale determined by theoretical minimum and maximum Previously, we examined the convergent validity between different multivariate synchrony metrics and an averaged dyadic measure when using synthetic Kuramoto data. With the ELEA data, we are able to examine convergent validity when using empirical data. The correlations between metrics across all 1222 windows of 300 time steps are presented in Table 4.Table 4Pearson correlation coefficient between metrics, windows of ELEA movement dataRhoCoherenceSymbolic entropySum-normalizedCSDMean dyadic correlationRecurrence (%).48.38–.40.23.37Rho.33–.27.26.58Coherence–.02.41.47Symbolic entropy-.04-.01Sum-normalized CSD.41 The correlations presented here are generally of lower magnitude than when using Kuramoto data, however they also show a similar positive correlation between different metrics, with the exception of symbolic entropy which decreases when synchronization is higher, as is expected. Where the magnitude is greater than.05, the results are significant. This gives some additional confirmation of convergent validity even when using empirical data. ## Triadic synchrony dataset A final component of our investigations into the performance of our metrics was to apply our analysis method to a dataset that has previously been used to study synchrony. We use the data analyzed by Dale and colleagues (Dale et al., 2020; Gervais et al., 2013) in their investigation of cross-correlation in triads. Their work looked at the interactions between groups of three participants in a period of open-ended conversation, with no goal or topic provided as part of the experiment. From video recordings, they extracted information about body movement and examined it for evidence of synchronization. Instead of using OpenPose, an ‘optical flow’ method was applied that measures the average change in pixel intensity over time to provide a proxy for body movement (Barbosa, 2017; Latif et al., 2014). The analysis focused on cross-correlation amongst the dyads that comprise a triad, and whether these cross-correlations themselves synchronized. Our approach is different in that it directly investigates synchrony as a group-level construct. Windows containing 300 time steps of data (equating to 10 s of recording) with high and low levels of synchronization are displayed below in Fig. 10 (with the movements of each participant corresponding to a different line/letter).Fig. 10Example triadic movement data with high and low synchronization We apply the same pre-processing as used in the original work, specifically a low-pass filter at 0.05 of the Nyquist frequency followed by excluding the first 200 frames from each meeting. The meetings are then split into non-overlapping windows of 300 frames, representing 10 s duration. We select the maximum observed synchrony across the windows for each meeting, and compare these per-meeting maxima to those observed in a surrogate baseline, where the variables have been swapped across recordings. In our surrogate baseline, we use a number of meetings and windows equal to those used in the main results. Consistent with our analysis of windows of ELEA data, a radius of 0.3 was used for mdRQA, the minimum rather than the maximum entropy per meeting is used, and the window size when computing power spectral density as part of the coherence calculation was reduced to 75. The mean values of the various synchrony metrics are presented in Table 5, along with standard deviations and results of Welch’s independent samples t tests and estimates of effect size. Table 5Synchrony metrics on triadic meeting dataMetricMSDM (surro- gate)SD (surro- gate)dftpCohen’s dRecurrence (%)12.824.5211.743.4363.41.13.2640.27Rho0.910.030.890.0467.21.70.0950.40Coherence0.590.080.510.0867.93.62<.0010.80Symbolic entropy2.500.102.550.1164.3– 2.45.017– 0.57Sum-normalized CSD0.500.070.460.0866.93.26.0020.73 The percentage of recurrence is higher in the real data compared to the surrogate baseline, consistent with the hypothesis that synchronization occurs during the conversations. The change is still quite small however, so this provides only weak evidence of multivariate synchrony. Similar to recurrence, symbolic entropy is slightly lower on the real data as would be expected if a small amount of synchrony is present in the data. Applying a Bonferroni adjustment to lower the significance level to.01 for our independent-samples t tests, recurrence, symbolic entropy and the cluster-phase ‘rho’ metric do not exhibit a significant change over the baseline. Coherence and the related sum-normalized CSD do suggest the presence of synchrony, with the difference from the surrogate baseline being significant according to a Welch’s independent samples t test. The results are mixed, with only two related metrics suggesting a significant change from the baseline, but there is some limited evidence of the presence of group-level synchronization. For most of our metrics, the average increase over the baseline is smaller for our metrics than was found in the investigation of cross-correlations in the original paper (Dale et al., 2020). This may be because the earlier work used a pairwise concept of synchrony rather than a full triadic/group-level metric. It may simply be less common for all three members of a group to synchronize together than it is for two individuals to synchronize, hence the difference in our results compared to the average cross-correlations reported in the original work (Dale et al., 2020). The relative distribution of the synchrony metrics is shown in Fig. 11, giving an indication of variability when using the triadic data. Fig. 11Distribution of scores for highest synchrony across windows per triadic meeting, with scale determined by theoretical minimum and maximum As with the data sets described previously, it is possible to examine the correlations between metrics on this empirical data. Table 6 displays Pearson correlation coefficients when using the full 1960 windows available from the triadic meetings. The metrics have small to medium correlations, all of which are significant at $p \leq .001$, giving further evidence of convergent validity between the metrics included in our package. Table 6Pearson correlation coefficient between metrics, windows of triadic movement dataRhoCoherenceSymbolic entropySum-normalizedCSDMean dyadic correlationRecurrence (%).40.17–.33.15.22Rho.33–.44.32.59Coherence–.20.41.47Symbolic entropy–.12–.27Sum-normalized CSD.49 ## Discussion In this paper, we presented the multiSyncPy package for computing multiple multivariate synchrony metrics. Our work aims to make such methods accessible while providing a good balance of alternatives, which the simple code examples presented earlier attempted to demonstrate. Relative to other software packages in the area of synchrony, multiSyncPy provides a valuable new contribution by focusing on multivariate synchrony, with applications in diverse areas of inquiry in the cognitive and behavioral sciences as well as other disciplines that might be interested in such phenomena (e.g., ecology, human–computer interaction). Another contribution of our work is to present an initial investigation of how different metrics perform on the same tasks. The methods collated in multiSyncPy have previously only been introduced in isolation from one another, and this initial comparison of multivariate metrics is novel. By examining two types of synthetic data, we observed that some metrics are more responsive than others to the addition of correlated noise to a multivariate signal, and some metrics appear more sensitive to the coupling strength in situations that can be modeled as coupled Kuramoto oscillators. The ‘rho’ metric, for example, seemed the least influenced by increased correlated noise, while being one of the metrics that increased most quickly with the Kuramoto coupling strength parameter. The synthetic data used in this paper are of course based on simplified mathematical models. They are useful, though, because they give the opportunity to change parameters of the data generation process and then observe corresponding trends in the values of synchrony metrics; however, there are some limitations worth noting. In particular, some of the interesting and complex properties of real-life signals may not be present in the data. One property that might be worthwhile to investigate in future work is quasi-periodicity, which is not reflected in our Kuramoto model since it is composed of oscillators following simple sinusoidal patterns. Methods for generating synthetic quasi-periodic data would make it possible to compare how different metrics perform under a wider range of conditions. Moreover, it is likely that a wide variety of nonlinear relationships between variables in a system are possible, and can be modeled in synthetic data. Future work could examine whether and how various nonlinear relationships impact synchrony metrics in different ways. Overall, on data from real-life experiments, our metrics showed limited increases over a surrogate baseline when considering windows of data, although a significant effect was observed for four out of five metrics on the full ELEA recordings. The fact that the increases were frequently quite small or not statistically significant might indicate that it may be hard to detect group-level synchrony in team tasks, it may be less common than the more frequently-investigated phenomenon of dyadic synchrony in teams, or it could be that this form of surrogation is quite conservative compared to simple randomization (Moulder et al., 2018). Looking at our results on the data analyzed by Dale and colleagues (Dale et al., 2020), it seems that the dyadic synchronization studied by the original authors was more noticeable against a surrogate baseline. This is to say that it is important to consider that pairs within larger teams may move in and out of coordination with each other over time. Future work could more systematically investigate not only in more detail the relationship between the multivariate metrics (see Schoenherr et al., 2019 for inspiration), but also convergence in pair-wise versus group-level coordination metrics, and changes over time in multivariate coordination (Amon et al., 2019). We expect that a key differentiator is in measuring synchrony as a system-level construct, which may not be the same as an aggregation of the synchrony between component dyads. Since the change above the surrogate baseline was generally small in our case studies on real data, future work could also search for examples of situations where multivariate synchrony is more obviously present. And, more generally, much remains to be known about the conditions under which system-level synchrony emerges in the variety of domains we mentioned involving social interactions. In terms of the practical utilization of our package, while these methods might now be more accessible than previously, we cannot understate the importance of carefully considering the methods, revisiting the original sources for these metrics, carefully inspecting the data to ensure the analyses are appropriate (e.g., periodic vs. non-rhythmic), choosing the correct pre-processing techniques (e.g., appropriate filtering, phase extraction, window size, time-norma etc.), analyzing multivariate systems with the same number of variables, and determining which segments of the data to analyze (removing transients, selecting for periods of activity vs. inactivity). As the field shifts from primarily bivariate to multivariate coordination dynamics, careful thinking, experimentation, and systematic comparison and validation of the multiple possible methods (including those presented in this paper and others such as (Zhang et al., 2020) and (Baboukani et al., 2019)) are required to fully understand these metrics. In conclusion, multiSyncPy provides a range of synchrony metrics that can be computed easily through simple function calls in Python. These metrics come from a range of theoretical backgrounds, and the context may make some metrics more appropriate or informative than others. All of the metrics apply to multivariate time series, and so can be used to investigate system-level constructs of synchrony. System-level synchrony is under-researched, even in contexts where synchrony has been studied previously, such as small group interactions. Our methods are also appropriate in situations where there are numerous variables and it would be difficult to make sense of a large number of pairwise synchronizations. In other words, we aim to contribute tools that may advance the field's understanding of how coordination functions across scales (Kelso, 2021; Zhang et al., 2019). For the benefit of future researchers interested in multivariate coordination dynamics, multiSyncPy is made freely available under the liberal LGPL license. ## Appendix I: Investigation of data characteristics on Kuramoto order parameter This subsection provides supplementary results concerning how the test of the Kuramoto order parameter is affected by characteristics of the data, in particular comparing results from Kuramoto oscillators to signals consisting of short bursts or peaks of activity in an otherwise flat progression (such as the movement data we extracted from the ELEA corpus). Recall that, when describing the empirical case study on the ELEA movement data, the test on the Kuramoto order parameter may have provided misleading results on the data because it was characterized by short bursts of activity. To confirm that this is the case, we generated synthetic data with the relevant characteristics, and compared this to results from data generated by a Kuramoto model, where the same problem was not expected to occur. First, we considered the unproblematic case using data from a Kuramoto model. Synthetic data generated by a Kuramoto model is moderated by the coupling strength, which is provided as a parameter. If the coupling strength is specified to be zero, then the components do not interact with each other and there is no mechanism by which they can coordinate and synchronize. In this situation, there is no synchrony expected. We generated a sample of 100 time series of five variables and 1000 time steps each from Kuramoto models where the coupling strength is 0. Performing the test of the Kuramoto order parameter, we find that, as expected, the result is not significant, t[99] = 0.25, $p \leq .05.$ By contrast, we would expect significant levels of synchronization when using a higher value of coupling. To verify this, we generated a sample of 100 time series of five variables and 1000 time steps each where the coupling strength is 0.5. The result of the test in this instance is significant, t[99] = 7.35, $p \leq .001.$ This gives some reassurance that the test works as expected when using signals that follow essentially sinusoidal progressions. Next, we considered what happens when applying the test to data characterized by short bursts. False identification of significance might occur with such data in situations where no synchrony is present. We constructed relevant synthetic data by generating variables which exhibit short bursts of activity, but where the bursts have random location, amplitude, and duration. Each variable takes the form of a signal with a duration of 1000 time steps in which five bursts occur. Each burst is constructed from a Gaussian curve. The standard deviation of the curve is selected from an exponential probability distribution, and the amplitude of the curve is modified by multiplying by a number which is also randomly chosen from an exponential distribution. Finally, the curve is given a location which is selected from a uniform distribution over the 1000 time steps of the signal. As stated, five curves (or ‘bursts’) are added to a signal which has a value of 0 at all other time steps. Gathering multiple such variables together leads to time series in which no synchrony is expected. The parameters for the exponential distributions were chosen using manual inspection, so that the bursts in different synthetic signals would be narrow enough in duration to have limited coincidental overlap. Examples of the data generated using this method are shown in Fig. 12.Fig. 12Example of synthetic data characterized by short bursts We generated a sample of 100 time series containing five variables each and applied the test on the Kuramoto order parameter. We found that although above chance-level synchrony was not expected, the results did suggest a significant degree of synchronization, t[99] = 62.6, $p \leq .001.$ This confirms, using synthetic data generated especially for the purpose, that data characterized by short bursts is inappropriate for analysis with the Kuramoto test. This conclusion supports the remarks made in the “Empirical Data Results” subsection when interpreting the ELEA movement data, which are also characterized by short bursts. The explanation we propose is that the test assumes a uniform distribution of phase values, whereas there is no progression in phase during lengthy periods of inactivity, leading to a skewed distribution in data where activity occurs in short bursts. If the variables in a multivariate time series have skewed distributions of phase values, then the distribution of the average phase value at each instant (averaged across variables, not time) could be quite different from what is assumed by the test of the Kuramoto order parameter. If the distribution of phase values is the source of the problem, it may be interesting to consider alternative phase extraction methods to the Hilbert transform used throughout this paper. Therefore, we tried again using two alternative methods. First, we used the continuous wavelet transform with the Morlet wavelet to extract the phase. Second, we took an approach similar to Gouhier and Guichard’s [2014] approach, in which we first find peaks (in our case using a method based on wavelet analysis) which are considered moments of maximal phase and then interpolate the phase linearly between these points. The approach based on the Morlet wavelet brings about results which do not indicate significance, as is desired for the synthetic data, t[99] = 0.47, $p \leq .05.$ However, further investigation shows that the problem is not entirely solved. When the synthetic data consists of bursts with longer duration, such that they can overlap more, then the test of the Kuramoto order parameter indicates significance, t[99] = 3.9, $p \leq .001.$ Moreover, the test also indicates significance when applied to a surrogate baseline constructed from shuffling the variables between time series within the ELEA movement data. Using a surrogate baseline constructed from ELEA meetings with four participants, t[19] = 8.2, $p \leq .001.$ This suggests that there are still issues when extracting phase using the continuous wavelet transform with the Morlet wavelet. The approach based on peak-finding gives more reasonable values for the t-statistic compared to the results when the Hilbert transform is used, although significance is still concluded in the synthetic data, t[99] = 2.3, $p \leq .05.$ Interestingly, when using the peak-picking approach, significance is not concluded from ELEA surrogate data, t[19] = 1.3, $p \leq .05.$ One thing to note is that the outputs seem to be highly sensitive to changes in the parameters used by the peak-picking algorithm. We used visual inspection to identify reasonable values for the parameters. 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--- title: Extracellular vesicles are dynamic regulators of maternal glucose homeostasis during pregnancy authors: - Hannah C. Zierden - Ruth Marx-Rattner - Kylie D. Rock - Kristen R. Montgomery - Pavlos Anastasiadis - Lillian Folts - Tracy L. Bale journal: Scientific Reports year: 2023 pmcid: PMC10027885 doi: 10.1038/s41598-023-31425-x license: CC BY 4.0 --- # Extracellular vesicles are dynamic regulators of maternal glucose homeostasis during pregnancy ## Abstract Homeostatic regulation of the maternal milieu during pregnancy is critical for maternal and fetal health. The placenta facilitates critical communication between maternal and fetal compartments, in part, through the production of extracellular vesicles (EVs). EVs enable tissue synchrony via cell–cell and long-distance communication and are at their highest circulating concentration during pregnancy. While much work has been done investigating how physiological challenges in pregnancy affect the fetus, the role of placental communication in maternal health has not been well examined. We previously identified placental O-glycosyl transferase (OGT), a glucose-sensing enzyme, as a target of maternal stress where OGT levels and activity affected the O-glycosylation of proteins critical for EV cargo loading and secretion. Here, we hypothesized that placental OGT plays an essential role in maternal homeostatic regulation during pregnancy via its regulation of maternal circulating EV concentrations. Our studies found that changes to key metabolic factors over the circadian cycle, including glucocorticoids, insulin, and glucose, were significantly associated with changes in circulating EV concentration. Targeting placental OGT in mice, we found a novel significant positive relationship between placental OGT and maternal circulating EV concentration that was associated with improving maternal glucose tolerance during pregnancy. Finally, an intravenous elevation in EVs, matching the concentration of EVs during pregnancy, shifted non-pregnant female glucose sensitivity, blunted glucose variance, and improved synchrony of glucose uptake. These data suggest an important and novel role for circulating EVs as homeostatic regulators important in maternal health during pregnancy. ## Introduction Disruptions to homeostasis during pregnancy, including chronic stress, are associated with adverse maternal health outcomes including preterm birth, gestational diabetes, and preeclampsia1–5. In our prior studies, we found that placental levels of the X-linked gene and glucose-sensing enzyme, O-glycosyl transferase (OGT), were directly related to sex-specific offspring outcomes resulting from maternal stress6–8. OGT serves as a link between maternal glucose regulation and placental function during pregnancy9. As the placenta reflects the fetal chromosomal sex, in both mouse and human placentae, females have twice as much OGT protein and enzymatic activity (O-GlcNAcylation) as males10–13. These higher levels seem to protect the female fetus from maternal insults, including stress14. However, despite these extensive mechanistic studies examining the importance of the placenta in fetal development, its role in maternal health has not been well examined. A key function of the placenta in the maintenance of pregnancy is cellular communication and coordination between fetal and maternal compartments15–20. A primary mechanism of this short- and long-distance communication is signaling via extracellular vesicles (EVs)21,22. EVs are biologically produced nanoparticles that transport proteins, nucleic acids, and small molecules to coordinate physiologic functions22–24. Circulating EVs are a dynamic and heterogeneous population of various sized particles (macro-vesicles > micro-vesicles > exosomes) from different cellular origins with diverse functions25,26. EVs produced during pregnancy are involved in implantation, immune activation, angiogenesis, glucose metabolism and uptake, and the onset of labor27–31. During pregnancy, the concentration of EVs in maternal circulation is dramatically increased, 3–fourfold, which is attributed to the large population of EVs produced by the placenta, supporting the hypothesis that placental EVs are involved in maternal homeostatic regulation in pregnancy21,31–33. In fact, changes in EV cargo and secretion associated with gestational diabetes have been well characterized across pregnancy suggesting their potential role to counter homeostatic imbalance34–38. Furthermore, we previously observed significant changes to O-GlcNAcylation of placental Annexin A1 (ANXA1), a protein involved in EV secretion, as a result of maternal stress during pregnancy, leading us to investigate the molecular mechanisms involved in how placental OGT acts as regulator of EV secretion into maternal circulation6,39. As our previous studies demonstrated a significant reduction of OGT by maternal stress, we first examine the effect of stress to alter EV secretion in both the nonpregnant and pregnant state. We then focus on the specific role of OGT to significantly regulate EV concentration, and finally the ability for an increase in pregnancy EVs to affect glucose homeostasis. ## Circulating extracellular vesicle (EV) concentrations significantly change in response to altered metabolic state. We first compared how the circadian rhythm affects circulating EVs in comparison with corticosterone levels over the course of the day. The concentration of circulating EVs significantly decreased (one-way ANOVA, main effect of time, F3,13 = 3.509, $$p \leq 0.0463$$) while the concentration of corticosterone significantly increased (Fig. 1A–D, one-way ANOVA, main effect of time, F3,13 = 25.62, $p \leq 0.0001$). While not significantly associated with exogenous changes to corticosterone, the size of EVs was significantly changed over the course of the day (Fig. 1C, one-way ANOVA, main effect of time, F3,13 = 8.454, $$p \leq 0.0023$$), with significant differences between 0700 h (87.5 ± 2.9) and 1100 h (75.2 ± 1.4, $$p \leq 0.006$$), and 0700 h and 1500 h (73.4 ± 0.803, $$p \leq 0.0031$$). The ζ-potential of EVs also changed over the course of the day (Fig. 1D, one-way ANOVA, main effect of time, F3,13 = 5.023, $$p \leq 0.0158$$), with EVs at 0700 h (− 30.9 ± 2.2 mV) being significantly more neutral than EVs at 1500 h (− 41.1 ± 2.0 mV, $$p \leq 0.0096$$).Figure 1Circulating extracellular vesicle (EV) concentrations significantly change in response to altered metabolic state. ( A) EV concentration in circulation significantly changes over the course of the day (one-way ANOVA, main effect of time, F3,13 = 3.509, $$p \leq 0.0463$$). 0700 shown in black, $$n = 4$$; 1100 in green, $$n = 5$$; 1500 in yellow, $$n = 4$$; 1900 in gray, $$n = 4$.$ ( B) The concentration of circulating EVs decreases over the course of the day (left y-axis, black, one-way ANOVA, main effect of time, F3,13 = 3.509, $$p \leq 0.0463$$), while corticosterone levels increase over the course of the day (right y-axis, red, one-way ANOVA, main effect of time, F3,13 = 25.62, $p \leq 0.0001$). ( C) The size of circulating EVs significantly changes over the course of the circadian rhythm (one-way ANOVA, main effect of time, F3,13 = 8.454, $$p \leq 0.0023$$), and (D) the ζ-potential is significantly changed (one-way ANOVA, main effect of time, F3,13 = 5.023, $$p \leq 0.0158$$). ( E,F) IP injections of corticosterone (red, $$n = 5$$) trend to decrease EV concentration in non-pregnant dams as compared to vehicle injections (blue, $$n = 5$$, two-tailed t-test, t8 = 1.351, $$p \leq 0.2137$$). Corticosterone injections did not impact (G) size (two-tailed t-test, t8 = 1.351, $$p \leq 0.2137$$) or (H) ζ-potential of circulating EVs (two-tailed t-test, t8 = 0.2309, $$p \leq 0.8232$$). ( I) Non-pregnant dams received either no injection ($$n = 4$$), vehicle injection (saline, $$n = 5$$), 6 h fasting with a vehicle injection ($$n = 5$$), 6 h fasting with a glucose injection (3 mg glucose/g body weight, $$n = 6$$), or 6 h fasting with an insulin injection (0.8 mU insulin/g body weight, $$n = 5$$). There was a significant effect of injection on concentration (one-way ANOVA, main effect of treatment, F4,20 = 4.883, $$p \leq 0.0065$$) and (J) size (one-way ANOVA, main effect of treatment, F4,20 = 4.764, $$p \leq 0.0073$$) of circulating EVs but not on (K) ζ-potential of circulating EVs. We next sought to probe the direct effect of the stress hormone, corticosterone, on circulating EVs using non-pregnant dams. We injected non-pregnant dams with vehicle (saline, $$n = 5$$) or corticosterone (300 μg/kg, $$n = 5$$) and evaluated the concentration of circulating EVs at 30 min. The vehicle group had an average concentration of 3.7 × 1010 ± 1.5 × 1010 particles/mL, while the corticosterone treated group had a concentration of 1.5 × 1010 ± 5.2 × 109 particles/mL (Fig. 1E–H, $$n = 5$$ per group). While not statistically significant, there was a trend towards decreased EV concentration with increasing corticosterone treatment (Fig. 1F, two-tailed t-test, t8 = 1.351, $$p \leq 0.2137$$). We observed no differences in either size (Fig. 1G, two-tailed t-test, t8 = 0.8502, $$p \leq 0.4200$$) or ζ-potential (Fig. 1H, two-tailed t-test, t8 = 0.2309, $$p \leq 0.8232$$) of EVs associated with corticosterone treatment. EVs from the vehicle group were 80.9 ± 1.8 nm, with a ζ-potential of -36.6 ± 1.9 mV, while EVs from corticosterone treated dams were 78.1 ± 2.8 nm, with a ζ-potential of − 35.9 ± 2.5 nm. To further examine homeostatic factors that change in response to stress and circadian rhythm, we examined the effect of fasting ($$n = 5$$), a glucose bolus injection ($$n = 6$$), and an insulin bolus injection ($$n = 5$$) on EV concentration in non-pregnant dams. As a comparison for the stress of the injection, non-injected dams ($$n = 4$$) averaged an EV concentration of 1.4 × 1010 ± 2.5 × 109 particles/mL, while the vehicle injected group (saline, $$n = 5$$) averaged 4.4 × 1010 ± 1.4 × 1010 particles/mL, fasting vehicle injected group averaged 2.5 × 1010 ± 1.0 × 1010 particles/mL, fasting glucose injected group averaged 5.8 × 1010 ± 1.5 × 1010 particles/mL (Fig. 1I), and fasting insulin injected group averaged 1.1 × 1011 ± 2.9 × 1010 particles/mL. The concentration of EVs in circulation significantly changed across treatment (one-way ANOVA, main effect of treatment, F4,20 = 4.883, $$p \leq 0.0065$$), where the insulin injection showed a significant increase in EV concentration compared to the non-injected and fasted groups ($$p \leq 0.0078$$, and $$p \leq 0.0125$$, respectively). The non-injected group had an average size of 135.0 ± 14.1 nm, vehicle 88.4 ± 7.1 nm, fasting 117.6 ± 15.1 nm, glucose 91.9 ± 6.1 nm, and insulin 83.1 ± 3.3 nm (Fig. 1J). We observed significant differences in the size of particles across these groups (one-way ANOVA, main effect of treatment, F4,20 = 4.764, $$p \leq 0.0073$$), where EVs from the non-injected group were significantly larger than EVs from vehicle ($$p \leq 0.0311$$), glucose ($$p \leq 0.041$$), and insulin ($$p \leq 0.014$$) injected groups. There were no significant differences in the ζ-potential of EVs across treatments, with the EVs from the non-injected group having a ζ-potential of − 30.1 ± 2.4 mV, vehicle − 34.2 ± 1.5 mV, fasting − 33.5 ± 2.4 mV, glucose − 32.1 ± 1.9 mV, and insulin − 33.5 ± 2.0 mV (Fig. 1K). These data demonstrate that EV production relies on many compounding factors, and we next used our mouse model of early pregnancy stress to understand the potential role of EVs in controlling homeostatic changes during pregnancy. ## Stress during pregnancy significantly decreases circulating EV concentration We first explored changes in circulating vesicles over the course of normal pregnancy, specifically on E12.5, E15.5 and E18.5, where plasma was collected from pregnant dams and used to isolate EVs. As expected, we observed a significant increase in the concentration of circulating EVs over the course of pregnancy (Fig. 2A, one-way ANOVA, main effect of treatment, F3,31 = 12.46, $p \leq 0.0001$). The concentration of EVs from non-pregnant dams (1.0 × 1011 ± 1.8 × 1010 particles/mL, $$n = 9$$) was significantly lower than EVs from pregnant dams at all timepoints (E12.5, 2.3 × 1011 ± 2.1 × 1010 particles/mL, $$p \leq 0.0007$$, $$n = 8$$; E15.5, 1.8 × 1011 ± 2.3 × 1010 particles/mL, $$p \leq 0.0489$$, $$n = 10$$; E18.5, 2.8 × 1011 ± 2.2 × 1010 particles/mL, $p \leq 0.0001$, $$n = 8$$).Figure 2Robust increase in circulating EV concentration during pregnancy is significantly decreased by stress in vivo, and glucocorticoids in vitro. ( A) There was a significant effect of pregnancy on the concentration of EVs in vivo (one-way ANOVA, main effect of treatment, F3,31 = 12.46, $p \leq 0.0001$). Non-pregnant dams shown in black ($$n = 9$$), E12.5 dams in orange ($$n = 8$$), E15.5 dams in green ($$n = 10$$), and E18.5 dams in blue ($$n = 8$$). ( B–D) We investigated changes to circulating EVs as a result of maternal stress (open circles). ( B) E12.5 stressed dams ($$n = 7$$, open orange circles) had a concentration of 9.1 × 1010 ± 1.6 × 1010 particles/mL plasma, at E15.5 ($$n = 6$$, open green circles) 4.2 × 1010 ± 8.6 × 109 particles/mL, and at E18.5 ($$n = 7$$, open blue circles) 1.1 × 1011 ± 2.2 × 1010 particles/mL. Stress produced a significant decrease in circulating EVs (E12.5, two-tailed t-test, t13 = 5.285, $$p \leq 0.0001$$; E15.5, two-tailed t-test, t14 = 4.502, $$p \leq 0.0005$$; E18.5, two-tailed t-test, t13 = 5.424, $$p \leq 0.0001$$) and (C) a significant increase in EV size. EVs from E12.5 control dams were 80.38 ± 2.7 nm, whereas EVs from stressed dams were 93.3 ± 5.5 nm (two-tailed t-test, t13 = 2.205, $$p \leq 0.046$$). EVs from E15.5 control dams were 88.0 ± 2.6 nm, whereas EVs from stressed dams were 104.8 ± 5.8 nm (two-tailed t-test, t14 = 3.028, $$p \leq 0.009$$). EVs from E18.5 control dams were 80.1 ± 1.9 nm, and EVs from E18.5 stressed dams were 98.7 ± 5.9 nm (two-tailed t-test, t13 = 3.143, $$p \leq 0.0078$$). ( D) E12.5 EVs from control dams were − 35.5 ± 1.1 mV, and E12.5 EVs from stressed dams were − 30.8 ± 1.6 mV (two-tailed t-test, t13 = 2.503, $$p \leq 0.0264$$). E15.5 EVs from control dams were − 34.0 ± 1.9 mV, and E15.5 EVs from stressed dams were − 34.1 ± 1.6 mV (two-tailed t-test, t14 = 0.03167, $$p \leq 0.9752$$). E18.5 EVs from control dams were − 32.7 ± 1.6 mV, and E18.5 EVs from stressed dams were − 33.3 ± 1.3 mV (two-tailed t-test, t13 = 0.2837, $$p \leq 0.7811$$). ( E) Cortisol treatment (red, $$n = 5$$) significantly decreased concentration of EVs in media of BeWo-b30 cells to 5.39 × 108 ± 1.71 × 108 particles/mL, compared to vehicle treated (blue, $$n = 5$$) which had a concentration of from 1.16 × 109 ± 1.77 × 108 particles/mL. Non-conditioned media (black, $$n = 8$$) had a concentration of 6.54 × 107 ± 4.89 × 106 particles/mL. (F) The concentration of EVs secreted into the media was significantly decreased following cortisol treatment (two-tailed t-test, t8 = 2.532, $$p \leq 0.0351$$). Cortisol exposure did not alter the (G) size (vehicle = 137.0 ± 3.1 nm v. cortisol = 137.1 ± 2.2 nm, two-tailed t-test, t8 = 0.0275, $$p \leq 0.9787$$) or (H) ζ-potential of EVs (vehicle = -34.6 ± 2.4 mV v. cortisol = − 38.5 ± 0.15 mV, two-tailed t-test, t8 = 1.652, $$p \leq 0.1372$$). As we previously identified changes to O-GlcNAcylation of placental ANXA1 as a result of maternal stress during pregnancy6, we next evaluated changes to circulating EVs following maternal stress (Fig. 2B–D). At E12.5 stressed dams ($$n = 7$$) had a concentration of 9.1 × 1010 ± 1.6 × 1010 particles/mL plasma, at E15.5 ($$n = 6$$) 4.2 × 1010 ± 8.6 × 109 particles/mL, and at E18.5 ($$n = 7$$) 1.1 × 1011 ± 2.2 × 1010 particles/mL (Fig. 2B). Across all three gestational days, there was a significant decrease in concentration of EVs associated with stress (Fig. 2B, E12.5, two-tailed t-test, t13 = 5.285, $$p \leq 0.0001$$; E15.5, two-tailed t-test, t14 = 4.502, $$p \leq 0.0005$$; E18.5, two-tailed t-test, t13 = 5.424, $$p \leq 0.0001$$). We also compared the size and ζ-potential of particles in each group. We observed an increase in particle size associated with stress (Fig. 2C). At E12.5, EVs from control dams were 80.38 ± 2.7 nm, whereas EVs from stressed dams were 93.3 ± 5.5 nm (two-tailed t-test, t13 = 2.205, $$p \leq 0.046$$). EVs from control dams at E15.5 were 88.0 ± 2.6 nm, whereas EVs from stressed dams showed an increase in size to 104.8 ± 5.8 nm (two-tailed t-test, t14 = 3.028, $$p \leq 0.009$$). Similarly, at E18.5 EVs from the control group were 80.1 ± 1.9 nm, and those from the stressed group were 98.7 ± 5.9 nm (two-tailed t-test, t13 = 3.143, $$p \leq 0.0078$$). We only observed significant differences in the ζ-potential of circulating EVs on E12.5 (Fig. 2D). On E12.5, EVs from control dams were − 35.5 ± 1.1 mV, whereas EVs from stressed dams were − 30.8 ± 1.6 mV (two-tailed t-test, t13 = 2.503, $$p \leq 0.0264$$). EVs from control dams at E15.5 were − 34.0 ± 1.9 mV, and EVs from stressed dams were − 34.1 ± 1.6 mV (two-tailed t-test, t14 = 0.03167, $$p \leq 0.9752$$). On E18.5, EVs from the control group were − 32.7 ± 1.6 mV, and those from the stressed group were − 33.3 ± 1.3 mV (two-tailed t-test, t13 = 0.2837, $$p \leq 0.7811$$). As previously reported, we found no difference in the litter size between control and stressed treatment groups (Supplemental Fig. 1A). In an in vitro model using a pure population of human trophoblast BeWo b30 cells, we investigated the effect of the stress hormone, cortisol, on EV production. Again, we saw a significant reduction in EV secretion in response to cortisol where vehicle treatment had an average concentration of 1.2 × 109 ± 1.8 × 108 particles/mL ($$n = 5$$), while the cortisol treated group had a reduction in concentration to 5.4 × 108 ± 1.7 × 108 particles/mL (Fig. 2E,F, $$n = 5$$). The non-conditioned media had a concentration of 6.5 × 107 ± 4.9 × 106 particles/mL ($$n = 8$$). As in other stress models, we observe that stress hormone significantly decreased EV concentration in this pure population of cells (two-tailed t-test, t8 = 2.532, $$p \leq 0.0351$$). We observed no difference in size (Fig. 2G, vehicle = 137.0 ± 3.1 nm v. cortisol = 137.1 ± 2.2 nm, two-tailed t-test, t8 = 0.0275, $$p \leq 0.9787$$) or ζ-potential (Fig. 2H, vehicle = − 34.6 ± 2.4 mV v. cortisol = − 38.5 ± 0.15 mV, two-tailed t-test, t8 = 1.652, $$p \leq 0.1372$$) for the two groups. ## Increasing circulating EV concentration shifts glucose dynamics Pregnancy is a highly metabolic and physiologically demanding state. Using glucose tolerance tests (GTT), clinical and preclinical studies previously demonstrated that glucose sensitivity is diminished as pregnancy progresses40. As we previously identified placental O-GlcNAc transferase (OGT), a nutrient sensing enzyme that drives the sex-specific effects of maternal stress on offspring, we sought to examine glucose uptake and processing as a readout of the role of EVs in maternal metabolic health6,7,41. We first tested the hypothesis that acutely increasing the concentration of EVs in circulation by injecting EVs from E18.5 pregnant dams into the tail vein of non-pregnant dams would impact glucose sensitivity and processing (Fig. 3). We observed significant differences in glucose tolerance between non-injected ($$n = 7$$), vehicle (saline, $$n = 5$$), and EV-injected treatment (Fig. 3A, $$n = 6$$, two-way ANOVA, main effect of time, F2.61,39.14 = 122.7, $p \leq 0.0001$, main effect of injection, F2,15 = 1.265, $$p \leq 0.3106$$, timeinjection interaction, F8,60 = 2.666, $$p \leq 0.0142$$). While we observed no statistically significant differences when analyzing glucose tolerance, we consistently observed glucose rise and slope recovery patterns suggesting that an increased EV concentration affects glucose processing. Increasing circulating EVs trended towards decreasing the area under the curve (AUC) (Fig. 3B, one-way ANOVA, main effect of injection, F2,15 = 2.02, $$p \leq 0.1672$$, Bartlett’s test, $$p \leq 0.2105$$, SDNoInj = 2296, SDVehInj = 2162, SDEVInj = 1005), decreasing the change in glucose rise from 0 to 15 min (Fig. 3C, one-way ANOVA, main effect of injection, F2,15 = 2.474, $$p \leq 0.1179$$, Bartlett’s test, $$p \leq 0.5110$$, SDNoInj = 44.05, SDVehInj = 29.23, SDEVInj = 27.52), and increasing glucose processing, as indicated by an increased slope of the curve from 30 to 60 min (Fig. 3D, one-way ANOVA, main effect of injection, F2,15 = 2.922, $$p \leq 0.0848$$, Bartlett’s test, $$p \leq 0.4621$$, SDNoInj = 1.045, SDVehInj = 0.7723, SDEVInj = 0.5935). Overall, acutely increasing EV concentration trended toward a synchronization of glucose response across individual animals, as indicated by a reduced group variance (Fig. 3E–G). These data, while only patterns and not statistically significant, suggest that circulating EVs may play a role in coordinating glucose sensitivity and uptake, a critical biologic process during pregnancy. Figure 3Dynamic increase of circulating EV concentration shifts glucose dynamics. ( A) Glucose tolerance curves for non-pregnant dams that received no injection ($$n = 7$$, black), vehicle injection ($$n = 5$$, green), or EV injection ($$n = 6$$, red) (two-way ANOVA, main effect of time, F2.61,39.14 = 122.7, $p \leq 0.0001$, main effect of injection, F2,15 = 1.265, $$p \leq 0.3106$$, timeinjection interaction, F8,60 = 2.666, $$p \leq 0.0142$$). There was no significant difference in the glucose tolerance test (B) area under the curve (one-way ANOVA, main effect of injection, F2,15 = 2.02, $$p \leq 0.1672$$, Bartlett’s test, $$p \leq 0.2105$$, SDNoInj = 2296, SDVehInj = 2162, SDEVInj = 1005), (C) change in glucose from 0 to 15 min (one-way ANOVA, main effect of injection, F2,15 = 2.474, $$p \leq 0.1179$$, Bartlett’s test, $$p \leq 0.5110$$, SDNoInj = 44.05, SDVehInj = 29.23, SDEVInj = 27.52), or (D) slope of glucose processing from 30 to 60 min (one-way ANOVA, main effect of injection, F2,15 = 2.922, $$p \leq 0.0848$$, Bartlett’s test, $$p \leq 0.4621$$, SDNoInj = 1.045, SDVehInj = 0.7723, SDEVInj = 0.5935). ( E–G) Individual dam glucose tolerance curves are shown (E) for non-injected dams, (F) vehicle injected dams, and (G) EV injected dams. ## O-glycosyl transferase (OGT) levels in the placenta affect circulating EVs OGT serves as a link between maternal glucose regulation and placental function during pregnancy9. The placenta contributes to the significant increase in circulating EVs during pregnancy. Therefore, we utilized genetic targeting of placental OGT to determine its contribution to overall circulating EVs. As mice have litters and, therefore, multiple placentae, we developed a scoring system to compare the amount of OGT in the uterus between dams, where each copy of OGT in the placenta received 1 point (XWT/XWT = 2 points; XOGT−/XWT or XWT/$Y = 1$ point; XOGT−/XOGT− or XOGT−/$Y = 0$ points). As with stress, altered OGT expression in the placenta did not affect litter size (Supplementary Fig. 1B). We found a significant positive correlation between the overall uterine OGT score and the concentration of EVs in maternal circulation (Fig. 4A, Pearson correlation, F1,27 = 23.43, $p \leq 0.0001$, R2 = 0.4646). Examining these data by gestational day, we found that the concentration of EVs at E12.5 ($$n = 10$$, Pearson correlation, F1,8 = 3.562, $$p \leq 0.0958$$, R2 = 0.308), E15.5 ($$n = 11$$, Pearson correlation, F1,9 = 9.286, $$p \leq 0.0139$$, R2 = 0.508), and E18.5 ($$n = 8$$, Pearson correlation, F1,6 = 7.159, $$p \leq 0.0367$$, R2 = 0.544) were correlated with overall uterine OGT score, with a statistically significant relationship between increasing uterine OGT and increased maternal circulating EV concentration. We observed no association between either particle size and OGT score (Fig. 4B), or between ζ-potential and OGT score (Fig. 4C). Importantly, we demonstrate that this is driven by OGT and not by the number of placentae (Supplementary Fig. 2). While OGT score is significantly correlated to the number of placentae (Supplementary Fig. 2A, Pearson correlation, F1,27 = 13.67, $$p \leq 0.001$$, R2 = 0.3361), when restricting the data analysis to include only litters of 7–8 pups, we observed a wide range of OGT scores (2–7, Supplementary Fig. 2B). Similarly, when restricting the analysis to include only litters of 7–8 pups, we observed a trend in increasing EV concentration with OGT score (Supplementary Fig. 2C, Pearson correlation, F1,10 = 2.279, $$p \leq 0.162$$, R2 = 0.1856).Figure 4Placental OGT levels significantly correlate with circulating EV concentration and maternal glucose sensitivity. ( A) Across pregnancy there was a significant correlation between OGT score and concentration of EVs (Pearson correlation, F1,27 = 23.43, $p \leq 0.0001$, R2 = 0.4646). The concentration EVs on E12.5 ($$n = 10$$, orange circles) was not significantly correlated with OGT score (Pearson correlation, F1,8 = 3.562, $$p \leq 0.0958$$, R2 = 0.308). The concentration of EVs from E15.5 dams ($$n = 11$$, green circles) was significantly with OGT score (Pearson correlation, F1,9 = 9.286, $$p \leq 0.0139$$, R2 = 0.508) and the concentration of EVs from E18.5 dams ($$n = 8$$, green circles) was significantly correlated with OGT score (Pearson correlation, F1,6 = 7.159, $$p \leq 0.0367$$, R2 = 0.544). $95\%$ CI are shown as dotted lines above and below the trend line. ( B) We observed no correlation for the size of circulating EVs with uterine OGT score. Similarly, no trends were observed for circulating EVs on E12.5 ($$n = 10$$, orange, Pearson correlation, F1,8 = 2.131, $$p \leq 0.182$$, R2 = 0.2104), E15.5 ($$n = 11$$, green, Pearson correlation, F1,9 = 2.3, $$p \leq 0.164$$, R2 = 0.2036), or E18.5 ($$n = 8$$, blue, Pearson correlation, F1,6 = 2.207, $$p \leq 0.188$$, R2 = 0.2689) (C) Across pregnancy, the ζ-potential of EVs from XOGT−/XWT dams did not significantly change with OGT score (Pearson correlation, F1,27 = 3.017, $$p \leq 0.094$$, R2 = 0.1005). There was no change in the ζ-potential of EVs on E12.5 ($$n = 10$$, orange, Pearson correlation, F1,8 = 0.3123, $$p \leq 0.592$$, R2 = 0.0376), E15.5 ($$n = 11$$, green, Pearson correlation, F1,9 = 2.971, $$p \leq 0.119$$, R2 = 0.2482), or E18.5 ($$n = 8$$, blue, Pearson correlation, F1,6 = 0.2253, $$p \leq 0.652$$, R2 = 0.0362). $95\%$ CI are shown as dotted lines above and below trend lines. ( D) Glucose tolerance for non-pregnant ($$n = 7$$, NP, black), E12.5 ($$n = 11$$, orange) and E15.5 ($$n = 10$$, green) dams. We observed significant variation in glucose sensitivity across gestational days (two-way ANOVA, main effect of time, F2.324,58.11 = 187.8, $p \leq 0.0001$, main effect of group, F2,25 = 2.241, $$p \leq 0.1273$$, timegroup interaction, F8,100 = 5.310, $p \leq 0.0001$). ( E) Glucose tolerance curves for pregnant dams with a low (< 7) OGT score. Individual E12.5 dams are shown in orange ($$n = 5$$) and E15.5 in green ($$n = 5$$). ( F) Glucose tolerance curves for pregnant dams with a high (≥ 7) OGT score. Individual E12.5 dams shown in orange ($$n = 6$$) and E15.5 in green ($$n = 5$$). ## Levels of placental OGT significantly correlate with circulating EV concentration and maternal glucose sensitivity We next examined the potential relationship between placental OGT and maternal glucose tolerance over the course of pregnancy. As observed in clinical and preclinical studies, we found a significant reduction in glucose sensitivity as pregnancy progressed (two-way ANOVA, main effect of time, F2.324,58.11 = 187.8, $p \leq 0.0001$, main effect of group, F2,25 = 2.241, $$p \leq 0.1273$$, timegroup interaction, F8,100 = 5.310, $p \leq 0.0001$) (Fig. 4D). Further linking placental OGT and EV secretion with glucose regulation, we then investigated the association of the uterine OGT score (as above) with maternal glucose tolerance. Based on the average OGT score across all pregnancies, each dam was characterized as having a low or high total uterine OGT score, ($$n = 58$$, Supplementary Fig. 1C). At the mid-gestational timepoint, E12.5, prior to the onset of glucose insensitivity in pregnancy, dams with an OGT score < 7 showed more efficient glucose processing ($$n = 5$$, Fig. 4E). However, by E15.5 dams with an OGT ≥ 7 showed more efficient glucose processing ($$n = 5$$, Fig. 4F). At the end of pregnancy, on E18.5, dams with an OGT ≥ 7 showed more efficient glucose processing ($$n = 6$$, Supplementary Fig. 3). The high variability we observed at the end of pregnancy may be attributed to the impending homeostatic changes related to parturition. As maternal glucose sensitivity is reduced later in pregnancy, increased placental EV secretion may be a compensatory attempt to improve glucose uptake, with this effect regulated by the placental glucose monitoring enzyme, OGT. Our results here demonstrate that placental OGT is associated with both changes in circulating EV concentration (Fig. 4A) and with changes in glucose sensitivity (Fig. 4D–F), supporting the hypothesis that circulating EVs may contribute to the energy balance required for pregnancy, and to overall maternal health during this physiologically demanding time period. ## Discussion Disruptions to maternal homeostatic regulation during pregnancy, such as those induced by chronic stress, are associated with adverse maternal health outcomes, including preterm birth, gestational diabetes, and preeclampsia1,42–44. The placenta plays a key role in homeostatic regulation by coordinating communication between maternal and fetal compartments, in part, through the production of extracellular vesicles (EVs)45. While studies have examined how physiological challenges in pregnancy affect fetal development, the role of EVs in maternal health has not been well examined. Our previous work identified placental O-glycosyl transferase (OGT) as a target of maternal stress, with reductions in OGT levels and activity, including modification of the OGT target, Annexin A1 (ANXA1), a protein involved in EV secretion6,7. OGT, a glucose-sensing enzyme, post-translationally O-glycosylates serine and threonine residues of hundreds of proteins46–49. The enzymatic activity of OGT is highly affected by glucose metabolism in a given cell, and changes to OGT function have been associated with metabolic disorders, including diabetes50–54. Glucocorticoids play a significant role in the regulation of glucose homeostasis55. Here, we examined the hypothesis that placental OGT, which is reduced by maternal stress, impacts maternal homeostatic regulation during pregnancy via a change to circulating EV concentrations. EVs are lipid bound, nano-sized particles secreted by cells that carry nucleic acids, proteins, and other signaling factors, facilitating cell–cell and long-distance communication in the body, and have garnered growing interest as biological communicators and biomarkers of disease in recent years22,28,45,56–59. We evaluated the concentration, size, and ζ-potential of isolated circulating EVs in response to changes in metabolic regulators important for homeostasis during pregnancy. Where the size of an EV may provide information regarding the type of vesicle predominantly in circulation (e.g., exosome, microvesicle, macrovesicle), the ζ-potential, or surface charge, indicates particle stability in circulation, as well as its potential for uptake45,60. Dynamic changes in EV circulating concentrations, such as the three to fourfold increase in circulating EVs observed during pregnancy, suggest important biological functions related to homeostatic regulation important in health outcomes, especially in pregnancy21,27,33,45,59,61. We first examined the influence of key metabolic factors that change in response to circadian and feeding rhythms on EV production. We found that over the circadian rhythm, as corticosterone increased, the concentration of EVs in circulation significantly decreased in non-pregnant animals. Similar observations were previously reported in samples from human patients, where increased EV concentration corresponded to decreased cortisol62–64. We also found that direct injection of metabolic factors, including corticosterone, glucose, or insulin, significantly and rapidly altered EV concentration. Indeed, others have shown changes to both EV production and diurnal variation of important metabolic functions in mice after deletion of secretory vesicle proteins62–64. Together, these data support a coordination of dynamic EV signals in homeostatic regulation. Pregnancy is a metabolically demanding state, and control of homeostatic regulation is critical for maternal health5,40,42,44,65–67. The profound increase in circulating EV concentration found in pregnancy is attributed to placental EV secretion31,37,45,68–70. Indeed, placental EVs have been implicated in a number of pregnancy morbidities, and identified as key communicators in maternal pathophysiology21,28,29,31,59,61,71. To assess changes to the concentration of EVs during pregnancy, we utilized our established model of maternal stress as a perturbation. Similar to effects seen with a corticosterone injection, we found a persistent and significant reduction in EV concentration throughout pregnancy. These data support a lasting effect of stress to alter EV concentrations in circulation throughout pregnancy. As EVs detected in circulation are contributed by most tissues in the body, assessing the effect of stress specifically on placental EV secretion required in vitro modeling. Therefore, we examined the effect of cortisol on EV secretion using human trophoblast-like BeWo cells to mimic the placental response to stress. Similar to effects following stress or corticosterone injection in vivo, EV secretion was significantly reduced following cortisol treatment of these cells. A decrease in EV concentration associated with stress has been observed in other model systems where exposure to stress or stress hormones resulted in decreased EV concentration58,72,73. Most Eutherian animals become less glucose sensitive as pregnancy progresses40. Similarly, circulating EV concentrations continue to increase across pregnancy in many species61,74. Placental EVs were previously shown to mediate glucose uptake in skeletal myocytes in vitro, with differential outcomes based on whether the EVs were derived from normal glucose tolerant or gestational diabetes patients31. Further, EV concentrations were significantly increased in gestational diabetes pregnancies compared to normal pregnancy levels, suggesting again the potential for compensatory responses by the placenta to increase EV production to counter the rise in glucose37. As OGT is a glucose-sensing enzyme, we hypothesized that the increasing concentration of circulating EVs found in pregnancy may contribute to glucose sensitivity and uptake41,52. To determine the potential role of placental EVs important to maternal health, we examined changes in glucose tolerance in response to an acute elevation in systemic EVs. We administered EVs from pregnant dams into nonpregnant dams via tail vein injection, thereby doubling the concentration of EVs in circulation and mimicking circulating EVs during pregnancy. While we observed no significant differences, we found that doubling the concentration of EVs in circulation trended toward decreasing the intra-group variance, potentially indicating a synchronization of glucose uptake across tissues. A limitation of our experimental design is that pregnancy is a unique biological state and non-pregnant females may not be the best proxy for the action of EVs during pregnancy. However, as pregnancy EV concentrations are so profoundly high, it was not experimentally feasible to further elevate levels in the pregnancy state. We next investigated the correlation between placental OGT and EV concentrations, important for glucose homeostasis, by directly targeting placental OGT levels. We utilized placental-specific transgenic targeting to produce litters which had variable OGT scores across dams, allowing us to assess changes in maternal circulating EV concentrations. We hypothesized that, similar to effects we found with maternal stress where both OGT and EVs were decreased by stress, directly reducing placental OGT would also impact EV concentrations. Indeed, we found that the total placental OGT score was significantly and positively correlated with the EV concentration in maternal circulation. Importantly, we established that this association is reflective of placental OGT levels, and not the number of placentae in the litter. We next wanted to determine if this positive association between placental OGT levels and maternal EV concentration was predictive of maternal glucose tolerance and regulation across pregnancy. As expected, we detected an overall shift to the right in maternal glucose tolerance across pregnancy in the mice, reflective of the reduction in insulin sensitivity known to occur40,75. However, when we divided our dams into low and high OGT score groups, with high placental OGT levels associated with high EV concentration, the shift to the right in the glucose curve between E12.5 and E15.5 was reversed, while in the low placental OGT group it was not. These results suggest that the placental response to increasing glucose levels may be an increase in EVs to offset insulin insensitivity. Recent studies reported changes in maternal circulating EV concentration associated with gestational diabetes, where increased EVs were detected, supporting a potential compensatory attempt by the placenta to resolve a non-homeostatic state31,37. Based on our experiments, it is not clear if or where EVs are acting to alter glucose homeostasis (e.g., liver, skeletal muscle), but this could be the focus of future studies. Our results demonstrate a potential novel relationship between placental OGT and the concentration of EVs in circulation during pregnancy. In our studies examining glucose tolerance by manipulating placental OGT using transgenic lines to vary total OGT between litters, we found that increasing EVs prevented the glucose insensitivity that occurs later in pregnancy. These data suggest a potential role of EVs in communicating glucose homeostasis, which is required for maintenance of both maternal and fetal health. It may be that the inability of the placenta to elevate EVs in circulation limits the signals necessary to coordinate or synchronize biological functions essential to balancing the energy demands of the mother and fetus; that the more EVs in circulation, the more equipped a system is to control glucose levels. There is evidence of this interplay between stress and glucose regulation in clinical studies, where a history of depression and trauma was associated with impaired glucose tolerance during pregnancy42. While many questions remain surrounding the function of EVs in maternal and fetal health, our current studies establish a potential role for placental OGT in maternal health during pregnancy. We highlight the importance of EV concentration in circulation as a key consideration in understanding how EVs regulate maternal homeostasis during pregnancy, and their potential role as detectable biomarkers in identifying health risks. Future studies could focus on unique EV cargo that are likely involved in the protein–protein targeting and signaling important for local tissue effects, the types of EVs produced by the placenta, and the specific mechanism by which EVs impact glucose uptake across various tissues76–80. ## Animals Mice used in these studies were bred in-house and were derived from a mixed background (C57Bl/6:I129) strain. Dams were between 10 and 16 weeks old. Virgin dams were used for all pregnancy experiments, and litters of 5–10 pups were included in analyses. To establish pregnancy, dams were paired with sires from 1900 to 0700 h. Embryonic day 0.5 (E0.5) was considered 1200 h on the day that a copulation plug was identified. Dams were evaluated on E12.5, E15.5, and E18.5. Unless otherwise specified, all collections were performed between 0700 and 1100 h. Mice used to determine the effects of O-GlcNAc transferase (OGT) on extracellular vesicles (EVs) and glucose tolerance were double-heterozygous (Ogttm1Gwh/J (XOGT/XWT); CYP19-Cre (P.Cre+/−)7,81. Dams were bred to either wildtype males (mixed background C57Bl/6:I129), or males which were hemizygous for OGT (XOGT/Y) and heterozygous for CYP19-Cre (P.Cre+/−) resulting in offspring representing all possible genotypes (XWT/XWT, XOGT−/XWT, XOGT−/XOGT−, XWT/Y, XOGT−/Y). Dams were euthanized at E12.5 ($$n = 10$$), E15.5 ($$n = 11$$), E18.5 ($$n = 8$$). Fetal tail DNA was isolated from each of the offspring and used for genotyping and calculating the OGT score. Based on previous findings, wildtype females (XWT/XWT) were assigned 2 points; wildtype males (XWT/Y) and XOGT−/XWT females were assigned 1 point; and knock-out males and females (XOGT−/Y, XOGT−/ XOGT−) were assigned 0 points. All animals used in this study were euthanized using a precision vaporizer with an induction chamber and waste gas scavenger in which isoflurane was administered in $2.5\%$ O2 for 1 min. Decapitation was performed after cardiac puncture to ensure euthanasia. All procedures in this study were approved by the University of Maryland Baltimore Institutional Animal Care and Use Committee. All procedures were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. Reporting of animal experiments is done in accordance with ARRIVE guidelines. ## Extracellular vesicle (EV) isolation and characterization Blood was collected via cardiac puncture using 26 G syringes (Becton Dickinson). Dipotassium EDTA coated tubes were used for blood collection (Becton Dickinson Microtainer™). Blood was spun at 1300 rcf for 10 min at 4 °C to separate plasma. EVs were isolated from 250 μL of blood plasma, as previously reported82. Plasma was mixed with 500 μL of freshly filtered (0.22 μm pore size filter, Sigma) phosphate-buffered saline (PBS). The plasma-saline mixture was centrifuged at 2000 rcf for 30 min at 4 °C, followed by centrifugation at 12,000 rcf for 45 min at 4 °C to pellet cell debris. EVs were immediately isolated from the resulting supernatant via size exclusion chromatography using the IZON qEVoriginal 70 nm columns in the IZON Automated Fraction Collector (Izon Science). Protein LoBind Tubes (Eppendorf™)were used for collection. Fractions 2 and 3 were combined and stored at – 80 °C until the sample was characterized, at which time samples were thawed on ice. The concentration, size, and ζ-potential of the isolated EVs were measured using a ZetaView® BASIC Nanoparticle Tracking Analysis Microscope (Particle Metrix)60. EVs were diluted in freshly filtered water for a final concentration in the range of 107 particles/mL (~ 200 particles per frame). The size and concentration of particles were measured by scanning 11 cell positions, with 30 frames per position, over 2 cycles. ζ-potential was measured across 11 positions. The sensitivity for video acquisition was set to 80, and the shutter speed to 100. ZetaView® software 8.05.14 was used to analyze all videos, with minimum brightness set to 20, minimum area to 10, and maximum area to 1000. ## Probing mediators of extracellular vesicle concentration Circadian collections in non-pregnant dams were done at 0700 ($$n = 4$$), 1100 ($$n = 5$$), 1500 ($$n = 4$$), and 1900 ($$n = 4$$) hours. To measure corticosterone levels, 10 μL of tail blood was collected and mixed with 5 μL of 50 mM EDTA (Sigma). Blood was kept on ice until centrifugation for 10 min at 5600 rcf at 4 °C. Plasma was collected and stored at − 80 °C until analysis. Cardiac puncture was performed to collect blood for EV isolation, as described above. Non-pregnant dams were separated into two groups: vehicle (saline, $$n = 5$$) and corticosterone (Cayman Chemical, 300 μg/kg, $$n = 5$$). At 0800, dams were given an intraperitoneal injection based on their assigned group. After 30 min, blood was collected via cardiac puncture and used for EV isolation, as above. Non-pregnant dams were separated into five groups: non-injected ($$n = 4$$), vehicle (saline, $$n = 5$$), fast (fasted 0800 h to 1400 h, saline intraperitoneal injection, $$n = 5$$), glucose (Sigma, fasted 0800 h to 1400 h, 3 mg glucose/g body weight in saline intraperitoneal injection, $$n = 6$$), insulin (Humulin, 0.8 mU insulin/g body weight in saline intraperitoneal injection, $$n = 5$$). Blood from control animals was collected at 1400 h. Vehicle, insulin, fasted, and glucose groups received respective intraperitoneal injections, and blood was collected after 2 h. ## Corticosterone radioimmune assay Corticosterone levels were determined using a corticosterone double antibody radioimmunoassay (RIA, MP Biomedicals), according to the kit instructions and as previously described83,84. Briefly, plasma was diluted 1:200 using the steroid diluent included in the kit. 125I corticosterone was added to each sample, followed by anti-corticosterone. Tubes were incubated for 2 h at room temperature. Precipitant was added to each tube, and tubes were spun at 2500 rpm for 15 min at 4 °C. Liquid was aspirated from the tubes, and the resulting pellet was measured using a gamma counter (Perkin Elmer, Wizard2). ## BeWo b30 cells Human choriocarcinoma trophoblastic cells, BeWo b30 (AcceGen, passage 7) were cultured in low glucose (1000 mg/L) Dulbecco’s modified Eagle’s medium (DMEM, Sigma) supplemented with $10\%$ charcoal-stripped fetal bovine serum (FBS, ThermoFisher) and $1\%$ penicillin/streptomycin (P/S, ThermoFisher) at 37 °C in $5\%$ CO270. Cells were plated at a density of 2.5 million cells per T-175 flask for 6 days, with media changes every 3 days. On day 6, cells were washed with sterile PBS, and grown in DMEM supplemented with $10\%$ exosome depleted FBS (Gibco) and $1\%$ P/S. Flasks were divided into two groups and treated with either vehicle ($0.1\%$ ethanol, $$n = 5$$) or 500 ng/mL cortisol ($$n = 5$$). After 3 days, media was collected and processed for EV isolation. To isolate EVs, media was spun at 2500 rcf for 30 min at 4 °C. The resulting supernatant was spun at 12,000 rcf for 45 min, followed by two rounds of ultracentrifugation at 100,000 rcf for 100 min in a swing bucket rotor. As above, pelleted EVs were resuspended in sterile filtered PBS and characterized using the ZetaView. Non-conditioned media ($$n = 8$$) was processed in the same way. ## Early pregnancy stress Pregnant dams assigned to the stress group underwent multimodal stress for seven days (0.5–6.5). As previously reported, non-habituating, painless stressors were grouped into three categories: odor, auditory, and tactile7,85–90. Odor stressors included ethanol (Sigma), fox odor (Sigma), or puma odor (Sigma), where dams were exposed to an open tube of the odor. Fox and puma odor were diluted 1:5000 in mineral oil (Sigma). Auditory stressors included owl screech and white noise, where the noise was played in the room at constant decibels (95–105 dB). Tactile stressors included wire mesh, wet bedding, and restraint. In the wire mesh stressor, chicken wire (Fencer Wire) cut to fit on the cage bottom was placed on top of the cage bedding. In wet bedding, 300 mL of water was poured onto the bottom of the cage, and in restraint, mice were put into a 50 mL Falcon Tube (Fisher) for the duration of stress. Dams were exposed to stressors from two categories from 0900 h until 1100 h, on a semi-random schedule. On E12.5 ($$n = 7$$), E15.5 ($$n = 6$$), and E18.5 ($$n = 7$$), dams were sacrificed, and blood was collected for EV isolation. Control non-pregnant dams ($$n = 9$$) were similarly sacrificed, and control pregnant dams were sacrificed on E12.5 ($$n = 8$$), E15.5 ($$n = 10$$), and E18.5 ($$n = 8$$). The number of pups in each litter was recorded. Stressors did not affect maternal food consumption, weight gain, litter size, or sex ratio7,85–90. ## EVs to alter glucose tolerance Non-pregnant dams were fasted starting at 0800 h. After 6 h of fasting, dams were given a tail vein injection of either vehicle (PBS, $$n = 5$$) or E18.5 EVs at a final concentration of 5 × 109 particles/g body weight in PBS ($$n = 6$$). EVs used in these experiments were collected from E18.5 control XWT/XWT dams, as described above. A third group of animals received no injection ($$n = 7$$). After 15 min, dams were challenged with an intraperitoneal injection of glucose (3 mg/g body weight) and glucose readings were measured at 0, 30, 60, and 120 min timepoints using the Contour Next Blood Glucose Monitoring System (Bayer Co, Germany), as previously reported91. ## Glucose tolerance test Glucose tolerance tests were performed, as previously reported91. Pregnant dams (XOGT−/XWT) were fasted beginning at 0800 h on E12.5 ($$n = 11$$), E15.5 ($$n = 10$$), and E18.5 ($$n = 8$$). A cohort of non-pregnant dams ($$n = 7$$) was also used. After 6 h of fasting, dams received an intraperitoneal injection of glucose in saline (3 mg/g body weight). Glucose readings were collected by tail blood at 0, 30, 60, and 120 min timepoints using the Contour Next Blood Glucose Monitoring System (Bayer Co, Germany). Following the glucose tolerance test, dams were sacrificed, fetal tails were collected to evaluate OGT score. ## Statistical analysis Sample size, mean, standard error of mean (SEM), and statistical significance are reported in the text and figure legends. GraphPad Prism was used to apply appropriate statistical analyses. For experiments of two groups, an unpaired t test was utilized. For experiments with three or more groups, analysis of variance (ANOVA) testing with Tukey post-hoc tests were used, with a significance set at $p \leq 0.05.$ Metabolic testing was analyzed using ANOVA with repeated-measures corrections and Tukey post-hoc tests. 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--- title: An exploratory approach to identify microRNAs as circulatory biomarker candidates for epilepsy-associated psychiatric comorbidities in an electrical post-status epilepticus model authors: - Eva-Lotta von Rüden - Heike Janssen-Peters - Maria Reiber - Roelof Maarten van Dijk - Ke Xiao - Isabel Seiffert - Ines Koska - Christina Hubl - Thomas Thum - Heidrun Potschka journal: Scientific Reports year: 2023 pmcid: PMC10027890 doi: 10.1038/s41598-023-31017-9 license: CC BY 4.0 --- # An exploratory approach to identify microRNAs as circulatory biomarker candidates for epilepsy-associated psychiatric comorbidities in an electrical post-status epilepticus model ## Abstract Patients with epilepsy have a high risk of developing psychiatric comorbidities, and there is a particular need for early detection of these comorbidities. Here, in an exploratory, hypothesis-generating approach, we aimed to identify microRNAs as potential circulatory biomarkers for epilepsy-associated psychiatric comorbidities across different rat models of epilepsy. The identification of distress-associated biomarkers can also contribute to animal welfare assessment. MicroRNA expression profiles were analyzed in blood samples from the electrical post-status epilepticus (SE) model. Preselected microRNAs were correlated with behavioral and biochemical parameters in the electrical post-SE model, followed by quantitative real-time PCR validation in three additional well-described rat models of epilepsy. Six microRNAs (miR-376a, miR-429, miR-494, miR-697, miR-763, miR-1903) were identified showing a positive correlation with weight gain in the early post-insult phase as well as a negative correlation with social interaction, saccharin preference, and plasma BDNF. Real-time PCR validation confirmed miR-203, miR-429, and miR-712 as differentially expressed with miR-429 being upregulated across epilepsy models. While readouts from the electrical post-SE model suggest different microRNA candidates for psychiatric comorbidities, cross-model analysis argues against generalizability across models. Thus, further research is necessary to compare the predictive validity of rodent epilepsy models for detection and management of psychiatric comorbidities. ## Introduction Epilepsy is a global health care issue affecting 70 million people worldwide and the prevalence of active epilepsy is usually between four and 12 per 1000 people each year1. The presence of comorbidities in epilepsy is almost the norm and epilepsy rarely occurs alone: more than $50\%$ of people suffering from epilepsy have additional psychiatric comorbidities2. Among these, anxiety and depression have been identified as prevalent and serious comorbidities, which have a major impact on quality of life in patients suffering from epilepsy3,4. Therefore, there is a need to detect and manage these comorbidities associated with epilepsy. While great progress has been made in the development of prognostic and diagnostic biomarkers for epilepsy5, the development of biomarkers associated with psychiatric comorbidities, such as depression and anxiety disorders, as well as cognitive dysfunction, is still in its infancy6–8. However, the vision of targeted treatments for epilepsy-associated comorbidities depends on the development of biomarkers that allow identifying patients at risk to develop psychiatric comorbidities and to further design individually tailored treatment. Per definition, a biomarker is “a characteristic that is measured as an indicator of normal biologic processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions”9. Per definition, microRNAs are a class of evolutionarily highly conserved, small (18–22 nucleotides in length), non-coding, regulatory RNAs10 with each microRNA regulating the expression of hundreds of target genes11. The identification of circulatory biomarkers might provide a basis for non-invasive approaches5, and could substantially improve the identification of people suffering from epilepsy-associated psychiatric comorbidities. Animal models with induced chronic epilepsy display various neurobehavioral and biochemical alterations and are therefore suitable to assess epilepsy-associated comorbidities including psychiatric disorders like depression or anxiety12,13. In this context, the selection of an appropriate animal model should also consider the assessment of the severity and burden on the animals. As part of a research consortium14, we have made relevant progress in identifying and validating parameters that form a basis for evidence-based severity assessment and reached a new level of sensitivity15–19. Improved sensitivity of severity assessment approaches may be of particular importance for animal welfare-based prioritization of models and validation of refinement measures. Besides the data from various behavioral assessments, we have also included the concentration of brain-derived neurotropic factor (BDNF) from serum samples of the animals in this study. There is growing evidence that the modulation of BDNF in the human brain may play a vital role in the development of depression20–24. In particular, BDNF levels tended to be lower in serum samples of patients diagnosed with depression20, and could be increased with antidepressant treatment22. The aim of the present exploratory and hypothesis-generating study was to identify circulating microRNAs as potential blood biomarkers with consistent differential expression across different rat models of epilepsy (amygdala kindling with focal/generalized seizures, chemical and electrical induced status epilepticus (SE) with the development of spontaneous recurrent seizures). Recent evidence suggests, for instance, a potential role for miR-132 as a general distress-associated biomarker in murine models for gastrointestinal diseases25. These microRNA candidates should be evaluated as potential biomarkers for identifying patients at risk to develop epilepsy-associated psychiatric comorbidities, and, in addition, to assess animal distress and cumulative severity in epilepsy models. ## TaqMan ArrayCard microRNA screening Screening of 750 candidate microRNAs (Fig. 1) revealed 48 significantly up- or down-regulated microRNAs in experimental animals ($$n = 6$$) compared to sham animals ($$n = 5$$) with a minimum Ct-value of 30 in all samples25–27.Figure 1MicroRNA screening of 750 microRNAs. ( a) rodent card A and (b) rodent card B. Heat maps representing up- (red) and down-regulation (green) of microRNAs in naïve control ($$n = 5$$), sham animals ($$n = 5$$) and animals with SE ($$n = 6$$). Comparison of animals with SE to sham animals revealed 48 significantly up- or down-regulated microRNAs with a minimum Ct-value of 30 in all samples. The heatmap was generated with Cluster3.0 (https://www.encodeproject.org/software/cluster/). cluster3 original website is http://bonsai.hgc.jp/~mdehoon/software/cluster/, original paper is https://pubmed.ncbi.nlm.nih.gov/14871861/ For the analysis of a microRNA expression profile, we selected the electrical post-SE model and a blinded and independent operator randomly selected five samples per group (sham and experimental condition), in addition one animal with a high number of seizures was included in the experimental group in order to increase the range concerning intrinsic disease severity. To consider a potential impact of the chronic electrode implant on microRNA expression, the sham group was selected as the control group for direct comparison with the rats with spontaneous recurrent seizures. After preamplification, TaqMan gene expression array cards—384-well microfluidic cards for rodents (Rodent A and B Card Set v3, ThermoFisher SCIENTIFIC, Darmstadt, Germany) were used according to manufacturer’s instructions. 750 different reactions of microRNAs highly conserved between mouse and rat plus 6 controls were measured. ## Filtering and selection of microRNA candidates Candidate microRNAs ($$n = 750$$) were subsequently subjected to a statistical validation procedure comprising the following steps (an overview is provided in Supplementary Fig. 1): first, 311 candidate microRNAs were removed ($$n = 439$$), because the expression assay revealed that the microRNA is not expressed in any of the samples ($$n = 166$$, median Ct-value according to Livak and Schmittgen27) or due to too many missing values to do a t test ($$n = 145$$). Next, 383 candidate microRNAs that were not significant in the t test (experimental cohort vs. sham cohort; $p \leq 0.05$) were removed ($$n = 56$$). 7 candidate microRNAs with a fold change between − 1 and 1, which may indicate no relevant increase or decrease of expression levels, were subsequently removed ($$n = 49$$). For the remaining candidate microRNAs, the correlation with seizure frequency and duration during the monitoring phase was assessed. Thereby, 35 candidate microRNAs displaying a spearman correlation coefficient higher than 0.5 in combination with a p value < 0.05 with either seizure frequency or duration were removed ($$n = 14$$). This was done to exclude any microRNA that might just reflect the intrinsic epilepsy severity, i.e. the density and duration of seizure events. Lastly, 3 candidate microRNAs with a minimum Ct-value higher than 30 were removed ($$n = 11$$)25–27. ## Principal component analysis (PCA) of behavior and microRNA expression Principal component analyses were conducted for behavioral and biochemical parameters as well as for expression data of selected microRNAs ($$n = 11$$) obtained in the electrical post-SE model. Data from behavioral assessments complemented with data on weight gain and serum BDNF levels were subjected to principal component analysis (naïve controls ($$n = 5$$); sham controls ($$n = 5$$), animals with SE and the development of spontaneous recurrent seizures ($$n = 6$$); Fig. 2). The analysis indicated a separation of SE animals from naïve control and sham animals along PC2. The individual behavioral data from SE animals suggest a higher inter-individual variance. PCA analysis of the individual microRNA expression values of the eleven selected microRNAs (miR-148b-5p, miR-203, miR-342-3p, miR376a, miR-429, miR-494, miR-598, miR-697, miR-712, miR-763, miR-1903) suggests a clear separation of data from SE animals and of data from naïve and sham controls with comparable inter-individual variance for all groups. Figure 2Principal component analysis (PCA) and correlation analysis of selected microRNAs (miR-148b-5p, miR-203, miR-342-3p, miR376a, miR-429, miR-494, miR-598, miR-697, miR-712, miR-763, miR-1903) in the electrical post-SE model. Illustration of the PCA of behavioral and biochemical parameters (a) and of expression data from selected microRNAs (b). Correlation matrix of the selected microRNAs and behavioral and biochemical parameters (c): the heat map illustrates the correlation (Spearman correlation coefficient) between the different parameters. The analysis identified a correlation between six microRNAs and weight gain in the early phase of epileptogenesis (positive correlation, blue) and with social interaction, saccharin preference and BDNF in the phase of epilepsy manifestation (negative correlation, red). All abbreviations are described in the Supplementary file. naïve naïve controls ($$n = 5$$), sham sham controls ($$n = 5$$), SE experimental animals with seizure history ($$n = 6$$), NB nestbuilding activity, BUR burrowing behavior, SI time animals spent in active social interaction, SP_percentage saccharin preference, OF open field, OF_distance open field distance moved in total, OF_rearing number of rearing postures in the open field, OF_immobile time the animal was immobile in the open field, OF_center time the animals spent in the center region of the open field, BWB black and white box, BWB_WB time the animals spent in the white box, BWB_entries number of transitions from black to white compartment, BWB_stretching number of stretching postures of the animal, BWB_LT latency to the first entry of the black box from the white box, EPM elevated plus maze, EPM_stretching number of stretching postures, EPM_head_dip number of the times the animal looked down from one of the open arms, EPM_closedarms time the animal spent in closed arms, EPM_openarms time the animal spent in open arms, EPM_open1_3 time the animal spent in the outer $\frac{1}{3}$ of the open arms, Adrenal_glands weight of adrenal glands (g), BDNF brain-derived neurotrophic factor in pg/ml, Seizures_n number of seizures, Seizures_duration duration of seizures. ## Correlation matrix between behavior and microRNA candidates A correlation matrix with the spearman correlation coefficients between the microRNA candidates ($$n = 11$$) and the various biochemical and behavioral parameters measured in the same animals is shown in Fig. 2. A detailed description of the parameters used for the correlation matrix can be found in Supplementary Table 1. Notable observations are that six out of eleven candidates exhibit a comparable correlation with selected behavioral and biochemical data. These six microRNAs include: miR-376a, miR-429, miR-494, miR-697, miR-763 and miR-1903: the correlation matrix indicates a positive correlation with weight gain and a negative correlation with social interaction (Fig. 3a,b). In addition, the heatmap suggests a negative correlation of miR-376a, miR-429, miR-494, miR-697, and miR-763 with saccharin preference as well as a negative correlation of miR-429, miR-494, miR-697, and miR-763 with plasma BDNF levels (Figs. 2 and 3c,d). Among the microRNAs for which a positive correlation with body weight gain is assumed, heatmap correlation plots also suggest a negative correlation for several of these microRNAs with social interaction, saccharin preference, and BDNF. All correlation values (p-value and correlation coefficient r) are provided in Supplementary Table 2.Figure 3Correlation analysis between three selected microRNA candidates and selected behavioral and biochemical parameters from the electrical post-SE model. Analysis of correlation (Spearman) between the three microRNA candidates (out of eleven) with the strongest positive/negative Spearman correlation coefficient and the following behavioral and biochemical parameters: weight gain (a), social interaction (b), saccharin preference (c), and BDNF expression levels (d). dCT delta Ct (cycle threshold) value. For each analysis $$n = 12$$–16. ## Validation of microRNA candidates using quantitative real-time PCR The microRNA candidates were further validated in three additional experimental epilepsy models: [1] the kindling model with focal seizures, [2] the kindling model with generalized seizures as well as [3] the chemical post-SE model. Among the eleven pre-selected microRNAs (miR-148b-5p, miR-203, miR-342-3p, miR376a, miR-429, miR-494, miR-598, miR-697, miR-712, miR-763, miR-1903), only three microRNAs were characterized by an expression level allowing differential expression analysis (miR-203, miR-429 and miR-712). Among them, miR-429 was significantly upregulated ($$p \leq 0.0383$$) in experimental animals ($$n = 40$$) in comparison to sham animals ($$n = 41$$) (Fig. 4). When we analyzed biochemical and behavioral parameters in these models, we only detected a moderate positive correlation between BDNF and miR-429 (FDR-corrected $$p \leq 0.01$$; $r = 0.057$; Fig. 4). All correlation values (FDR-corrected p-values and correlation coefficient r) are provided in Supplementary Tables 2 and 3. The expression levels of miR-429 have been analyzed in the individual models (Fig. 4). dCT-values did not differ between the different models. Figure 4Validation of miR-429 expression in other epilepsy models. ( a) MicroRNA miR-429 is upregulated in experimental animals ($$n = 40$$) when compared to sham animals ($$n = 42$$) across models ($$p \leq 0.0383$$). ( b) Cross-model-correlation analysis of miR-429 expression levels and BDNF concentrations revealed a significantly positive correlation (Spearman correlation). ( c) Correlation of miR-429 with several behavioral and biochemical parameters across models (positive correlation, blue; negative correlation, red). ( d) Kindling model with focal seizures (sham/exp $$n = 12$$), (e) Kindling model with generalized seizures (sham $$n = 12$$, exp $$n = 11$$). ( f) Chemical post-SE model (sham $$n = 12$$, exp $$n = 13$$) and (g) electrical post-SE model (sham $$n = 6$$, exp $$n = 4$$). In the kindling model with focal seizures (d) and in the chemical post-SE model (f), miR-429 expression levels were not increased in animals with seizure activity ($$p \leq 0.0628$$ and $$p \leq 0.0619$$, respectively; t test). sham sham controls, exp experimental animals with seizure history, dCT delta Ct (cycle threshold) value. ## Discussion In this study, we could identify several candidate microRNAs significantly upregulated in plasma samples from distinct chronic rat epilepsy models. Several microRNAs including miR-376a, miR-429, miR-494, miR-697 miR-763, and miR-1903 were identified as interesting biomarker candidates based on findings from the electrical post-SE model. Considering the predictive validity of the different paradigms28,29, the negative correlation with social interaction and saccharin preference suggests that these microRNAs may be linked with psychiatric comorbidities including depression and autism. A potential link is further supported by the negative correlation with BDNF. In a chronic rat epilepsy model, low serum BDNF levels have previously been linked with stress vulnerability and a predisposition to behavioral alterations that may recapitulate psychiatric comorbidities in patients30. An increase in anhedonia-associated behavior detected in the saccharin preference test and a decrease in social interaction have previously been suggested as sensitive parameters for evidence-based severity assessment in different rodent models15–18. Thus, the correlation between the microRNAs and the behavioral patterns also points to a potential informative value of these circulatory microRNAs for the assessment of severity in rodent models. Considering the potential link between the pre-selected microRNAs and the behavioral changes in the electrical post-SE model, it is of particular interest to further explore the potential of these pre-selected microRNAs as biomarker candidates for psychiatric comorbidities on one hand, and severity in laboratory rodents on the other hand, and to explore the generalizability of these results. The results from the PCA show that the entity of microRNAs that had successfully passed the validation procedure are suitable to distinguish between individual animal data from the epileptic cohort and the control group. For this purpose, we have selected two different chronic epilepsy models including electrical kindling with repeated electrically induced seizures and a chemical post-SE model. In the cross-model analysis considering data from all models, miR-429 was standing out as the only molecule with an upregulation in experimental rats versus electrode-implanted rats. To our knowledge a regulation of miR-429 has not yet been reported in epileptic tissue from rodent models or human patients. However, it has been identified as a promising candidate biomarker especially in the field of cancer research, and, as a member of the miR-200 family, it is involved in the regulation of epithelial to mesenchymal transition, which can be considered as a crucial step in tumor metastasis31. At the first glimpse our findings might indicate a potential value as a biomarker for psychiatric comorbidities in patients and severity in laboratory animals. However, the subsequent correlation analysis did not confirm a consistent cross-correlation of the microRNA with behavioral and biochemical alterations in the different chronic epilepsy models used. In this context, it needs to be considered that the kindling paradigm with the common exposure to repeated stimulations does not result in spontaneous seizures32. Thus, while the models used for comparison in this study share the generation of a hyperexcitable network, the kindling paradigm commonly lacks the formation of a network and epileptic focus that spontaneously triggers recurrent seizures32,33. Moreover, in comparison with models with spontaneous recurrent seizures, behavioral and biochemical alterations in this model are rather subtle and mild15–18,34. Concerning a direct comparison between the electrical and chemical post-SE model, we and others have previously reported relevant differences in behavioral patterns and molecular alterations. In this context, it should be considered that exposure to the convulsant can be associated with compound-related effects32,35. In addition, it is well known that the neuropathology of the pilocarpine model is rather extreme often exceeding respective pathological alterations in patients with temporal lobe epilepsy32,36. Taken together our molecular findings further confirm substantial differences between the chronic models. Thus, it will be of particular relevance to invest more time and efforts to explore and compare the predictive validity of the models when it comes to behavioral alterations as a basis for translational research in the context of psychiatric comorbidities. As this is an exploratory, hypothesis-generating study to identify potential circulatory biomarker candidates for epilepsy-associated psychiatric comorbidities, we are aware that we have to avoid overinterpretation, and that some limitations of the experimental design have to be considered. When further evaluating the significance of the findings in this study, it also has to be considered that blood sampling represents an invasive means, which was carried out at the end of the experiments. In addition, time points of blood sampling (36 h after the last seizure, between 9:00 and 10:30 a.m.) can affect the outcome since microRNA expression patterns can be regulated following the circadian rhythm of rats. Besides, there is a general question of specificity, in particular, concern exists that the increased microRNA levels detected in circulatory fluids originate from damage in brain tissue37. Considering the entire filtering and selection process for microRNA candidates with the sequential steps for validation, it needs to be emphasized that this is an exploratory process, in which optimal quantity and quality of samples cannot always be guaranteed. On the one hand, the conduction of 439 comparisons (experimental cohort vs. sham cohort) for expression analyses without post-hoc tests as a first filtering step increases the risk of inflated type I error rates. On the other hand, the correction of p values afterwards to mitigate false discovery rates would have carried the risk of excluding promising candidates, which can still be filtered and validated in subsequent steps. Considering the exclusion of microRNAs with a fold change between − 1 and 1, it needs to be emphasized that microRNAs with small fold changes are not necessarily irrelevant to the exploratory research question38. So far, however, no standard method exists on normalization, preprocessing, and downstream analyses of microRNA expression patterns39. An appropriate normalization technique, however, should strengthen analytical rigor by minimizing technical and experimental bias without introducing noise39,40. In the present study, for qRT-PCR data, in particular for microRNA relative expression assay, we used a classical ddCT approach according to Livak and Schmittgen27, taking median Ct-values as normalizer to perform a global normalization of each array card. Moreover, in order to enhance specificity, it is necessary to define criteria that make it possible to exclude microRNAs whose expression is altered only by the intrinsic severity of epilepsy itself. However, the positive correlation of a candidate microRNA with duration and/or frequency of seizures, which was considered an exclusion criterion in the validation process, can reduce the sensitivity of the candidate search. More precisely, there is convincing evidence that seizure frequency in patients with epilepsy can correlate with depression and anxiety disorders in a relevant manner (e.g.41–43). As a consequence of the last validation step, the removal of microRNAs with Ct-values > 30 increased the risk of missing CNS-derived microRNAs whose intrinsic concentration is low, which should be considered a limitation in the context of sensitivity. For all models used in this study, female animals have been selected, as the mortality of male rats in the electrical post-status-epilepticus model is quite high44. When using male rats, the required number of animals must be dramatically increased to obtain reliable data. From an ethical point of view, this is concerning. In addition, the NIH called for a gender balance and the inclusion of female animals in preclinical studies, as male animals usually are overrepresented45. Furthermore, based on the estrous cycle, we expect a higher variance in female rats, and on the long-term we want to identify robust microRNA expression alterations. Thus, we decided to use female rats in this first exploratory study. However, in the future it will be of interest to conduct a comparative study in male rats. With respect to translational validity, it needs to be considered that the extrapolation from animal behavioral and/or biochemical data to a patient’s clinical disease phenotype is a conceptual issue46–48. In particular, the level to which preclinical data may be extrapolated from animal models to human disorders, and, more specifically, the association of animal behavioral alterations with human psychiatric comorbidities, requires further validation. Moreover, the biopsychosocial model, as proposed by Engel49, remains particularly applicable in the field of clinical psychiatry due to the complexity and polymorphism of the clinical phenotype of psychiatric diseases50. Therefore, animal models of depression are generally considered less ‘valid’ with respect to the three criteria [1] predictive, [2] construct, and [3] face validity51. While the latter can be enhanced in a relevant manner, predictive and construct validity of animal models in psychiatry research remain a controversially discussed issue46,51–53. Finally, with regard to the exploratory nature of the first part of the study, the interpretation and conclusions of the results must be made with caution: the study presents an approach for a first, not fully comprehensive exploratory screening for microRNA candidates associated with psychiatric comorbidities. Therefore, the limitations which underly the exploratory character of the study design need to be taken into account. ## Conclusion Data sets from the electrical post-SE model suggest different circulatory microRNA biomarker candidates for psychiatric comorbidities and for severity and distress in laboratory animals. However, cross-model analysis argues against generalizability across different chronic epilepsy models. Thus, further research is necessary to compare the predictive validity of rodent epilepsy models for detection and management of psychiatric comorbidities. ## Ethic statement All animal experiments were conducted and reported in accordance with German law for animal protection and with the European Directive, $\frac{2010}{63}$/EU. All animal experiments were approved by the government of Upper Bavaria (Munich, Germany, license number 55.2‐1‐54‐2531‐119‐14 and 55.2-1-54-2532-105-16). All experimental procedures have been carried out in compliance with the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines and the Basel declaration (http://www.basel.declaration.org) including the 3R principle. ## Animal models Female Sprague Dawley rats (Envigo, Italy) were used to generate three rat models of epilepsy. The three rat epilepsy models were generated at the Institute of Pharmacology, Toxicology, and Pharmacy (Ludwig-Maximilians-University Munich, Germany): [1] the kindling model with repeated electrical induction of seizures; [2] the electrical, and [3] the chemical post-status epilepticus (SE) model with development of spontaneous seizures. The samples used in this study originated from three published studies, a study overview is provided in Supplementary Fig. 2. An extensive description of each of the three models used can be found in the respective publications: electrical post-SE model18, chemical post-SE model17, and kindling model16. In this study, subgroups of animals have been included: electrical post-SE model (exp $$n = 6$$, naïve $$n = 5$$, sham $$n = 5$$), chemical post-SE model (sham $$n = 12$$, exp $$n = 13$$), and kindling model (generalized sham $$n = 12$$, exp $$n = 11$$; focal sham/exp $$n = 12$$). The experimental group in the post-SE model comprised five randomly selected animals which had developed spontaneous recurrent seizures, and one additional animal which had developed high-frequency seizures to increase intragroup variability. The first part of the present study has an exploratory, hypothesis-generating character, therefore sample sizes could not be calculated. The second part was designed as a confirmatory and hypothesis-testing study and post hoc power was computed. For all models, female animals have been used. Please note that we used female rats based on the model characterization of Brandt and colleagues44 demonstrating a high mortality rate in male rats. Accordingly, female animals were also used in the other two models. All experimental (exp) and sham animals received an electrode implanted in either their right basolateral amygdala (AP-2.2 mm, L + 4.7 mm, DV + 8.5 mm, kindling and electrical post-SE models) or their right hippocampus (AP-3.9 mm, L + 1.7 mm, DV + 4.0/+ 4.1 mm, chemical post-SE model). The operative state was reached with multimodal anesthesia under the combination of chloral hydrate (360 mg/kg i.p.), inducing hypnosis and akinesia without affecting seizure thresholds, bupivacaine ($0.5\%$ up to 1 ml, Jenapharm, Germany, s.c.) for local anesthesia, and meloxicam (Metacam®, Boehringer Ingelheim, Germany, 1 mg/kg, 30 min pre- and 24 h post-surgery, s.c.) as perioperative analgesic. Details on group allocation, randomization, and the surgical procedures are provided in the Supplementary file. For the electrical post-SE model, an additional naïve, non-implanted control group (ctr) was included to determine potential effects of the chronic electrode implant. For all other models, electrode implanted, non-epileptic animal groups served as controls (sham). ## Blood sampling For the microRNA expression analysis, plasma samples from the electrical post-SE model [naïve ($$n = 5$$), sham ($$n = 5$$), and experimental condition ($$n = 6$$)] were used. Blood samples were collected via cardiac puncture during endpoint-measurements under injection anesthesia [metamizole (100 mg/kg) and chloralhydrate (360 mg/kg)] followed by euthanasia (pentobarbital 600 mg/kg). To obtain liquid supernatant by separating the corpuscular components, total blood was centrifuged (2000g for 10 min), and plasma was stored in RNase/DNase clean tubes at − 80 °C till RNA-isolation. ## Behavioral and biochemical analysis Behavioral and biochemical data from the electrical and chemical post-SE models and the kindling model were evaluated to identify and compare differences between the different chronic models as a means of validation. All animals were subjected to a comprehensive assessment of behavioral and biochemical parameters. The data from microRNA profiling and from behavioral and biochemical parameters originate from the same experimental and sham cohort. BDNF concentrations were measured in re-thawed serum samples using highly sensitive and specific fluorometric two-site enzyme-linked immunosorbent assays (ELISAs, Promega Inc, Germany) according to Hellweg and colleagues and Deuschle and colleagues22,23. A detailed description of the behavioral tests performed and the determination of BDNF serum concentrations can be found in the Supplementary file, the corresponding results for each model can be found in the respective publications16–18. ## RNA isolation from plasma samples The miRNeasy plasma Mini Kit (Qiagen, Hilden, Germany) was utilized according to instructions for the RNA isolation. 30 μl of RNA-solution were obtained from an amount of 100 μl plasma. Synthetic Caenorhabditis elegans miR-39 (5 μl of 1 fmol/μl) was added as spike-in control during RNA isolation to the Qiazol/chloroform/plasma mixture. ## Statistical analysis For array card analysis, p-values < 0.05 defined significance using a two-sided t test. Differential expression of microRNAs (Ct-values) was analyzed by Kruskal–Wallis test (Nonparametric One-way ANOVA multi-comparison test). Global normalization for qRT-PCR data was applied using the ddCT approach with the calculation of median values27. For correlation analysis of selected microRNAs with behavioral and biochemical parameters, Spearman correlation was calculated, and heatmaps were visualized using R version 3.6.154 and the R package “corrplot”55. For correlation analysis of microRNA-429 with behavioral and biochemical parameters, a correlation coefficient r of < − 0.5 or > 0.5 in combination with $p \leq 0.05$ defined significant results. P values retrieved from Spearman correlation analysis of miR-429 were further corrected for multiple comparisons using false discovery rate (FDR) correction with the Benjamini–Hochberg method. Principal component analysis (PCA) was conducted using R version 4.1.254, and graphical illustrations of the PCA were created with ggplot256. Expression data were plotted as individual data points. Direct comparisons were computed by two-tailed unpaired t test. The significance level for all statistical tests performed was set at $p \leq 0.05.$ For statistical analysis and graphical illustration of phenotypic and expression data, Graph Pad Prism 6 was utilized. The correlation matrix was visualized using R version 3.6.154 and the R package “corrplot”55. Post-hoc power analyses were conducted using GPower version 3.1.9.7; details on effect sizes and power calculation protocols are provided in the Supplementary file. ## Supplementary Information Supplementary Information. 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--- title: 'Geriatric nutritional risk index predicts all-cause mortality in the oldest-old patients with acute coronary syndrome: A 10-year cohort study' authors: - Ying Li - Jian Shen - Xiaoling Hou - Yongkang Su - Yang Jiao - Jihang Wang - Henan Liu - Zhenhong Fu journal: Frontiers in Nutrition year: 2023 pmcid: PMC10027908 doi: 10.3389/fnut.2023.1129978 license: CC BY 4.0 --- # Geriatric nutritional risk index predicts all-cause mortality in the oldest-old patients with acute coronary syndrome: A 10-year cohort study ## Abstract ### Background and objective Nutritional status assessment in acute coronary syndrome (ACS) patients has been neglected for a long time. The geriatric nutritional risk index (GNRI) is a sensitive indicator for assessing the nutritional status of the elderly. This study aims to explore the association between GNRI and all-cause mortality in the oldest-old patients with ACS. ### Methods The patients who met the inclusion criteria were consecutively enrolled from January 2006 to December 2012. Clinical data were collected on admission, and all subjects were followed after being discharged. The nutritional status was evaluated using GNRI. The relationship between GNRI and all-cause mortality was assessed by using different analyses. ### Results A total of 662 patients with a mean age of 81.87 ± 2.14 years old were included in our study, and followed (median: 63 months, IQR 51–71). Patients whose GNRI ≤ 98 were reported as at risk of malnutrition ($31.11\%$, $$n = 206$$). In multivariable analysis, we found that for each SD increase in GNRI, the risk of all-cause mortality lowered by $23\%$, and the HR for GNRI ≤ 98 was 1.39 ($95\%$ CI 1.04–1.86). After stratifying patients into three groups by tertiles of GNRI, we found that the HRs for tertile 2 and tertile 3 were 1.49 ($95\%$ CI 1.02–2.19) and 1.74 ($95\%$ CI 1.22–2.50), respectively. The trend test revealed a dose–response relationship between GNRI and all-cause mortality in the oldest-old with ACS. Lastly, in subgroup analyses, we found a reliable association between GNRI and all-cause mortality. ### Conclusion Malnutrition is common in the oldest-old patients with ACS, and GNRI could predict their long-term all-cause mortality in a dose-dependent manner. GNRI may be a prospective index for risk-stratification and secondary-prevention in the oldest-old patients with ACS. ## Introduction Acute coronary syndrome (ACS) is one of the leading causes of morbidity and mortality worldwide [1, 2]. Aging is a vital risk factor for its prevalence and poor clinical outcomes. Almost a third of patients admitted for ACS and two-thirds of those dying from ACS are >75 years old [3, 4]. Multi-comorbidities, complicated coronary artery lesions, and high prevalence of frailty in the oldest-old have increased the risk of re-infarcted, bleeding complications, and mortality when compared to younger patients (5–7). Malnutrition is a common but under-recognized problem in hospitalized patients and caused detrimental and extensive impacts on clinical results with negative and far-reaching consequences for clinical outcomes [8, 9]. As estimated, about 30–$60\%$ of hospitalized patients are malnourished. It not only leads to a high economic burden but is associated with longer hospital stays and higher mortality [10, 11]. A higher prevalence of malnutrition has been found in the elderly [12]. Recently, the side effects of malnutrition in cardiovascular diseases have come under the researchers’ spotlight. Clinical studies have demonstrated that malnutritional status has negative effects on people affected by cardiovascular diseases including ACS (13–16). Geriatric nutritional risk index (GNRI) was first created by Bouillanne et al. to identify nutritional-related complications among the elderly (17–19). It has been used to assess the nutritional status of patients with heart failure [20], chronic kidney disease (CKD) [21], tumors [22], etc. And many studies have revealed that GNRI was significantly associated with vascular calcification, length of hospital stay, and mortality [23, 24]. However, studies about the relationship between GNRI and the prognosis of ACS have seldom been conducted [25]. Herein our study aimed to explore the relationship between GNRI and all-cause mortality of the oldest-old ACS patients, investigate the predictive value of GNRI on patients; long-term prognosis, and assess the effectiveness of the risk-stratify for them. ## Study design and population From January 2006 to December 2012, 720 patients aged ≥80 admitted to the cardiology department of the Chinese People’s Liberation Army (PLA) General Hospital for coronary angiography due to ACS symptoms, were enrolled (Figure 1). A total of 699 patients signed their informed consent. The exclusion criteria were: [1] patients with severe valvular heart disease, severe pulmonary hypertension, severe liver or renal insufficiency, rheumatoid arthritis, infectious diseases, and malignant tumors; [2] patients with familial hypertriglyceridemia (triglyceride ≥5.65 mmol/L); and [3] patients with neuropsychiatric problems that prevent them cooperating with the researchers. Most critically, our study followed the Declaration of Helsinki and was certified by the Chinese PLA General Hospital’s Ethics Service Center. Figure 1: Flow chart of patient enrollment, grouping, and follow-up. To confirm the diagnosis of coronary heart disease, the cardiac intervention center of the PLA General Hospital performed coronary intervention and perioperative treatment according to current guidelines. All of the angiography results were analyzed using the same image analysis tool. Loading doses of aspirin (300 mg) and clopidogrel (300 mg) were administered before the intervention. The degree of coronary stenosis was determined using the Gensini score [26], and two experienced experts trained the recorders. Individualized interventions, such as intensive medication therapy, percutaneous coronary intervention (PCI), or coronary artery bypass grafting (CABG), were administered based on coronary angiography results, and long-term follow-up was conducted after being discharged. ## Data collection and index evaluation We collected demographic data (age and gender), anthropometric data [height, body weight, BMI, heart rate, systolic blood pressure (SBP), and diastolic blood pressure (DBP)], laboratory data [total cholesterol (TC), triglycerides (TG), low-density lipid-cholesterol (LDL-C), high-density lipid-cholesterol (HDL-C), estimated glomerular filtration rate (eGFR), fasting plasma glucose (FPG), uric acid (UA), albumin], left ventricular ejection fraction (LVEF), comorbidity data [diabetes mellitus type 2 (T2DM), hypertension, stroke, prior myocardial infarction (MI), hyperlipidemia and chronic kidney disease (CKD)], smoking, drug-usage data [aspirin, clopidogrel, statins, β-blocker and angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor blocker (ARB)], coronary lesions data[left anterior descending branch (LAD), left circumflex branch (LCX), right coronary artery (RCA), left main coronary artery (LM), multivessel lesions and Gensini score] and treatment data (intensive medications, PCI and CABG). The body mass index (BMI) was calculated as follows: According to WHO criteria in the Asian population, patients could be classified as overweight (BMI > 24.9 kg/m2), normal-weight (BMI 18.5–24.9 kg/m2), and under-weight (BMI < 18.5 kg/m2) [27]. The eGFR was calculated by the Chinese modified Modification of Diet in Renal Disease equation: Standardized creatinine (Scr) was calculated by the calibration equation: Chronic kidney disease was defined as eGFR <60 ml/min/1.73 m2. The diagnostic criteria for diabetes mellitus type 2 (T2DM) were: FPG ≥ 7.0 mmol/L; and (or) random blood glucose (RBG) ≥ 11.1 mmol/L; blood glucose ≥11.1 mmol/L 2 h after oral glucose tolerance test (OGTT). Hyperlipidemia was defined as the use of lipid-lowering drugs or total serum cholesterol ≥240 mg/dl. Based on coronary angiography results, the multivessel lesion was defined as having more than 2 vessels with significant diameter stenosis of $50\%$. ## Assessment of nutritional status The geriatric nutritional risk index (GNRI) was used in this study to assess the nutritional status of the oldest-old patients with ACS. GNRI is calculated as follow [17]: The ideal weight was calculated as follows: 22 × square of height (m2) [18]. It is worth noting that GNRI was created to identify and predict nutritional-related complications [17]. The original GNRI cut-off values and grades of nutrition-related risk were: severe risk (GNRI <82), moderate risk (GNRI 82–92), low risk (GNRI 92–98), and no risk (GNRI >98). Patients were often considered as having a normal nutritional status if their GNRI >98 [19]. Hence, we used 98 as the cut-off value in the present study. GNRI >98 was defined as well-nourished, while GNRI≤98 was defined as at malnutrition risk. ## Endpoint and follow-up The follow-up period lasted up to 10 years performed every 12 months after discharge via outpatient visits, telephone records, or medical records of outcomes. During the follow-up period, 37 patients were lost follow-up, leaving 662 ($94.7\%$) patients enrolled in the final statistical analysis. All-cause mortality (cardiac and non-cardiac) was the ultimate endpoint of our research. ## Statistical analysis The baseline characteristics of the participants were shown according to GNRI >98 and GNRI ≤98. The measurement data of normal distribution were expressed as mean ± SD, and the T-test was used for homogeneity of variance. If the variance is not uniform, the rank-sum test was used. Non-normally distributed measurements were represented by the median and interquartile range (IQR). Statistical data were expressed by quantity, the chi-square test was used to evaluate differences between groups, and an analysis of variance was used to compare data between groups. Pearson correlation test was used to evaluate the correlation between GNRI and clinical parameters. Unadjusted survival curves were generated using log-rank tests in Kaplan–Meier plots. Univariate Cox regression analysis (HR, $95\%$ CI) was used to identify the factors associated with all-cause mortality. $p \leq 0.05$ was considered statistically significant. The Cox proportional hazards model was used to estimate the association between GNRI and all-cause mortality. We built three regression models: model 1 is the unadjusted model, model 2 is the partially adjusted model (age and gender), and model 3 is the completely adjusted model (age, gender, diabetes, stroke, CKD, aspirin, eGFR, FPG, UA, EF, Gensini score, LM lesions, multivessel lesions, and HDL-C). The GNRI was transformed into three classification variables for the primary analysis. For the trend test, the new categorical variables were recorded as continuous variables and entered into the regression model. We also standardized the GNRI and then put it into a regression model to determine the relationship between the change in GNRI per SD and all-cause mortality. In addition, we performed a subgroup analysis to explore whether the relationship between GNRI and all-cause mortality could be modified by the following variables: gender, diabetes, hypertension, prior MI, hyperlipidemia, CKD, smoking, LM lesions, and multivessel lesions. Interactions between GNRI and the above variables were tested. Results were reported as HR and $95\%$ CI. Two-sided $p \leq 0.05$ was considered statistically significant. All analyses were performed using the statistical software packages R [28] and Empower Stats [29]. ## Baseline characteristics and malnutrition assessment There were 662 oldest-old patients with ACS enrolled in our study, $71.9\%$ ($$n = 476$$) of whom were males (Table 1). The mean age of the participants was 81.87 ± 2.14 years old (IQR 80–89), and the average GNRI at admission was 102.47 ± 11.06. According to GNRI, the patients were classified as well-nourished (GNRI >98, $$n = 456$$, $68.89\%$), and at malnutrition risk (GNRI ≤98, $$n = 206$$, $31.11\%$). Patients with low GNRI had lower body weight, BMI, SBP, LVEF, albumin, and eGFR, while they have higher height and heart rate. There were no statistical differences in TC, TG, LDL-C, HDL-C, FPG, and UA between the two groups. Patients at malnutrition risk had a higher proportion of CKD but a lower proportion of hypertension. There was no significant difference in the prevalence of other common complications such as diabetes, hyperlipidemia, or stroke. What is more, we noticed that patients with low GNRI were more likely to have RCA lesions and multivessel lesions, and got a higher Gensini score. In terms of smoking behavior, medication usage, and treatment manners, there was no significant difference between the two groups. Table 1 \| Variable \| Total \| GNRI >98 \| GNRI ≤98 \| d-value \| p-Value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| N \| 662 \| 456 \| 206 \| \| – \| \| Demographic data \| \| \| \| \| \| \| Age (year old) \| 81.87 ± 2.14 \| 81.83 ± 2.11 \| 81.98 ± 2.20 \| −0.0701 \| 0.398 \| \| Female \| 186 (28.10%) \| 135 (29.61%) \| 51 (24.76%) \| 0.1079 \| 0.199 \| \| Anthropometric data \| \| \| \| \| \| \| Height (cm) \| 165.32 ± 8.25 \| 164.81 ± 8.61 \| 166.46 ± 7.27 \| −0.1806 \| 0.017 \| \| Body weight (kg) \| 67.20 ± 10.68 \| 69.74 ± 10.16 \| 61.55 ± 9.63 \| 0.8191 \| <0.001 \| \| BMI (kg/m2) \| 24.57 ± 3.40 \| 25.64 ± 2.97 \| 22.19 ± 3.08 \| 1.1482 \| <0.001 \| \| Heart rate (bpm) \| 74.81 ± 14.02 \| 74.01 ± 13.47 \| 76.57 ± 15.07 \| −0.183 \| 0.029 \| \| SBP (mmHg) \| 137.08 ± 21.79 \| 138.41 ± 21.10 \| 134.15 ± 23.02 \| 0.1962 \| 0.020 \| \| DBP (mmHg) \| 71.44 ± 12.12 \| 71.73 ± 12.07 \| 70.78 ± 12.23 \| 0.0784 \| 0.350 \| \| Laboratory data \| \| \| \| \| \| \| TC (mmol/L) \| 4.11 ± 0.97 \| 3.94 ± 1.09 \| 3.87 ± 1.18 \| 0.0626 \| 0.443 \| \| TG (mmol/L) \| 1.39 ± 0.72 \| 1.43 ± 1.02 \| 1.30 ± 0.95 \| 0.1302 \| 0.133 \| \| LDL-C (mmol/L) \| 2.36 ± 0.84 \| 2.21 ± 0.77 \| 2.11 ± 0.76 \| 0.1304 \| 0.109 \| \| HDL-C (mmol/L) \| 1.13 ± 0.36 \| 1.15 ± 0.31 \| 1.15 ± 0.39 \| 0 \| 0.803 \| \| eGFR (ml/min/1.73 m2) \| 70.85 ± 23.11 \| 72.16 ± 24.16 \| 67.97 ± 20.35 \| 0.1818 \| 0.031 \| \| FPG (mmol/L) \| 7.05 ± 4.13 \| 6.91 ± 4.45 \| 7.34 ± 3.32 \| −0.1041 \| 0.224 \| \| UA (μmol/L) \| 351.59 ± 149.66 \| 355.77 ± 164.12 \| 342.34 ± 110.98 \| 0.0897 \| 0.285 \| \| Albumin (g/L) \| 37.54 ± 5.98 \| 39.85 ± 4.22 \| 32.45 ± 6.17 \| 1.5073 \| <0.001 \| \| GNRI \| 102.47 ± 11.06 \| 107.93 ± 7.31 \| 90.38 ± 7.94 \| 2.3365 \| <0.001 \| \| LVEF (%) \| 55.65 ± 9.91 \| 56.73 ± 9.23 \| 53.28 ± 10.92 \| 0.3525 \| <0.001 \| \| Comorbidity data \| \| \| \| \| \| \| T2DM \| 231 (34.89%) \| 157 (34.43%) \| 74 (35.92%) \| 0.0313 \| 0.709 \| \| Hypertension \| 511 (77.19%) \| 363 (79.61%) \| 148 (71.84%) \| 0.1849 \| 0.028 \| \| Stroke \| 138 (20.85%) \| 94 (20.61%) \| 44 (21.36%) \| 0.0184 \| 0.827 \| \| Prior MI \| 120 (18.13%) \| 82 (17.98%) \| 38 (18.45%) \| 0.012 \| 0.886 \| \| Hyperlipidemia \| 151 (22.81%) \| 111 (24.34%) \| 40 (19.42%) \| 0.1175 \| 0.162 \| \| CKD \| 78 (11.78%) \| 43 (9.43%) \| 35 (16.99%) \| 0.2364 \| 0.005 \| \| Smoking \| 164 (24.77%) \| 112 (24.56%) \| 52 (25.24%) \| 0.0158 \| 0.851 \| \| Medication data \| \| \| \| \| \| \| Aspirin \| 642 (96.98%) \| 444 (97.37%) \| 198 (96.12%) \| 0.0731 \| 0.384 \| \| Clopidogrel \| 632 (95.47%) \| 435 (95.39%) \| 197 (95.63%) \| 0.0114 \| 0.892 \| \| Statins \| 615 (92.90%) \| 426 (93.42%) \| 189 (91.75%) \| 0.0651 \| 0.438 \| \| β-blocker \| 418 (63.14%) \| 286 (62.72%) \| 132 (64.08%) \| 0.0282 \| 0.737 \| \| ACEI/ARB \| 364 (54.98%) \| 252 (55.26%) \| 112 (54.37%) \| 0.018 \| 0.830 \| \| Coronary lesions \| \| \| \| \| \| \| LAD lesions \| 560 (84.59%) \| 385 (84.43%) \| 175 (84.95%) \| 0.0145 \| 0.863 \| \| LCX lesions \| 383 (57.85%) \| 256 (56.14%) \| 127 (61.65%) \| 0.1116 \| 0.184 \| \| RCA lesions \| 429 (64.80%) \| 282 (61.84%) \| 147 (71.36%) \| 0.1991 \| 0.018 \| \| LM lesions \| 109 (16.47%) \| 73 (16.01%) \| 36 (17.48%) \| 0.0395 \| 0.638 \| \| Multivessel lesions \| 466 (70.39%) \| 306 (67.11%) \| 160 (77.67%) \| 0.2314 \| 0.006 \| \| Gensini score \| 53.65 ± 42.65 \| 50.89 ± 41.63 \| 59.77 ± 44.31 \| −0.209 \| 0.013 \| \| Treatment \| \| \| \| 0.0158 \| 0.851 \| \| Intensive medication \| 241 (36.40%) \| 167 (36.62%) \| 74 (35.92%) \| \| \| \| PCI \| 405 (61.18%) \| 279 (61.18%) \| 126 (61.17%) \| \| \| \| CABG \| 16 (2.42%) \| 10 (2.19%) \| 6 (2.91%) \| \| \| We further analyzed GNRI in patients with different BMI and albumin levels (Figure 2). The high prevalence of malnutrition was found in patients with BMI <18.5 kg/m2 ($94.12\%$), and patients with albumin<35 g/L ($74.68\%$). What is more, there were substantial malnutritional patients in the normal-to over-weight groups. Figure 2: Percentage of malnutrition according to BMI and albumin. (A) Under-weight (BMI <18.5 kg/m2); (B) Normal-weight (BMI 18.5–24.9 kg/m2); (C) overweight (BMI >24.9 kg/m2); (D) normal-albumin (≥35 g/L); (E) hypo-albumin (<35 g/L). ## Association between GNRI and all-cause mortality The participants were followed for a median of 63 months (IQR 51–74). There were 201 endpoint-events during the follow-up, with 82 occurring in the low GNRI group and 119 in the other. As a nutritional screening index, GNRI was associated with many traditional cardiovascular risk factors (Supplementary Table 1). In univariate analysis (Table 2), a strong positive association was found between all-cause mortality and age, diabetes, stroke, CKD, FPG, Gensini scores, LM lesions, and multivessel lesions. Meanwhile, aspirin, HDL-C, eGFR, LVEF, albumin, and GNRI (HR = 0.97, $95\%$ CI [0.96, 0.99]) were closely associated with a reduction in all-cause mortality. However, male, hypertension, BMI, TC, and LDL-C did not show a significant association with the outcome. Table 2 \| Variable \| All-cause mortality \| All-cause mortality.1 \| \| --- \| --- \| --- \| \| Variable \| HR (95% CI) \| p value \| \| Male \| 1.05 (0.77, 1.43) \| 0.777 \| \| Age (year) \| 1.08 (1.02, 1.15) \| 0.012 \| \| BMI (kg/m2) \| 0.96 (0.92, 1.00) \| 0.084 \| \| SBP (mmHg) \| 0.99 (0.99, 1.00) \| 0.058 \| \| DBP (mmHg) \| 0.99 (0.98, 1.01) \| 0.337 \| \| TC (mmol/L) \| 1.09 (0.98, 1.20) \| 0.101 \| \| TG (mmol/L) \| 1.06 (0.95, 1.19) \| 0.314 \| \| LDL-C(mmol/L) \| 1.05 (0.88, 1.26) \| 0.594 \| \| HDL-C(mmol/L) \| 0.53 (0.33, 0.83) \| 0.006 \| \| eGFR(ml/min/1.73m2) \| 0.98 (0.97, 0.98) \| <0.001 \| \| FPG (mmol/L) \| 1.05 (1.03, 1.06) \| <0.001 \| \| UA (μmol/L) \| 1.00 (1.00, 1.00) \| 0.004 \| \| albumin \| 0.96 (0.94, 0.98) \| <0.001 \| \| GNRI \| 0.97 (0.96, 0.99) \| <0.001 \| \| LVEF \| 0.96(0.95, 0.98) \| <0.001 \| \| T2DM \| 1.46 (1.10, 1.93) \| 0.008 \| \| Hypertension \| 1.07 (0.76, 1.49) \| 0.712 \| \| Stroke \| 1.50 (1.09, 2.04) \| 0.012 \| \| Prior MI \| 1.05 (0.75, 1.49) \| 0.764 \| \| Hyperlipidemia \| 0.81 (0.57, 1.15) \| 0.230 \| \| CKD \| 2.30 (1.62, 3.25) \| <0.001 \| \| Smoking \| 1.08 (0.78, 1.48) \| 0.648 \| \| Aspirin \| 0.38 (0.21, 0.70) \| 0.002 \| \| Clopidogrel \| 0.60 (0.34, 1.05) \| 0.071 \| \| Statins \| 0.98 (0.59, 1.64) \| 0.951 \| \| β-blocker \| 1.06 (0.80, 1.42) \| 0.672 \| \| ACEI/ARB \| 1.11 (0.84, 1.48) \| 0.450 \| \| LM lesions \| 1.53 (1.09, 2.14) \| 0.014 \| \| Multivessel lesions \| 1.44 (1.04, 2.00) \| 0.030 \| \| Gensini score \| 1.01 (1.00, 1.01) \| <0.001 \| We further assessed the association between GNRI and all-cause mortality by multivariable Cox regression analysis (Table 3 and Figure 3). For each SD increase in GNRI, the risk of all-cause mortality was lowered by $23\%$. And when compared with the high GNRI group, the HR of all-cause mortality in the low GNRI group was 1.39 ($95\%$ CI [1.04, 1.86], $p \leq 0.05$). Then we divided the patients into three groups according to the tertiles of GNRI. Compared with tertile 1, the HRs of tertile 2 and tertile 3 were 1.49 ($95\%$ CI [1.02, 2.19], $p \leq 0.05$) and 1.74 ($95\%$ CI [1.22, 2.50], $p \leq 0.01$), respectively. Subsequently, the trend test revealed a dose–response between GNRI and all-cause mortality. ## Survival analyses of GNRI and all-cause mortality According to the Kaplan–Meier survival analysis (Figure 4A), we found that patients with high GNRI had a longer survival time ($p \leq 0.001$). Then we further divided GNRI into the tertiles and analyzed them. As shown in Figure 4B, all-cause mortality in tertile 3 was significantly lower than in tertile 1 and tertile 2 ($p \leq 0.001$). Figure 4: Kaplan–Meier curve of GNRI and all-cause mortality. (A) Kaplan–Meier survival curve for all-cause mortality by GNRI 98. (B) Kaplan–Meier curve for all-cause mortality by tertiles of GNRI. The result was shown as the Kaplan–Meier curve and p-value. ## Subgroup analyses In the subgroup analysis (Figure 5), we adjusted sex, age, diabetes, stroke, CKD, aspirin, eGFR, FPG, UA, LVEF, Gensini score, LM lesions, multivessel lesions, and HDL-C except for the stratified variables. The results showed that low GNRI was associated with increased all-cause mortality in those males, with diabetes mellitus, with previous MI, without multivessel lesions, and without LM lesions. But the values of p for interaction were all >0.05, suggesting the inverse association between low GNRI with all-cause mortality across all those subgroups. Figure 5: Subgroup analyses for the association between GNRI and all-cause mortality. Adjusted sex, age, diabetes, stroke, CKD, aspirin, eGFR, FPG, UA, LVEF, Gensini score, LM lesions, multivessel lesions, and HDL-C. ## Discussion In this study, we collected clinical data from 662 oldest-old patients with ACS who received coronary angiography and followed for 10 years. As far as we know, this’s the first study to investigate the association between GNRI and all-cause mortality in the oldest-old patients with ACS. Our key findings were the following: [1] the prevalence of malnutrition risk was high in the oldest-old patients with ACS; [2] as a reliable tool for assessing the nutritional status of the elderly, GNRI is associated with many traditional cardiovascular risk factors; [3] low GNRI is an independent risk factor of all-cause mortality in a dose-dependent manner; [4] GNRI has a stable predictive ability for all-cause mortality; and [5] GNRI is a prospective indicator to stratify the risk of all-cause mortality in the oldest-old patients with ACS. Aging and diseases, especially cardiovascular diseases, are the risk factors for malnutrition (30–32). The oldest-old (elderly >80 years old) tend to have a higher malnutrition risk than those 65–80 years old [33]. Despite its high prevalence and negative impact on short-and long-term prognosis, malnutrition remains underdiagnosed. One of the reasons is the lack of a broadly acknowledged definition and diagnostic criterion, and assessing patients’ nutritional status in an emergency like ACS is even more challenging. GNRI seems a promising index because that it can be readily calculated using three objective measurements: serum albumin concentration, height, and body weight. GNRI was strongly associated with poor outcomes in elderly emergency surgery patients, according to Jia et al. [ 34]. Therefore, we picked GNRI as the screening tool for malnutrition in the oldest-old patients with ACS and further assess the association between GNRI and the prognosis of the oldest-old ACS patients. Serum albumin and BMI are common nutrition indicators; however, they are affected by dehydration, heart failure, inflammation, and other factors [9, 35]. In comparison, GNRI is more reliable. It is not simply an overlap of albumin and BMI. Adding GNRI to a baseline model of established risk factors increased the predictive effect of mortality beyond BMI or serum albumin [36, 37]. GNRI performed much better than serum albumin level alone in predicting MACE in patients receiving the PCI with rotational atherectomy in the previous study [38]. Lots of studies have been conducted to investigate the relationship between GNRI and the prognosis of some chronic diseases, such as chronic heart failure, CKD, and tumors. And the results revealed that low GNRI correlated with longer hospital stays and high mortality. However, few studies evaluated the association between GNRI and the prognosis of elderly patients with ACS, let alone the oldest-old patients. To our knowledge, this was the first time to confirm that a low GNRI was an independent predictor of long-term prognosis in the oldest-old patients with ACS. Common nutrition screening tools include subjective global assessment (SGA), mini nutritional assessment (MNA), MNA-short form (MNA-SF), prognostic nutritional index (PNI), controlling nutritional status (CONUT), etc. [ 35, 39]. These methods are sensitive to subjective biases and have limitations in terms of time, personnel, and the potential for overdiagnosis. Several studies have compared GNRI with them [40, 41]. Wafaa et al. [ 25] demonstrated that GNRI had a stronger predictive value for describing and classifying nutritional status and nutritional-related problems in elderly hospitalized patients. The GNRI was created as a nutrition-related risk index to recognize and anticipate nutritional-related problems. In the present study, we noticed that a low GNRI at admission was a robust predictor for all-cause mortality in the oldest-old individuals with ACS. In our study, the GNRI was found to have a positive correlation with BMI, eGFR, LVEF, and serum albumin concentration. The roles of abnormal glucose and lipid metabolism in the pathogenesis of ACS have been widely acknowledged [26]. Surprisingly, we did not find an association like in other studies [36, 37, 42] between GNRI and FPG, TC, TG, LDL-C, or HDL-C. Our distinct subjects may explain the differences. As we all know, malnutrition is more common in the elderly due to decreased appetite, impaired digestion and absorption ability, and the impact of diseases [43]. So, in our oldest-old patients, hyperglycemia and hyperlipidemia were relatively less prevalent. And our patients followed prescriptions more strictly ($92.90\%$ vs. $78.7\%$) so that maintained their superior blood glucose and cholesterol levels. CKD is a severe risk factor for coronary artery disease (CAD) [44, 45]. Patients with CKD generated atherosclerotic plaque as a result of traditional (hyperlipidemia, hypertension, diabetes mellitus, smoking, and so on) and non-traditional (uremia-related cardiovascular disease risk factors such as inflammation, aberrant calcium-phosphorus metabolism, and so on) risk factors [46, 47]. As eGFR declines, this progress gets even worse. Herein our study, we discovered the correlation between GNRI and eGFR which may further inspire researchers to explore the relationship between nutritional status and cardio-renal syndrome. Obesity has been considered a traditional risk factor for cardiovascular diseases. Herein the present study, we found that there were substantial malnutritional patients in the normal-to over-weight patients who may be seen as relatively strong before, and our findings may provide evidence supporting the existence of the obesity paradox [48, 49]. Inflammation is a crucial factor in the development of ACS, and it also makes a significant contribution to malnutrition. The malnutrition-inflammation-atherosclerosis (MIA) syndrome has already been proposed as a crucial component of geriatric syndrome (50–52). GNRI was strongly associated with the progression of atherosclerosis in elderly CAD patients [53]. In the present study, we found that patients with a low GNRI were more prone to developing multivesicular lesions and got a higher Gensini score. This was the first to report an association between GNRI and the location and severity of the culprit coronary arteries. There have been reported that effective nutritional interventions could significantly reduce the length of hospital stay and mortality of malnutritional patients [54, 55]. By using GNRI, one can quickly recognize the risk of malnutrition and then take measures to improve nutritional status, and the prognosis gets ultimately enhanced. The GNRI was created to assess and forecast nutritionally associated problems. In multi-variate analysis and subgroup analysis, we demonstrated that GNRI stably predicts all-cause mortality of the oldest-old patients in a dose-manner. Based on these, we concluded that the GNRI could serve as a perspective index for risk-stratification and secondary prevention of the oldest-old patients with ACS. ## Strengths and limitations This was the first time enrolled oldest-old patients with ACS, assessed their nutritional status with GNRI on admission, and followed for 10 years. Then we evaluated the association between GNRI and long-term all-cause mortality. In the end, we demonstrated that GNRI is a nutrition assessing tool with prominent advantages, and it may be a hopeful tool to quickly and accurately evaluate nutritional status and predict their prognosis. The results of our study were reliable and practical for clinical use. However, there were some deficits inevitably. First, this was a single-center cohort study and all participants were Chinese and our was something old, so some selection bias existed. Second, we did not record dietary intake, physical activity, and other factors during the follow-up that may affect nutritional status. Finally, we took 98 as the cut-off value of GNRI, it may need further adjustment according to race or age, etc. ## Conclusion In this study, we confirmed that low GNRI was an independent predictive factor for all-cause death in the oldest-old patients with ACS, and the relationship between the two was dose-dependent. GNRI may be a perspective index for risk stratification in the oldest-old patients with ACS. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by Chinese PLA General Hospital’s Ethics Service Center. The patients/participants provided their written informed consent to participate in this study. ## Author contributions YL, JS, and XH: conducted research and wrote the paper. YL and YS: analyzed the data. YJ, JW, and HL: conducted research. ZF: designed research and had primary responsibility for final content. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: 'Conjunctival sac microbiome in anophthalmic patients: Flora diversity and the impact of ocular prosthesis materials' authors: - Hejia Zhao - Yanjun Chen - Yixu Zheng - Jing Xu - Chenyu Zhang - Min Fu - Ke Xiong journal: Frontiers in Cellular and Infection Microbiology year: 2023 pmcid: PMC10027910 doi: 10.3389/fcimb.2023.1117673 license: CC BY 4.0 --- # Conjunctival sac microbiome in anophthalmic patients: Flora diversity and the impact of ocular prosthesis materials ## Abstract ### Purpose To explore the changes of bacterial flora in anophthalmic patients wearing ocular prosthesis (OP) and the microbiome diversity in conditions of different OP materials. ### Methods A cross-sectional clinical study was conducted, involving 19 OP patients and 23 healthy subjects. Samples were collected from the upper, lower palpebral, caruncle, and fornix conjunctiva. 16S rRNA sequencing was applied to identify the bacterial flora in the samples. The eye comfort of each OP patient was determined by a questionnaire. In addition, demographics information of each participant was also collected. ### Results The diversity and richness of ocular flora in OP patients were significantly higher than that in healthy subjects. The results of flora species analysis also indicated that in OP patients, pathogenic microorganisms such as Escherichia Shigella and Fusobacterium increased significantly, while the resident flora of Lactobacillus and Lactococcus decreased significantly. Within the self-comparison of OP patients, compared with Polymethyl Methacrylate (PMMA), prosthetic material of glass will lead to the increased colonization of opportunistic pathogens such as Alcaligenes, Dermabacter and Spirochaetes, while gender and age have no significant impact on ocular flora. ### Conclusions The ocular flora of OP patients was significantly different from that of healthy people. Abundant colonization of pathogenic microorganisms may have an important potential relationship with eye discomfort and eye diseases of OP patients. PMMA, as an artificial eye material, demonstrated potential advantages in reducing the colonization of opportunistic pathogens. ## Introduction The loss of eyes poses huge burdensome on the living quality of anophthalmic patients (Kulkarni et al., 2018). Anophthalmic patients are distributed all over the world. In China alone, there are at least 300000 patients without eyes, and the population incidence rate can reach 0.3‰; Statistics show that in most cases, the common reason for the occurrence of anophthalmia was due to the eye enucleation of the eyeball in the treatment of severe terminal eye diseases such as trauma, tumor and congenital malformation of the eye (Saeed et al., 2006; Gupta and Padmanabhan, 2012; Yousuf et al., 2012; Ibanga et al., 2013; Zhao et al., 2013). Aphthalmia can cause a great burden on the life and work of anophthalmic patients; wearing ocular prosthesis is often the main solution, which help anophthalmic patients to repair esthetics and function, contribute social reintegration and improve their quality of life (Goiato et al., 2014; de Caxias et al., 2019; Society for Maternal-Fetal Medicine et al., 2019; Makrakis et al., 2021). The wearing of OP significantly contributes to patients’ aesthetic satisfaction. However, the wearing/implantation of OP could cause a large amount of latent adverse effects on patients, among which the most frequently-reported symptoms include the increased tears and secretion, pain, burning sensation, etc. At present, these adverse symptoms are mostly attributed to the stimulation of ocular prosthesis to the conjunctival sac, poor installation, insufficient daily care of ocular prosthesis and allergy to ocular prosthesis materials (Koch et al., 2016; Altin Ekin et al., 2020; Wang et al., 2020). To sum up, although the wearing of OP significantly contributes to patients’ aesthetic satisfaction, symptoms such as the increase of chronic secretions are often reported due to the heterogeneity of OP materials (Pine et al., 2011). OP may provide a suitable living environment for bacterial community, which might trigger the dysbiosis of ocular flora. Over the last decades, bacterial culture has served as the mainstream approach to identify pathogenic bacteria (Christensen and Fahmy, 1974; Miller et al., 1976; López-Sánchez et al., 2001). In more recent studies, the ocular flora identified by culture methods in OP patients was characterized mainly by the presence of gram-positive bacteria including Staphylococcus, Corynebacterium, Enterococcus, etc. as well as the gram-negative bacteria of Klebsiella, Pseudomonas, Stenotrophomonas, etc (Mela et al., 2010; Toribio et al., 2019). However, only a limited number of microbial species can be found in the flora culture, for the difference between the in-vitro environment and the in-vivo environment will also lead to unstable bacterial growth and may affect the bacterial flora. At present, bacterial identification based on 16S rRNA high-throughput sequencing reduces underlying confounding factors in bacterial culture and it has been widely used in the study of intestinal (Millar et al., 1996), vaginal (Yoshimura et al., 2011), skin (Rainer et al., 2020) and respiratory (Deng et al., 2022) microbiota. As the changes of ocular flora in patients with ocular prosthesis and the possible adverse effects of its dominant communities remains still unclear, this study aimed to focus on the composition of ocular flora in OP patients. Therefore, the purpose of this study is to clarify the changes of ocular flora in OP patients based on 16s rRNA sequencing. Focus of: 1) the difference of ocular flora between normal subjects and OP patients; 2) Influence of gender, age, OP material and wearing time on ocular flora in OP patients. The specific goal is to identify the colonization of pathogenic and beneficial bacteria according to the dominant communities among different groups, so as to analyze the potential risk factors causing eye discomfort of OP patients. We successfully identified that 1) PMMA shows more advantages than Glass as OP materials; 2) Age and sex have no significant effect on the ocular flora of OP patients. ## Research participants inclusion method This study was registered in the Chinese clinical trial registry (ChiCTR1800016357). The study protocol was approved by the Ethics Committee of Southern Medical University and written informed consents were obtained from all subjects. The study was conducted in accordance with the Declaration of Helsinki. All 42 participants were enrolled in this study between November 2021 and April 2022, including 23 male and 19 female participants. Inclusion criteria are listed as follows: 1) The participants did not have any type of acute conjunctivitis, conjunctival cyst, orbital implant exposure, nor the usage of any scratched or ill-fitting prosthesis, also including phthisis bulbi and cosmetic scleral shells. 2) The participants had not been administrated with any antibiotics, drugs, probiotics or fiber supplements within past three months, nor received relevant treatments that may affect the homeostasis of the flora. 3) The participants did not have a history of anemia, gastrointestinal diseases, and chronic diseases. 4) The participants were not pregnant or nursing. 5) The participants did not receive eye drops (antibiotics, corticosteroids and NSAIDs) in the past 6 months. 6) No oral antibiotics or antibiotic eye drops have been used recently. 7) The participants had not used contact lenses in recent 2 months. 8) Because PMMA and Glass were two commonly used ocular prosthesis materials for OP patients at this stage, which were highly representative, so we selected OP patients who used PMMA and Glass as ocular prosthesis materials as the subjects of this study (Schellini et al., 2015). ## Participants grouping method All subjects who met the screening criteria were divided into ocular prosthesis group (OP) ($$n = 19$$) and control group (Con) ($$n = 23$$) according to whether the participant is an anophthalmic patient. Besides, OP patients are a small group, and sample collection is relatively difficult; and the number of OP patients who go to the hospital is not many, which makes it difficult to track and follow them up; Finally, due to the lack of data on the incidence rate of OP patients, only using the incidence rate of blind patients for sample size calculation will cause data bias. Therefore, we did not use the calculation of sample size, but collected samples of existing patients for experiment and analysis. At the same time, in order to compare the differences of ocular surface flora among ocular prosthesis patients in multiple dimensions, we further divided all participants of OP group into [1] Gender term: Male group ($$n = 11$$) and Female group ($$n = 8$$); [2] Age term: Under 25 ($$n = 8$$) and over 25 ($$n = 11$$); Since the number of anophthalmic patient is very small, and the inducements leading to anophthalmia widely occur in the adult population, so that we did not use the traditional threshold of 18 years old as the age, but used the threshold of 25 years old as the age group, which was also reflected in other research reports (Pine et al., 2017; Rokohl et al., 2018). And from our data collection, there was only one patient who was below 18 years old. [ 3] Material of ocular prosthesis term: Glass group ($$n = 5$$) and PMMA (polymethyl methacrylate) group ($$n = 14$$); [4] Material and wearing time terms: participants who wear glass ocular prosthesis for less than one year and more than one year are defined as Glass-down ($$n = 3$$) and Glass-up ($$n = 2$$), respectively. Similarly, participants who wear PMMA are defined as PMMA-down ($$n = 5$$) and PMMA-up ($$n = 8$$). ## Questionnaire and information summary All participants were requested to complete a questionnaire, which included two parts: the basic information section, the ocular prosthesis condition section and the eye discomfort section, the participants of OP group are required to fill in all sections, while the participants of Control group only need to fill in the first section. The basic information included: gender and age; the ocular prosthesis condition section included: reason for wearing the ocular prosthesis, implant, eye seat material, ocular prosthesis material, use time, and the eye discomfort section included: secretion degree, tears degree, foreign body sensation, and pain degree. The severity of eye discomfort is ranked from 0 to 4: 0), no discomfort; 1) some of the time, 2) half of the time; 3) most of the time; 4) all the time (Kulkarni et al., 2018). The total eye discomfort degree was calculated by adding up the scores above. The higher the score, the more uncomfortable the ocular prosthesis will be. ## Sample collection For bacterial analysis, each participant received ophthalmologic examinations at Nanfang Hospital and Zhujiang Hospital of Southern Medical University. Topical anesthesia was applied before collection. Subjects were arranged to sit in a clean room, and the ocular specimens were collected from the upper, lower palpebral, and fornix conjunctiva using one single disposable aseptic dry cotton swab containing the topical anesthetic agent from a random eye. Another one single aseptic dry cotton swab containing the topical anesthetic agent was used as a blank control. 42 samples were collected from all participants (19 OP patients and 23 Control subjects). After collection, the samples were stored at -80 °C until genome DNA extraction. ## Extraction of genome DNA DNA was extracted using a DNA extraction kit (Mabio, Guangzhou, China) for the corresponding sample. The concentration and purity were measured using the NanoDrop One (Thermo-Fisher Scientific, MA, USA), measuring the OD value of genomic DNA solution (for the determination of concentration and purity, OD260/OD280 should be around 1.8, high indicates RNA pollution, low indicates protein pollution). In addition, we added CK bands (water control) during the DNA Extraction in order to avoid experimental bias (Supplement data: ‘16sRNA_Data’). ## Amplicon generation 16S rRNA/18SrRNA/ITS genes of distinct regions (e.g. Bac 16S: V3-V4/V4/V4-V5; Fug 18S: V4/V5; ITS1/ITS2; Arc 16S: V4-V5 et. al) were amplified using specific primer (e.g. 16S: 338F and 806R/515F and 806R/515F and 907R; 18S: 528F and 706R/817F and 1196R; ITS5-1737F and ITS2-2043R/ITS3-F and ITS4R; Arc: Arch519F and Arch915R et. al) with 12bp barcode. Primers were synthesized by Invitrogen (Invitrogen, Carlsbad, CA, USA). PCR reactions, containing 25 μL 2x Premix Taq (Takara Biotechnology, Dalian, China), 1 μL each primer (10 mM) and 3 μL DNA sample (20 ng/μL) in a volume of 50 µL, were amplified by thermocycling: 5min at 94°C for initialization; 30 cycles of 30 s denaturation at 94°C, 30 s annealing at 52°C, and 30 s extension at 72°C; followed by 10 min final elongation at 72°C. The PCR instrument was BioRad S1000 (Bio-Rad Laboratory, CA, USA). ## PCR products detection, pooling and purification First of all, the length and concentration of the PCR product were detected by $1\%$ agarose gel electrophoresis. Samples with bright main strip between (e.g. 16S V4: 290-310bp/16S V4V5: 400-450bp et. al) could be used for further experiments. We used 12 bp barcode specific primers to amplify all single samples, and the amplified PCR products were mixed according to the same amount of DNA. After 20-40 samples were mixed, the library index label was added to build the library. So the mixing here refers to the equal amount of mixing of the PCR products of a single sample, in preparation for the next step of building the library. PCR products were mixed in equidensity ratios according to the GeneTools Analysis Software (Version4.03.05.0, SynGene). Then, the PCR products of each sample were sequenced. Secondly, mixture PCR products were purified with E.Z.N.A. Gel Extraction Kit (Omega, USA). Each project selects the appropriate primers for amplification. Finally, when the final primer sequence was not known, it could be viewed in the mapping file of the analysis result package. ## Library preparation and sequencing Sequencing libraries were generated using NEBNext® Ultra™ II DNA Library Prep Kit for Illumina® (New England Biolabs, MA, USA) following manufacturer’s recommendations and index codes were added. The library quality was assessed on the Qubit2.0Fluorometer (Thermo Fisher Scientific, MA, USA). At last, the library was sequenced on an Illumina Nova6000 platform and 250 bp paired-end reads were generated (Guangdong Magigene Biotechnology. Guangzhou, China). ## Species annotation analysis For each of the representative sequence, the silva (for 16S, 18S, chloroplast and mitochondria, self-organized, https://www.arb-silva.de/), Unite (for ITS, http://unite.ut.ee/index.php), RDP(for 16S, http://rdp.cme.msu.edu/index.jsp), Greengenes (for 16S, http://greengenes.lbl.gov/) database were used to annotate the taxonomic information by usearch-sintax (set the confidence threshold to default to ≥ 0.8). The taxonomy of the species annotation was divided into seven levels: Kingdom(L1), Phylum(L2), Class(L3), Order(L4), Family(L5), Genus(L6) and Species(L7). ## Species diversity, correlation and functional cluster analysis Firstly, alpha diversity was applied in analyzing complexity of species diversity for each sample through 14 indices, including richness, chao1, shannon_2, shannon_e, shannon_10, jost, jost1, simpson, dominance, equitability, robbins, berger_parker, reads and buzas_gibson. Beta diversity analysis was used to evaluate differences of samples in species complexity through 9 algorithm, including bray_curtis, Euclidean, abund_jaccard, Canberra, chisq, chord, gower, weighted_unifrac and unweighted_unifrac by R software. The 16S ribosomal RNA (rRNA) gene sequencing has revolutionized the study of microbial communities in environments within the human body as well as other environment such soil or aquatic habitats (Cho and Blaser, 2012; Pflughoeft and Versalovic, 2012). Data analysis in such studies typically assigns 16S rRNA sequences to Operational Taxonomic Units (OTUs). There are multiple OTU clustering methods that had been proposed, in which the majority uses a threshold of $97\%$ sequence identity (Seguritan and Rohwer, 2001; Schloss and Handelsman, 2005; Westcott and Schloss, 2017). LDA Effect Size (LEfSe) analyse was used to find the biomarker of each group based on homogeneous operational taxonomic unit (OTU)_table. At last, the abundance OTU_table was standardized by PICRUS to remove the influence of copy of 16S marker gene in the genome of species, then compared the Greengene ID corresponding to each OTU to the COG database to obtain cog family information and conduct subsequent analysis. ## 16S RNA sequencing result In the process of DNA extraction, the quality of each sample was controlled: OD260/OD280 were around 1.8. Then, in the sequencing results, the range of Raw reads is 84258-92168, the range of Clean reads is 83612-91138, and the range of Clean tags is 73563-87411 Among them, when Qphred=20, the range is $99.7\%$ - $99.9\%$, which is far greater than $90\%$. When Qphred=30, the range is $99\%$ - $99.4\%$, which is far greater than $85\%$ (Supplement Table 1). When the dilution curve is about $25\%$ of the reflection percentage, the Richness of all samples has become flat (Figure 1). The Qphred value of the sequencing results is far beyond the qualified line. The dilution curve measures the species richness of different samples by randomly selecting a certain number of individuals from the samples, to show whether the amount of sequencing data of the sample is reasonable. Our results tend to be flat. To sum up, our sequencing depth is reasonable and can cover all bacterial groups in the sample. Figure 1: The abscissa of the raffection curve represents the proportion of the number of sequences extracted by resampling, and the ordinate represents the number of different species or diversity values. ## Analysis method and participates information In this study, the statistical analysis on the basic information of all participants showed that the difference on the history of endophthalmitis between healthy subjects and OP patients was statistically significant ($$P \leq 0.001$$), while the difference on other variants were not statistically significant ($P \leq 0.05$) (Table 1). No significant differences could be drawn in terms of gender, age and materials of ocular prosthesis among all participants in present study. Therefore, the analysis results of the included participants have a good representativeness for the population data. The statistical analysis on the clinical symptoms showed that OP patients using different OP materials had statistical significance in Secretion and Tears ($$P \leq 0.001$$, $$P \leq 0.005$$), but not in Foreign body perception and Pain ($P \leq 0.05$) (Table 2). ## Ocular flora analysis for control vs OP group First of all, we used the stack histogram of evolutionary tree to detect the species diversity at the OTU level, and verified the superiority of sample grouping according to the clustering network (Figure 2B); Then, we showed the dominate flora between the control group and the OP group through stacked histograms and thermograms. In the control group, Acinetobacter, Uruburuella, Ralstonia, Vibrio, Arcobacter, Leuconostoc, Lactobacillus, Lactococcus, Enterococcus were the main dominant floras, and in the OP group Prevotella_2, Escherichia Shigella, Castellaniella, Fusobacterium, Truepera, Prevotella, Porphyromonas, Alloprevotella, Bacillus; Cloacibacterium were the main dominant flora (Figures 2A, E). In addition, the infiltration level of each dominant flora between Control and OP group was shown in Figure 3; Supplement Table 2. Based on the chao1 index ($P \leq 0.0001$) and Shannon index ($P \leq 0.01$) The alpha diversity analysis of OS microbial community in OP group was significantly higher than that in Control group, indicating that the OS microbial community in OP group was more abundant and homogeneous than that in Control group (Figure 2C). The beta diversity analysis results were shown in Figure 2D, which indicated the relationships among bacterial communities. This result indicated that the conjunctival microbiota in the Control group was significantly dissimilar to that in the OP group (R² = 0.319, $$P \leq 0.001$$) based on the OTU and genus profiles, which indicated the ocular flora compositions of OP patients were distinct from those of healthy subjects. Figure 2: (A) Differences in relative mean abundances of genus in ocular microbiota between OP patients and Control subjects. (B) Evolution tree shows OTU level. (C) Alpha diversity analysis of conjunctival microbiota between OP patients and Control subjects.(D) Beta diversity analysis of ocular flora communities in OP patients and Control subjects visualized by PCoA plot. (E) The heatmap shows the dominant bacteria of OP group and Control group. : P<0.01,**: P<0.0001. Figure 3: Histogram of dominate bacteria between Control group and OP group. (: P<0.05; **:P<0.001; ns: No Sense). ## Ocular flora analysis for age and gender terms in OP group In order to further explored the impact of age and gender on ocular flora in OP patients, we regrouped OP patients in terms of age and gender. Alpha (Figures 4A, C) and beta (Figures 4B, D) diversity analyses were carried out on both terms. The results showed that there were no statistical significances for alpha diversity analysis ($P \leq 0.05$) and beta diversity analysis (R² < 0.1, $P \leq 0.05$) at the age and gender levels. Therefore, age and gender of OP patients may not have significant influence on the diversity of ocular flora. Figure 4: (A) Alpha diversity analysis of age level in OP patients. (B) PCoA plot shown the Beta diversity analysis of age level in OP patients. (C) Alpha diversity analysis of gender level in OP patients. (D) PCoA plot shown the Beta diversity analysis of gender level in OP patients. ns: No Sense. ## Ocular flora analysis for material and wearing time terms in OP group In the further exploration of the diversity in ocular flora caused by materials and wearing time, by means of diversity analysis, it had been discovered that not a statistical significance but a remarkable difference was found between respective characteristic species of each group. Figures 5A–D show the dominant flora of Glass group and PMMA group. And infiltration level of each dominant flora between Control group, PMMA group and Glass Group was shown in Figure 6 and Supplement Table 2. After further grouping the wearing time of ocular prosthesis, Figure 5D, E shows the dominant flora of Glass-down group, Glass-up group, PMMA-down group and PMMA-up group. The results showed that there was no statistical significance for alpha diversity analysis ($P \leq 0.05$) and beta diversity analysis (R² = 0.0604, $$P \leq 0.292$$) at material and wearing time terms. To further identify the specific flora that can differentiate material and wearing time terms in OP patients, LEfSe analysis was performed (LDA score > 2.0, $P \leq 0.05$). Results showed that the abundances of Pseudonocardiaceae, Porphyrobacter, Dermabacter, Cyclobacterium_lianum, Spirochaetes_bacterium_RBG_16_49_21, Patulibacter, Cellvibrio, Pseudomonadaceae, Alcaligenes, Pseudomonas, Kocuria, Thiopseudomonas, Sphingobium, Pseudomonas_sp:A842010, Geobacillus, Stappiaceae, Pannonibacter, Hydrogenophilus and Thermoanaerobacterales were significantly higher in the Glass group than in the PMMA group, and those of Parvibaculales, Aquabacterium, Anaerococcus_mediterraneensis, Omnitrophicaeota and Devosia were lower in the Glass group than in the PMMA group (Figure 5F). And the abundances of Peredibacter, Amoebophilaceae, Treponema_2 and Candidatus_Amoebophilus were significantly higher in the PMMA-down group than in the PMMA-up group; Spongiibacteraceae, Planctomicrobium, Rubripirellula and Selenomonas_3 were lower in the PMMA-down group than in the PMMA-up group. What’s more, the abundances of Peredibacter, Micrococcus, Paenibacillaceae and Luteimonas were higher in the Glass-down group than in the Glass-up group; Curtobacterium, Corynebacterium_kroppenstedtii, NS5_marine_group and Selenomonas_3 were lower in the Glass-down group than in the Glass-up group (Figures 5G, H). Venn plots shown the intersection of PMMA-down group and Glass-down group (Figure 5I) and the intersection of PMMA-up group and Glass-up group (Figure 5J) were taken respectively to obtain early common species (Peredibacter) and late common species (Selenomonas_3). Figure 5: (A) Differences of ocular microbiota between Glass, PMMA group and Control subjects. (B) Alpha diversity analysis between Glass group and PMMA group. (C) PCoA plot shown the Beta diversity analysis between Glass group and PMMA group. (D) The heatmap shows the dominant bacteria of Material term. (E) The heatmap shows the dominant bacteria of Material & Wearing Time term. (F) LEfSe analysis for characteristic colony search between Glass group and PMMA group. (G) LEfSe analysis for characteristic colony search between PMMA-down group and PMMA-up group. (H) LEfSe analysis for characteristic colony search between Glass-down group and Glass -up group. (I) Venn plot shows the intersection colony of PMMA-down group and Glass-down group. (J) Venn plot shows the intersection colony of Glass-up group and PMMA-up group. ns: No Sense. Figure 6: Histogram of dominate bacteria between Control group, PMMA group and Glass Group. (: P<0.05; : P<0.01; :P<0.001; ns: No Sense). ## Discussion Although many factors can affect the eye comfort of OP patients. However, one of the latent inducements might be attributed to the heterogeneity of exogenous OP used by patients, which easily leads to the changes of eye microecology. The disturbance of ocular flora may be another essential factor affecting the ocular comfort of OP patients. Existing studies have proved that changes in intestinal, oral, vaginal and skin microbial communities will significantly affect the homeostasis of the body (Goldsmith and Sartor, 2014; Guenin-Macé et al., 2020; Oh et al., 2021; Uehara et al., 2021). Therefore, the investigation of ocular microbiota of patients with ocular prosthesis may provide a new method to improve ocular comfortableness. In earlier studies, bacterial culture revealed significant differences in ocular flora between healthy subjects and OP patients. The results of bacterial culture experiment showed that the richness of pathogenic microorganisms on the ocular surface of OP patients was significantly higher than that of healthy subjects, especially the increase of Staphylococcus aureus and Staphylococcus epidermidis (Guiotti et al., 2018; Altin Ekin et al., 2020). In addition, research showed that the proportion of gram-negative bacteria in patients who frequently operate and adjust prostheses will be significantly elevated (Vasquez and Linberg, 1989). However, the traditional bacterial culture method has limitations in the study of microbial communities. Some pathogens are difficult to be cultured under normal conditions, which leads to lower detection rate of bacteria compared with 16S rRNA sequencing or molecular metagenomics (Zhou et al., 2014). The emerging molecular biotechnology (16S rRNA sequencing) demonstrates a higher precision in microbial community detection. Compared with sequencing results obtained by using traditional bacterial cultures, 16S rRNA sequencing has been proven for higher sensitiveness on detecting microbial diversity (Ozkan et al., 2017). In this study, we used high-throughput 16S rRNA sequencing to deeply identify the difference of ocular surface flora between OP patients and healthy subjects. In the comparison of ocular flora abundance between OP patients and healthy subjects, the contents of Escherichia Shigella and Fusobacterium increased significantly, while the Lactobacillus and Lactococcus decreased significantly. The existing research results show that Escherichia Shigella is an important ocular pathogen, which can increase patients’ susceptibility of ocular inflammation, and its increased abundance may also cause the imbalance of intestinal flora homeostasis and further affect the immune system (de Paiva et al., 2016; Huang et al., 2021). The increased of Fusobacterium may cause the disorder of ocular intestinal axis regulation, and lead to the occurrence and disease progression of autoimmune uveitis. Other studies showed that the infection of anaerobic bacteria may aggravate the symptoms of ocular diseases, and the detection of anaerobic bacteria may have the potential to develop into a new generation of diagnostic markers (Brook, 2001; Brook, 2008; Gunardi et al., 2021). However, compared with healthy subjects, the abundance of Lactobacillus and Lactococcus in OP patients decreased significantly. As permanent bacteria in the body, they were considered as the potential driving forces for the evolution of human immune system. As anti-inflammatory probiotics, they can also regulate the expression of Tumor Necrosis Factor - alpha (TNF - α), Interleukin – 10 (IL-10) and the infiltration level of immune cells, so as to alleviate the ocular symptoms (O'Callaghan and O'Toole, 2013; Feher et al., 2014; Yun et al., 2021). In addition, the alpha and beta diversity of OP patients was more variable than that of healthy subjects, and there was a significant statistical difference between them. The dominant ocular flora of healthy subjects was more stable than that in OP patients, indicating a change occurred in the metabolic environment. The increased in the colonization capacity of pathogenic microorganisms might be attributed to the microporous structure of ocular prosthesis materials, which may not be the suitable niche for normal bacterial species. Research have shown that the prosthesis implanted into the body for relatively longer period is easy to be colonized by pathogenic microorganisms (De Cicco et al., 1995; de Freitas et al., 2012; Veerachamy et al., 2014), which may be an important reason for eye discomfort in OP patients. In the subsequent analysis, we determined that age and gender did not affect the diversity of ocular flora in patients with ocular prosthesis. However, alpha and beta analysis showed that age term and gender term had no effect on the ocular flora diversity of OP patients, and there was no statistical difference of them. It can’t be considered that there was a definite impact on the ocular flora between men and women in OP patients as well as the age level at present. Interestingly, in the LEfSe analysis of OP patients’ ocular prosthesis materials and use time, we found that the abundance of Alcaligenes, Dermabacter, and Spirochaetes in OP patients using glass as ocular prosthesis materials was higher than that in OP patients using PMMA. Current studies have shown that Alcaligenes is a conditional pathogen, which can cause blood flow, urinary tract, skin, soft tissue and middle ear infections (Huang, 2020; Spencer et al., 2020); Dermabacter has a high correlation with many diseases and has been reported to cause bacteremia in patients (Gómez-Garcés et al., 2001; Schaub et al., 2020); The colonization of Spirochaetes can cause uveitis, ocular syphilis and ocular leptospirosis (Rathinam, 2002; Chao et al., 2006; Duan et al., 2020). The ocular flora of OP patients using PMMA has a high abundance of Aquabacterium and Devosia. As neutral bacteria, the pathogenicity of these two bacteria remains unclear to human, but animal experiments have indicated Devosia to be protective for the mice kidneys (Zhao et al., 2016; Sun et al., 2021). According to the results of LEfSe analysis, glass as the ocular prosthesis material may have more pathogenic microorganisms colonized on its surface, while PMMA is mainly colonized by neutral bacteria, which seems to have more beneficial as an ocular prosthesis material. Within the limitation of 16S rRNA sequencing technique, present study cannot provide the detection of fungi or viruses in the eyes of OP patients (Mahmoudi et al., 2018; Merle et al., 2018). Secondly, the enrolled number of OP patients is still insufficient and for future study, a larger-scale sample collection will be required. Although our study successfully presented a rather detailed exploration on the diversity of flora, functional prediction of flora in OP patients could be deepen. Thus, further extension of our work could be carried out on the analysis of functions and pathways of ocular flora. In line with these findings, we can reach the following conclusions: First of all, the ocular flora species of OP patients and healthy subjects were significantly different, and the colonization of bacteria species experienced a major change. Secondly, in OP patients, age and gender terms have not been found to have significant influence on the ocular surface flora. Last but not least, compared with glass, PMMA manifested a certain degree of anti-colonized ability for pathogenic bacteria and featured as a potentially better ocular prosthesis material. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by Ethics Committee of Southern Medical University. The patients/participants provided their written informed consent to participate in this study. ## Author contributions KX and MF conceived the study, critically reviewed the intellectual content of the manuscript and made substantive revisions to the important contents of the manuscript. HZ was the major contributor to the research and the writing of the manuscript. YC and CZ provided technical support and revised the manuscript. YZ and JX provided valuable suggestions. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: T lymphocyte characteristics and immune repertoires in the epicardial adipose tissue of heart failure patients authors: - Xu-Zhe Zhang - Xian-Li Chen - Ting-Ting Tang - Si Zhang - Qin-Lin Li - Ni Xia - Shao-Fang Nie - Min Zhang - Zheng-Feng Zhu - Zi-Hua Zhou - Nian-Guo Dong - Xiang Cheng journal: Frontiers in Immunology year: 2023 pmcid: PMC10027920 doi: 10.3389/fimmu.2023.1126997 license: CC BY 4.0 --- # T lymphocyte characteristics and immune repertoires in the epicardial adipose tissue of heart failure patients ## Abstract ### Background Epicardial adipose tissue (EAT) acts as an active immune organ and plays a critical role in the pathogenesis of heart failure (HF). However, the characteristics of immune cells in EAT of HF patients have rarely been elucidated. ### Methods To identify key immune cells in EAT, an integrated bioinformatics analysis was performed on public datasets. EAT samples with paired subcutaneous adipose tissue (SAT), heart, and peripheral blood samples from HF patients were collected in validation experiments. T cell receptor (TCR) repertoire was assessed by high-throughput sequencing. The phenotypic characteristics and key effector molecules of T lymphocytes in EAT were assessed by flow cytometry and histological staining. ### Results Compared with SAT, EAT was enriched for immune activation-related genes and T lymphocytes. Compared with EAT from the controls, activation of T lymphocytes was more pronounced in EAT from HF patients. T lymphocytes in EAT of HF patients were enriched by highly expanded clonotypes and had greater TCR clonotype sharing with cardiac tissue relative to SAT. Experiments confirmed the abundance of IFN-γ+ effector memory T lymphocytes (TEM) in EAT of HF patients. CCL5 and GZMK were confirmed to be associated with T lymphocytes in EAT of HF patients. ### Conclusion EAT of HF patients was characterized by pronounced immune activation of clonally expanded IFN-γ+ TEM and a generally higher degree of TCR clonotypes sharing with paired cardiac tissue. ## Introduction Due to its unique anatomic and functional features [1], epicardial adipose tissue (EAT) and its critical role in the pathogenesis of cardiovascular diseases have received increasing attention in recent years. EAT covers nearly $80\%$ of the heart’s surface and accounts for approximately $15\%$ of the total heart mass [2]. EAT is mainly located in the atrioventricular and the interventricular sulcus [3]. EAT is in direct contact with the myocardium without fascial interruption, allowing mutual crosstalk. Under normal conditions, EAT is cardio-protective by maintaining lipid homeostasis and providing mechanical protection to the adjacent myocardium. Under pathological conditions, however, EAT transforms into a pro-inflammatory and pro-fibrotic phenotype and is cardiac deleterious [4]. Heart failure (HF) is a complex clinical condition with a poor prognosis characterized by cardiac diastolic or systolic dysfunction [5]. Emerging evidence has linked EAT to the pathogenesis of HF [4, 6]. Sodium-glucose cotransporter 2 (SGLT2) inhibitors is a novel agent for the treatment of HF [7]. A reduction in EAT volume has been linked to the beneficial effects of SGLT2 inhibitor in HF patients [8]. The mechanisms by which EAT contributes to HF remain unclear, but likely involve enhanced inflammation. EAT is populated by immune cells including macrophages, T lymphocytes, mast cells, etc., and serves as the source of pro-inflammatory mediators (9–11). Pro-inflammatory cytokines and pro-fibrotic factors, such as leptin, TNF-α, IL-1β, and IL-6 are up-regulated in EAT under pathological conditions [12, 13] and may diffuse into the adjacent myocardium to promote cardiac dysfunction. However, a better understanding of the relationship between EAT and HF requires a full-scale knowledge of the changes in the immune microenvironment within EAT in HF. Here, we performed integrated bioinformatics and immune cell infiltration analyses on public datasets to characterize the immune features and immune cell profiles of EAT in HF patients. The results suggested that EAT of HF patients was characterized by pronounced immune activation, particularly by the accumulation of T lymphocytes. Further analyses indicated that T lymphocytes in EAT of HF patients were highly expanded, closely related to those in cardiac tissue, and dominated by IFN-γ+ effector memory T lymphocytes (TEM). GZMK and CCL5 identified by bioinformatics analyses may act as the key effector molecules of T lymphocytes in EAT of HF patients. The overall flowchart of this study is shown in Figure 1. Figure 1: Overall flowchart of this study. ## Public datasets in transcriptomic analysis GSE64554 [14], GSE120774 [15], GSE192886 [16] and GSE24425 [17] were obtained from Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo). Array or sequencing data of paired EAT and SAT in GSE64554 ($$n = 46$$), GSE120774 ($$n = 36$$), and GSE24425 ($$n = 12$$) were from patients undergoing cardiac valve or coronary artery bypass graft surgery. GSE192886 contained sequencing data of EAT from HF patients ($$n = 5$$) and non-HF patients ($$n = 5$$) undergoing coronary artery bypass graft surgery. Clinical characteristics for analyzed patients can refer to the original citations of these datasets and Tables S1 - S3. ## Patients and samples in the experimental validation In the validation experiments, fresh EAT with paired SAT, heart, and peripheral blood samples were collected from HF patients undergoing heart transplantation in Wuhan Union Hospital. Peripheral blood samples were obtained before surgery. SAT samples were obtained from the suprasternal region, heart and EAT samples were obtained from the left ventricle. We obtained informed consent from all enrolled subjects. The experimental protocol and sample collection were in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of Wuhan Union Hospital of Huazhong University of Science and Technology (METC number: 20200462). Information on the involved subjects was listed in Table S4. ## Identification of differentially expressed genes, functional enrichment analysis, PPI network construction, and identification of hub genes The data obtained from GSE64554 and GSE120774 were processed by log2 transformation and quantile normalization via limma package [18] using R separately. The differential expression matrixes of the datasets were also identified by the limma package separately and P values were adjusted by the Benjamini-Hochberg method. We then applied the Robust Rank Aggregation (RRA) method [19] to filter the differential expression matrixes, so as to obtain the comprehensive differentially expressed genes (DEGs) across two different microarray platforms. DEGs with RRA score less than 0.05 were selected for further analyses. Functional enrichment analyses were performed using the DAVID [20] by inputting the official gene symbols of obtained DEGs. Figures for functional enrichment analyses were plotted by R and Sangerbox (http://www.sangerbox.com/tool). Construction of the protein-protein interaction (PPI) network and identification of hub genes were performed as the previous description [21]. ## Weighted gene co-expression network analysis To explore the gene modules responsible for the phenotypic differences between EAT and SAT, we performed the Weighted gene co-expression network analysis (WGCNA)to identify co-expressed gene modules [22]. First, we screened the top $25\%$ of the genes in the variance variability between samples in a pooled matrix and used them as input data. Next, we obtained the soft threshold and set the minimum gene number in the module to 30 to get gene co-expression modules. By analyzing the correlation between each module with the EAT/SAT phenotypes, we screened out the gene modules that need further exploration. Finally, functional enrichment analyses were performed on the obtained modules, and the modules significantly related to the immune process were identified. By taking the intersection of immune-related key modules and DEGs identified by RRA, we obtained a set of key immune-related genes. ## Immune cell infiltration and correlation analyses xCell [23] and CIBERSORT [24] are signature-based methods to infer the immune cell landscape according to expressional profiling. We performed immune cell infiltration analyses and obtained the immune cell landscapes for EAT and SAT based on the pooled matrix. “ Lymphoid cells” and “myeloid cells and others” were categorized. Results were evaluated by t-test to determine the significance of differences. The correlation relationship between immune cell types, WGCNA modules, and target genes was evaluated by Pearson correlation coefficients. ## T cell receptor repertoires sequencing and analyses Paired EAT, SAT, and heart samples were used for T cell receptor (TCR) repertoire sequencing. Tissue genomic DNA was extracted using Universal Genomic DNA Kit (CWBio, China). DNA quality was evaluated using Nanodrop2000 (Thermo, USA) with concentration >20ng/uL and OD$\frac{260}{280}$ between 1.7 and 2.0. Multiplex PCR reactions were run to specifically amplify the third complementarity-determining region (CDR3) of the TCRβ chain for libraries construction. The constructed libraries were deeply sequenced by Illumina NextSeq500. Primers and sequencing were provided by SEQHealth (China). Raw sequences filtered by SOAPnuke (version 1.6.0) were used for TCR sequencing analyses, and the sequencing data were mapped to the ImMunoGeneTics (IMGT) database using MiXCR (version 3.0.3) to define the V, D, and J fragments and CDR3 sequence [25]. The terms TCR clonotype and TCR clone describe the CDR3 sequence composed of a unique amino acid sequence and CDR3 sequence composed of unique V, D, and J fragments, respectively. Antigen matching analysis was performed via the IEDB database (http://www.iedb.org/). ## Flow cytometry Peripheral blood mononuclear cells (PBMCs) were isolated by density gradient centrifugation using lymphocyte separation medium (MPbio, USA). Fresh EAT samples were digested at 37°C in Hepes buffer containing collagenase D (1mg/mL, Sigma, USA) and dispase II (2mg/mL, Sigma, USA), and then filtered by 100μm and 40μm filters (Falcon, USA) sequentially to collect the stromal vascular fraction (SVF) for subsequent flow cytometric analyses. Memory phenotypes of T lymphocytes were categorized into naïve T cell (TN, CD62L+CD45RA+), central memory T cell (TCM, CD62L+CD45RA-), effector memory T cell (TEM, CD62L-CD45RA-) and CD45RA+ effector memory T cell (TEMRA, CD62L-CD45RA+). For the detection of interferon (IFN)-γ, cells were re-suspended in RPMI-1640 medium (Gibco, USA) with $10\%$ heat-inactivated FBS (Gibco, USA) at a concentration of 106 cells/ml and stimulated with Cell Stimulation Cocktail (eBioscience, USA). After 6 hours of stimulation, cells were harvested, permeabilized, and then stained with fluorescence-conjugated antibodies. Used antibodies were as follows: PE-Cy7-anti-human CD3(BD Biosciences, USA), PE-anti-human IFN-γ (BD Biosciences, USA), BV421-anti-human CD45RA (BD Biosciences, USA), APC-anti-human-CD62L (Biolegend, USA), Fixable Viability Stain 510 (BD Biosciences, USA). The stained cells were washed with Flow Cytometry Staining Buffer (eBioscience, USA) and fixed with IC Fixation Buffer (eBioscience, USA). Flow cytometry analyses were performed with a FACS Calibur flow cytometer (BD Biosciences, USA) and analyzed by FlowJo software. ## Histological staining For immunohistological or immunofluorescence staining, paired EAT and SAT samples were fixed in $4\%$ paraformaldehyde at 25 °C for 24 hours and embedded in paraffin. Slides were sectioned in 5μm and blocked with $1\%$ BSA PBS buffer and then stained with target antibodies and DAPI following routine procedures. The slides were scanned with a digital scanner (3D-HISTECH, Hungary). CaseViewer software was used for observation and statistics. For immunohistological statistics, 3 areas under the 20x field of view from each slide were randomly selected, and the average number of positive cells per mm2 was calculated (3 slides included for each sample). Used antibodies were as follows: human CD3 antibody (Servicebio, China), human CCL5 antibody (R&D systems, USA), and human GZMK antibody (R&D systems, USA). ## Statistical analysis Data processing and analyses were performed using SPSS 22.0, GraphPad Prism, and R. Normality were evaluated by the Shapiro-Wilk test. Differences were evaluated using Student’s t-test and $P \leq 0.05$ was considered statistically significant unless indicated otherwise. ## Integrated bioinformatics analyses revealed pro-inflammatory characteristics of EAT The DEGs between EAT and SAT in GSE64554 and GSE120774 were identified separately and shown in Figure 2A. Next, we applied the RRA algorithm to integrate DEGs of the two datasets and obtain a more comprehensive DEGs list. The RRA method identified 131 genes that were up-regulated in EAT compared to SAT, while 159 genes were down-regulated. DEGs identified by RRA presented significant differences (adjusted P value<0.05 and \|log2FC\| ≥0.5) in at least one dataset, most of which ($90\%$) showed consistent expressing trends across datasets. The top10 up- and down-regulated DEGs recognized by RRA were shown in Figure 2B. Next, we applied Gene Ontology (GO) enrichment analysis on the DEGs identified by RAA that were up- and down-regulated in EAT versus SAT to explore their potential functions, respectively. As shown in Figure 2C, the up-regulated DEGs in EAT were mainly enriched in complement activation and immune response, while the down-regulated DEGs were mainly related to embryonic skeletal system morphogenesis, suggesting immune activation in EAT compared to paired SAT. The PPI network of DEGs identified by STRING was further analyzed by cytoHubba to identify hub genes. As shown in Figure S2 and Table S5, we obtained the top 10 hub genes including COL1A1, FGF2, BGN, C3, TIMP1, CD44, POSTN, COL3A1, CCL2 and APOB. Figure 2: Bioinformatics analyses reveal pro-inflammatory characteristics and key genes of EAT. (A) Volcano plot of DEGs between EAT and SAT in GSE64554 and GSE120774. (B) Top 10 up- and down-regulated DEGs identified by RRA method. (C) GO-BP functional enrichment analyses of up- and down-regulated DEGs in EAT compared to SAT. (D) Cluster dendrogram of WGCNA. (E) WGCNA key modules and EAT/SAT phenotype correlation. (F) GO-BP functional enrichment analyses of WGCNA-identified blue and greenyellow gene modules. Further, we applied the WGCNA method to identify immune-related key gene modules associated with EAT. By filtering the expression profiles of the top $25\%$ variance in all EAT and SAT samples, a total of 3869 highly variable genes were included in WGCNA. Then, filtered genes were clustered into 18 different modules based on WGCNA clustering (Figure 2D). The correlation analyses between all modules and EAT/SAT phenotype were carried out and 7 modules were found to be significantly associated with the EAT/SAT phenotype (Figure 2E). Functional enrichment analyses suggested that the blue and greenyellow modules were closely related to immune response (Figure 2F). The overlap of DEGs and the two modules were identified and 9 key genes were obtained for further analyses. Of the 9 key genes, all were up-regulated DEGs in EAT and listed in Table S6, including SLCO2B1, F13A1, C1QA, C1QB, and C1QC from the blue module and IGLL1, GZMK, CCL5, and SLC38A1 from the greenyellow module. ## Immune cell infiltration analyses showed a potential enrichment of lymphocytes in EAT We used xCell to explore the differences in the immune cell landscape between EAT and SAT. As shown in Figure 3A, EAT was infiltrated by more lymphocytes and dendritic cells (DC), while the abundance of macrophages and M1 macrophages showed no significant difference. In SAT, M2 macrophages, basophils, and mast cells showed higher degrees of infiltration. The correlation between different cell subtypes was calculated to infer their potential interaction. In Figure 3B, CD4+ T cells and CD8+ T cells presented a strong positive correlation ($r = 0.82$), indicating that the two subtypes of T cells had a consistent tendency of infiltration. Figure 3: Immune cell infiltration and correlation analyses. (A) Violin charts of xCell immune infiltration score between EAT and SAT. (B) Correlation matrix of immune cell subtypes (Pearson correlation coefficients are displayed in the box). (C) Correlation matrix of immune cell infiltration scores with 19 identified key genes (Pearson correlation coefficients are displayed in the box). (D) Correlation matrix of immune cell infiltration scores with WGCNA key modules. P < 0.05, P < 0.01, P < 0.001 and ns refers to no significance. Next, we analyzed the correlation between the infiltrated immune cells with hub genes, key genes and key modules identified in EAT from the previous PPI network and WGCNA analyses. As shown in Figure 3C, the expression of GZMK, CCL5, IGLL1, and SLC38A1 presented a strong positive correlation with CD4+ T cells, CD8+ T cells, and B cells, while the expression of SLCO2B1, BGN, C3, TIMP1, C1QA, C1QB and C1QC showed a strong positive correlation with DC. As shown in Figure 3D, the 7 key modules related to EAT and SAT obtained by WGCNA were all related to different subtypes of immune cells. In particular, the blue and greenyellow modules closely related to the immune process presented a strong positive correlation with lymphocyte abundance. The correlation coefficients between the greenyellow module and T or B cells were more than 0.8. Based on the above analyses, we concluded that EAT acts as a pro-inflammatory adipose tissue characterized by abundant lymphocyte infiltration compared with SAT. ## More activated T lymphocytes in EAT from HF patients To further explore the characteristics of EAT from HF patients, we analyzed a public dataset GSE192886 containing transcriptome profiles of EAT from 5 HF patients and 5 patients without HF as controls (CON). We obtained 196 up-regulated and 261 down-regulated DEGs in EAT from HF patients versus that from controls. Function enrichment analysis of up-regulated DEGs suggested immune activation, particularly lymphocyte activation in EAT from HF patients (Figure 4A). Up-regulated DEGs were mainly enriched in the lymphocyte activation pathway relative to the myeloid leukocyte activation pathway (Figure 4B). Next, we used CIBERSORT to compare the immune cell composition between EAT from HF patients and non-HF controls. As shown in Figure 4C, the frequencies of T cells and B cells were higher in EAT from HF patients compared to non-HF controls, indicating lymphocyte activation as the hallmark of EAT from HF patients. Figure 4: Amplified lymphocyte activation features in EAT of HF patients. (A) GO-BP functional enrichment analyses of up-regulated DEGs in EAT of HF patients. (B) “Lymphocyte activation” and “myeloid leucocyte activation” GO term genes in DEGs of HF-EAT. (C) Immune cell infiltration analyses of HF-EAT and control EAT by CIBERSORT. (D) Differentially expressed key genes in HF-EAT. (E) Expression of T cell-inflamed GEPs in HF-EAT and control EAT. (F) Correlation of CCL5 and GZMK expression with T cell-inflamed GEPs score in GSE24425. (G) Top 10 potential key TFs of DEGs in HF-EAT identified by ChEA3 database. (H) PPI network of top 10 potential TFs. P < 0.05, *P < 0.01. We examined the expression levels of genes associated with the inflammatory characteristics of EAT (10 hub genes and 9 key genes identified above). As shown in Figure 4D and Figures S3, S4 (Mann-Whitney test), of these genes (IGLL1 not included), the expression of CCL5, GZMK, and POSTN showed a further increase in EAT from HF patients indicating an enhanced degree of inflammation and fibrosis while CCL5 and GZMK presented the strongest positive correlation with infiltrated T lymphocytes in previous analyses (Figure 3C). Next, we examined the expressions of T cell-inflamed gene expression profiles (GEPs) between EAT from HF patients and non-HF controls. T cell-inflamed GEPs (composite genes listed in Table S7) has been reported to be associated with inflammatory T-lymphocyte infiltration and prediction of sensitivity to immunotherapy in tumors [26, 27]. As shown in Figure 4E (Mann-Whitney test), the expressions of T cell-inflamed GEPs were higher in EAT of HF patients, providing further evidence of an enhanced T-lymphocyte response. In addition, the expression of CCL5 and GZMK were also strongly positively correlated with T cell-inflamed GEPs in EAT and SAT samples from validation dataset GSE24425 (Figure 4F). Next, we identified the potential key TFs regulating the phenotypic transition of EAT from HF patients using the ChEA3 database [28] (Figure 4G). The PPI network of top 10 predicted key TFs suggested a crucial role of lymphocyte-specific TFs in EAT of HF patients, especially for those were differentially expressed including TBX21, PAX5, NFATC2, and STAT4 (Figure 4H). ## Characteristics of TCR repertoires in EAT from HF patients The numbers of TCR clones and TCR clonotypes were higher in EAT than in paired SAT from HF patients, indicating enhanced T lymphocyte infiltration in EAT (Figure 5A). Next, we compared the distribution of the low (fraction>$0.1\%$), middle (fraction>$0.5\%$), and high (fraction>$1\%$) frequency TCR clonotypes between EAT and paired SAT. The results suggested the enrichment of highly expanded TCR clonotypes in EAT compared to paired SAT (Figure 5B). Accordingly, the proportion of top 10 TCR clonotypes was higher in EAT than in SAT (Figures 5C, D). These results suggested that T lymphocytes from EAT of HF patients exhibited higher clonal expansion than those from SAT. TCR clones with high frequency in EAT were listed and evaluated by antigen matching analysis via the IEDB database (Tables S8, S9). Figure 5: Characteristics of TCR repertoires in EAT. (A) TCR clone counts and TCR clonotype counts in EAT and paired SAT. (B) Fraction of low (proportion>0.1%), middle (proportion>0.5%) and high (proportion>1%) frequency TCR clonotypes between EAT and paired SAT. (C, D) The ratio and difference of the top 100 TCR clonotypes in EAT and paired SAT. (E) Total TCR clonotypes sharing between paired EAT and SAT. (F) Total cardiac TCR clonotypes sharing in paired EAT and SAT. (G) Spearman’s correlation of cardiac TCR clonotypes with paired EAT and SAT. (H) Heat map of V-J usage between EAT, paired SAT and heart. (I) Spearman’s correlation of V-J combination (average usage>0.01) in EAT, SAT and heart. P < 0.05 and ns refers to no significance.* A relatively low proportion of shared TCR clonotypes was observed between EAT and paired SAT (Figure 5E). However, the degree of TCR clonotype sharing between cardiac tissue and EAT was higher than that between cardiac tissue and SAT (Figure 5F). Further, we found the Spearman’s correlation coefficients between frequencies of TCR clonotypes in cardiac tissue and paired EAT was higher compared to that of SAT (Figure 5G). Next, we examined the usages of TRBV-TRBJ fragments in EAT, SAT, and cardiac tissue (Figure 5H). For the frequency distribution of V-J fragments with an average frequency >$1\%$ in the heart (19 V-J fragments ranked in Figure S5), the correlation between cardiac tissue and EAT was greater than that between EAT and SAT while no obvious correlation was observed between cardiac tissue and SAT (Figure 5I). Thus, the above results suggested a similar antigenic microenvironment between the heart and adjacent EAT. ## Characteristics of T lymphocytes functional phenotypes in EAT from HF patients In order to verify the accumulation of T lymphocytes in EAT and the role of key genes, we collected EAT together with paired SAT and peripheral blood samples from HF patients undergoing heart transplantation. Immunohistomical staining showed abundant CD3-positive T lymphocytes in EAT compared to SAT (Figures 6A, B). Further flow cytometry showed that enriched T lymphocytes in EAT were mainly composed of TEM expressing high levels of IFN-γ (Figures 6C–F). Further immunofluorescence staining results confirmed that CCL5 and GZMK were co-localized with CD3-positive T lymphocytes (Figure 6G). Taken together, we concluded that EAT from HF patients were populated by inflammatory TEM cells expressing high levels of effector molecules including IFN-γ, CCL5, and GZMK and thus contributing to EAT pro-inflammatory conversion in HF patients. Figure 6: Verification of T cells infiltration and key molecules in EAT. (A, B) CD3-specific immunohistochemical staining in EAT and SAT. (C, D) Gating strategy and representative flow cytometry results of IFN-γ+ T lymphocytes in EAT. (E, F) Representative flow cytometry results for proportion of T lymphocytes memory subtypes in EAT. (G) Representative fluorescent staining images of CCL5 and GZMK with CD3 from EAT of HF patients (scale: 50 μm). P < 0.01, *P < 0.001 and ns refers to no significance. ## Discussion Previous understanding of the pro-inflammatory characteristics of EAT was limited to the paracrine and endocrine effects of adipokines and cytokines produced by EAT. The profiles of immune cells in EAT have rarely been elucidated. A pioneering work by Hirata et al. [ 10] suggested that macrophages in EAT from patients with coronary artery disease tend to be polarized towards the pro-inflammatory M1 phenotype. Recently, Vyas et al. [ 9] found that EAT was highly enriched in adaptive immune cells. Given the relatively simple cellular composition of adipose tissue, integrated bioinformatics analyses based on the high-throughput array or sequencing data could expand our knowledge of the roles and characteristics of immune cells in EAT. Based on our analyses, EAT was enriched in immune activation-related pathways and T lymphocytes compared to paired SAT and this trait was more pronounced in EAT from HF patients. Further, we used high-throughput TCR sequencing to explore the characteristics of TCR repertoires in EAT and found enrichment of highly expanded TCR clonotypes in EAT from HF patients. In addition, we found a higher degree of TCR clonotypes sharing between EAT and paired cardiac tissue from HF patients relative to SAT, suggesting a similar antigenic microenvironment between the heart and adjacent EAT. Furthermore, we demonstrated the dominance of pro-inflammatory IFN-γ+ effector memory T lymphocytes in EAT from HF patients. Considering our previous work has revealed a tissue-specific T-cell response predominated by clonally expanded Th1 and cytotoxic CD8+T lymphocytes in failing human hearts [29], the present work may provide further evidence of a similar immune microenvironment at the cellular level between EAT and heart. CCL5 and GZMK may be the key effector molecules of T lymphocytes in EAT. GZMK produced by cytotoxic T lymphocytes mediates cell death by displaying tryptase-like activity [30]. It has been reported that GZMK assists transcellular diapedesis of TEM by inducing the expression of ICAM1 in endothelial cells [31]. CCL5 belongs to the C-C motif chemokine family and binds to its receptor CCR5 [32]. CCL5 can be produced by a variety of cells including T lymphocytes, macrophages, fibroblasts, and epithelial cells, and regulates the migration of T lymphocytes and monocytes [32]. CCL5 expression was found to be higher in visceral adipose tissue (VAT) compared to SAT and positively correlated with CD3 and CD11b expression [33], while Zhou et al. [ 34] further identified CD8+ T lymphocytes as the major cellular sources of CCL5 in the VAT of obese mice. A recent study showed that clonally expanded GZMK+CD8+ T cells producing a high level of CCL5 may promote the recruitment of pro-inflammatory immune cells and elevate tissue inflammation [35]. Taken together, GZMK and CCL5 may act as key effectors in mediating the adaptive immune response of T lymphocytes in EAT of HF patients. Existing evidence suggest that increased EAT volume was associated with an increased risk of HF with preserved ejection fraction (HFpEF) [36]. However, EAT volume was reduced in HF patients with reduced ejection fraction (HFrEF) [37]. In HFpEF patients, increased EAT volume was associated with higher concentrations of troponin T, hs-CRP, IL-6, and increased risk of cardiovascular death and hospitalization, while these associations were reversed in HFrEF patients [6]. The reason for the discrepancy may be due to the increased intra-myocardial fat energy requirement in patients with HFrEF because of the progression to cachexia state [38]. The reduction of EAT may exacerbate the progression of HFrEF by diminishing the ability of the myocardium to nourish from adjacent EAT. Since the pro-inflammatory conversion of EAT often precedes the clinical diagnosis of HF [4], the specific causal relationship between EAT and different types or stages of HF remains unclear. To conclude, EAT of HF patients was characterized by pronounced immune activation, particularly by the accumulation of IFN-γ+ TEM and a generally higher degree of TCR clonotypes sharing with paired cardiac tissue. GZMK and CCL5 may act as the key effector molecules of T lymphocytes in EAT of HF patients. Our study has certain limitations. First, we used expression profiles from public datasets to infer immune cell infiltration scenarios of EAT, which may have discordance with actual situations. Second, the samples were obtained from end-stage HF patients and the sample size was small in the validation experiments. More detailed exploration of immune cell profiles in EAT from different stages of HF patients is deserved in the future. ## Conclusion EAT of HF patients was characterized by pronounced immune activation of clonally expanded IFN-γ+ TEM and a generally higher degree of TCR clonotypes sharing with paired cardiac tissue. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: PRJNA925305 (SRA). ## Ethics statement The studies involving human participants were reviewed and approved by Medical Ethics Committee of Wuhan Union Hospital of Huazhong University of Science and Technology. The patients/participants provided their written informed consent to participate in this study. ## Author contributions XC, X-ZZ, X-LC, and T-TT contributed to experiments design, data analyses, and manuscript writing. SZ and Q-LL contributed to reviewing the bioinformatics analyses. NX, S-FN, MZ, and Z-FZ contributed to reviewing the manuscript. Z-HZ contributed to reviewing and revising the manuscript. N-GD contributed to collecting clinical samples. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Risk factors for and outcomes of poststroke pneumonia in patients with acute ischemic stroke treated with mechanical thrombectomy authors: - Ping Zhang - Lei Chen - Yi Jiang - Hui Yuan - Xuan Zhu - Minmin Zhang - Tao Wu - Benqiang Deng - Pengfei Yang - Yongwei Zhang - Jianmin Liu journal: Frontiers in Neurology year: 2023 pmcid: PMC10027925 doi: 10.3389/fneur.2023.1023475 license: CC BY 4.0 --- # Risk factors for and outcomes of poststroke pneumonia in patients with acute ischemic stroke treated with mechanical thrombectomy ## Abstract ### Objective The purpose of the study was to assess the risk factors for poststroke pneumonia (PSP) and its association with the outcomes in patients with acute ischemic stroke (AIS) due to large artery occlusion treated with mechanical thrombectomy (MT). ### Methods Consecutive patients with AIS who underwent MT from January 2019 to December 2019 in the stroke center of Changhai Hospital were identified retrospectively. All of the patients were evaluated for the occurrence of PSP while in the hospital, and their modified Rankin scale (mRS) scores were assessed 90 days after having a stroke. Logistic regression analysis was conducted to determine the independent predictors of PSP, and the associations between PSP and clinical outcomes were analyzed. ### Results A total of 248 patients were enrolled, of whom $33.47\%$ [83] developed PSP. Logistic regression analysis revealed that body mass index (BMI) [unadjusted odds ratio (OR) 1.200, $95\%$ confidence interval (CI) 1.038–1.387; $$p \leq 0.014$$], systemic immune-inflammation index (SII) (OR 1.001, $95\%$ CI 1.000–1.002; $$p \leq 0.003$$), dysphagia (OR 9.498, $95\%$ CI 3.217–28.041; $p \leq 0.001$), and intubation after MT (OR 4.262, $95\%$ CI 1.166–15.581; $$p \leq 0.028$$) were independent risk factors for PSP. PSP was a strong predictor of clinical outcomes: it was associated with functional independence (mRS score ≤ 2) (OR 0.104, $95\%$ CI 0.041–0.260; $p \leq 0.001$) and mortality at 90 days (OR 3.010, $95\%$ CI 1.068–8.489; $$p \leq 0.037$$). ### Conclusion More than one in three patients with AIS treated with MT developed PSP. Dysphagia, intubation, higher BMI, and SII were associated with PSP in these patients. Patients with AIS who develop PSP are more likely to experience negative outcomes. The prevention and identification of PSP are necessary to reduce mortality and improve clinical outcomes. ## Introduction Mechanical thrombectomy (MT) has been proven to be effective for patients with acute ischemic stroke (AIS) due to large artery occlusion [1]. Although most of these patients achieve complete recanalization after MT, many patients with AIS die of complications [2]. The most common complication is pneumonia, which negatively affects clinical outcomes and increases the cost and duration of hospitalization [3]. The prediction of poststroke pneumonia (PSP) remains challenging. No single biomarker pattern predicting PSP or outcome has been identified [3]. The severity of stroke and dysphagia are considered risk factors for PSP [4]. However, there is still no conclusive evidence regarding the risks and effects of PSP after MT [5]. Whether thrombolysis before MT or anesthesia adds potential health risks for patients and increases PSP rates is still uncertain [6, 7]. Therefore, it is necessary to investigate the risk factors for PSP and its association with outcomes in patients with AIS treated with MT. The goal of this study was to evaluate the predictive factors of PSP in patients with AIS with MT and the association between PSP and clinical outcomes. ## Patient cohort In this retrospective study, patients who presented at the Department of Stroke Center at Changhai Hospital between January 2019 and December 2019 were included. The inclusion criteria were as follows: [1] diagnosis of acute ischemic stroke; [2] age ≥ 18 years; [3] large artery occlusion confirmed by computed tomographic angiography or digital subtraction angiography; [4] treatment with mechanical thrombectomy with or without intravenous alteplase according to the American Heart Association (AHA)–American Stroke Association (ASA) guidelines [1]; and [5] hospitalization in Changhai Hospital after MT. The exclusion criteria were as follows: [1] diagnosis with community-acquired pneumonia; [2] other infectious diseases or treatment with broad-spectrum antibiotics or corticosteroid therapy for the 2 weeks before MT; [3] cancer; and [4] loss to follow-up. Written informed consent was obtained from all the participants. The study was approved by the Shanghai ethical committee. ## Diagnosis of PSP Poststroke pneumonia was defined as a clinical diagnosis of pneumonia within 7 days after stroke onset during the hospital stay that did not fulfill the criteria for community-acquired pneumonia [3, 5], regardless of whether the patient was intubated. The clinical diagnosis of pneumonia was made based on the following findings: a new or progressive infiltrate, consolidation, or ground glass opacity revealed on chest computed tomography (CT) or radiography plus two or more of the following three criteria: [1] fever (>38°C) without another cause; [2] leukopenia (<4,000 leukocytes/mm3) or leukocytosis (>10,000 leukocytes/mm3); and [3] for patients older than 70 years old, at least two of the following: (a) a positive sputum culture; (b) new onset or worsening cough, or respiratory rate; (c) rales, crackles, or bronchial breath sounds; and (d) worsening gas exchange. ## Data collection and follow-up Patient demographics, medical histories, laboratory findings, and clinical characteristics were extracted from the clinical records. These data included age, sex, body mass index (BMI = weight/height2), pre-stroke modified Rankin Scale (mRS) score, comorbidity, smoking, and drinking status, National Institutes of Health Stroke Scale (NIHSS) score, Glasgow Coma Scale (GCS) score, and systemic immune-inflammation index (SII, platelet × neutrophil/lymphocyte ratio) on admission, stroke onset to revascularization time (ORT), location of the lesion, the occlusion site, Alberta Stroke Program Early Computed Tomography Score (ASPECTS), treatment with intravenous alteplase, general anesthesia (GA) in the MT (including using GA at the beginning or converting from sedation during the MT operation), the presence of dysphagia, intubation after MT, and the degree of vessel recanalization after MT. The mRS at 90 days was used to evaluate the functional outcome, and an mRS score of ≤2 was considered a good outcome. Modified thrombolysis in cerebral infarction (mTICI) was used to measure vessel recanalization, of which mTICI ≥2b was defined as successful recanalization. All intracranial hemorrhage and symptomatic intracranial hemorrhage were diagnosed according to the Heidelberg criteria [8]. Overall, two physicians blindly evaluated the imaging and procedural characteristics. If there was a disagreement, a third experienced physician made the final decision. The patients were followed up by outpatient clinics or telephone at 90 days after stroke (within a window of ±14 days). The mRS at 90 days was used to evaluate the functional outcome, and mRS ≤2 was considered a good outcome. Trained physicians conducted interviews blindly. Mortality in the hospital and mortality at 90 days were included as safety outcomes. ## Statistical analysis Statistical analysis was performed with SPSS Statistics version 25. Continuous variables with normal distribution were expressed as the means (SE), categorical variables were expressed as counts (%), and continuous variables not normally distributed were expressed as median (P25, P45) values. Differences between the groups were calculated using the t-test, the χ2-test, or the Mann–Whitney U-test as needed. Univariate analysis was used to identify the predictive parameters at a p-value of < 0.05. Variables with a p-value of < 0.05 in univariate analysis were included in the logistic regression analysis. In addition, $p \leq 0.05$ was considered statistically significant. ## Results There were 265 patients with AIS treated with MT between January 2019 and December 2019 in our stroke center. In total, six patients were excluded because they were diagnosed with community-acquired pneumonia on admission, eight patients were excluded because they were diagnosed with other infectious diseases or cancer, and three patients were lost to follow-up. A total of 248 patients were included in the study (Figure 1), and 83 ($33.47\%$) patients developed PSP. Of the 248 patients, the mean age was 67.19 ± 11.47 years, and 160 ($64.5\%$) patients were men. The median time from stroke onset until the clinical diagnosis of PSP (marked by the prescription of antibiotics) was 2 days. The comparison of the patients with or without PSP is shown in Table 1. The univariate analysis suggested that BMI, hypertension history, serum glucose, SII, NIHSS score, GCS score, ASPECTS, white blood cell (WBC) count on admission, stroke ORT, NIHSS score at 24 h, median volume of lesions with CBF <$30\%$ on CT, dysphagia, intubation after MT, the posterior circulation AIS, and the use of sedatives after MT were significantly different between the PSP and non-PSP groups. The MT-related factors, stroke ORT, the median volume of lesions with CBF <$30\%$ on CT, and NIHSS score at 24 h after MT were significantly different between the two groups. Figure 1: Enrollment flow diagram. AIS, acute ischemic stroke; MT, mechanical thrombectomy. TABLE_PLACEHOLDER:Table 1 Variables with $p \leq 0.05$ in univariate analysis were included in the logistic regression model. The independent predictors of PSP were BMI (OR 1.200, $95\%$ CI 1.038–1.387; $$p \leq 0.014$$), SII (OR 1.001, $95\%$ CI 1.000–1.002; $$p \leq 0.003$$), dysphagia (OR 9.498, $95\%$ CI 3.217–28.041; $p \leq 0.001$), and intubation after MT (OR 4.262, $95\%$ CI 1.166–15.581; $$p \leq 0.028$$). The details are shown in Table 2. Table 2 \| Parameter \| OR \| 95% CI of Exp (B) \| 95% CI of Exp (B).1 \| p \| \| --- \| --- \| --- \| --- \| --- \| \| BMI \| 1.2 \| 1.038 \| 1.387 \| 0.014 \| \| Hypertension \| 0.897 \| 0.321 \| 2.508 \| 0.835 \| \| GCS score on admission \| 1.165 \| 0.921 \| 1.474 \| 0.204 \| \| NIHSS score on admission \| 1.026 \| 0.963 \| 1.092 \| 0.428 \| \| Serum glucose on admission \| 0.947 \| 0.827 \| 1.085 \| 0.434 \| \| SII on admission \| 1.001 \| 1.0 \| 1.002 \| 0.003 \| \| ASPECTS on admission \| 0.965 \| 0.757 \| 1.229 \| 0.771 \| \| ORT \| 1.0 \| 0.998 \| 1.001 \| 0.574 \| \| Intubation \| 4.262 \| 1.166 \| 15.581 \| 0.028 \| \| NIHSS at 24 h \| 1.026 \| 0.963 \| 1.092 \| 0.428 \| \| Median volume of lesions with CBF <30% on CT \| 1.002 \| 0.988 \| 1.016 \| 0.763 \| \| WBC on admission \| 0.884 \| 0.763 \| 1.025 \| 0.102 \| \| Dysphagia \| 9.498 \| 3.217 \| 28.041 \| 0.0 \| \| Sedatives \| 1.708 \| 0.506 \| 5.759 \| 0.388 \| \| posterior circulation AIS \| 0.384 \| 0.099 \| 1.492 \| 0.167 \| The overall 90-day mortality of the cohort was $18.6\%$, and this rate was associated with the presence of PSP. Compared with the patients with non-PSP, the patients with PSP had a higher in-hospital mortality (18.07 vs. $9.09\%$; $$p \leq 0.061$$) and 90-day mortality (33.73 vs. $10.30\%$; $p \leq 0.001$) and had a lower functional independence rate (mRS score ≤2) (13.25 vs. $68.48\%$; $p \leq 0.001$) (Figures 2, 3). PSP was negatively associated with functional independence (mRS score ≤2) (OR 0.104, $95\%$ CI 0.041–0.260; $p \leq 0.001$) and positively associated with 90-day mortality (OR 3.010, $95\%$ CI 1.068–8.489; $$p \leq 0.037$$) (Table 3). Figure 2: Distribution of the modified Rankin scale scores between patients with and without poststroke pneumonia. Figure 3: Distribution of clinical outcomes in-hospital and at 90 days. TABLE_PLACEHOLDER:Table 3 ## Discussion The incidence of PSP after AIS varies from 5.4 to $44\%$ depending on the clinical setting and the definition based on the literature [9, 10]. In our study, $33.47\%$ of the patients with AIS treated with MT developed PSP, which was significantly higher than the ~ $12\%$ incidence of pneumonia in patients who have had AIS of any type [9]. Moreover, our study demonstrated that PSP was negatively associated with functional independence (mRS ≤2) and was positively associated with mortality rates in patients with AIS who received MT. Similar findings were also reported in many other studies [11]. Infection is already considered a determinant of outcomes after stroke [12]. Among all infections, pneumonia had the greatest impact on the outcome of patients with stroke. The proportion of deaths attributed to pneumonia occurring within the 1st week after stroke onset accounted for one-third of all deaths in patients with acute ischemic stroke. A nationwide 4-year study confirmed that the patients with hospital-acquired pneumonia (HAP) after the stroke had an elevated risk of death (OR, 1.2; $95\%$ CI, 1.1–1.3) [13]. Our research subjects had PSP, including not only HAP but also ventilator-associated pneumonia (VAP) after stroke. PSP significantly elevated 90-day mortality (OR 3.010; $95\%$ CI, 1.068–8.489) in our study. Therefore, identifying risk factors and the early identification of PSP are important for patients with AIS treated with MT. This study demonstrated that patients with higher BMI, higher SII on admission, dysphagia, and intubation after MT were more likely to develop PSP. As risk factors for PSP, dysphagia and intubation after MT have been frequently reported by various studies [8, 11]. This indicates that the predictive risk factors remain mostly identical in patients with AIS treated with MT. However, they suffer more severe stroke symptoms and have an overall higher risk of developing PSP. Intubation due to the severity of the stroke, not due to GA, is an independent risk factor for PSP, which indicates that the severity of the stroke itself is the key point for PSP. The NIHSS of patients with PSP was higher than that of patients with non-PSP ($p \leq 0.001$) but was not an independent factor for PSP according to logistic regression. This may be because other stroke factors affecting respiration directly, such as dysphagia and intubation, are more closely associated with PSP in patients with AIS with MT [5]. Another reason may be that the sample size in our study was also small. The effect of anesthesia on pneumonia has always been a controversial issue. Our study did not find that GA was an independent risk factor for PSP. Many previous studies and meta-analyses have confirmed that there is no difference in complications and outcomes after stroke between patients who underwent GA and those who underwent conscious sedation [7, 14, 15]. Interestingly, we found that BMI was also an independent predictor of PSP. One study reported that obesity was a predictor of an increased risk of in-hospital complications in patients with cerebral hemorrhage [16]. Another study reported that for patients treated with MT, a high BMI was independently associated with lower rates of functional independence among recanalized patients [17]. A meta-analysis also demonstrated an association between obesity and increased postoperative complications [18]. However, the relationship between obesity and stroke outcomes is still unclear. A post-hoc analysis of the MR CLEAN trial found that obesity was associated with better functional outcomes after a stroke in patients treated with MT [19]. The NIH FAST-MAG study reported that obesity was associated with increased survival but had a U-shaped or J-shaped relation to disability and stroke-related quality of life [20]. Although BMI was an independent factor affecting PSP, the BMI values did not differ between the patients with good and poor outcomes in our study. Higher quality evidence is needed to clarify the relationship between obesity and outcome in patients with stroke. The SII, which combines platelets, lymphocytes, and neutrophils to reflect thrombosis and inflammation, was also an independent predictor of PSP based on our study. The inflammatory mechanism after stroke plays a critical role in the development of AIS ischemic stroke [21]. Stroke induces the activation of the inflammatory cascade in both the CNS and PNS, which is called stroke-induced immunosuppression (SIIS). It occurs within hours of stroke onset and increases the host's susceptibility to poststroke infection. The inflammatory response of AIS is complex. The damage caused by neutrophils causes systemic inflammation and damages the blood–brain barrier. Platelets become excessively active and begin to accumulate. Inflammatory cytokines trigger lymphocyte apoptosis [22]. The infiltration of leukocytes and the release of various inflammatory mediators may also result in adverse outcomes. A high SII was an independent risk factor for poor outcomes at 3 months in patients with AIS [23, 24]. Ahmet Adiguzel et al. reported that the SII reached statistical significance in terms of discriminability for pneumonia development [25] based on daily measurement of the SII. Our study only recorded the SII on admission but still indicated an association with PSP. Therefore, the SII, which is a relatively integrated index that can be quickly calculated from blood, maybe a potential prognostic factor for PSP in clinical practice. Large-scale population data are needed to verify the reliability of SII in predicting PSP. The study had some limitations. First, as a retrospective single-center study, the sample size was small, which may lead to selection bias. Second, the degree of dysphagia and feeding actions were not analyzed. Third, only SII on admission was calculated. The correlation between the dynamic monitoring of SII and PSP was unclear. The associations between PSP and other inflammatory markers, such as C-reactive protein, were not discussed in this study. Finally, our study included all patients with pneumonia except the patients with community-acquired pneumonia and did not distinguish ventilator-associated pneumonia. ## Conclusion This study found that nearly one in three patients with stroke with AIS treated with MT developed PSP during acute care. Patients with AIS who developed PSP had worse outcomes than those without PSP. Dysphagia, intubation after MT, higher BMI, and SII on admission were all associated with PSP in these patients. The findings of the current study may help to prevent the development of PSP by identifying patients who are at risk. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Naval Medical University (M2019-010-2019-12-10). The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. ## Author contributions PZ and LC contributed to draft the manuscript. HY and YJ contributed to collect the data. XZ and MZ contributed to follow the patients. TW, BD, and JL contributed to polish the language. PY and YZ contributed to revise the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K. American Heart Association Stroke Council 2018. Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke (2018) 49 e46-110. 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--- title: 'The prognostic significance of human ovarian aging-related signature in breast cancer after surgery: A multicohort study' authors: - Xin Hua - Qi-Wei Zhu - Yi-Nuan Zhang - Lu Cao - Meng-Di Wang - Yun-Sheng Gao - Jia-Yi Chen journal: Frontiers in Immunology year: 2023 pmcid: PMC10027938 doi: 10.3389/fimmu.2023.1139797 license: CC BY 4.0 --- # The prognostic significance of human ovarian aging-related signature in breast cancer after surgery: A multicohort study ## Abstract ### Background Recent studies have shown that ovarian aging is strongly associated with the risk of breast cancer, however, its prognostic impact on breast cancer is not yet fully understood. In this study, we performed a multicohort genetic analysis to explore its prognostic value and biological features in breast cancer. ### Methods The gene expression and clinicopathological data of 3366 patients from the The Cancer Genome Atlas (TCGA) cohort, the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort and the GSE86166 cohort were analyzed. A total of 290 ovarian aging-related genes (OARGs) were included in the establishment of the prognostic model. Furthermore, functional mechanisms analysis, drug sensitivity, and immune cell infiltration were investigated using bioinformatic methods. ### Results An eight OARG-based signature was established and validated using independent cohorts. Two risk subgroups of patients with distinct survival outcomes were identified by the OARG-based signature. A nomogram with good predictive performance was developed by integrating the OARG risk score with clinicopathological factors. Moreover, the OARG-based signature was correlated with DNA damage repair, immune cell signaling pathways, and immunomodulatory functions. The patients in the low-risk subgroup were found to be sensitive to traditional chemotherapeutic, endocrine, and targeted agents (doxorubicin, tamoxifen, lapatinib, etc.) and some novel targeted drugs (sunitinib, pazopanib, etc.). Moreover, patients in the low-risk subgroup may be more susceptible to immune escape and therefore respond less effectively to immunotherapy. ### Conclusions In this study, we proposed a comprehensive analytical method for breast cancer assessment based on OARG expression patterns, which could precisely predict clinical outcomes and drug sensitivity of breast cancer patients. ## Introduction Breast cancer is a hormone-sensitive tumor and its development and progression are closely related to the host’s hormone levels [1, 2]. The decline in ovarian function, known as ovarian aging, results from a decrease in the quantity and quality of oocytes and is one of the key intrinsic determinants of hormonal changes [3]. Numerous studies have shown that ovarian aging is strongly associated with the risk of breast cancer, but its prognostic impact on breast cancer is not yet fully understood. Therefore, it is of great significance to explore the prognostic implications of ovarian aging and its potential as an alternative individual therapeutic target for breast cancer. Menarche and menopause mark the origin and end points in the process of ovarian ageing, as well as affect breast cancer risk. It has been well-documented that women who experienced menarche at an early age have an exponentially increased risk of developing breast cancer (4–7). Large cohort studies have also demonstrated that breast cancer incidence decreases with an earlier onset of menopause (8–10). Ovarian aging is a complex process with multi-linked genetic, etiological, or influencing factors and its molecular mechanisms remains largely unelucidated [3, 11]. Fortunately, a new study in Nature conducted a large-scale genome-wide association study of ovarian ageing and identifies 290 genetic determinants of ovarian aging [12]. Therefore, a comprehensive understanding of the relationship between the expression of the 290 ovarian aging-related genes (OARGs) and survival outcomes in breast cancer, would be important in determining the effects of ovarian aging in breast cancer. Herein, this study was conducted to evaluate the prognostic profiles of OARGs in breast cancer. A novel ovarian aging-based signature for evaluating breast cancer prognosis was developed and validated in multiple cohorts. Furthermore, the present study aimed to present the prognostic landscape of OARGs in breast cancer, and screen for survival-related OARGs as biomarker candidates and potential therapeutic targets. ## Data collection RNA-sequencing (HTSeq-fragments per kilobase per million [FPKM]), clinicopathological, and survival data were obtained from three individual large breast cancer cohorts, namely The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/repository, accessed in July 2022), The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (https://www.cbioportal.org/, accessed in July 2022) and the GSE86166 dataset from Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/, accessed in July 2022). Subjects who met the following criteria were included in the study: (a) had a histologically confirmed breast cancer without metastatic disease; (b) from post-surgery; (c) with available follow-up data of overall survival (OS), and an OS of not less than 30 days. The OS was defined as the time from the date of diagnosis to the date of death due to any cause or to the date of the last follow-up. A total of 290 OARGs were identified from the study of Ruth et al. ( Table S1) [12]. The overall workflow followed in this study was presented in Figure 1. Figure 1: The flow chart detailing the comprehensive analysis of ovarian aging patterns in postoperative breast cancer patients. ## Screening for prognostic genes The Kaplan-Meier and univariate Cox regression analyses, using OS as an outcome, were employed to estimate the predictive values of the 290 OARGs and screen for prognostic genes (with both $P \leq 0.05$) in the TCGA cohort. ## The prognostic pattern of ovarian aging in breast cancer Consensus cluster analysis was carried out based on the identified prognostic genes to classify patients into different groups by a non-negative matrix factorization (NMF) algorithm using the NMF package [13]. This was done to ensure maximum differences between the groups and minimum differences within the groups. The samples were clustered using the Brunet criterion. The K’s range was set at 2 to 10. According to cophenetic, dispersion, and silhouette, the ideal K was found. The prognostic pattern of ovarian aging in breast cancer The selected 22 prognostic OARGs were subjected to cluster analyses using the Brunet selection criterion for 50 iterations. The classification of clusters (K) was limited to 2-10. Three were chosen as the optimal cluster number based on the homogeneity, discreteness, and silhouette (Figures S2A, B). The results show that the OS ($P \leq 0.001$; Figure S2C) and DFS ($P \leq 0.001$; Figure S2E) of C2 were worse than those of C1 and C3. ## Development and validation of the prognostic OARG signature To further screen candidate genes for the prognostic model, the identified prognostic genes were subjected to LASSO Cox regression analysis to avoid potential co-linearity and simplify the number of independent variables [14]. Then, multivariate Cox regression analysis was performed to evaluate the prognostic contributions of the selected candidate genes from the LASSO Cox regression analysis (hazard ratio, HR, $95\%$ confidence interval, CI should not cross HR 1; $P \leq 0.05$), and establish the OARG risk score using the following formula: risk score = sum (each OARG normalized expression level × corresponding coefficients). Based on this, we calculated the OARG risk score for each patient and determined the optimal cut-off value for the OARG risk score according to maximally selected rank statistics method with OS for an outcome [15]. Thus, according to the cutoff value, we divided each patient into different risk-stratified groups: the patient would be assigned into high-risk group if the patient’s calculated OARG risk score was larger than the cutoff value; otherwise assigned into low-risk group. The survival differences between the two risk groups were compared using Kaplan-Meier analyses with a log-rank test. Furthermore, in the TCGA cohort, a nomogram was constructed, which incorporated the OARG risk score and additional prognostic clinicopathological characteristics identified from the multivariate Cox regression analysis. Calibration curves for the survival probability at one, three, and five years were also plotted to assess the prognostic precision of this nomogram. The same procedures and calculations were performed in the METABRIC and GSE86166 cohorts for validation. The selected 22 prognostic OARGs were also subjected to LASSO Cox regression analysis to avoid potential co-linearity and simplify the number of independent variables in the prognostic signature (Figures 2A, B). Subsequently, the LASSO Cox analysis yielded a total of 17 genes and therefore multivariate Cox regression analysis was performed to establish the prognostic OARG signature (Figure 2C). Finally, an 8-OARG risk signature was established in the TCGA cohort. The corresponding risk score of each patient was calculated using the following formula: risk score = HLA-B × (-0.24351) + RBBP8 × (-0.34470) + SPRY4 × 0.31174 + WT1 × 0.29836 + WWOX × 0.39556 + UPRT × 0.40719+ PELO × 0.43603+ ZNF208 × (-0.23972). The patients in the TCGA cohort were grouped into risk-stratified groups (high-risk group, $$n = 337$$; low-risk group, $$n = 680$$) based on the cut-off value of 4.49 which was determined using maximally selected rank statistics (Figure S2). The distributions of patient risk score and survival status, as well as each patient’s 8-OARGs expression levels, are summarized in Figures 3A, B, respectively. The Kaplan-Meier survival curves demonstrated that the high-risk group patients had significantly worse survival OS ($P \leq 0.001$; Figure 3C) and DFS ($P \leq 0.001$; Figure 3D) than the low-risk group patients. Moreover, the OARG risk signature remained significantly associated with OS (HR = 3.79, $95\%$ CI = 2.42-5.95, $P \leq 0.001$; Figure 3E) and DFS (HR = 2.20, $95\%$ CI = 1.28-3.76, $$P \leq 0.004$$; Figure 3F) after adjusting for other clinicopathological variables. Figure 2: Screening and identification of prognostic ovarian ageing-related genes (OARGs) in the TCGA cohort. (A) Selection of the optimal candidate genes in the LASSO model. (B) LASSO coefficients of prognosis-associated OARGs, each curve represents a gene. (C) Forest plots showing results of univariate Cox regression analysis between the candidate OARGs expression and overall survival. Figure 3: Estimate the prognostic value of ovarian ageing-related gene (OARG) signature model in TCGA cohort. (A) The distribution of risk scores in the TCGA and patient distribution in the high- and low-risk group according to overall survival (OS) status. (B) The heatmap showing expression profiles of the 8 OARGs. (C) Kaplan-Meier curves for the OS of patients in the high- and low-risk groups. (D) Kaplan-Meier curves for the diseases-free survival (DFS) of patients in the high- and low-risk groups. (E) Multivariate Cox regression analysis of OS. (F) Multivariate Cox regression analysis of DFS. Using the same formula and the cut-off value from the TCGA cohort, the risk scores and risk-stratified groupings weredetermined for patients in the METABRIC and GSE86166 cohorts for validation (Figures S3, S4). Consistently, the Kaplan-Meier survival curves also showed that the high-risk group patients had significantly worse OS ($P \leq 0.001$; Figure S3C) and RFS ($P \leq 0.001$; Figure S3D) in the METABRIC cohort, and worse OS ($$P \leq 0.016$$; Figure S4C) and RFS ($$P \leq 0.022$$; Figure S4D) in the GSE86166 cohort, respectively. Furthermore, after adjusting for other clinicopathological variables, the OARG risk signature remained associated with OS (HR = 1.35, $95\%$ CI = 1.14-1.60, $P \leq 0.001$; Figure S3E) and RFS (HR = 1.22, $95\%$ CI = 1.00-1.49, $$P \leq 0.050$$; Figure S3F) in the METABRIC cohort and OS (HR = 1.94, $95\%$ CI = 1.05-3.60, $$P \leq 0.035$$; Figure S4E) and RFS (HR = 1.86, $95\%$ CI = 0.91-3.82, $$P \leq 0.090$$; Figure S4F) in the GSE86166 cohort, respectively. ## Functional enrichment analysis of the OARG signature Gene Set Variation Analysis (GSVA) using the “GSVA” package and Gene Set Enrichment Analysis (GSEA, https://www.gsea-msigdb.org/gsea/index.jsp) were conducted to determine the pathway and biological function differences between the two risk groups [16, 17]. We used the c2.cp.kegg.v7.4.symbols.gmt in the Molecular Signatures Database (MSigDB) for board hallmarkers [17]. Gene sets with normal $P \leq 0.05$ and false discovery rate < 0.10 were considered to be significantly enriched. Gene ontology (GO) enrichment analysis was performed using Metascape (https://metascape.org/gp/index.html#/main/step1) and plotted using the “ClusterProfiler” and “Cytoscape” package. ## Identification of potential target drugs for high-risk group patients The “pRRophetic” package, which was developed upon statistical models calculated from huge collections of cancer cell lines gene expression and drug sensitivity data [18], was used to predict the drug sensitivity of the two risk groups. The half maximal inhibitory concentrations (IC50) of potential target drugs were compared between the two risk groups. ## Estimation of the immune cell infiltration landscape The “GSVA” package with single-sample GSEA (ssGSEA) was used to evaluate the infiltration scores of immune cell types and immune-related pathways between the two risk groups. In addition, the variations in the compositions of immune cell types between the two risk groups were evaluated using the CIBERSORT method [19]. Then, the differences in the reported famous six immune subtypes of wound healing (Immune C1), IFN-γ dominant (Immune C2), inflammatory (Immune C3), lymphocyte depleted (Immune C4), immunologically quiet (Immune C5), and TGF-β dominant (Immune C6) subtypes [20] were compared between the two groups. We also estimated the immunogenicity and immunome infiltration characteristics of breast cancer using the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) and Tumor Immune Dysfunction and Exclusion (TIDE) approaches [21, 22], and further investigated how well the risk signature performed in predicting the effects of immunotherapy. More specifically, a higher TIDE score means a higher likelihood of immune escape and a lower likelihood that the patient will benefit from immunotherapy. ## Statistical analysis Continuous data were reported as medians with interquartile ranges (IQR), while categorical data were reported as frequencies with percentages, and compared using the Mann-Whitney U test, chi-square test, continuity corrected chi-square test, or Fisher’s exact test, whichever is appropriate. Disease-free survival (DFS) was defined as the time from the date of diagnosis to the date of recurrence/metastasis or to the date of death due to any cause or to the last follow-up. Meanwhile, recurrence-free survival (RFS) was defined as the time from the date of diagnosis to the date of recurrence or to the date of death due to any cause or to the last follow-up. The survival outcomes were estimated using the Kaplan-Meier method and compared by the log-rank test. The Cox proportional hazards model was performed to calculate the adjusted HRs and corresponding $95\%$ confidence intervals (CIs). All statistical analyses were conducted with R version 4.1.2 (http://www.r-project.org). Statistical significance was set at two‐sided $P \leq 0.05.$ ## Screening for prognostic OARGs A total of 1096 subjects from the TCGA cohort, 1904 subjects from the METABRIC cohort, and 366 subjects from the GSE86166 cohort were included in this study. After filtering out subjects who did not meet our selection criteria, a total of 3267 subjects were enrolled in the final analysis, including 1017 subjects in the TCGA cohort for training, as well as1888 subjects in the METABRIC cohort and 362 subjects in the GSE86166 cohort for validation. The Kaplan-Meier and univariate Cox regression analyses, using OS as an outcome, were conducted to screen for prognostic genes among the 290 OARGs. In total, the expression of 22 genes was found to be significantly related to OS, with 11 genes having a negative association and 11 genes with a positive association (Figure S1). ## Establishment of a prognostic nomogram based on the OARG signature A risk score-based visualized nomogram, which integrates the risk signature and three important clinicopathological factors (age, stage and subtype) selected from the multivariate Cox regression analysis, was established to individually quantify and assess the OS probability at 1-, 3- and 5-years of breast cancer patients in TCGA cohort (Figure 4A). We conducted a bootstrap validation and calculated the nomogram’s C-index to be 0.812 ($95\%$ CI: 0.768-0.856) in the TCGA cohort and 0.757 ($95\%$ CI: 0.734-0.779) in the METABRIC cohort, respectively. To evaluate the predictive efficacy and clinical application of the nomogram, calibration curves were plotted for both the TCGA cohort (Figure 4B) and the METABRIC cohort (Figure 4C). The calibration curves demonstrated satisfactory consistency among the actual and anticipated OS probabilities at 1-, 3- and 5-years. Figure 4: Development of a nomogram based on ovarian ageing-related genes (OARGs) signature for predicting overall survival (OS) of patients with breast cancer. (A) The nomogram plot integrating OARG risk score, age, stage and subtype in the TCGA training cohort. (B) The calibration plot for the probability of 1-, 3-, and 5-year OS in the TCGA training cohort. (C) The calibration plot for the probability of 1-, 3-, and 5-year OS in the METABRIC validation cohort. ## Gene set variation analysis of OARG signature We performed GSVA to determine the potential biological functions of the OARG signature in breast cancer. In the training cohort of TCGA, the pathway sets DNA sensing, primary immunodeficiency, and nutrients metabolism were found to be activated in the high-risk group (Figure S5A). Meanwhile, the pathway sets with the immune network, autoimmune system, and immune disease were activated in the low-risk group (Figure S6D). GO enrichment analysis confirmed that the immune-related biological processes were enriched in the low-risk group (Figure S6A). These results were further validated in the METABRIC (Figures S5B, S6B, E) and GSE86166 (Figures S5C, S6C, F) cohorts and similar functional results were found. These results support the comprehensive DNA repair and immunomodulatory function effects of the OARG signature in the development and progression of breast cancer. ## Clinical implications of the OARG signature in predicting therapeutic effects The potential intrinsic connections between the OARG signature and therapeutic effects of chemotherapeutic, endocrine, and targeted agents were further explored. In the training cohort of TCGA, the low-risk group had a lower IC50 for chemotherapeutics such as doxorubicin, etoposide, gemcitabine, paclitaxel, vinorelbine and 5-fluorouracil, indicating the predictive potential of the model for chemosensitivity (Figures 5A–F). For the endocrine and targeted drugs, the low-risk patients had a lower IC50 for tamoxifen and fulvestrant (Figures 5G, H), as well as for lapatinib, sunitinib, dasatinib, crizotinib, pazopanib, and ruxolitinib (Figures 5I–N). Most of the results were validated in the METABRIC (except for crizotinib; Figure S7) and the GSE86166 (except for vinorelbine, crizotinib, and ruxolitinib; Figure S8) cohorts. The better prognosis for the low-risk group could be partially explained by these findings. These findings also imply that the low-risk group would benefit more from therapy with traditional and novel drugs. Figure 5: Analysis of the association between the risk model and chemotherapeutics, endocrine therapy, and targeted therapy. (A–F) The model predicting the sensitivity to chemosensitivity. It was estimated that low-risk patients had lower IC50 for chemotherapeutics of doxorubicin, etoposide, gemcitabine, paclitaxel, vinorelbine and 5-fluorouracil. (G, H) The model predicting the sensitivity to endocrine therapy. It was estimated that low-risk patients had lower IC50 of tamoxifen and fulvestrant. (I–N) The model predicting the sensitivity to targeted therapy. It was estimated that low-risk patients had lower IC50 of lapatinib, sunitinib, dasatinib, crizotinib, pazopanib and ruxolitinib. ## Immunocyte infiltration profiling of the OARG signature in breast cancer The profiling of immune infiltration was performed using the ssGSEA and CIBERSORT methods, and the outcomes showed noticeably different immune infiltration landscapes between the two risk categories. Specifically, functions such as APC_co_inhibition, APC_co_stimulation, CCR, Check-point, Cytolytic_activity, HLA, Inflammation-promoting, MHC_class_I, Parainflammation, T_cell_co-inhibition, T_cell_co-stimulation and Type_I_IFN_Reponse were elevated in the low-risk group patients (Figure 6A). Moreover, the patients in the low-risk group exhibited a higher percentage of B cells naive, Macrophages M0 and Macrophages M2. In contrast, the percentages of B cells memory, T cells CD8, T cells CD4 memory activated, T cells follicular helper, NK cells activated, Monocytes, Macrophages M1, Dendritic cells resting and Dendritic cells activated were all higher in high-risk group individuals (Figure 6B). In addition, the high-risk group had significantly lower immune and ESTIMATE scores than the low-risk group (Figure 6C). There was no immune C5 subtype in our cohort and the risk scores between the immune subtypes significantly differed. The immune C4 subtype had the highest risk score and the immune C2 subtype had the lowest risk score (Figure 6D). In contrast, the low-risk group presented with higher TIDE scores indicating that the low-risk group patients may be more susceptible to immune escape (Figure 6E). The patients responding to immunotherapy also had higher risk scores than those non-responding to immunotherapy (Figure 6F). We also discovered that the proportion of patients responding to immunotherapy in the high-risk group was higher than that in the low-risk group ($33.5\%$ vs $18.5\%$, $P \leq 0.001$, Figure 6G). Overall, these findings showed that the immune infiltration profiles in breast cancer are linked with the risk stratification based on the OARG signature, and the immunotherapy effects could be also predicted. Figure 6: The landscape of immune function and immune cell infiltration between the high- and low-risk group in the TCGA cohort. Red represents high-risk samples; blue represents low-risk samples. P < 0.05, P < 0.01, P < 0.001. (A) Barplot showing differences of immune functions between the low- and the high-risk group. (B) Violin plot showing differences of infiltrating immune cell types between the low- and the high-risk group. (C) Comparison of tumor microenvironment scores calculated by ESTIMATE between the low- and the high-risk group. (D) Comparison of risk scores between different immune subgroups. (E) Comparison of tumor microenvironment scores calculated by TIDE between the low- and the high-risk group. (F) Comparison of risk scores between different responder subgroups. (G) Comparison of the immunotherapy responding proportion between the low- and the high-risk group. ## Discussion The current multicohort genetic association research provided a bioinformatics-based analysis model, which incorporated clinical information collection, transcriptome profiling, survival analysis, functional evaluation, and immune infiltration estimation to interpret the possible molecular mechanisms of ovarian aging and its implication in breast cancer. Moreover, this analysis model proposes a comprehensive perspective to explore the ovarian aging microenvironment in breast cancer and could reveal the potential outcomes and mechanisms related to the prognostic OARG signature. Ovarian aging, involves complex genetic variants regulation and elaborate biological mechanisms. It is linked to several unfavorable consequences of hormone-sensitive cancers [23, 24]. In recent years, increasing evidence suggests that ovarian aging is crucial in the female reproductive longevity biological processes, which have been demonstrated to be associated with the tumorigenesis and development of endocrine tumors (25–29). This study developed a signature featuring 8 OARGs (HLA-B, RBBP8, SPRY4, WT1, WWOX, UPRT, PELO, ZNF208) and determined its prognostic and functional implications in breast cancer patients. HLA-B has been previously demonstrated to have significant immunogenic involvement in breast cancer by supporting multiple downstream immunogenic pathways [30, 31]. Our research showed that a better prognosis was related to a relatively higher expression of HLA-B. On the other hand, RBBP8 functions as a tumor suppressor protein in breast cancer by interacting with some distinct tumor-suppressing factors, including BRCA1 and retinoblastoma [32, 33]. Our findings also suggest that RBBP8 served as a protective factor for breast cancer. An in vivo research revealed that SPRY4 may influence the characteristics of cancer stem cells, as well as tumor cell migration and proliferation [34]. Numerous studies have demonstrated that WT1 plays an oncogenic role in various solid cancers including breast cancer, by promoting epithelial-to-mesenchymal transition and lowering chemotherapy efficacy [35, 36]. Although previous studies found that WWOX expression was reduced in various cancers, our study has shown that it may be a risk factor affecting the prognosis of breast cancer [37]. Moreover, the current study found that the overexpression of UPRT was associated with a worse prognosis in breast cancer and is closely related to cancer gene-therapy efficacy [38]. PELO is a new HER-signaling regulator and was suggested to play a role in inhibiting tumor cell proliferation and metastasis [39, 40]. ZNF208 is a member of the zinc finger family of proteins and its mutations were found in many cancers, such as pancreatic cancer, gastric cancer, esophageal cancer and laryngeal cancer (41–43). We discovered its prognostic significance for breast cancer in our investigation. The functional analysis results support the comprehensive DNA damage repair and immunomodulatory functions of the OARG signature in the development and progression of breast cancer. DNA damage repair mechanisms can trigger an innate immune response, resulting in a reduction in cell proliferation and the production of interferon, which is a crucial mechanism for promoting immune regulation (44–46). The tumor microenvironment enables tumor cells to avoid immune monitoring and medication interference, which permits them to survive [47]. Previous studies have found that numerous pathways and genes associated with DNA damage repair networks play a role in genetic instability and immune activity (46, 48–50). Our results revealed that patients in the low-risk group exhibited a higher percentage of B cells naive, Macrophages M0 and Macrophages M2. Macrophages M0 have been polarized into M1-like and M2-like subtypes, both of these two macrophages are strongly linked to inflammatory reactions. Specifically, M1-like macrophages are primarily involved in pro-inflammatory reactions, while M2-like macrophages primarily participate in anti-inflammatory reactions [51]. Ovarian aging activity is typically connected to the trigger of the anti-inflammatory signal, which is consistent with our results. Many studies have revealed that a better outcome is associated with the abundance of M1-like macrophages, while a worse outcome is suggested by the predominance of M2-like macrophages in breast cancer [52, 53]. Therefore, the increased enrichment of M2-like macrophages that occurs with ovarian aging may be a possible explanation for the poor prognosis and may serve as a novel prognostic biomarker for breast cancer. Additionally, patients in the low-risk group had lower IC50 values for chemotherapeutic agents (doxorubicin, etoposide, gemcitabine, paclitaxel, vinorelbine, and 5-fluorouracil), endocrine agents (tamoxifen and fulvestrant), and targeted agent (lapatinib), which may have contributed to their better prognosis, since they were more responsive to systemic therapeutic drugs. Moreover, patients in the low-risk group have a higher sensitivity to sunitinib, pazopanib, ruxolitinib and crizotinib, which are currently being tested in ongoing clinical trials and may be potential targets for breast cancer therapy. Although the present study shows that the OARG signature has an excellent performance in multicohort of breast cancer datasets, the study also has some limitations. Firstly, the participants were retrospectively enrolled, which may inevitably introduce bias to some extent. Secondly, the functional results of OARG genes from our bioinformatics analyses have not yet been confirmed in in vitro and in vivo experimental studies. Thirdly, we recognize that it is essential for well-designed clinical trials to investigate the prognostic significance of this model and its therapeutic implications in selecting novel drugs for breast cancer. In conclusion, the current multicohort genetic association research comprehensively explored the OARGs in breast cancer based on their biological functions, linked pathways, regulatory immune infiltration, efficacy levels, and clinical implications. The survival-related OARG signature proposed in the current study has the potential to distinguish prognosis and may be clinically applied as useful biomarker and candidate targets in breast cancer. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author. ## Author contributions XH did the literature search. XH designed the study. XH, Q-WZ, Y-NZ, LC, M-DW, Y-SG, and J-YC participated in the analysis and interpretation of data. XH and J-YC developed an early draft. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1139797/full#supplementary-material ## References 1. Loibl S, Poortmans P, Morrow M, Denkert C, Curigliano G. Breast cancer. Lancet (2021) 397. DOI: 10.1016/S0140-6736(20)32381-3 2. 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--- title: Aldehyde dehydrogenase 2 polymorphism is associated with chemotherapy‐related cognitive impairment in patients with breast cancer who receive chemotherapy authors: - Senbang Yao - Wen Li - Shaochun Liu - Yinlian Cai - Qianqian Zhang - Lingxue Tang - Sheng Yu - Yanyan Jing - Xiangxiang Yin - Huaidong Cheng journal: Cancer Medicine year: 2022 pmcid: PMC10028021 doi: 10.1002/cam4.5319 license: CC BY 4.0 --- # Aldehyde dehydrogenase 2 polymorphism is associated with chemotherapy‐related cognitive impairment in patients with breast cancer who receive chemotherapy ## Abstract It may be possible to infer the risk of CRCI by detecting the single‐nucleotide locus of ALDH2 in patients with breast cancer who receive chemotherapy, which is conducive to strengthening clinical interventions for these patients and improving their QOL. ### Background Chemotherapy‐related cognitive impairment (CRCI) is a common but easily overlooked condition that markedly affects the quality of life (QOL) of patients with breast cancer. The rs671 is a common gene polymorphism of aldehyde dehydrogenase 2 (ALDH2) in Asia that is involved in aldehyde metabolism and may be closely related to CRCI. However, no study has yet summarised the association between ALDH2 and CRCI. ### Methods This study enrolled one hundred and twenty‐four patients diagnosed with breast cancer according to the pathology results, genotyped for ALDH2 single‐nucleotide polymorphisms (SNP) to explore these. The mini‐mental state exam (MMSE), verbal fluency test (VFT), and digit span test (DST) results were compared in these patients before and after chemotherapy (CT). ### Results We found that patients with ALDH2 gene genotypes of rs671_GG, rs886205_GG, rs4648328_CC, and rs4767944_TT polymorphisms were more likely to suffer from cognitive impairment during chemotherapy. A trend toward statistical significance was observed for rs671_GG of DST ($z = 2.769$, $$p \leq 0.006$$), VFT ($t = 4.624$, $P \leq 0.001$); rs886205_GG of DST ($z = 3.663$, $P \leq 0.001$); rs4648328_CC of DST ($z = 2.850$, $$p \leq 0.004$$), VFT ($t = 3.477$, $$p \leq 0.001$$); and rs4767944_TT of DST ($z = 2.967$, $$p \leq 0.003$$), VFT ($t = 2.776$, $$p \leq 0.008$$). The cognitive indicators of these patients significantly decreased after chemotherapy ($p \leq 0.05$). The difference in ALDH2 rs671 was most obvious. ### Conclusion Our results showed what kinds of ALDH2 genotyped patients that are more likely to develop CRCI. In the future, it may be possible to infer the risk of CRCI by detecting the single‐nucleotide locus of ALDH2 that is conducive to strengthening clinical interventions for these patients and improving their QOL. More importantly, this study has important implications for Asian women with breast cancer as ALDH2 rs671 is a common polymorphism in Asians. ## INTRODUCTION Breast cancer is the most common type of malignant tumour in women worldwide, 1 representing nearly $25\%$ of all cases 2 and accounting for $6.5\%$ of mortalities globally. 3 In China, the incidence of breast cancer is $7.33\%$, which ranks first among malignant tumours in females, with a death rate of approximately $6.9\%$. 4, 5, 6 Patients with breast cancer often need multiple chemotherapy treatments. 7, 8 The physical health of patients with breast cancer is severely affected by the somatic side effects of chemotherapy. 9 However, a cognitive decline caused by chemotherapy also affects these patients' quality of life (QOL). Chemotherapy‐related cognitive impairment (CRCI) is used to define chemotherapy‐induced cognitive impairment in patients with cancer. 10 Currently, many countries and scientific research institutions are committed to exploring the neurobiological mechanisms of CRCI. 11, 12 Further study is required on how to predict which patients' cognitive function is more likely to be affected by chemotherapy. Functional MRI is effective for exploring patient cognition; however, it is expensive and not widely available. Studies have found that the effects of chemotherapy on cognition may be due to the accumulation of metabolites such as aldehyde, tau protein, 13 conjugated dienes, 14 hydroperoxides, 15, 16 and so on. Among these, the aldehyde is more important in inducing oxidative stress and neuroinflammation. Because oxidative stress is one important mechanism for CRCI, 17 the normal metabolism of aldehyde can reduce neuroinflammation responses and maintain normal cognitive function. The protein encoded by the ALDH2 (aldehyde dehydrogenase 2) gene is a key enzyme in toxic aldehyde metabolism that converts aldehyde into acid. 18 Therefore, ALDH2 may be involved in influencing the extent of cognitive impairment in patients with breast cancer who receive chemotherapy. The ALDH2 single‐nucleotide polymorphisms (SNP) in rs671 is significant in Asian populations as $70\%$ of these patients possess ALDH2 wild‐type (GG, rs671), while $30\%$ have ALDH2 variants (G/A or A/A, rs671). Those with ALDH2 variants have greatly reduced ability to metabolise aldehydes and are more susceptible to cognitive impairment. 19, 20 Therfore, ALDH2 has received extensive attention in cancer and cardiovascular disease. 21, 22 The cognitive regulatory function of ALDH2 in Alzheimer's disease (AD), 23 Parkinson's disease, 24 and diabetes‐associated cognitive impairments 25 has received extensive attention. Yu et al. found that the ALDH2 rs671 genotype was correlated with cognitive status in patients with Parkinson's disease. 24 Ying et al. found that ALDH2 upregulation can significantly improve cognitive function in AD mice. 23 A Stanford University study found that low ALDH2 activity could contribute to increased cytotoxicity by producing more reactive oxygen species (ROS) during chemotherapy, 26 and ROS is an important factor affecting cognitive function. 27 Therefore, it is worth exploring whether ALDH2 plays a similar role in CRCI. Previous studies on the genetic risk of CRCI have focused on the catechol‐O‐methyltransferase (COMT) gene. The COMT (rs165599) polymorphism has been reported to contribute to CRCI in patients with breast cancer. 28 Recent studies have also found that three SNPs in genes related to cognitive function (APOE rs429358, ANKK1 rs1800497, and TOMM40 rs10119) are closely associated with CRCI. 29 As ALDH2 rs671 is an important site that determines its functional activity, it is of great significance to explore the activity of its related sites. The current study confirmed that ALDH2's role in cognitive function is important. The ALDH2 genotype may be associated with cognitive function after chemotherapy. Currently, no studies have explored this topic. This study was conducted to determine whether ALDH2 genotyping can identify which groups are more likely to be affected by cognitive function during chemotherapy. Positive results will have important implications for improving their QOL. ## Patients This study enrolled one hundred and twenty‐four patients diagnosed with breast cancer according to pathology results. The histopathological type of breast cancer was determined on the basis of the WHO classification criteria of breast tumours. The tumour specimens were histologically graded depending on the Nottingham grading system. IHC staining was performed in all the cases for ER, PR, HER‐2, and Ki‐67 biomarkers for molecular pathology examination. The corresponding reference guide containing the WHO classification of breast tumours 30 are the AJCC TNM staging system for breast cancer, 31 the NCCN Guidelines for Breast Cancer, 32 and St. Gallen International Expert Consensus. 33 The patient cohort consisted of 124 females with an average age of 49.5 ± 10.50 years (mean and standard deviation). The baseline clinical and demographic characteristics of enrolled patients are presented in Table 1. The patients were recruited at the Second Affiliated Hospital of Anhui Medical University between September 2017 and August 2021. The exclusion criteria were as follows:[1] received radiotherapy and other treatments in the past; [2] had brain metastasis or cachexia; [3] suffered from bipolar disorders such as depression and anxiety; and [4] had impaired cognition as a result of other diseases, such as Alzheimer's disease, vascular dementia, and cerebral trauma. All patients provided informed consent before participating in this study. TABLE 1 \| Unnamed: 0 \| N/Mean ± SD \| Percentage \| \| --- \| --- \| --- \| \| Total number of patients \| 124 \| 100% \| \| Age (years) \| 49.5 ± 10.50 \| / \| \| Education (years) \| 10.3 ± 3.86 \| / \| \| Molecular classification \| \| \| \| Luminal A \| 23 \| 18.55% \| \| Luminal B \| 47 \| 37.90% \| \| HER‐2 overexpression \| 34 \| 27.42% \| \| TNBC \| 20 \| 16.13% \| \| Pathological type \| \| \| \| Invasive Carcinoma No Special Type \| 115 \| 92.74% \| \| Invasive Carcinoma Special Type \| 1 \| 0.81% \| \| Noninvasive carcinoma \| 7 \| 5.65% \| \| Other type \| 1 \| 0.81% \| \| KPS \| \| \| \| 100 \| 2 \| 1.61% \| \| 90 \| 71 \| 57.26% \| \| 80 \| 33 \| 26.61% \| \| 70 \| 18 \| 14.52% \| ## Genotyping The collected peripheral venous blood (3–5 ml) was stored in an ethylenediamine tetraacetic acid dipotassium salt (EDTA‐K2) tube at −80°C. A genomic QIAGENE kit (Cat#69504, Shanghai Genesky Bio‐Tech Co, Ltd) was used to extract genomic DNA from the blood. The DNA samples were kept at −20°C. An improved multiplex ligase detection reaction (iMLDR) was used for genotyping. Different fluorescently labelled allele‐specific oligonucleotide probe pairs were used to distinguish the alleles of each SNP. Different SNPs were recognised by distinct extended lengths at the 3'end. Two negative controls were used; one was a double‐distilled water template and the other was a DNA sample without primers, with other conditions remaining the same. Repeated tests were conducted, and the results were consistent. Finally, $5\%$ of the total DNA samples were randomly selected and repeated for iMLDR genotyping using an ABI3730XL automated sequencer (Applied Biosystems), and the results were consistent to confirm the iMLDR results. The sequencing results were uploaded as supplementary raw data which can be opened with GeneMapper 4.1 (AppliedBiosystems, USA) software. The picture of the iMLDR SNP genotyping schematic diagram was uploaded in the manuscript as Figure S1. The picture of iMLDR (Sample 110) was uploaded in the manuscript as Figure S2, and other samples can also be opened with GeneMapper. ## Cognitive screening test The mini‐mental state exam (MMSE) was used to assess cognizant ability, including spatial and time recognition, calculation, short‐term memory, language, and visual–spatial abilities. It is mainly used to quickly check whether the subject has intellectual disabilities such as mental retardation and dementia. MMSE is one of the most influential cognitive screening tools. It has been widely used in families and communities because of its simple and convenient characteristics. ## Neuropsychological tests Programmatic neuropsychological tests were performed after starting chemotherapy and after six cycles of postoperative adjuvant chemotherapy to evaluate memory and cognitive function. The verbal fluency test (VFT) was used, in which patients were required to say as many animal names as possible within 60 s. The digit span test (DST) was used to evaluate short‐term memory, in which patients were asked to repeat a random sequence of numbers. The number of digits repeated in the correct order determined the score. ## The expression of ALDH2 in brain The Human Protein Atlas (HPA) has been used to explore the expression of ALDH2 in the brain. 34 HPA was launched in 2003 as a project initiated by the Swedish scientific research sector to explore and summarise the expression of all human proteins in tissues and organs using ‐omics techniques (https://www.proteinatlas.org). The expression of ALDH2 in the brain was explored using the BRAIN section of the HPA. ALDH2 was also analysed in hippocampal formation and amygdala. ## Statistical analysis The proportions of qualitative variables were calculated. Means and standard deviations (SD) of quantitative variables were calculated. The data were tested for normality. The paired t‐test and paired Wilcoxon signed‐rank test were performed for normal and non‐normal distributions in continuous variable data, respectively. The independent‐samples t‐test and Mann–Whitney U‐test were performed for normal and non‐normal distribution in continuous variable data, respectively. Differences were considered statistically significant when the probability value was <0.05. The mean and SD of cognitive outcomes from similar studies and our previous pre‐experimental tests were used for sample size estimation. 24, 35, 36, 37, 38, 39, 40, 41 With the test level of 0.05 on both sides and $80\%$ power (1–β), the sample size was estimated using PASS (Power Analysis and Sample Size Software, version 15). GraphPad Prism 5.0, R language, and SPSS software (version 21.0) were used for statistical analysis and visualisation. ## Process and enrolled patient profiles of the research A flow diagram of our research strategy and the analytical method is shown in Figure 1. The baseline clinical and demographic characteristics of the 124 enrolled patients are presented in Table 1. General information about the four genotyped SNPs loci of the ALDH2 gene is shown in Table 2. FIGURE 1: Technical process of this study TABLE_PLACEHOLDER:TABLE 2 ## Cognitive abilities of ALDH2_rs671 patients before and after chemotherapy The comparison results of cognitive function before and after chemotherapy for different SNPs in ALDH2_rs671 are shown in Figure 2 and Table 3. No significant difference was noted in cognitive function between the two groups before chemotherapy. No significant difference was found in cognitive function between the BC and AC groups in rs671(AG/AA) patients. For rs671(GG) patients, the DST and VFT scores after chemotherapy were significantly reduced, suggesting a decline in cognitive ability. FIGURE 2: Comparison of cognitive abilities of patients before and after chemotherapy. (A) Score of MMSE of those with ALDH2_rs671_GG. (B) Score of DST of those with ALDH2_rs671_GG. (C) Score of VFT of those with ALDH2_rs671_GG. (D) Score of MMSE of those with ALDH2_rs886205_GG. (E) Score of DST of those with ALDH2_rs886205_GG. (F) Score of VFT of those with ALDH2_rs886205_GG. BC means before chemotherapy, AC means after chemotherapy, $p \leq 0.05$, $p \leq 0.01$, *$p \leq 0.0001$, and $p \leq 0.05$ were considered significant TABLE_PLACEHOLDER:TABLE 3 ## Cognitive abilities of ALDH2_rs886205 patients before and after chemotherapy The comparison results of cognitive function before and after chemotherapy for different SNPs in ALDH2_rs886205 are shown in Figure 2 and Table 4. Before chemotherapy, the difference between the two groups was statistically significant only for the DST score. A statistically significant difference was found in the VFT scores between the BC and AC groups in rs886205 (AG/AA) patients. For rs886205 (GG) patients, the MMSE, DST, and VFT scores after chemotherapy were significantly reduced, suggesting a decline in cognitive ability. TABLE 4 \| rs886205 \| N \| MMSE \| DST \| VFT \| \| --- \| --- \| --- \| --- \| --- \| \| Before chemotherapy \| \| \| \| \| \| rs886205 (GG) group \| 101.0 \| 27.4 (1.69) \| 6.27 (0.78) \| 11.5 (1.51) \| \| rs886205 (AG/AA) group \| 23.0 \| 27.5 (1.27) \| 5.87 (0.57) \| 11.6 (1.53) \| \| t/z \| \| −0.216 \| 2.346 \| −0.409 \| \| p \| \| 0.806 \| 0.016 * \| 0.683 \| \| rs886205 (GG) patients \| \| \| \| \| \| BC group \| 101.0 \| 27.4 (1.69) \| 6.27 (0.78) \| 11.5 (1.51) \| \| AC group \| 101.0 \| 26.8 (1.73) \| 5.92 (0.93) \| 10.4 (1.91) \| \| t/z \| \| 2.405 \| 3.663 a \| 4.709 \| \| p \| \| 0.018 * \| 0.000* \| 0.000* \| \| rs886205 (AG/AA) patients \| \| \| \| \| \| BC group \| 23.0 \| 27.5 (1.27) \| 5.87 (0.57) \| 11.6 (1.53) \| \| AC group \| 23.0 \| 27.0 (1.74) \| 5.83 (0.73) \| 10.2 (2.37) \| \| t/z \| \| 0.828 \| 0.253 \| 2.273 \| \| p \| \| 0.417 \| 0.803 \| 0.033 * \| ## Cognitive abilities of ALDH2_rs4648328 patients before and after chemotherapy The comparison results of cognitive function before and after chemotherapy for different SNPs in ALDH2_rs4648328 are shown in Figure 3 and Table 5. No significant difference was observed in cognitive function between the two groups. A statistically significant difference was noted in the VFT scores between the BC and AC groups in rs4648328 (CT/TT) patients. For rs4648328 (CC) patients, the DST and VFT scores after chemotherapy were significantly reduced, suggesting a decline in cognitive ability. FIGURE 3: Comparison of cognitive abilities of patients before and after chemotherapy. (A) Score of MMSE of those with ALDH2_rs4648328_CC. (B) Score of DST of those with ALDH2_rs4648328_CC. (C) Score of VFT of those with ALDH2_rs4648328_CC. (D) Score of MMSE of those with ALDH2_rs4767944_TT. (E) Score of DST of those with ALDH2_rs4767944_TT. (F) Score of VFT of those with ALDH2_rs4767944_TT. BC means before chemotherapy, AC means after chemotherapy, $p \leq 0.05$, $p \leq 0.01$, *$p \leq 0.0001$, and $p \leq 0.05$ were considered significant. TABLE_PLACEHOLDER:TABLE 5 ## Cognitive abilities of ALDH2_rs4767944 patients before and after chemotherapy The comparison results of cognitive function before and after chemotherapy for different SNPs in ALDH2_rs4767944 are shown in Figure 3 and Table 6. No significant difference was found in cognitive function between the two groups. A statistically significant difference was observed in the VFT scores between the BC and AC groups in rs4767944 (CT/CC) patients. For rs4767944 (TT) patients, the DST and VFT scores after chemotherapy were significantly reduced, suggesting a decline in cognitive ability. TABLE 6 \| rs4767944 \| N \| MMSE \| DST \| VFT \| \| --- \| --- \| --- \| --- \| --- \| \| Before chemotherapy \| \| \| \| \| \| rs4767944 (TT) group \| 48.0 \| 27.2 (1.81) \| 6.31 (0.80) \| 11.4 (1.44) \| \| rs4767944 (CT/CC) group \| 76.0 \| 27.5 (1.48) \| 6.10 (0.72) \| 11.6 (1.56) \| \| t/z \| \| −1.109 \| 1.418 \| −0.619 \| \| p \| \| 0.270 \| 0.159 \| 0.537 \| \| rs4767944 (TT) patients \| \| \| \| \| \| BC group \| 48.0 \| 27.2 (1.81) \| 6.31 (0.80) \| 11.4 (1.44) \| \| AC group \| 48.0 \| 27.0 (1.80) \| 5.89 (0.89) \| 10.3 (1.74) \| \| t/z \| \| 0.669 \| 2.967 a \| 2.776 \| \| p \| \| 0.507 \| 0.003 \| 0.008 \| \| rs4767944 (CT/CC) patients \| \| \| \| \| \| BC group \| 76.0 \| 27.5 (1.48) \| 6.10 (0.72) \| 11.6 (1.56) \| \| AC group \| 76.0 \| 26.8 (1.68) \| 5.90 (0.91) \| 10.3 (2.15) \| \| t/z \| \| 2.560 a \| 1.878 \| 3.757 \| \| p \| \| 0.010 \| 0.064 \| 0.000* \| ## Correlation analysis of cognitive outcomes in patients with SNPs characteristic of cognitive decline after chemotherapy A significant positive correlation exists between MMSE and VFT scores in patients with ALDH2_rs671_GG after chemotherapy (Figure 4A). A significant positive correlation was found between the MMSE and DST scores in patients with ALDH2_rs886205_GG after chemotherapy (Figure 4B). A significant positive correlation was observed between DST and VFT in patients with ALDH2_rs4648328_CC after chemotherapy (Figure 4C). A significant positive correlation was observed between DST and VFT in patients with ALDH2_rs4767944_TT after chemotherapy (Figure 4D). FIGURE 4:** Correlation analysis of cognitive outcomes in patients with single‐nucleotide polymorphisms characteristic of cognitive decline after chemotherapy. (A) Correlation between MMSE and VFT in ALDH2_rs671_GG patients after chemotherapy. (B) Correlation between MMSE and DST in ALDH2_rs886205_GG patients after chemotherapy. (C) Correlation between DST and VFT in ALDH2_rs4648328_CC patients after chemotherapy. (D) Correlation between DST and VFT in ALDH2_rs4767944_TT patients after chemotherapy ## The expression of ALDH2 in the brain ALDH2 expression differs in different parts of the brain. The highest expression was observed in the cerebral cortex and the lowest in the olfactory bulb. We further analysed the expression of ALDH2 in hippocampal formation and amygdala, and the results are shown in Figure 5. FIGURE 5: Expression of ALDH2 in the brain. (A) Expression of ALDH2 in different parts of the brain. (B) Expression of ALDH2 in different parts of the hippocampal formation. (C) Expression of ALDH2 in different parts of the amygdala ## DISCUSSION This study explored the decline in cognitive function in patients with different molecular types of ALDH2 after chemotherapy and found that patients with rs671_GG, rs886205_GG, rs4648328_CC, and rs4767944_TT were more likely to have cognitive decline after chemotherapy. This method can help identify the population susceptible to cognitive decline in chemotherapy and help these patients by providing early intervention to improve their QoL, which is meaningful. As we know, the ALDH2 gene is a key gene for aldehyde metabolism. 42 In addition, aldehydes can damage the nerve cells in the brain. 43, 44, 45 Therefore, one of the possible reasons for cognitive impairment in patients undergoing chemotherapy is the accumulation of aldehydes in the body and nervous system. There are common polymorphisms of ALDH2 in Asians: ALDH2_rs671 (AG/AA) and wild‐type ALDH2_rs671 (GG). These two genotypes are almost halved in the Asian population. 19 Studies have found that patients with an inactivating point gene polymorphism coding for ALDH2_rs671 (AG/AA) are markedly protected against alcoholism. Cognitive impairment caused due to alcohol is similar to that caused by chemotherapy. 46, 47 Interestingly, our results indicated that patients with ALDH2_rs671 (AG/AA) had no significant changes in cognitive function after chemotherapy, while those with ALDH2_rs671 (GG) showed a significant cognitive decline (Table 2). Therefore, we propose that patients with ALDH2_rs671 (GG) may be more susceptible to CRCI. Such patients should receive early cognitive interventions to help improve their QoL. To improve the sensitivity of CRCI‐susceptible population discrimination, we analysed three other common ALDH2 alleles: rs886205, rs4648328, and rs4767944. These polymorphism sites also correlate with cognitive function and can be used to identify Alzheimer's disease, 48, 49 alcohol‐related cognitive impairment, and other diseases. 50, 51 We found that patients with breast cancer with ALDH2_rs886205_GG, ALDH2_rs4648328_CC, and ALDH2_rs4767944_TT also showed a significant cognitive decline after chemotherapy (TABLE 3‐5). A possible reason is that patients with different ALDH2 genotypes have different abilities to metabolise aldehydes. 52 Therefore, there are differences in the degree of cognitive impairment caused by aldehyde accumulation during chemotherapy. 53, 54 Recent studies have confirmed that ALDH2 in astrocytes is involved in the regulation of behavioural and cognitive functions related to ethanol metabolism in the brain. 55 Both chemotherapy‐ and ethanol metabolism‐induced cognitive impairment can be caused by aldehydes 42, 56; thus, ALDH2 in the brain may also affect CRCI by participating in aldehyde regulation. In this study, we analysed the expression of ALDH2 in the brain, hippocampal formation, and amygdala based on the HPA database. In future studies, it may be possible to improve and prevent CRCI by regulating the function of ALDH2 in the brain, which will greatly enhance the QoL of patients with cancer undergoing chemotherapy. The limitations of this study include the following aspects that need to be addressed in future studies. Patients with both primary and metastatic breast cancer were enrolled in this study. This heterogeneity of the sample will affect the persuasiveness of the results. This was a cross‐sectional study, and future studies should include a control group and conduct long‐term follow‐ups. Our study focused on Asian populations. Although Asians have a higher mutation rate of ALDH2, aldehyde detoxification of ALDH2 is also worthy of investigation in other ethnic groups with CRCI. The MMSE lacks sensitivity for CRCI, and more effective cognitive testing methods should be used in future research. Repeated measurements will increase the results' accuracy. It would be meaningful to measure cognitive function at different time points after chemotherapy to explore the correlation between CRCI symptoms and the time after chemotherapy. ## CONCLUSION Since breast cancer CRCI is very common and markedly affects patients' QoL, it is clinically meaningful to identify which patients are more likely to develop CRCI. 37, 57 This study indicated that patients with ALDH2 rs671_GG, rs886205_GG, rs4648328_CC, and rs4767944_TT polymorphisms were more likely to suffer from cognitive impairment during chemotherapy. For these patients, their cognitive status should be monitored while paying attention to somatic side effects to improve QoL (Figure 6). More importantly, as ALDH2 rs671 is a common polymorphism site in Asians, this study has important implications for Asian women with breast cancer. Broadly, this study highlighted the direction for the study of ALDH2 in CRCI; however, the specific mechanism can be explored through animal experiments in the future. FIGURE 6: Scientific value of this research ## AUTHOR CONTRIBUTIONS Senbang Yao: Conceptualization (lead); formal analysis (lead); investigation (lead); resources (lead); software (lead); writing – original draft (lead). Wen Li: Conceptualization (equal); data curation (equal); investigation (equal); resources (equal). Shaochun Liu: Formal analysis (equal); investigation (equal); supervision (equal); writing – original draft (equal). Yinlian Cai: Data curation (supporting); supervision (supporting). Qianqian Zhang: Formal analysis (supporting); methodology (supporting); resources (supporting). Lingxue Tang: Data curation (supporting); investigation (supporting); visualization (supporting). Sheng Yu: Data curation (supporting); supervision (supporting); writing – review and editing (supporting). Yanyan Jing: Data curation (supporting); methodology (supporting); project administration (supporting). Xiangxiang Yin: Data curation (supporting); funding acquisition (supporting); validation (supporting). Huaidong Cheng: Conceptualization (lead); funding acquisition (lead); writing – review and editing (lead). ## FUNDING INFORMATION This study was supported by The National Natural Science Foundation of China (Nos. 81872504 and 81372487). ## CONFLICT OF INTEREST The authors declare there are no competing interests. ## ETHICS APPROVAL All procedures involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. 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--- title: Tumor immune microenvironment in therapy‐naive esophageal adenocarcinoma could predict the nodal status authors: - Andromachi Kotsafti - Matteo Fassan - Francesco Cavallin - Valentina Angerilli - Luca Saadeh - Matteo Cagol - Rita Alfieri - Pierluigi Pilati - Carlo Castoro - Ignazio Castagliuolo - Melania Scarpa - Marco Scarpa journal: Cancer Medicine year: 2022 pmcid: PMC10028023 doi: 10.1002/cam4.5386 license: CC BY 4.0 --- # Tumor immune microenvironment in therapy‐naive esophageal adenocarcinoma could predict the nodal status ## Abstract Low infiltration of activated CD8+CD28+ T cells was observed in both intratumoral and peritumoral mucosa of patients with nodal metastasis suggesting that immune surveillance failure is the main driver of nodal metastasis onset. ### Background Currently, preoperative staging of esophageal adenocarcinoma (EAC) has modest reliability and accuracy for pT and pN stages prediction, which heavily affects overall survival. The interplay among immune checkpoints, oncogenes, and intratumoral and peritumoral immune infiltrating cells could be used to predict loco‐regional metastatic disease in early EAC. ### Methods We prospectively evaluated immune markers expression and oncogenes status as well as intratumoral and peritumoral immune infiltrating cells populations in esophageal mucosa samples obtained from neoadjuvant therapy‐naïve patients who had esophagectomy for EAC. ### Results Vascular invasion and high infiltration of lamina propria mononuclear cells resulted associated with nodal metastasis. Low infiltration of activated CD8+CD28+ T cells was observed in both intratumoral and peritumoral mucosa of patients with nodal metastasis. Low levels of CD69, MYD88, and TLR4 transcripts were detected in the intratumoral specimen of patients with lymph node involvement. Receiver operating characteristic curve analysis showed good accuracy for detecting nodal metastasis for all the markers tested. Significant lower infiltration of CD8 T cells and M1 macrophages and a lower expression of CD8A, CD8B, and TBX21 were found also in Esophageal Adenocarcinoma TCGA panCancer Atlas in the normal tissue of patients with nodal metastasis. ### Conclusions Our data suggest that immune surveillance failure is the main driver of nodal metastasis onset. Moreover, nodal metastasis containment also involves the immune microenvironment of the peritumoral healthy tissue. ## INTRODUCTION Esophageal adenocarcinoma (EAC) is an increasingly frequent malignancy characterized by a poor prognosis. Multimodality therapy protocols combining neoadjuvant radiation and/or chemotherapy followed by surgery are the present treatment option. 1, 2 Neoadjuvant chemoradiotherapy successfully down‐staged locally advanced EAC patients, 3, 4, 5, 6 but according to the NCCN guidelines patients with early EAC (pT1N0 or pT2N0) usually do not have neoadjuvant therapy. 7 The accuracy of the preoperative evaluations of several staging parameters is often inadequate with several underestimations. 8 Endoscopic ultrasound scan shows poor accuracy in preoperative staging of the esophagogastric junction and esophageal adenocarcinoma. In fact, for node (N) staging, sensitivity is $77.3\%$ and specificity $67.4\%$, with an accuracy of $77.9\%$. 9 Baseline SUVmax of 18‐fluorine‐fluorodeoxyglucose positron emission tomography/computed tomography (18F‐FDG PET/CT) exhibits a high predictive value of the preoperative CT stage, as it can predict a locally advanced tumor with high accuracy. 10 Preoperative staging of esophageal adenocarcinoma has modest reliability and accuracy for pT and pN stages prediction, with as much as $25\%$ of patients having conflicting clinical and pathological staging, which heavily affects overall survival. 11 The immune system can recognize and eliminate tumor cells based on their expression of tumor‐specific antigens. 12 The antitumor action of the immune system is exerted by the activation of T lymphocytes that infiltrate the neoplasm, inhibiting its proliferation. The tumor cell can be recognized and activate the lymphocytes through a two‐signal model that involves HLA molecules and costimulatory molecules. On the other hand, immune checkpoints such as programmed death PD‐1 have the main function of inhibiting T cell activity 13, 14, 15, 16 through interaction with its ligands PD‐L1 and PD‐L2. 17 Several oncogenes may modulate the tumor microenvironment. Mutations in TP53 are a negative predictor of survival after surgery 18 and CD80 expression shows a robust correlation with TP53 activation confirming the connection between TP53 and immune surveillance in cancer. 19 The overexpression of HER2 is found in about $20\%$ of gastroesophageal carcinomas and is associated with a poor prognosis. 20 BRAF encodes a cytoplasmic serine/threonine kinase that acts as an oncogene. 21 Sommerer et al. detected activating BRAF mutations in $11\%$ of adenocarcinomas. 22 Mismatch repair (MMR) genes are involved in the recognition and repair of the nucleotide mismatch during DNA replication 23 and their alterations result in microsatellite instability (MSI). The prevalence of MSI in EAC ranges from $5\%$ to $10\%$. 24 Therefore, we hypothesize that the interplay among immune checkpoints, oncogenes, and intratumoral and peritumoral immune microenvironment could be used to implement cancer staging to choose the best therapy in the early EAC stage. ## Study design A prospective cohort of 206 patients who had esophagectomy for EAC was examined in the MICCE1 project and among them, all the consecutive 30 patients with therapy‐naive pTis, pT1, pT2, or pT3 EAC were selected. Esophageal samples were obtained from surgical specimens of normal and neoplastic mucosa and processed for subsequent analysis as described below. The diagnosis was supported by histological, clinical, and radiological parameters. The study was performed according to the guidelines of the Declaration of Helsinki, all patients signed the informed consent, and IRB approval (Veneto Institute of Oncology, Padua, Italy) was obtained. ## Preoperative staging and neoadjuvant therapy The staging was performed with upper gastrointestinal endoscopy, thorax, and abdominal CT scan, and 18F‐FDG PET/CT. Patients staged below cT3N0 or any cT pN1 were considered suitable for surgery alone. Patients staged below cT3N0 were eligible for resection when there was no evidence of distant metastases or locally advanced tumors with evident periesophageal involvement at staging. ## Surgery Surgical techniques details have been described elsewhere. 25 In brief, the Ivor‐Lewis esophagectomy was performed, through a laparotomy and then a right thoracotomy, for cancers of the esophagogastric junction and mid‐lower esophagus. To avoid neoplastic involvement of the resection margins, at least 6–8 cm of the healthy esophagus was removed above the proximal edge of the tumor. Lymph nodes (LN) were dissected en bloc. Patients were examined at scheduled intervals after 1, 3, 6, and 12 months and every 6–12 months thereafter. Fresh specimens were immediately frozen in liquid nitrogen or fixed in formalin for subsequent analysis. ## Histopathology Histopathology was performed according to international protocols and with a standardized protocol of sampling and processing of the biospecimens. Two gastrointestinal pathologists jointly reassessed all cases. The 7th edition of the TNM classification was used to evaluate the pathological Nodal status (pN0, pN1). For this study, the number of metastatic LN and their site were also examined. 26 ## Immunohistochemistry The Bond Polymer Refine Detection Kit (Leica Biosystems) on BOND‐MAX automated IHC stainer (Leica Biosystems) was used for immunohistochemical stainings. The following primary antibodies were used according to the manufacturer's directions: MLH1 (clone ES05, 1:100; Dako), MSH2 (clone FE11, 1:100; Dako), MSH6 (clone EP49, 1:100; Dako), PMS2 (clone EP51, 1:100; Dako), PD‐L1 (clone 22C3, 1:50; Dako), PD‐L2 (clone 176611, 1:1000; R&D Systems, Inc.), p53 (clone DO7, 1:50; Dako), CD80 (clone 37711, 1:40; R&D Systems, Inc.), CD8 (clone C$\frac{8}{144}$B, 1:200; Dako), LAMP1 (clone H5G11, 1:50; Santa Cruz Biotechnologies) and HER2 (Hercept test; Dako). ## Flow cytometry Fresh mucosa samples were minced and filtered through a sterile Nylon Filter (BD Falcon). The single cell suspension was incubated with human Fc Receptor binding inhibitor (eBioscience) for 20 min, pelleted, and stained in FACS buffer (phosphate‐buffered saline/$2\%$ Flow cytometry staining/$0.02\%$ sodium azide) for 30 min at 4°C. The following fluorochrome‐conjugated antibodies were used: anti‐human CD8A PE (clone HIT8a), anti‐human CD28 FITC (clone CD28.2), anti‐human CD80 FITC (clone 2D10.4), anti‐human HLA ABC FITC (clone W$\frac{6}{32}$) all from eBioscience and anti‐pan Cytokeratin PE (clone C‐11; Abcam). Stained samples were acquired on a FACSCalibur based on CellQuest software (Becton Dickinson). The percentage of cells positive for the molecules of interest was reported. ## RNA extraction and qRT‐PCR RNA was purified from snap‐frozen esophageal mucosa using the SV Total RNA Isolation System (Promega) according to the manufacturer's directions. The iScript cDNA Synthesis Kit (Bio‐Rad) was used for complementary DNA synthesis. The ABI PRISM 7000 Sequence Detection System (Applied Biosystems) was utilized to quantify specific mRNA transcripts with SYBR Green PCR Master Mix. The expression of the ACTB housekeeping gene was used to normalize the expression of the target molecules. Sequences of PCR primer pairs were for CD69 fw 5′ CAAGTTCCTGTCCTGTGTGCT 3′ rv 5′ GCCCACTGATAAGGCAATGAG 3′; CD80 fw 5′ CTCA CTTCTGTTCAGGTGTTATCCA 3′ rv 5′ TCCTTTTGCCA GTAGATGCGA 3′; TLR4 fw 5′ TTTCCTGCAATGGA TCAAGGA 3′ rv 5′ TTATCTGAAGGTGTTGCACATTCC 3′; MYD88 fw 5′ GGATGGTGGTGGTTGTCTCT 3′ rv 5′ AGGATGCTGGGGAACTCTTT 3′; ACTB fw 5′ CTG GACTTCGAGCAAGAGATG 3′ rv 5′ AGTTGAAGGTAGT TTCGTGGATG 3′. ## MSI and BRAF mutational status DNA was isolated from four consecutive 5 μm thick sections obtained from tumor tissue and matched normal mucosa. DNA was extracted from manually micro‐dissected neoplastic cells by using the Mini Amp kit (Qiagen) following the manufacturer's directions. DNA quality was assessed on the TapeStation 4200 microfluidic platform (Agilent Technologies) using the Genomic ScreenTape device (Agilent Technologies), following the manufacturer's instructions. The extracted DNA was analyzed with the MSI Titano kit (Diatech Pharmacogenetics) following the manufacturer's instructions. Exon 15 BRAF status was analyzed by conventional Sanger sequencing. 27 ## External series Publicly accessible Esophageal Adenocarcinoma TCGA panCancer Atlas data were considered for external cohort in‐silico analysis. 28 Seventy tumor specimens and seven paired adjacent normal esophageal tissues from 70 therapy‐naive EAC patients staged pTis, pT1, pT2, or pT3 ($$n = 70$$, 22 pN0 vs. 48 pNx) were selected. Tumor staging, LN metastasis, and gene mutational status‐related data were extracted through the Computational Biology Center Portal (cBioportal). 29, 30 Data of gene expression were downloaded through UCSC Xena browser. 31 Gene expression profiles of our panel of selected immune genes were analyzed, and the association between expression or genomic alteration and nodal metastasis was tested with a non‐parametric Mann–Whitney U test. The CIBERSORTx web portal (https://cibersortx.stanford.edu) 32 was exploited to run the validated 22‐phenotype leukocyte signature (LM22) in absolute mode with 1000 permutations and B‐mode batch correction. RSEM normalized gene expression data were utilized as input data. Samples with accurate CIBERSORTx deconvolution ($p \leq 0.05$) were used for further immunophenotyping analysis. ## Statistical analysis Data are shown as median with interquartile range (IQR) (continuous variables) or frequency with percentage (categorical data) with a descriptive purpose. In all patients, data were compared using the Kruskal‐Wallis test (continuous variables) or Fisher's test (categorical data). Factors associated with LN involvement were investigated with univariate logistic regression models. Receiver operating characteristic (ROC) analysis was used to assess the accuracy of the potential markers for the prediction of the presence of nodal metastasis. Immune cell rate and immune gene mRNA expression levels were used for ROC curve analysis. All tests were two‐sided and a p‐value less than 0.05 was considered statistically significant. Statistical analysis was performed using R 3.3 (R Foundation for Statistical Computing). 33 ## Patient characteristics Between April 2011 and May 2017, 206 consecutive patients with esophageal adenocarcinoma were evaluated for inclusion in the MICCE1 project at the Veneto Institute of Oncology. One hundred fifty‐nine patients treated with neoadjuvant therapy were excluded. In total, a cohort of 30 patients staged pTis, pT1, pT2, or pT3 was included in this study (Figure 1). Patient characteristics are outlined in Table 1. The median age was 71 (IQR 62–78) and 26 of them were males. LN involvement was identified in 11 patients ($35\%$). The comparison of the demographic data between the whole group of patients who had neoadjuvant therapy and accepted to participate to MICCE1 project ($$n = 121$$) and the selected group only showed an expected difference in terms of T‐category distribution. The group of patients who had neoadjuvant therapy had generally a more advanced ypT stage or, paradoxically, a ypT0 stage after the complete response to neoadjuvant therapy. This comparison is shown in Table S1. FIGURE 1: Consort diagram of esophageal adenocarcinoma patients selected for the study. The comparison of the demographic data between the whole group of patients and the selected group was carried out between similarly selected patients. TABLE_PLACEHOLDER:TABLE 1 ## Intratumoral and peritumoral immune markers as predictors of nodal metastases To determine the predictive value of immune markers in the intratumoral and peritumoral esophageal mucosa of therapy‐naïve EAC patients, real‐time qRT‐PCR, flow cytometry as well as histopathology techniques were used to assess the effects of the markers on different LN stages (pN0 and pN1). In our series, no association between a mutation in TP53, BRAF, MMRd/MSI, or alterations in c‐Myc, p16, and nodal metastasis was observed (data not shown). As shown in Figure 2, LN involvement was associated with vascular invasion (odds ratio [OR] 27.5, $95\%$ confidence interval [CI] 2.6–289; $$p \leq 0.005$$) and with high intratumoral infiltration of lymphomononuclear cells ($$p \leq 0.02$$). However, nodal metastasis was associated with low levels of activated CD8 T cells expressing CD28 within the tumor ($$p \leq 0.08$$). ROC curve showed a good diagnostic accuracy of nodal involvement (AUC [area under the curve] = 0.81 [$95\%$ CI: 0.48–0.97], $$p \leq 0.01$$) (Figure 3A). Moreover, nodal metastasis tended to be associated with low levels of CD69, MYD88, and TLR4 mRNA expression within the tumor ($$p \leq 0.11$$, $$p \leq 0.06$$, and $$p \leq 0.06$$, respectively). ROC curve analysis of CD69, MYD88 and TLR4 mRNA expression levels showed a good accuracy in diagnosis of nodal involvement (AUC = 0.76 [$95\%$ CI: 0.47–0.93], $$p \leq 0.04$$; AUC = 0.80 [$95\%$ CI: 0.52–0.95], $$p \leq 0.01$$; and AUC = 0.80 [$95\%$ CI: 0.52–0.95], $$p \leq 0.01$$) (Figure 3B–D). Low levels of CD8+CD28+ T cells were associated with nodal metastasis also when detected in the peritumoral healthy mucosa ($$p \leq 0.06$$) (Figure 4A,B). ROC curve analysis activated CD8 T cell infiltration levels and showed good accuracy in the diagnosis of nodal involvement (AUC = 0.80 [$95\%$ CI: 0.46–0.97], $$p \leq 0.03$$). Besides, in peritumoral healthy mucosa CD80 mRNA levels directly correlated with MYD88, TLR4, and CD69 mRNA levels (Rho = 0.65, $$p \leq 0.005$$; Rho = 0.47, $$p \leq 0.047$$, and Rho = 0.82, $$p \leq 0.0005$$, respectively), indicating a possible pathway leading to CD8 T cells activation (Figure 4C and Table S4). Finally, the small sample size did not allow for evidence of any correlation between immune parameters and cancer recurrence as shown in Figure S1. FIGURE 2: Histochemical analysis of histopathological sections of esophageal adenocarcinoma: (A) Vascular invasion (scored as 0 = absent and 1 = present); (B) infiltrating lamina propria mononuclear cells (scored as 0 = absent, 1 = low, 2 = high). FIGURE 3: (A) Flow cytometric analysis of CD8+CD28+ T cells within the tumor. Representative images of flow cytometric analysis of CD8+CD28+ T cells are shown. The percentage of cells positive for CD8 and CD28 is reported. (B) MYD88 mRNA expression levels within the tumor. (C) TLR4 mRNA expression levels within the tumor. (D) CD69 mRNA expression levels within the tumor. The expression of the ACTB housekeeping gene was used to normalize the expression of the target genes. Data are reported as 2[CT(housekeeping) − CT(target)]. FIGURE 4: (A) Flow cytometric analysis of CD8+CD28+ T cells in the peritumoral healthy tissue. The percentage of cells positive for CD8 and CD28 is reported. (B) Representative images of peritumoral CD8+ according to nodal status. (C) Correlation between CD80 and MYD88, TLR4, or CD69 mRNA expression levels in the peritumoral healthy tissue. The expression of the ACTB housekeeping gene was used to normalize the expression of the target molecules. ## External cohort Esophageal Adenocarcinoma TCGA PanCancer Atlas dataset comprised 70 therapy‐naive pT1, pT2, or pT3 EAC, with LN involvement identified in 48 patients ($68\%$) (Tables S2 and S3). No significant association between nodal metastasis and the expression of the selected putative immune markers or genomic alterations was found in the intratumoral tissue (data not shown). Among the selected cohort, there were data available also for seven matched adjacent normal tissue samples (four from patients with LN metastasis). Interestingly, in these peritumoral tissue samples CD8A, CD8B, and TBX21 expression were reduced in patients with nodal metastasis ($$p \leq 0.05$$, $$p \leq 0.05$$, and $$p \leq 0.05$$, respectively) (Figure 5A). Moreover, CD80 mRNA levels directly correlated with CD38 and CD69 mRNA levels (Rho = 0.85, $$p \leq 0.03$$; and Rho = 0.77, $$p \leq 0.05$$, respectively), confirming the possible role of CD80 in the pathway leading to T cell activation observed in our series (Figure 5B). Furthermore, immune cell composition was inferred in these peritumoral specimens using CIBERSORTx. Interestingly, the CD8 T cell and M1 macrophage populations resulted significantly enriched in the normal esophageal mucosa of patients without nodal metastasis ($$p \leq 0.05$$ and $$p \leq 0.05$$, respectively) (Figure 5C). FIGURE 5: (A) TCGA panCancer Atlas Esophageal Adenocarcinoma‐peritumoral normal tissue: CD8A, CD8B, and TBX21 mRNA expression according to nodal status. (B) Correlation between CD80 and CD38 and CD69 mRNA expression levels in TCGA panCancer Atlas Esophageal Adenocarcinoma‐ peritumoral normal tissue (C) CIBERSORTx Digital Cytometry for CD8 T cells and M2 macrophages populations in TCGA panCancer Atlas Esophageal Adenocarcinoma—peritumoral normal tissue according to nodal status. ## DISCUSSION Preoperative staging in patients undergoing oncological esophagectomy for adenocarcinoma is fundamental for treatment decisions and heavily affects overall survival. However, it has modest accuracy and reliability for pT and pN prediction, and even patients with early EAC (stage I and II) not receiving neoadjuvant therapy have a significant risk of recurrence. A recent study shows a lack of agreement among gastrointestinal pathologists in measuring the depth of submucosal invasion in esophageal endoscopic resections despite formulating a consensus approach for scoring. 34 Moreover, currently, minimally invasive esophagectomy with two‐field lymphadenectomy is the standard of care for early EAC LN metastasis and sentinel node navigation surgery has been successfully investigated to tailor the extent of lymphadenectomy. 35 Finally, $15\%$ of patients with cT1b esophageal cancer were found to have a positive nodal disease and, thus, to not be a suitable candidate for endoscopic resection. 36 Striving to fill the unmet need for reliable predictors of nodal metastasis, we investigated mucosal samples among neoadjuvant therapy‐naïve patients with early EAC to help plan possible adjuvant chemotherapy. In our series, LN involvement was associated with vascular invasion. Similarly, Barbetta et al. observed that vascular invasion is an independent predicting factor of pathologic nodal involvement. 37 Moreover, we previously reported that CD31, a marker of angiogenesis, was associated with nodal metastasis in EAC. 38 In our opinion, histological vascular invasion within primary cancer can be an easily detectable marker of nodal involvement in therapy‐naive EAC. A study assessing the best biopsy pattern to detect it is warranted to validate the use of this marker in clinical practice. To our knowledge, there is not a known association between most commonly mutated oncogenes and nodal metastases in therapy‐naive EAC. No association was established between mutations in TP53, cMyc, p16, BRAF, or MMR proteins and nodal metastasis in our cohort of patients. Similarly, no association between genomic alterations of the most commonly mutated oncogenes and nodal metastasis was observed in the TCGA panCancer Atlas series. Thus, we hypothesized that the main driver of nodal metastasis could be an immune surveillance failure. In fact, in our series, nodal metastasis was associated with high intratumoral infiltration of lymphomononuclear cells. In our opinion, this is probably due to unbalanced immune surveillance with high infiltration of immunosuppressive cells such as regulatory T cells (Tregs) and M2 macrophages. In fact, in breast cancer, tumor‐draining LN invasion by metastatic cells is associated with local immunosuppression, which can be partially attributed to Treg. 39 Similarly, the infiltration of Tregs and M2 tumor‐associated macrophages (TAMs) is significantly associated with nodal metastasis and the progression of premalignant lesions to oral squamous cell carcinoma. 40 Indeed, in our series, nodal metastasis was associated with low levels of activated cytotoxic T cells within the tumor and ROC analysis revealed a good diagnostic accuracy of nodal involvement. Moreover, nodal metastasis tended to be associated with low levels of CD69, MYD88, and TLR4 mRNA expression, T cells, and innate immunity activation markers, respectively, that showed good accuracy in the diagnosis of nodal involvement. Similarly, in early rectal cancers absence of CD8+ T‐cell infiltration was strongly associated with nodal metastasis presence. 34 In a seminal study, Galon and colleageus showed colorectal cancers with signs of early metastatic invasion had lower infiltrates of immune cells and lower transcripts levels of genes related to type 1 helper effector T cells. 35 In our opinion, our data may suggest that even in esophageal adenocarcinoma the immune surveillance failure within cancer might be associated with the presence of nodal metastasis. Therefore, these findings could provide the basis for larger studies on the clinical use of these biomarkers. On the other hand, in our series, low levels of activated CD8 T cells in the peritumoral healthy mucosa were associated with nodal metastasis and the ROC curve demonstrated a good diagnostic accuracy of nodal involvement for low levels of activated CD8 T cell infiltration levels. These data seemed to be confirmed also in the normal adjacent to tumor mucosa of esophageal adenocarcinoma TCGA panCancer Atlas, even if their series was extremely limited in terms of sample size. Similarly, in colorectal cancer patients, low numbers of intraepithelial CD8 in the biopsy predicted the presence of nodal metastasis, tumor deposits, and lymphatic and venous invasion in the primary tumor. 36 Moreover, in peritumoral healthy mucosa, CD80 mRNA levels directly correlated with MYD88, TLR4, and CD69 mRNA levels, suggesting a possible activation pathway. These findings suggest that even in EAC part of the battle for nodal metastasis containment is not only within the tumor but also in the peritumoral healthy tissue. The main limit of our study is the small sample size that made the differences in the infiltration rate of immune cells and expression levels of immune genes, in somehow, blurred. This study aims at investigating the immune microenvironment in naïve‐therapy EAC and, currently, this is a relatively small subgroup among all EAC patients as it is evident also in the TCGA series. On the other hand, the sample size is adequate for ROC curves analysis, which seems to confirm what is only suggested in comparative analysis. In conclusion, our study showed that vascular invasion could be a marker of nodal involvement in therapy‐naive EAC that can be easily detected with standard histology. Our data suggest that immune surveillance failure might be the main driver of nodal metastasis onset, whereas no association between mutations in common oncogenes and nodal metastasis was observed. Finally, our findings suggest that nodal metastasis containment also seems to involve the immune microenvironment of the peritumoral healthy tissue. These findings could provide the basis for larger studies on the clinical use of these biomarkers. ## AUTHOR CONTRIBUTIONS Andromachi Kotsafti: Conceptualization (equal); investigation (equal); writing – original draft (equal); writing – review and editing (equal). Matteo Fassan: Conceptualization (equal); investigation (equal); writing – original draft (equal); writing – review and editing (equal). Francesco Cavallin: Data curation (equal); writing – review and editing (equal). Valentina Angerilli: Investigation (equal). Luca Saadeh: Investigation (equal). Matteo Cagol: Investigation (equal). Rita Alfieri: Investigation (equal). Pierluigi Pilati: Funding acquisition (equal); investigation (equal); supervision (equal). Carlo Castoro: Funding acquisition (equal); investigation (equal); supervision (equal). Ignazio Castagliuolo: Funding acquisition (equal); investigation (equal); supervision (equal). Melania Scarpa: Conceptualization (equal); investigation (equal); visualization (equal); writing – review and editing (equal). Marco Scarpa: Conceptualization (equal); data curation (equal); funding acquisition (equal); project administration (equal); supervision (equal); writing – original draft (equal); writing – review and editing (equal). ## FUNDING INFORMATION Open access funding provided by BIBLIOSAN. ## CONFLICT OF INTEREST The authors have no conflict of interest. ## DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. ## References 1. Gebski V, Burmeister B, Smithers BM. 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--- title: RUNX2 interacts with SCD1 and activates Wnt/β‐catenin signaling pathway to promote the progression of clear cell renal cell carcinoma authors: - Xiandong Song - Junlong Liu - Bitian Liu - Chiyuan Piao - Chuize Kong - Zhenhua Li journal: Cancer Medicine year: 2022 pmcid: PMC10028032 doi: 10.1002/cam4.5326 license: CC BY 4.0 --- # RUNX2 interacts with SCD1 and activates Wnt/β‐catenin signaling pathway to promote the progression of clear cell renal cell carcinoma ## Abstract ### Background Previous studies have demonstrated that Runt‐associated transcription factor 2 (RUNX2) serves as the main transcription factor for osteoblast differentiation and chondrocyte maturation. RUNX2 is related to a variety of tumors, particularly tumor invasion and metastasis, while the expression and molecular mechanisms of RUNX2 in clear cell renal cell carcinoma (ccRCC) keep to be determined. Stearyl CoA desaturase 1 (SCD1), an endoplasmic reticulum fatty acid desaturase, transfers saturated fatty acids to monounsaturated fatty acids, is expressed highly in numerous malignancies. ### Methods The Cancer Genome Atlas (TCGA) datebase and Western blot was used to analyzed the mRNA and protein levels of the target gene in ccRCC tissues and adjacent tissues. The proliferation ability of ccRCC cells was tested by colony forming and EdU assay. The migration ability of cells was detected by transwell assay. Immunoprecipitation was utilized to detect protein–protein interaction. Cycloheximide chase assay was used to measure the half‐life of SCD1 protein. ### Results In this study, the expressions of RUNX2 and SCD1 are increased in ccRCC tissues as well as ccRCC cell lines. Both RUNX2 and SCD1 could promote proliferation and migration in ccRCC cells. Furthermore, RUNX2 could physically interact with SCD1. In addition, the functional degradation and the inactivation of Wnt/β‐catenin signaling pathway triggered by the downregulation of RUNX2 could be partly offset by the overexpression of SCD1. ### Conclusion The findings indicate that the RUNX2/SCD1 axis may act as a potential therapeutic target via the Wnt/β‐catenin signaling pathway of ccRCC. ## INTRODUCTION Renal cell carcinoma (RCC) is considered a kind of common malignant tumor in the urinary system, after bladder cancer and prostate cancer in the aspect of incidence, with a proportion of $4.18\%$ in whole adult malignancy and $21.82\%$ in urinary malignancy. 1, 2 RCC accounts for about 300,000 new cancer cases all over the world annually, contributing to almost 100,000 deaths every year. 3 Clear cell renal cell carcinoma (ccRCC), as the most common histologic subtype, is a tumor regarded as originating from epithelial cells in the proximal convoluted tubule of the nephron. 4 Eventually, around $50\%$ of ccRCC patients would develop metastases. Although in the last few years the systemic treatment of metastatic RCC has come a long way, the patients' 5‐year survival rate with metastatic ccRCC turns out to be lower than $10\%$. 5, 6 Thus, for improvement of the prognosis of patients with ccRCC, effective biomarkers along with brand‐new therapeutic targets are needed to recognize. As a major transcriptional regulator of skeletogenesis, transcription factor Runt‐related transcription factor 2 (RUNX2) could regulate the expression of stage‐specific osteoblast genes and promote the transition from the immature to the mature osteoblast phenotype, thereby promoting bone formation. 7, 8 Furthermore, the parts of RUNX2 played in migration, invasion, and metastasis have been recorded in different tumors including multiple myeloma, T‐cell lymphoma, acute myeloid leukemia, and prostate cancer. 9, 10, 11, 12 According to the report, the expression of RUNX2 in tumors is at a higher level than that of normal tissue. Besides, the RUNX2 overexpression is significantly related to poor prognosis in various malignant cancers. 13, 14 But, the pathophysiologic influence of RUNX2 on the progression of ccRCC is still undiscovered. Lately, dysregulated fatty acid metabolism has been found among numerous kinds of cancers, for instance, breast cancer, lung cancer, prostate cancer, and ccRCC. 15, 16, 17, 18 It has been demonstrated that aberrant lipid homeostasis is critical for sustained tumorigenesis in ccRCC. 19 Stearyl CoA desaturase 1 (SCD1), as an endoplasmic reticulum fatty acid desaturase, is able to transform convert saturated fatty acids (SFAs) into monounsaturated fatty acids (MUFAs) during de novo synthesis of cellular fatty acids. 20 Studies have revealed that the elevated expression of SCD1 could accelerate cell proliferation, enhance cell invasiveness, improve cell survival, and ultimately lead to greater tumorigenicity. 21 SCD1 expression is evidently elevated in all types of human cancers, becoming a new key factor in tumorigenesis. 22, 23 The study has been indicated that RUNX1 which is also a member of Runx family could regulate the expression of SCD1 in human skin squamous cell carcinoma. And Lipids and membrane organization are altered in response to RUNX1. Therefore, in this paper, we decided to detect the rationale that RUNX2 correlates with SCD1 in ccRCC. 24 Abnormal activation in the Wnt/β‐catenin signaling pathway is taken as being able to increase the malignancy of human cancers of all kinds. 25, 26 There is evidence that the Wnt/β‐catenin signaling pathway plays a dominant role in ccRCC growth together with metastasis. 27 According to this research, it was revealed that RUNX2 and SCD1 were overexpressed in ccRCC, and RUNX2 might physically interact with SCD1. Moreover, RUNX2 might enhance the proliferation along with the migration of ccRCC via Wnt/β‐catenin signaling pathway activation. ## Bioinformatics analyses For the purpose of accessing the mRNA expression levels of RUNX2 along with SCD1 in RCC tissues, the Cancer Genome Atlas (TCGA) was interrogated. Analysis of the TCGA platform is acquired by visiting the following website: http://gepia.cancer‐pku.cn (KIRC = clear cell renal cell carcinoma, KICH = chromophobe renal cell carcinoma, KIRP = papillary renal cell carcinoma). ## Tissue samples The tissue samples utilized in this research (tumor and normal tissues) came from 120 patients who were received and treated in the First Hospital of China Medical University (Shenyang, China) from November 2020 to February 2021. The tissue samples were frozen in liquid nitrogen at once when resection was done, then saved at −80°C. With the permission of the Ethics Committee of the First Hospital of China Medical University granted for the research, written informed consents were also achieved among every related patient. ## Cell lines and cell culture Each cell line, offered with $95\%$ air and $5\%$ CO2 at the temperature of 37°C, was incubated in a humidified incubator. The human ccRCC cell lines, including 769‐P, 786‐O, and OS‐RC‐2, were incubated in an RPMI‐1640 medium (HyClone; GE Healthcare Life Sciences), and CAKI‐1 cells were incubated in McCoy's 5A medium (Gibco; Thermo Fisher Scientific, Inc.), and ACHN cells were incubated in an MEM medium (HyClone; GE Healthcare Life Sciences). HK‐2 cells (normal cortex/proximal tubule cells) were incubated in a DMEM/F12 medium (HyClone; GE Healthcare Life Sciences). Each medium was with supplementation of $10\%$ fetal bovine serum (HyClone, USA). With regards to seeding and subcultivation, first, phosphate‐buffered saline (PBS) was used to wash cells and later digested by trypsin/EDTA solution until cells detached. Cycloheximide (CHX) was purchased from MCE (MedChemExpress, USA). To determine the half‐life of SCD1 protein by CHX, cells were treated with CHX (100 μg/ml) for the indicated time and then harvested the cell protein for immunoblotting. ## Western blot RIPA buffer plus protease inhibitor cocktail was applied to lyse tissues and cells, and a BCA protein assay kit was employed for detecting the protein concentration. Equivalent protein was extracted electrophoretically by $10\%$ SDS‐PAGE. Following an electrophoretic transfer of proteins onto PVDF membranes (Bio‐Rad Laboratories, Inc.), nonspecific binding was blocked by being incubated in $5\%$ skim milk for 1 h at the temperature of 37°C, then the membranes were cultured at 4°C for the whole night by primary antibody as follows: anti‐RUNX2 (1:1000, 12,556, Cell Signaling Technology), anti‐SCD1(1:500, sc‐58,420, Santa Cruz Biotech), anti‐GSK‐3β (1:1000, D5C5Z, Cell Signaling Technology), anti‐P‐GSK‐3β (1:1000, D85E12, Cell Signaling Technology), anti‐AXIN1 (1:1000, C95H11, Cell Signaling Technology), anti‐DVL2 (1:1000, 30D2, Cell Signaling Technology), anti‐β‐tubulin (1:1000, 2128S, Cell Signaling Technology), or anti‐GAPDH (1:5000, 5174, Cell Signaling Technology). TBST ($0.2\%$ Tween) was used to wash the membranes thrice, 15 min for each time, and then the membranes were incubated with the proper horseradish peroxidase (HRP)‐conjugated secondary antibodies for 1 h at 37°C. At last, the luminescence system (Bio‐Rad, CA, USA) and ECL luminescence reagent were utilized to detect (Absin Biotechnology, Shanghai, China). The densitometric values of every band were calculated by ImageJ software (version 1.51; National Institutes of Health). ## Quantitative real‐time PCR Total RNA was separated through the usage of TRIzol reagent (Takara Biotechnology, Dalian, China). A ThermoFisher Scientific NanoDrop ND‐100 was used to determine the quality and quantity of RNA. The mRNA was reverse transcribed for cDNA synthesis accessing PrimeScript RT Master Mix (Takara, Dalian). Real‐time quantitative PCR was accessed with the LightCycler™480 II system (Roche diagnostics, Switzerland). Relative quantification was performed by the method of 2−ΔΔCT with GAPDH as the internal reference. The primers (forward and reverse, respectively) used were as follows: RUNX2 (5’‐GCGCATTCCTCATCCCAGTA‐3′ and 5’‐GGCTCAGGTAGGAGGGGTAA‐3′), SCD1 (5’‐TCTAGCTCCTATACCACCACCA‐3′ and 5′‐ TCGTCTCCAACTTATCTCCTCC‐3′), β‐catenin (5′‐ GGAGAACTGGTCGCCATCAAG −3′ and 5′‐ ACATTGGGTTCTCCTCGGACC ‐3′), and GAPDH (5’‐GGAGCGAGATCCCTCCAAAAT‐3′ and 5’‐GGCTGTTGTCATACTTCTCATGG‐3′). ## Lentiviral transduction Stable cell lines were made by lentivirus infection. The infected cells were chosen for at least four passages by putting 10 μg/ml puromycin into a growth medium. Lentiviral‐based plasmids for RUNX2 and SCD1 knockdown and those for RUNX2 and SCD1 overexpression were purchased from GeneChem (Shanghai, China). Stable RUNX2 or SCD1 knockdown ccRCC cell lines were generated using the lentivirus vector containing short hairpin (sh) RNAs targeting RUNX2 (shRNA‐RUNX2), SCD1 (shRNA‐SCD1), or negative control vector (shRNA‐Ctrl). Viral particles containing the full‐length fragment RUNX2 or SCD1 were used to generate overexpression ccRCC cell lines (LV‐RUNX2 or LV‐SCD1). The negative control vectors (LV‐Ctrl) were also generated. shRNA‐RUNX2 sequence: CCGGCTAGTGATTTAGGGCGCATTCTCGAGAATGCGCCCTAAATCACTGAGTTTTTG and shRNA‐SCD1 sequence: CCGGGCACATCAACTTCACCACATTCTCGAGAATGTGGTGAAGTTGATGTGCTTTTTG. ## Co‐immunoprecipitation assay For immunoprecipitation, RIPA buffer plus protease inhibitor cocktail was utilized to lyse cells, and centrifugation was done with cells at 12,000 g for 20 min at 4°C. Then, the supernatants were incubated with 10 μl anti‐RUNX2 antibody (Cell Signaling Technology), 10 μl of anti‐SCD1 antibody (Santa Cruz Biotech), or 1 μl of anti‐IgG antibody (negative control, Cell Signaling Technology) overnight at 4°C in rotation. Then put into 10 μl Pierce Protein A/G Magnetic Beads (Thermo Fisher Scientific) and rotated the mixture at 4°C for another 2 h. By means of mild lysis buffer washed the beads thrice, followed by adding protein loading buffer (5×) and denatured at a temperature of 100°C for 10 min. Next perform a western blot. ## Chromatin immunoprecipitation assay The chromatin immunoprecipitation assay was conducted by a SimpleChiP™ Enzymatic Chromatin IP kit (Cell Signaling Technology, Danvers, MA, USA). In short, cells were cross‐linked by $37\%$ formaldehyde for 15 min at 37°C, used glycine to quench the cross‐linking reaction, and then gathered the cells. The cross‐linked chromatin was digested until a length of around 150–900 bp with the micrococcal nuclease added to the collected cells. The cross‐linked chromatin was then, respectively, incubated with 10 μl of anti‐RUNX2 antibody (Cell Signaling Technology), 1 μl of anti‐IgG antibody (negative control, Cell Signaling Technology), or 10 μl of anti‐histone H3 antibody (positive control, Cell Signaling Technology) overnight at 4°C in rotation and incubated with Protein A/G‐Sepharose for 2 h. Beads were then recovered by centrifugation and washed two times with ChIP Wash Buffer. The antibody/protein/DNA cross‐link complexes were reversed by heating at the temperature of 65°C for 2 h, and DNA Purification Kit was performed to purify the DNA (Cell Signaling Technology, Danvers, MA, USA). Purified DNA was scrutinized by RT‐qPCR with promoter‐specific primers (forward and reverse, respectively): SCD1 Primer 1 (5′‐ CCAGTCAACTCCTCGCACTT‐3′ and 5’‐AAGGCTAGAGCTGGCAACG‐3′), SCD1 primer 2 (5’‐CCATTGTTCGCAGGCGTACC‐3′ and 5′‐ ACATCTCCGTCCCGTCTTCC‐3′). ## Colony formation assays Cells that grew at $80\%$ confluence were trypsinized and moved to a fresh medium in a single‐cell suspension. Cells were diluted properly to seed on 6‐well plates (500 cells per well). Cells shall be permitted to develop for 10 days prior to being stained with crystal violet solution. ImageJ software was used to quantify Colony areas. ## Transwell assays Cells were serum‐starved for 24 h and seeded 2 × 104 cells in transwell inserts (8‐μm pore size, Corning). Cell suspensions were put into inserts involving 200 μl serum‐free media on the top as well as 600 μl media with $10\%$ FBS at the base chamber. After that, keep cell suspensions incubated at 37°C for 48 h. By use of cotton swabs wiped off cells in the upper chamber gently. Took images with an optical microscope to analyze via Image J software. ## 5‐ethynyl‐2′‐deoxyuridine (EdU) proliferation assay Cells were planted into 6‐well plates with EdU (BeyoClick™, EDU‐488, China) and put into the medium (1:1000) for labeling. After being labeled, took away the culture medium followed by EdU staining, and the cells were incubated for 30 min with a click reaction cocktail (Tris–HCl, pH 8.5, 100 mmol/L; CuSO4, 1 mmol/L; Apollo 488 fluorescent azide, 100 μmol/L; and ascorbic acid, 100 mmol/L) in the darkroom, at indoor temperature. The cells were washed twice with 1 × PBS. Prior to observation under a fluorescence microscope, nuclei were stained with Hoechst followed by mounting and imaging. ## Statistical analysis For all the bar graphs, all experimental data are presented as mean ± SEM as indicated. Mann–Whitney U test, Pearson Chi‐square test, Pearson correlativity analysis, as well as Student's t test were implemented based on the instruction. p value of <0.05 was regarded as evidence. GraphPad Prism 9 software was used to perform statistical analyses (LaJolla, CA, USA). ## mRNA expression levels of RUNX2 and SCD1 in RCC from the TCGA database The expression of RUNX2 along with SCD1 in renal cell carcinoma via analyzing The Cancer Genome Atlas (TCGA) data sets was analyzed with the web link: http://gepia.cancer‐pku.cn. RUNX2 was upregulated in ccRCC and papillary renal cell carcinoma, and RUNX2 was downregulated in chromophobe renal cell carcinoma. SCD1 was upregulated in ccRCC, papillary renal cell carcinoma, and chromophobe renal cell carcinoma (Figure 1A, B). What is more, the expression of RUNX2 and SCD1 was not obviously correlated with the pathological stage in ccRCC (Figure 1C, D). TCGA platform revealed that SCD1 mRNA expression was in positive correlation with RUNX2 mRNA expression in ccRCC (Figure 1E). FIGURE 1: mRNA expression levels of RUNX2 and SCD1 in RCC from TCGA database. (A, B) RUNX2 was expressed at higher levels in KIRC and KIRP, whereas RUNX2 was downregulated in KICH. SCD1 was highly expressed in KICH, KIRC, and KIRP. (C, D) RUNX2 (left) or SCD1 (right) expression was not correlated with the pathological stage. (E) The mRNA expression of SCD1 was positively correlated to the mRNA expression of RUNX2 which is revealed by the TCGA platform at http://gepia.cancer‐pku.cn. KIRC, clear cell renal cell carcinoma; KICH, chromophobe renal cell carcinoma; KIRP, papillary renal cell carcinoma; red, tumor; blue, normal. p < 0.05 was considered significant, ns, not significant. ## RUNX2 and SCD1 were highly expressed in ccRCC tissues and ccRCC cell lines RUNX2 and SCD1 expressions were analyzed in 120 pairs of ccRCC and adjacent normal tissues to explore potential clinical significance in ccRCC. As revealed by Western blot, the protein level of RUNX2 and SCD1 was prominently higher in tumor tissues than that in the adjacent normal tissues (Figure 2A–C). The expression of RUNX2 and SCD1 protein was significantly correlated with Fuhrman grade but failed to be remarkably related to age, gender, tumor stage, and tumor size (Table 1). According to this research, owing to the shortage of advanced tumors, the relationship with metastases could not be examined. Next, the interaction between the protein expression of RUNX2 and SCD1 in 120 cases of ccRCC was tested using linear correlation analysis, and the expression of RUNX2 was correlated positively with the protein level of SCD1 (Figure 2D). In addition, the protein expression level of RUNX2 was differentially expressed among different ccRCC cell lines (Figure 2E, F). FIGURE 2: RUNX2 and SCD1 were expressed at high levels in ccRCC cell lines and ccRCC tissues. (A) RUNX2 and SCD1 protein levels in ccRCC tissues (T) and adjacent normal tissues (N). (B, C) RUNX2 and SCD1 were expressed at a significantly higher level in tumor tissues in comparison with normal tissues. (D) The protein expression of SCD1 was positively correlated to the protein expression of RUNX2 in ccRCC tissues. (E, F) The protein expression of RUNX2 was differently expressed among ccRCC cell lines. $p \leq 0.05$ was considered significant, ns, not significant. TABLE_PLACEHOLDER:TABLE 1 ## RUNX2 played a crucial part in the proliferation and migration of ccRCC Our previous study has provided evidence that RUNX2 could regulate epithelial–mesenchymal transition in renal cell carcinomas, 28 and RUNX2 was found to mediate tumor growth and metastasis in clear cell renal cell carcinoma. 29 Stable RUNX2 knockdown and RUNX2 overexpression ccRCC cell lines were generated to functionally dissect the potential role of RUNX2 in ccRCC cell growth and migration. The protein expression level of RUNX2 was greatly lowered owing to short hairpin (sh) RNAs targeting RUNX2 in 786‐O and ACHN cells (Figure 3A). Upregulation at the protein level of RUNX2 was observed in CAKI‐1 and OS‐RC‐2 cells transfected with viral particles containing the full‐length fragment of RUNX2 (Figure 4A). According to the migration assay, the migration capacity of 786‐O and ACHN cells was noticeably inhibited by the silence of RUNX2 while the migration ability of CAKI‐1 and OS‐RC‐2 cells was promoted as a result of RUNX2 overexpression (Figure 3B and 4B). EdU assay indicated that cellular growth was impaired by RUNX2 knockdown, and the proliferation ability of ccRCC cells was enhanced with RUNX2 overexpression (Figures 3C and 4C). Colony formation experiments revealed that the colony ability was impeded by depletion of RUNX2, whereas a significantly increased number of colonies were yielded when RUNX2 was overexpressed (Figure 3D and 4D). Given that the Wnt/β‐catenin signaling pathway holds the key to the progress of a variety of diseases and cancers and RUNX2 could control bone resorption through the downregulation of the Wnt/β‐catenin signaling pathway in osteoblasts, 30, 31 the part of RUNX2 played in the activity of Wnt/β‐catenin signaling pathway was examined. As shown in Western blot, protein expression of β‐catenin was downregulated when RUNX2 was knocked down, and the expression of β‐catenin grew at the protein level when RUNX2 was upregulated (Figure 5A, B). Then the protein expression of RUNX2 and β‐catenin was analyzed in 16 pairs of ccRCC and adjacent normal tissues. As revealed by the western blot, RUNX2 and β‐catenin protein expression were higher in tumor tissues than that in the adjacent normal tissues (Figure 5C). Next, a linear correlation analysis was used to test the interaction between the protein expression of RUNX2 and SCD1 in 16 cases of ccRCC, and the expression of RUNX2 was correlated positively with the protein level of β‐catenin (Figure 5D). Collectively, these in vitro findings demonstrated that RUNX2 could promote the growth and migration of ccRCC cells by Wnt/β‐catenin signaling pathway activation. However, what is the rationale that RUNX2 could regulate Wnt/β‐catenin signaling? The mRNA expression level of RUNX2 and β‐catenin was then determined to explore the regulatory mechanism. It was found that when RUNX2 was knocked down, the mRNA expression level of RUNX2 was significantly declined, while there was no observed change in β‐catenin mRNA level (Figure S1). It could be conducted that RUNX2 does not regulate β‐catenin expression transcriptionally. Some studies have demonstrated that GSK‐3β, AXIN1, and DVL2 are the important regulators of Wnt/β‐catenin signaling. 32, 33, 34 Therefore, we wondered if RUNX2 could activate Wnt/β‐catenin signaling by regulating GSK‐3β, AXIN1, and DVL2. As shown in (Figure S1), the expression of β‐catenin was significantly declined at the protein level in the absence of RUNX2. However, the protein expression of GSK‐3β, AXIN1, and DVL2 has no change. These data suggested that RUNX2 could activate Wnt/β‐catenin signaling but not via regulating GSK‐3β, AXIN1, and DVL2. FIGURE 3: Downregulation of RUNX2 protein could inhibit proliferation and migration of ccRCC. (A) The protein level of RUNX2 was detected by western blot in 786‐O and ACHN cells transfected with shRNA‐RUNX2. (B) The migration ability of 786‐O and ACHN cells was detected by transwell assay (magnification×20). (C) The proliferation ability of 786‐O and ACHN cells was detected by EdU assay (magnification×200). (D) The colony formation ability of 786‐O and ACHN cells was detected by colony formation assay. p < 0.05 was considered significant, ns, not significant. FIGURE 4: Upregulation of RUNX2 protein could promote proliferation and migration of ccRCC. (A) The protein level of RUNX2 was detected by western blot in CAKI‐1 and OS‐RC‐2 cells transfected with LV‐RUNX2. (B) The migration ability of CAKI‐1 and OS‐RC‐2 cells was detected by transwell assay (magnification×20). (C) The proliferation ability of CAKI‐1 and OS‐RC‐2 cells was detected by EdU assay (magnification×200). (D) The colony formation ability of CAKI‐1 and OS‐RC‐2 cells was detected by colony formation assay. p < 0.05 was considered significant, ns, not significant. FIGURE 5: Wnt/β‐catenin pathway was activated by the overexpression of RUNX2. (A) The expression of β‐catenin was detected by western blot in 786‐O and ACHN cells transfected with shRNA‐RUNX2. (B) The expression of β‐catenin was detected by western blot in CAKI‐1 and OS‐RC‐2 cells transfected with LV‐RUNX2. (C) RUNX2 and β‐catenin protein levels in ccRCC tissues (T) and adjacent normal tissues (N). (D) The protein expression of β‐catenin was positively correlated to the protein expression of RUNX2 in ccRCC tissues. p < 0.05 was considered significant, ns, not significant. ## RUNX2 regulated the SCD1 expression in ccRCC cell lines Stable RUNX2 knockdown and RUNX2 overexpression ccRCC cell lines were used to study whether RUNX2 could regulate SCD1 expression in ccRCC cells. In the absence of RUNX2, the expression of SCD1was significantly declined at the mRNA and protein level (Figure 6A, B), but no striking change was perceived in the mRNA and protein expression of SCD1 with the overexpression of RUNX2 in ccRCC cells (Figure 6C, D). Given that RUNX2 serves as a recognized transcription factor and SCD1 expression regulation is mediated by a variety of different transcription factors, notably sterol response element‐binding protein (SREBP), peroxisome proliferator‐activated receptor (PPAR), LXR, NF‐1, and AP‐2, 35, 36, 37 chromatin immunoprecipitation assay was conducted. However, it was revealed that RUNX2 could not bind to the promoters of SCD1 (Figure 6E). Therefore, it could be hypothesized that the upregulation of RUNX2 expression could not affect the expression of SCD1, and there might be some co‐mediating factors or some other regulation modes between them. Co‐immunoprecipitation assay indicated that anti‐RUNX2 antibody could co‐immunoprecipitate with SCD1 in ccRCC cells. Similarly, RUNX2 was co‐immunoprecipitated using an anti‐SCD1 antibody (Figure 6F). Taking together, RUNX2 could physically interact with SCD1. The study has indicated that SCD1 protein could be degraded in the ubiquitin‐proteasome‐dependent pathway. 38 The half‐life of the SCD1 protein was then analyzed by the cycloheximide chase assay. First, 786‐O cells were harvested with CHX (100 μg/ml) for 0, 3, 6, and 9 h. As the western blot is shown (Figure S2), the protein content of SCD1 at 3 h was degraded to about $70\%$ of the protein content at 0 h, and at 6 h, the protein content was degraded to about $30\%$ of the protein content of SCD1 at 0 h. Then, 786‐O cell was treated with CHX (100 μg/ml) for 0, 3, 4, 5, and 6 h. It was found that the protein content of SCD1 was degraded to about $50\%$ at 5 h compared with that at 0 h (Figure S2). Therefore, it could be concluded that the half‐life of the SCD1 protein was 5 h. Subsequently, cells stably expressing shRNA‐Ctrl and shRNA‐RUNX2 were treated with CHX (100 μg/ml) for the indicated time, then harvested the cell protein for immunoblotting. The half‐life of the SCD1 protein was reduced after RUNX2 depletion (Figure S2). These results suggest that RUNX2 might regulate the stability of the SCD1 protein, probably by inhibiting its ubiquitination degradation pathway. FIGURE 6: RUNX2 regulated the SCD1 protein expression in ccRCC cells. (A) The mRNA level of RUNX2 and SCD1 was determined by RT‐qPCR in 786‐O and ACHN cells transfected with shRNA‐RUNX2. (B) The protein level of RUNX2 and SCD1 was determined by western blot in 786‐O and ACHN cells transfected with shRNA‐RUNX2. (C) The mRNA level of RUNX2 and SCD1 was determined by RT‐qPCR in CAKI‐1 and OS‐RC‐2 cells transfected with LV‐RUNX2. (D) The protein level of RUNX2 and SCD1 was determined by western blot in CAKI‐1 and OS‐RC‐2 cells transfected with LV‐RUNX2. (E) The fold enrichment of RUNX2 on SCD1 promotor in 786‐O and ACHN cells with high RUNX2 expression. (F) The physical interaction between RUNX2 and SCD1 in 786‐O and ACHN cells was examined by co‐immunoprecipitation assay. p < 0.05 was considered significant, ns, not significant. ## SCD1 partially restored the effect of RUNX2 in the progression of ccRCC For the sake of investigating the part of SCD1 played in RUNX2‐mediated ccRCC cell growth, migration, and invasion, the RUNX2 overexpression cells were transfected with short hairpin (sh) RNAs targeting SCD1 (LV‐RUNX2/shRNA‐SCD1) and the RUNX2 knockdown cells were transfected with viral particles containing the full‐length fragment of SCD1 (shRNA‐RUNX2/LV‐SCD1). The RUNX2 expression was elevated at the mRNA and protein level when SCD1 was overexpressed and the protein expression level of the β‐catenin signaling pathway also rose after overexpression of SCD1 (Figure 7A, B). The migration assay showed that the migration potential in RUNX2 knockdown ccRCC cells was enhanced as a result of SCD1 overexpression (Figure 7C). Additionally, EdU assay and colony formation assay revealed that ccRCC cell proliferation was impaired when deficiency of RUNX2, whereas the decline in cell growth resulted from the silence of RUNX2 was incompletely restored with SCD1 overexpression (Figure 7D, E). Conversely, the mRNA and protein expression of RUNX2 was downregulated with the silence of SCD1, and the expression of β‐catenin was also decreased at a protein level (Figure 8A, B). The enhanced ability of migration, proliferation as well as colony formation caused by RUNX2 overexpression was partially reversed by SCD1 knockdown (Figure 8C–E). These data suggested that the RUNX2‐SCD1 axis was likely to be conducive to the development of ccRCC. FIGURE 7: The decline of cell ability in proliferation and migration caused by RUNX2 knockdown was partially reversed with SCD1 overexpression. (A) Cells transfected with shRNA‐Ctrl and shRNA‐RUNX2 were transfected with LV‐Ctrl or LV‐SCD1, and the mRNA expression of RUNX2 and SCD1was determined by RT‐qPCR. (B) The protein expression of β‐catenin, RUNX2, and SCD1 was detected by Western blot. (C) The migration ability of 786‐O and ACHN cells was examined by transwell assay (magnification × 20). (D) The proliferation ability of 786‐O and ACHN cells was determined by EdU assay (magnification × 200). (E) The colony formation ability of 786‐O and ACHN cells was determined by colony formation assay. p < 0.05 was considered significant, ns, not significant. FIGURE 8: The elevated potential of cell proliferation and migration caused by RUNX2 overexpression was partially restored by the downregulation of SCD1. (A) Cells transfected with LV‐Ctrl and Lv‐RUNX2 were transfected with shRNA‐Ctrl or shRNA‐SCD1, and the mRNA expression of RUNX2 and SCD1was determined by RT‐qPCR. (B) The protein expression of β‐catenin, RUNX2, and SCD1 was detected by Western blot. (C) The migration ability of CAKI‐1 and OS‐RC‐2 cells was examined by transwell assay (magnification × 20). (D) The proliferation ability of CAKI‐1 and OS‐RC‐2 cells was determined by EdU assay (magnification × 200). (E) The colony formation ability of CAKI‐1 and OS‐RC‐2 cells was determined by colony formation assay. p < 0.05 was considered significant, ns, not significant. ## DISCUSSION In this research, RUNX2 was expressed at a higher level in ccRCC cell lines and ccRCC tissues by demonstration. Overexpression of RUNX2 was able to enhance ccRCC cell growth and migration as well as activate the Wnt/β‐catenin signaling pathway. Furthermore, this study indicates that RUNX2 might be a potential biomarker and a therapeutic target for the clinical treatment of ccRCC. Besides, RUNX2 could physically interact with SCD1, contributing to ccRCC cell proliferation and migration. Such data show that RUNX2 could intermodulate with SCD1 to accelerate the development of ccRCC by activating the Wnt/β‐catenin signaling pathway. RUNX2 is thought to relate to the proliferation, invasion, and metastasis of diverse malignant tumor cells. 13, 39, 40 Until now, RUNX2 expression has been mainly reported in cells of the osteoblast lineage, and RUNX2 plays a critical part as bone marrow mesenchymal stem cells (BMSCS) differentiate and mature into osteoblasts during the development of bone. 41, 42, 43 Nevertheless, the effect of RUNX2 on ccRCC is open to clarification. From this research, RUNX2 was discovered to be highly expressed in both ccRCC cell lines and ccRCC tissues. RUNX2 was irrelevant to gender, age, tumor size, and tumor stage. Nevertheless, RUNX2 was markedly related to Fuhrman grade. A recent study suggests that the clinical survival rate of ccRCC patients with high expression of RUNX1 is lower than that of ccRCC patients embodied with low RUNX1 expression. 44 These data reveal that RUNX2 might have a protumorigenic effect in ccRCC. To functionally dissect the potential influence of RUNX2 on the pathogenesis of ccRCC, the expression of RUNX2 in ccRCC cell lines was examined. With regards to the expression of RUNX2, high levels in 786‐O and ACHN cells, moderate levels in 769‐P cells, and CAKI‐1 and OS‐RC‐2 cells at low levels were observed. The 786‐O, ACHN, CAKI‐1, and OS‐RC‐2 cells were selected for finding out the biological roles of RUNX2 in the invasion as well as proliferation of ccRCC cells. Given that dysregulation of the Wnt/β‐Catenin signaling pathway is also an independent predictor of oncologic results in patients with ccRCC, 45 it was discovered that the clone formation, proliferation, and migration ability of ccRCC cells was weakened by RUNX2 knockdown and the Wnt/β‐catenin signaling pathway was also inactivated, while there was an opposite outcome when RUNX2 was upregulated. And the protein expression of RUNX2 and β‐catenin was upregulated in ccRCC tumor tissues. There was an obvious correlation between RUNX2 and β‐catenin expression at protein levels. Studies have demonstrated that RUNX2 could alleviate high glucose‐suppressed osteogenic differentiation via the Wnt/β‐catenin pathway and it has also been discovered that RUNX2 could control osteosarcoma apoptosis via the Wnt/β‐catenin signaling pathway. 46, 47 It was demonstrated that GSK‐3β, AXIN1, and DVL2 could regulate the Wnt/β‐catenin signaling pathway. 48, 49, 50 In our paper, it was revealed that the β‐catenin expression could not be regulated via RUNX2 transcriptionally at the mRNA level, and the protein expression of important regulators of the Wnt/β‐catenin signaling pathway such as GSK‐3β, AXIN1, and DVL2 was not affected by RUNX2 expression. It could be concluded that RUNX2 could activate Wnt/β‐catenin signaling but not via regulating GSK‐3β, AXIN1, and DVL2. In this study, it is demonstrated that RUNX2 might promote the development and progression of ccRCC via Wnt/β‐catenin signaling pathway activation. However, how RUNX2 could regulate Wnt/β‐catenin signaling. More experiments still need to be done in the future. SCD1 is a lipid‐modifying enzyme that catalyzes the ttransformation of saturated fatty acid to a monounsaturated fatty acid. Besides, the expression of SCD1 is commonly upregulated in diverse tumor types. 22, 51, 52 Studies have demonstrated the involvement of SCD1 in the promotion of proliferation, migration, metastasis, and tumor growth in cancer cells of different origins including the kidneys, bladder, liver, colon, thyroid, and endometrium. 23, 53, 54, 55, 56, 57 Western blot analysis of ccRCC tissues derived from clinical tumor specimens revealed that SCD1 protein expression was in positive correlation with RUNX2 protein expression. The mRNA and protein expression of SCD1 was decreased on account of the downregulation of RUNX2, whereas no notable change was found in SCD1 expression at the mRNA and protein level when RUNX2 was overexpressed. It could be inferred that there might be some unknown factor between RUNX2 and SCD1 that participated in the regulatory relationship between them. Chromatin immunoprecipitation assay revealed that RUNX2 could not bind to the promoters of SCD1, whereas the RUNX2 protein could interact with SCD1 protein physically, which was demonstrated by the co‐immunoprecipitation assay. Studies have indicated that SCD1 is ubiquitinylated prior to proteasomal degradation and there is supporting evidence illuminating that the 66‐residue N‐terminal segment primarily takes charge of SCD1 ubiquitin degradation and this segment induces instability in an otherwise stable endoplasmic reticulum membrane protein. 38, 58 It could be hypothesized that the decrease of SCD1 may be induced by RUNX2 via the ubiquitin‐proteasome‐dependent pathway. Cycloheximide tracing experiment showed that the degradation rate of SCD1 protein was significantly accelerated in the absence of RUNX2 protein. In summary, RUNX2 might activate and maintain the stability of the SCD1 protein. Finally, it was found that the ability of ccRCC cells proliferation and migration was partially restored by a protein rescue experiment. A report has identified that the overexpression of SCD1 is in relation to the growth, migration, and invasion of numerous neoplastic lesions. 59, 60 From this research, the mRNA and protein expression of RUNX2 was enhanced when SCD1 was overexpressed, and the RUNX2 expression was lowered at the mRNA and protein level as a result of SCD1 knockdown. Furthermore, in RUNX2 knockdown cells, the ccRCC progression was enhanced by SCD1 overexpression and the Wnt/β‐catenin signaling pathway was activated as SCD1 was overexpressed. Conversely, the migration and proliferation ability of ccRCC was inhibited by SCD1 silencing in RUNX2 overexpression cells and the activation of Wnt/β‐catenin signaling pathway was also decreased when SCD1 was knockdown. Therefore, it could be speculated that co‐expression of RUNX2 and SCD1 might play an essential role in ccRCC cell proliferation and migration by activating the Wnt/β‐catenin signaling pathway. In conclusion, RUNX2 could intermodulate with SCD1 and activate the Wnt/β‐catenin signaling pathway to facilitate the progress of ccRCC. However, there might be some co‐mediating factors or some other regulatory modes between RUNX2 and SCD1. Consequently, it is in need of exploring the regulatory mechanism between them further. ## AUTHOR CONTRIBUTIONS XS carried out experiments and also completed the manuscript; JL and BL gathered the data; CP carried out data analysis; and CK and ZL proceeded with experimental design and supervised all experiments and manuscripts. All related authors have examined and approved the final manuscript. ## Funding information No particular grant that belonged to funding agencies in public, commercial, or nonprofit fields was given to such a study. ## CONFLICT OF INTEREST The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## ETHICAL APPROVAL STATEMENT With the permission of the Ethics Committee of the First Hospital of China Medical University granted for the research, written informed consents were also achieved among every related patient. ## DATA AVAILABILITY STATEMENT The data sets employed and/or analyzed in the current study are attainable from the relevant author at reasonable request. ## References 1. Siegel RL, Miller KD, Jemal A. Cancer statistics. CA Cancer J Clin (2019) 69 7-34. PMID: 30620402 2. Capitanio U, Bensalah K, Bex A. Epidemiology of renal cell carcinoma. 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--- title: 'Internet‐administered, low‐intensity cognitive behavioral therapy for parents of children treated for cancer: A feasibility trial (ENGAGE)' authors: - Ella Thiblin - Joanne Woodford - Christina Reuther - Johan Lundgren - Nina Lutvica - Louise von Essen journal: Cancer Medicine year: 2022 pmcid: PMC10028033 doi: 10.1002/cam4.5377 license: CC BY 4.0 --- # Internet‐administered, low‐intensity cognitive behavioral therapy for parents of children treated for cancer: A feasibility trial (ENGAGE) ## Abstract A single‐arm feasibility trial showed overall acceptability and feasibility of an internet‐administered, guided, low‐intensity cognitive behavioral therapy intervention for parents of childhood cancer survivors, and progression criteria were met for recruitment, retention, missing data, and harms, indicating methods, study procedures, and the overall the trial and intervention was feasible and acceptable. However, completion of assessments at each timepoint and intervention adherence were under progression criteria, meaning some modifications to the study protocol intervention are required before commencing a pilot randomized controlled trial. ### Background Parents of children treated for cancer may experience mental health difficulties, such as depression and anxiety. There is a lack of evidence‐based psychological interventions for parents, with psychological support needs unmet. An internet‐administered, guided, low‐intensity cognitive behavioral therapy‐based (LICBT) self‐help intervention may provide a solution. ### Methods The feasibility and acceptability of such an intervention was examined using a single‐arm feasibility trial (ENGAGE). Primary objectives examined: [1] estimates of recruitment and retention rates; [2] feasibility and acceptability of data collection instruments and procedures; and [3] intervention feasibility and acceptability. Clinical outcomes were collected at baseline, post‐treatment (12 weeks), and follow‐up (6 months). ### Results The following progression criteria were met: sample size was exceeded within 5 months, with $11.0\%$ enrolled of total population invited, study dropout rate was $24.0\%$, intervention dropout was $23.6\%$, missing data remained at ≤$10\%$ per measure, and no substantial negative consequences related to participation were reported. Intervention adherence was slightly lower than progression criteria ($47.9\%$). ### Conclusion Findings suggest an internet‐administered, guided, LICBT self‐help intervention may represent a feasible and acceptable solution for parents of children treated for cancer. With minor study protocol and intervention modifications, progression to a pilot randomized controlled trial (RCT) and subsequent superiority RCT is warranted. ## INTRODUCTION Advances in cancer treatment have resulted in increased childhood cancer survival rates worldwide. 1, 2 Parents are the primary source of support for children with cancer, with many actively involved in care years after treatment completion. 3 While treatment completion is an important milestone, it is also a period of vulnerability for parents. 4, 5 Psychological difficulties such as anxiety ($19.7\%$ to $43.4\%$), 6, 7 and depression ($14.4\%$ to $43.4\%$) 6, 7 are reported. Parents also report post‐traumatic stress symptoms 8, 9 and $19.1\%$ of mothers and $7.8\%$ of fathers report at least partial post‐traumatic stress disorder (PTSD) 5 years after treatment. 10 Further, parents face socioeconomic impacts 11 and restrictions on daily life activities. 12 However, parents' psychological needs are unmet 13 and barriers to seeking support include lack of time, guilt, and putting the child's needs first. 14, 15 Solutions to increase access to psychological interventions are being implemented globally, 16 for example low‐intensity cognitive behavioral therapy (LICBT). 17 LICBT is delivered through self‐help interventions (e.g., print or digital format), including internet‐administered CBT (iCBT) 18 rather than by traditional psychologists. Guided iCBT (supported by a trained professional) is associated with higher effect sizes than unguided interventions 19 and show equivalent overall effects to traditional face‐to‐face interventions. 20 An internet‐administered, guided, LICBT based self‐help intervention may also address barriers to seeking support given increased privacy and flexibility. 21 In previous research, we have shown that an iCBT self‐help intervention decreases symptoms of anxiety, depression, and post‐traumatic stress in parents of children on cancer treatment. 22, 23 Recent research has also demonstrated a video‐conference‐based internet‐administered intervention to be effective for parents of children living with a life‐threatening illness (including cancer). 24 However, to the best of our knowledge, the only existing internet‐administered intervention for parents of children who have completed treatment, with published results, is an online group‐based, intervention delivered in real time via videoconferencing by psychologists. 25, 26 As such, there is currently no internet‐administered, LICBT based self‐help intervention available for parents of children who have completed cancer treatment. A program of phase I (development) research, following the Medical Research Council complex interventions framework 27, 28 informed development of the internet‐administered LICBT intervention EJDeR. 10, 29, 30, 31, 32, 33, 34 Following phase II (feasibility) 27, 28 we conducted the current study, the single‐arm feasibility trial ENGAGE. Primary objectives examined methodological, procedural, and clinical uncertainties 35 to prepare for the design and conduct of a future pilot RCT and subsequent superiority RCT. Information was gathered on: [1] estimates of recruitment and retention rates; [2] feasibility and acceptability of data collection instruments and procedures; and [3] feasibility and acceptability of the intervention. An embedded mixed‐method process evaluation examined the feasibility of collecting weekly assessments and semi‐structured interviews at baseline and post‐treatment explored: [1] self‐reported psychological concerns, healthcare utilization, and productivity losses; [2] treatment expectations; [3] intervention acceptability; and [4] perceived impact of the intervention on difficulties and mechanisms of change. Findings from semi‐structured interviews at baseline and post‐treatment to inform the embedded process evaluation will be reported elsewhere. ## MATERIALS AND METHODS The study protocol is published 36 and registered, with results following the Consolidated Standards of Reporting Trials (CONSORT) 2010 statement extension for randomized pilot and feasibility trials. 37 ## Study design A single‐arm feasibility trial of a guided, internet‐administered LICBT‐based intervention (EJDeR), with data collected at baseline, post‐treatment (12 weeks), and follow‐up (6 months) with an embedded mixed‐methods process evaluation. EJDeR is delivered via the U‐CARE‐portal (Portal), a web‐based platform, designed to deliver internet‐administered interventions and support the execution of study procedures. ## Participants Eligible participants were: [1] parent of a child diagnosed with childhood cancer (0–18 years) who completed treatment 3 months to 5 years previously (timespan informed by our previous longitudinal research that has identified this as a time period of vulnerability for parents) 9, 10, 29, 30; [2] resident in Sweden; [3] able to read and understand Swedish; [4] able to access e‐mail, internet, and Bank‐ID (a Swedish citizen authentication system); and [5] self‐reporting a need for psychological support related to the child's cancer. Exclusion criteria were: [1] a self‐reported or clinician assessed (with the Mini‐International Neuropsychiatric Interview, M.I.N.I., version 7.0.0) 38 severe and enduring mental health difficulty (e.g., PTSD) and/or misuse of alcohol, street drugs, or prescription medication; [2] acute suicidality; and [3] ongoing psychological treatment respectively. ## Postal study invitations Personal identification numbers of children who had completed treatment 3 months to 5 years previously were provided by the Swedish Childhood Cancer Registry (CCR), and linked to parents' names and addresses via NAVET, a population registry from the Swedish Tax Agency. The first recruitment block was pre‐selected with parents of children who had ended treatment near to 5 years previously. The following four blocks were randomly selected by a member of the Portal team, independent to the research team, using a computer‐generated simple randomization procedure. Postal study invitation packs, sent to parents' home addresses, included a: [1] study invitation letter; [2] study information sheet and link to a secure website on the Portal (information in text and video format); [3] paper reply‐slip to register interest in participation; [4] paper‐based opt‐out form and reasons for non‐participation questionnaire; and [5] freepost envelope. Parents could register interest in participation and request more study information via the Portal, post, telephone or e‐mail. ENGAGE included an embedded recruitment RCT, investigating the effect of personalized versus non‐personalized study invitations on recruitment and retention 39 with results reported separately. 40 ## Online advertisements Advertisements were placed on social media sites, websites, and newsletters of 12 cancer organizations and interest groups. ## Opt‐out and reminders Parents invited via the post could opt‐out of ENGAGE via the Portal, post, telephone, or e‐mail. Up to five reminder telephone contact attempts were made if parents did not respond within 4 weeks of invitation. Telephone numbers were identified using internet search engines. Contact attempts were documented in paper‐based case report forms (CRFs). If a telephone number was not identified, a postal study invitation reminder letter was sent. ## Reasons for non‐participation Parents opting out of ENGAGE were asked to complete a reason for non‐participation questionnaire including a closed, multiple choice question and an open question for other reason(s). 41 Reasons for non‐participation were collected to enable the identification of potential modifiable barriers to participation (e.g., treatment preferences, interest in internet‐administered self‐help, burden of trial procedures). ## Consent, eligibility, and baseline Parents provided consent via the Portal. Parents who registered interest in participation but did not provide consent, or opt out, within 2 weeks, were contacted to confirm interest in participation (maximum five reminders via telephone, SMS or e‐mail). Parents providing consent were contacted to organize a telephone eligibility interview with a licensed psychologist. Interviews included: [1] questions concerning eligibility criteria; with those eligible completing specific modules of the M.I.N.I., and; [2] questions concerning parent and child sociodemographic and clinical characteristics (Table 1). TABLE 1 \| Variable \| Measure \| Time‐point \| Time‐point.1 \| Time‐point.2 \| Time‐point.3 \| Time‐point.4 \| Mode of administration \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variable \| Measure \| Eligibility interview \| Baseline \| Post‐treatment \| Weekly process evaluation \| Follow‐up \| Mode of administration \| \| Child age, legal gender, cancer diagnosis, date of first diagnosis, date of end of treatment (where available), type of treatment \| Childhood Cancer Registry \| \| \| \| \| \| Swedish Childhood Cancer Registry \| \| Eligibility (inclusion and exclusion) criteria; parent sociodemographic and clinical characteristics (age, gender, relationship status, highest level of education, employment status, number and ages of children, current housing situation, region of birth, previous psychological treatment, physical health problem, previous traumatic/difficult life events and internet usage); child sociodemographic and clinical characteristics (age, gender, cancer diagnosis, time since end of treatment, type of treatment, cancer recurrence) \| Structured questions \| ✓ \| \| \| \| \| Telephone \| \| Psychiatric (mood and anxiety) disorders, drug and alcohol misuse, suicidality \| M.I.N.I. \| ✓ \| \| ✓ \| \| ✓ \| Telephone \| \| PTSS \| PCL‐5 \| \| ✓ \| ✓ \| ✓ \| ✓ \| Portal/Telephone Weekly process evaluation: Portal only \| \| PTSS \| PCL‐C \| \| ✓ \| ✓ \| ✓ \| ✓ \| Portal/Telephone Weekly process evaluation: Portal only \| \| Depression \| PHQ‐9 \| \| ✓ \| ✓ \| ✓ \| ✓ \| Portal/Telephone Weekly process evaluation: Portal only \| \| Anxiety \| GAD‐7 \| \| ✓ \| ✓ \| \| ✓ \| Portal/Telephone \| \| Fear of recurrence \| FRHC \| \| ✓ \| ✓ \| \| ✓ \| Portal/Telephone \| \| Fear of serious health condition \| FRHC \| \| ✓ \| ✓ \| \| ✓ \| Portal/Telephone \| \| Psychological inflexibility and experiential avoidance \| AAQ‐6 \| \| ✓ \| ✓ \| ✓ \| ✓ \| Portal/Telephone Weekly process evaluation: Portal only \| \| Depressed inactivity \| BADS \| \| ✓ \| ✓ \| ✓ \| ✓ \| Portal/Telephone Weekly process evaluation: Portal only \| \| Fatigue \| FSS \| \| ✓ \| ✓ \| \| ✓ \| Portal/Telephone \| \| Quality of life \| EQ‐5D \| \| ✓ \| ✓ \| \| ✓ \| Portal/Telephone \| \| Self‐compassion \| SCS‐SF \| \| ✓ \| ✓ \| \| ✓ \| Portal/Telephone \| \| Health economics \| TIC‐P \| \| ✓ \| ✓ \| \| \| Portal/Telephone \| Eligible participants were enrolled and invited to an optional semi‐structured telephone interview with a licensed psychologist to explore concerns, needs, healthcare utilization, and productivity loss, alongside expectations on the trial and intervention. Participants gained access to the Portal assessment at baseline (Table 1) and were required to complete the assessment within 28 days. Participants who had not completed within 14 days were reminded up to five times (telephone, SMS, or e‐mail). Upon completion of the Portal assessment at baseline, participants gained access to EJDeR and were allocated to an e‐therapist. ## Intervention The EJDeR protocol is published following the Template for Intervention Description and Replication (TIDieR) checklist. 34, 42 The first version of the intervention used a multi‐strand approach utilizing several CBT techniques, including third‐wave CBT (e.g., mindfulness and compassion focused therapy), delivered over 10 modules. 32, 36 Following public and professional involvement 34 the number of CBT techniques were minimized to reduce complexity and length 34 and a LICBT approach was adopted. EJDeR is a guided internet‐administered LICBT intervention delivered over 12 weeks on the Portal and includes text, illustrations, film, audio files, in‐module exercises, and homework exercises. EJDeR includes two LICBT techniques: behavioral activation (BA) for depression, and worry management (WM) for generalized anxiety disorder (GAD). 34 It consists of four modules: [1] introduction and psychoeducation; [2] BA; [3] WM, and; [4] relapse prevention (Figure S1). After completing the first module and an initial assessment session with an e‐therapist, participants work with BA or WM, dependent on their main difficulty. After completion of BA or WM, parents may use the remaining LICBT technique. All participants gain access to the relapse prevention module. E‐therapist guidance is provided via an initial assessment session (video‐conferencing or telephone, ≈45 min); weekly support via written messages via the Portal (≈20–30 min/week), and a mid‐intervention booster session (video‐conferencing or telephone, ≈30–45 min) following structured protocols. 43, 44, 45 E‐therapists also provided at‐need written messages to participants if requested. A 2‐day training program with two experts in LICBT, and weekly group clinical supervision via video‐conferencing with a Swedish licensed psychologist with expertise in iCBT were provided. ## Outcomes Feasibility outcomes are informed by the CONSORT 2010 statement extension for randomized pilot and feasibility trials, 35, 37 and relate to methodological uncertainties (e.g. estimates of recruitment and retention rates, reasons for non‐participation and study drop‐out), procedural uncertainties (e.g. feasibility and acceptability of data collection instruments and procedures, including percentages completing assessments and numbers of missing items), and clinical uncertainties (e.g. intervention feasibility and acceptability, including participants' adherence to the intervention and impressions and experiences of working with the intervention). All feasibility outcomes are shown in Table 2 alongside progression criteria. 46 Some feasibility outcomes 36 were revised to improve clarity and reflect protocol modifications (Table S1). Intervention acceptability is further explored in the embedded process evaluation (reported elsewhere). Progression criteria were informed by the researchers' previous experience, our previous longitudinal research with the population 6 and relevant literature on recruitment, 47, 48 attrition, 49 adherence, 50, 51 and missing data. 52 TABLE 2 \| Outcome \| Evaluation \| Progression criteria to controlled trial a \| \| --- \| --- \| --- \| \| Recruitment and eligibility \| Number identified via postal study invitations (Swedish Childhood Cancer Registry and the Swedish Tax Agency [NAVET]) and/or via Online advertisements via cancer organizations and interest groups \| No criteria set \| \| Recruitment and eligibility \| Percentage consented to participate, assessed for eligibility, fulfilling eligibility criteria, and enrolled (of total number invited) \| ≥9% enrolled of total participant population invited (e.g., included of total participant population invited) \| \| Recruitment and eligibility \| Reasons for ineligibility \| No criteria set \| \| Recruitment and eligibility \| Ambiguities regarding eligibility criteria including diagnostic uncertainties in M.I.N.I. \| No criteria set \| \| Recruitment and eligibility \| Reasons for non‐participation \| No criteria set \| \| Data collection \| Percentage completing assessments M.I.N.I. (eligibility interview, post‐treatment, and follow‐up) Semi‐structured interview (baseline and post‐treatment) Portal assessment (baseline, post‐treatment, and follow‐up) Weekly Portal assessment \| ≥70% answering all questions at all assessments \| \| Data collection \| Numbers of missing items M.I.N.I. (eligibility interview, post‐treatment, and follow‐up) Portal assessment (baseline, post‐treatment, and follow‐up) Weekly Portal assessment \| ≤10% per measure \| \| Attrition \| Rate of study dropout Rate of intervention dropout \| ≤30% ≤30% \| \| Resources needed to complete the study and the intervention \| Length of time required for: Participants to work through the intervention Participants to complete the initial assessment session and mid‐intervention booster session with e‐therapist Participants to complete the eligibility interview, M.I.N.I., semi‐structured interview, Portal assessment at each time‐point E‐therapists to deliver the intervention \| No criteria set \| \| Resources needed to complete the study and the intervention \| Number of: Internal and external study personnel Reminder contacts needed during recruitment Reminder contacts needed to complete Portal assessment at each time‐point Contacts needed to arrange eligibility interview, M.I.N.I. and semi‐structured interview over the telephone at each time‐point \| No criteria set \| \| Participants' adherence to intervention \| Number of: Participants adhering to the minimum treatment dose (MTD) Opened modules Completed LICBT modules started with Completed initial assessment sessions Completed mid‐intervention booster sessions Completed homework sheets \| ≥50% adhering to MTD, i.e., attending the initial assessment session, completing the introduction and psychoeducation module and one LICBT treatment module (i.e. behavioral activation or worry management) and attending the mid‐intervention booster session. \| \| Participants' use of the intervention \| Number of: Participant logins Participant written messages E‐therapist written messages \| No criteria set \| \| E‐therapists' adherence to intervention \| Content of initial assessment session, mid‐intervention booster session, and written messages via the Portal \| No criteria set \| \| Participants' acceptability of the intervention and data collection \| Reasons for low adherence and dropout from study and intervention Number of risk assessments Impressions and experiences of working with the intervention (including positive and negative consequences) and of completing assessments and interviews b \| No criteria set No criteria set <1 participant reporting substantial negative consequences related to participation in the study and/or intervention \| The post‐treatment time‐point was set at 12 weeks, immediately after the EJDeR intervention had finished. A 6‐month follow‐up time‐point was selected to examine the feasibility of longer‐term data collection. Sociodemographic data on parents and children, specific modules of the M.I.N.I. assessing current and past psychiatric disorders and suicidality, and psychological and health economic measures are reported in Table 1, alongside data collection time‐point and mode of administration. A random $10\%$ sample of M.I.N.I.s were coded by a member of the research team, with inter‐rater reliability calculated as satisfactory (α = 0.92). 53 Semi‐structured interviews were conducted at baseline and post‐treatment with licensed psychologists (data reported elsewhere). ## Sample size Following recommendations for feasibility trial sample sizes the target sample size was 50. 54 ## Double data entry Paper‐based CRFs were used for data collected outside the Portal, with data independently entered onto a Microsoft® Access database by two research assistants, exported into Microsoft® Excel spreadsheets, with accuracy checked using Microsoft® Spreadsheet. ## Reminders A prompt (SMS and/or e‐mail) was sent when it was time to complete Portal assessments with automatic reminders (SMS and/or e‐mail) sent if not completed within 1 week. Participants who did not complete Portal assessments within 2 weeks, were offered to complete over the telephone, with up to six reminder attempts made via telephone, SMS, or email. Informed by evidence suggesting study newsletters can improve retention 55 a newsletter was sent via the Portal 6 weeks before post‐treatment and follow‐up. ## Participant adherence The minimum treatment dose (MTD) (i.e., full intervention adherence) was defined as: [1] attendance of the initial assessment session; [2] completion of the introduction and psychoeducation module; [3] completion of one LICBT module (BA or WM); and [4] attendance of the mid‐intervention booster session. ## E‐therapist adherence A $15\%$ random sample of initial assessment and mid‐intervention booster sessions and written messages via the Portal from e‐therapists were marked for adherence, with each item within the structured support protocols marked as absent/present. ## Statistical methods Feasibility outcomes relating to recruitment and eligibility, data collection, attrition, resources needed to complete the study and the intervention, participants' adherence to the intervention, participants' use of the intervention, e‐therapists' adherence to the intervention, and participants' sociodemographic characteristics are reported using descriptive statistics. Numbers and percentages (and $95\%$ CIs where appropriate) are reported for categorical variables, means and SDs for continuous variables. Numbers and percentages of participants meeting criteria for each M.I.N.I. diagnosis is reported at each time‐point. Means and SDs for continuous variables and numbers and percentages for categorical variables are reported for all outcomes at each time‐point. Mean change scores (with $95\%$ CIs) are reported for Portal assessments of psychological outcomes at each time‐point, to describe the study sample. ## Risk and safety procedures Participants scoring >0 on PHQ‐9 (depression) question 9 (suicidal ideation), or a total score >20 (severe depression) were risk assessed by a licensed psychologist within one working day. If needed, participants were directed to appropriate support and excluded. ## Public involvement A Parent Research Partner (PRP) group was established consisting of four parents with lived experience of being a parent of a child treated for cancer (two fathers and two mothers, aged between 45 and 54 years of age). The PRP group was involved in optimizing the acceptability of EJDeR e.g., relevancy, ease of understanding, content, language, and structure. 34 The group was also consulted on the development of participant invitation letters. 39, 40 ## RESULTS Data supporting feasibility objectives pertaining to recruitment and eligibility, data collection, attrition, and resources needed to complete the study and intervention are available in Zenodo. 56 ## Recruitment and eligibility Participant flow is summarized in an adapted CONSORT diagram (Figure 1). Recruitment took place over 5 months (03‐07‐2020 and 30‐11‐2020). Of 509 study invitations sent via CCR and NAVET, 60 consented ($11.8\%$, $95\%$CI, [9.1–14.9]); 57 were assessed for eligibility ($11.2\%$, $95\%$CI, [8.6–14.3]); and 56 fulfilled eligibility criteria and were enrolled ($11.0\%$, $95\%$CI, [8.4–14.1]) exceeding progression criteria of ≥$9\%$ enrolled of total potential participant population invited. An additional 21 consented from other recruitment strategies (online advertisements and parents invited by the CCR passing the invitation to their partner), 19 were assessed for and fulfilled eligibility, and enrolled. Nine parents were excluded prior to consent and one was excluded during the eligibility interview (acute suicidality). In total, 75 participants were enrolled, exceeding sample size expectations (Figure 1). FIGURE 1: Study flow of participants in the ENGAGE feasibility trial. Solid black lines denote participant flow through the study, including study drop outs i.e., those who discontinued the study. Dashed gray lines represent participants that were lost to follow‐up during assessments at post‐treatment (12 weeks) and follow‐up (6 months) respectively, but had not dropped out of the study. Ambiguities regarding eligibility arose in six cases. In three cases, parents met criteria for PTSD according to the M.I.N.I. but were included as symptoms were mild. In one case a parent met criteria for Alcohol Use Disorder, and was included due to being in early remission. One was attending a psychological support group; study inclusion was delayed until the group ended. One reported their child had recently relapsed, however, as treatment had not started, the parent was included. Out of 509 parents identified via the CCR and NAVET, 164 ($32.2\%$) opted out, and 137 provided a response to the multiple‐choice question regarding reasons for non‐participation. Not experiencing any need for psychological support ($\frac{93}{137}$, $67.9\%$) was most commonly reported (Table S2). Full results concerning opt‐out rates and reasons for non‐participation have been reported separately. 41 ## Sociodemographic and clinical characteristics Baseline sociodemographic and clinical characteristics for participants ($$n = 75$$) are summarized in Table 3. TABLE 3 \| Sociodemographic and clinical characteristics \| n (%) \| \| --- \| --- \| \| Age (years) \| Age (years) \| \| Mean (SD) range \| 42.8 (7.1) 26–62 \| \| Gender \| Gender \| \| Female \| 48 (64.0) \| \| Male \| 27 (36.0) \| \| Relationship status \| Relationship status \| \| Partner \| 63 (84.0) \| \| Single \| 12 (16.0) \| \| If partner, cohabiting \| If partner, cohabiting \| \| Yes \| 62 (98.4) \| \| No \| 1 (1.6) \| \| Highest level of education \| Highest level of education \| \| Lower secondary \| 1 (1.3) \| \| Upper secondary \| 15 (20.0) \| \| Post‐secondary non‐tertiary \| 3 (4.0) \| \| Tertiary \| 54 (72.0) \| \| PhD \| 2 (2.7) \| \| Employment status \| Employment status \| \| Employed \| 66 (88.0) \| \| Unemployed \| 9 (12.0) \| \| Number of children \| Number of children \| \| Median (range) \| 2.0 (1–5) \| \| Age of children \| Age of children \| \| Mean (SD) range \| 12.0 (7.1) 0.5–37 \| \| Housing situation \| Housing situation \| \| Rental \| 8 (10.7) \| \| Apartment ownership \| 17 (22.7) \| \| House ownership \| 47 (62.7) \| \| Other \| 3 (4.0) \| \| Region of birth \| Region of birth \| \| Nordic countries \| 63 (84.0) \| \| Asia \| 6 (8.0) \| \| Europe (excluding. Nordic countries) \| 5 (6.7) \| \| Africa \| 1 (1.3) \| \| Previous psychological treatment \| Previous psychological treatment \| \| Yes \| 40 (53.3) \| \| No \| 35 (46.7) \| \| Physical health problems \| Physical health problems \| \| Yes \| 24 (32.0) \| \| No \| 51 (68.0) \| \| Type of physical health problem a \| Type of physical health problem a \| \| Diseases of the musculoskeletal system and connective tissue \| 9 (12.0) \| \| Endocrine, nutritional and metabolic diseases \| 5 (6.7) \| \| Diseases of the genitourinary system \| 3 (4.0) \| \| Diseases of the circulatory system \| 2 (2.7) \| \| Diseases of the digestive system \| 2 (2.7) \| \| Diseases of the nervous system \| 2 (2.7) \| \| Diseases of the respiratory system \| 2 (2.7) \| \| Diseases of the skin and subcutaneous tissue \| 1 (1.3) \| \| Neoplasm \| 1 (1.3) \| \| Other cannot classify \| 3 (4.0) \| \| Previous traumatic/difficult life event \| Previous traumatic/difficult life event \| \| Yes \| 60 (80.0) \| \| No \| 15 (20.0) \| \| Type pf previous traumatic/difficult life event a \| Type pf previous traumatic/difficult life event a \| \| Child's cancer disease \| 34 (45.3) \| \| Death in family and miscarriage \| 21 (28.0) \| \| Severe disease/illness own/family/friends \| 18 (24.0) \| \| Divorce or separation \| 12 (16.0) \| \| Exposure to violence or sexual abuse \| 6 (8.0) \| \| Suicide/suicide attempt among family/friends \| 4 (5.3) \| \| War/terrorist attacks \| 3 (4.0) \| \| Other traumatic experiences \| 13 (17.3) \| Participants' internet usage is reported in Table S10. According to participant self‐report data, children treated for cancer were predominantly male ($$n = 38$$, $54.3\%$), had been diagnosed with Leukemia ($$n = 32$$, $45.7\%$) and treated with chemotherapy ($$n = 55$$, $78.6\%$). The children's mean age at the time of the eligibility interview was 10.6 years (SD 5.2, range, 2–24). Baseline sociodemographic and clinical characteristics for children are provided in Table S10. ## Data collection Data collection (baseline, post‐treatment, and follow‐up) took place between 24‐07‐2020 and 04‐10‐2021. Percentage completing assessments at each time‐point are reported in Table 4, and progression criteria of $70\%$ of participants answering all questions at all assessments was not met. Completion rates of weekly Portal assessments decreased from $65.7\%$ (week one) to $38.9\%$ (week 11) (Table S3). TABLE 4 \| Assessment \| Completed assessment \| Completed assessment.1 \| Completed assessment.2 \| Completed assessment.3 \| Completed assessment.4 \| Completed assessment.5 \| Completed assessment.6 \| Completed assessment.7 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Assessment \| Total study sample a \| Total study sample a \| Total study sample a \| Total study sample a \| Study sample at time‐point b \| Study sample at time‐point b \| Study sample at time‐point b \| Study sample at time‐point b \| \| Assessment \| N \| n \| % \| 95% CI \| N \| n \| % \| 95% CI \| \| Baseline \| Baseline \| Baseline \| Baseline \| Baseline \| Baseline \| Baseline \| Baseline \| Baseline \| \| Semi‐structured interview \| 75 \| 74 \| 98.7 \| 92.8, 100.0 \| 75 \| 74 \| 98.7 \| 92.8, 100.0 \| \| Portal assessment \| 75 \| 72 \| 96.0 \| 88.8, 99.2 \| 74 \| 72 \| 97.3 \| 90.6, 99.7 \| \| Post‐treatment \| Post‐treatment \| Post‐treatment \| Post‐treatment \| Post‐treatment \| Post‐treatment \| Post‐treatment \| Post‐treatment \| Post‐treatment \| \| M.I.N.I \| 75 \| 54 \| 72.0 \| 60.4, 81.8 \| 64 \| 54 \| 84.4 \| 73.1, 92.2 \| \| Semi‐structured interview \| 75 \| 53 \| 71.0 \| 59.0, 80.6 \| 64 \| 53 \| 82.8 \| 71.3, 91.1 \| \| Portal assessment \| 75 \| 42 \| 56.0 \| 44.1, 67.5 \| 64 \| 42 \| 65.6 \| 52.7, 77.1 \| \| Follow‐up \| Follow‐up \| Follow‐up \| Follow‐up \| Follow‐up \| Follow‐up \| Follow‐up \| Follow‐up \| Follow‐up \| \| M.I.N.I \| 75 \| 48 \| 64.0 \| 52.1, 74.8 \| 59 \| 48 \| 81.4 \| 69.1, 90.3 \| \| Portal assessment \| 75 \| 40 \| 53.3 \| 41.5, 65.0 \| 59 \| 40 \| 67.8 \| 54.4, 79.4 \| Missing data ranged from $0.01\%$–$4.1\%$ items missing per measure, bettering progression criteria (≤$10\%$). Missing data from the M.I.N.I. is reported in Table S4 and missing data for measures included in Portal assessments are reported in Table S5. Missing items for measures included in weekly Portal assessments are provided in Table S6. ## Attrition In total, $\frac{18}{75}$ ($24.0\%$ [$95\%$CI, 14.9–35.3]) of participants enrolled into the study dropped out of the study, bettering progression criteria (≤$30\%$). In total, $\frac{17}{72}$ ($23.6\%$ [$95\%$CI, 14.4–35.1]) of participants gaining access to EJDeR, dropped out of EJDeR, bettering progression criteria (≤$30\%$). ## Resources needed to complete the study and the intervention Length of time for participants to work through EJDeR and complete assessments at each time‐point are provided in Table S7. The number of reminder contacts needed during recruitment and for participants to complete Portal assessments are reported in Table S8. The number of contacts needed to arrange interviews at each time‐point are reported in Table S9. Seventy‐two participants gained access to EJDeR and 71 were allocated to an e‐therapist (one dropped out before allocation). Psychology program students ($$n = 10$$) supported 27 participants (mean = 2.7, range, 1–7) and spent a mean of 76.9 h (SD 29.7, range, 22.3–109.8) delivering EJDeR, attending training, supervision, and administration, equating to a mean of 2.9 h per participant each week (SD 1.3, range, 0.9–4.6). Due to students not having adequate time to support participant caseloads, the majority were supported by a CBT‐therapist internal to the research team ($$n = 32$$), a licensed psychologist in the research team ($$n = 5$$), and a licensed psychologist external to the research team ($$n = 7$$). The clinical supervisor worked for 155 h, including training, supervision, and administration. Difficulties recruiting research personnel was identified as a challenge. 57 The research team included the principal investigator, a researcher, a PhD student/e‐therapist, a research assistant, and an e‐therapist/research assistant. External study personnel included licensed psychologists ($$n = 7$$) and e‐therapists ($$n = 10$$). Paper‐based CRFs for study data were considered time and resource intensive, as was coordinating external study personnel. ## Participants' adherence to intervention Seventy‐two participants gained access to EJDeR. One was excluded shortly after access (severe and enduring mental health difficulty) and $\frac{34}{71}$ ($47.9\%$) adhered to the MTD, nearly meeting progression criteria of $50\%$. The mean number of modules opened was 2.3 (SD 0.9, range, 1–4), parents completed a mean of 1.7 modules (SD 1.3, range, 0–4), and a mean of 2.7 homework sheets (SD 2.8, range, 0–11). Initial assessment sessions were attended by $\frac{61}{71}$ ($85.9\%$) and mid‐intervention booster sessions were attended by $\frac{44}{71}$ ($62.0\%$). Visual inspection of data indicated differences in adherence rates by first LICBT module started and by gender. A post hoc descriptive analysis was performed. In total, $\frac{54}{71}$ ($76.1\%$) started a LICBT module, with 26 starting with BA and 28 with WM. In total, $\frac{20}{26}$ ($76.9\%$) starting with BA, and $\frac{14}{28}$ ($50.0\%$) starting with WM adhered to the MTD. Of the 71 participants, 25 were fathers, and 46 were mothers. For fathers: $\frac{8}{25}$ ($32.0\%$) started with BA and $\frac{7}{8}$ ($87.5\%$) adhered to the MTD; $\frac{12}{25}$ ($48.0\%$) started with WM, and $\frac{5}{12}$ ($41.7\%$) adhered to the MTD. For mothers, $\frac{18}{46}$ ($39.1\%$) started working with BA and $\frac{13}{18}$ ($72.2\%$) adhered to the MTD; $\frac{16}{46}$ ($34.8\%$) started with WM and $\frac{9}{16}$ ($56.3\%$) adhered to the MTD. ## Participants' use of the intervention A mean of 20 participant logins were made (SD 14.9, range, 1–72). A mean of 8.5 participant written messages were sent to e‐therapists (SD 7.6, range, 0–33), and a mean of 28.8 e‐therapist written messages (SD 16.3, range, 0–74) were sent to participants. ## E‐therapists' adherence to intervention Adherence rates were $90.5\%$ for initial assessment sessions, $85.2\%$ for mid‐intervention booster sessions, and $87.5\%$ for written communication between participants and e‐therapists. ## Participants' acceptability of the intervention and data collection Reasons for study dropout are reported in Figure 1. Nineteen risk assessments were conducted and two resulted in study exclusion. No participant reported substantial negative consequences related to study and/or intervention. A structured question asking participants whether the intervention was helpful was omitted by researcher error and it was therefore not possible to assess whether ≥$70\%$ of participants using the intervention reported it as helpful (Table 2). ## Psychological and health economics outcomes M.I.N.I. data at baseline, post‐treatment, and follow‐up are provided in Table S12. The mean and SD of outcomes at baseline, post‐treatment, and follow‐up, with $95\%$ CIs, are reported in Table 5, alongside observed changes from baseline to post‐treatment and from baseline to follow‐up (with $95\%$ CI). From baseline to follow‐up depressive symptoms decreased by an average of 3.1 PHQ‐9 points. From baseline to follow‐up anxiety symptoms decreased by an average of 2.9 GAD‐7 points. Descriptive data from the Treatment Inventory of Costs in Patients with psychiatric disorders (TIC‐P) are reported in Table S14. However, due to a large amount of missing data on the TIC‐P it is difficult to interpret this data in a meaningful way. TABLE 5 \| Outcome measures \| Baseline \| Baseline.1 \| Baseline.2 \| Baseline.3 \| Post‐treatment \| Post‐treatment.1 \| Post‐treatment.2 \| Post‐treatment.3 \| Follow‐up \| Follow‐up.1 \| Follow‐up.2 \| Follow‐up.3 \| Change from baseline to Post‐treatment \| Change from baseline to Post‐treatment.1 \| Change from baseline to Post‐treatment.2 \| Change from baseline to follow‐up \| Change from baseline to follow‐up.1 \| Change from baseline to follow‐up.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Outcome measures \| n \| M \| SD \| 95% CI a \| n \| M \| SD \| 95% CI a \| n \| M \| SD \| 95% CI a \| n \| M \| SD \| n \| M \| SD \| \| PCL‐5 \| 72 \| 16.0 \| 12.2 \| 10.5, 14.6 \| 42 \| 9.0 \| 8.7 \| 7.2, 11.1 \| 40 \| 5.2 \| 4.8 \| 3.9, 6.2 \| 42 \| −5.4 \| 6.3 \| 40 \| −8.2 \| 8.8 \| \| PCL‐C \| 72 \| 31.5 \| 10.9 \| 9.4, 13.0 \| 42 \| 25.1 \| 8.4 \| 6.9, 10.7 \| 40 \| 22.0 \| 4.7 \| 3.9, 6.0 \| 42 \| −4.9 \| 6.8 \| 40 \| −7.3 \| 7.9 \| \| PHQ‐9 \| 72 \| 6.6 \| 5.0 \| 4.3, 6.0 \| 42 \| 3.8 \| 2.9 \| 2.4, 3.7 \| 40 \| 2.8 \| 2.1 \| 1.7, 2.7 \| 42 \| −2.0 \| 3.1 \| 40 \| −3.1 \| 4.7 \| \| GAD‐7 \| 72 \| 6.1 \| 4.7 \| 4.0, 5.6 \| 42 \| 3.4 \| 2.9 \| 2.4, 3.7 \| 40 \| 2.6 \| 2.8 \| 2.3, 5.6 \| 42 \| −2.5 \| 3.9 \| 40 \| −2.9 \| 4.1 \| \| FRHC \| 72 \| 6.5 \| 1.9 \| 1.6, 2.3 \| 42 \| 5.5 \| 1.5 \| 1.2, 1.9 \| 40 \| 5.0 \| 1.7 \| 1.4, 2.2 \| 42 \| −0.8 \| 1.5 \| 40 \| −1.1 \| 1.5 \| \| AAQ‐6 \| 72 \| 16.2 \| 8.1 \| 7.0, 9.7 \| 41 \| 12.8 \| 6.8 \| 5.6, 8.7 \| 40 \| 12.4 \| 6.0 \| 4.9, 7.7 \| 41 \| −2.0 \| 4.4 \| 40 \| −2.4 \| 5.1 \| \| BADS \| 72 \| 96.1 \| 25.9 \| 22.3, 31.0 \| 42 \| 113.1 \| 16.3 \| 13.4, 20.8 \| 40 \| 118.8 \| 15.2 \| 12.5, 19.5 \| 42 \| 11.5 \| 17.3 \| 40 \| 16.6 \| 22.5 \| \| FSS \| 72 \| 34.2 \| 13.7 \| 11.8, 16.4 \| 42 \| 28.0 \| 10.9 \| 9.0, 13.9 \| 40 \| 26.2 \| 12.3 \| 10.1, 15.8 \| 42 \| −3.8 \| 7.9 \| 40 \| −7.0 \| 13.6 \| \| EQ‐5D \| 72 \| 8.1 \| 2.3 \| 2.0, 2.8 \| 42 \| 7.3 \| 1.8 \| 1.5, 2.3 \| 40 \| 6.6 \| 1.5 \| 1.2, 1.9 \| 42 \| −0.5 \| 1.6 \| 40 \| −1.0 \| 1.6 \| \| EQ‐5D‐VAS \| 72 \| 66.0 \| 17.2 \| 14.8,20.6 \| 42 \| 74.1 \| 11.2 \| 9.2,14,3 \| 40 \| 77.4 \| 11.7 \| 9.6,15.0 \| 42 \| 6.5 \| 11.6 \| 40 \| 10.4 \| 12.3 \| \| SCS‐SF \| 72 \| 37.8 \| 5.1 \| 4.4, 6.1 \| 42 \| 37.6 \| 7.1 \| 5.8, 9.1 \| 40 \| 37.5 \| 6.3 \| 5.2, 8.1 \| 42 \| −0.1 \| 6.8 \| 40 \| −0.6 \| 6.2 \| ## DISCUSSION The ENGAGE feasibility trial demonstrated it is possible to recruit and retain parents of children treated for cancer into a single‐arm feasibility trial of an internet administered, guided, LICBT based, self‐help intervention. In summary: [1] $12.0\%$ of invited parents consented and $11.0\%$ of invited parents were enrolled, exceeding progression criteria of ≥$9\%$; [2] $24.0\%$ dropped out of the study, and $23.6\%$ dropped out of the intervention, bettering progression criteria of ≤$30\%$; [3] missing items per questionnaire ranged from $0.01\%$ to $3.9\%$, remaining under ≤$10\%$ for all measures, bettering progression criteria; [4] percentage of participants completing assessments ranged from $65.6\%$ to $98.7\%$, bettering progression criteria of ≥$70\%$ for M.I.N.I interviews at all time‐points and Portal assessments at baseline, and marginally under progression criteria of ≥$70\%$ for Portal assessments at post‐treatment and follow‐up; [5] intervention adherence was $47.9\%$, marginally under progression criteria of ≥$50\%$; and [6] no participant reported a substantial negative consequence related to the study and/or intervention, meeting progression criteria. This study was not designed to detect differences in parental depression or anxiety at follow‐up, however reductions in depressive and anxiety symptoms were observed via visual inspection. ## Strengths and limitations To the best of our knowledge, ENGAGE is first trial worldwide designed to test the feasibility of an internet administered, guided, LICBT based, self‐help intervention for parents of children treated for cancer. Robust methods examined a range of feasibility objectives, alongside a priori specified progression criteria. The intervention protocol is published in accordance with TIDieR guidelines 34 and reporting of methods and results are transparent and complete in accordance with calls for better reporting of feasibility studies. 58 A novel recruitment strategy was adopted with participants identified via the CCR, meaning invited participants are a nationally representative sample of parents of children treated for cancer. We also successfully adopted an opt‐out recruitment strategy and explored reasons for non‐participation, 41 which will inform recruitment strategies used in the future pilot RCT. Use of retention strategies, including telephone reminders 59 and use of a study newsletters 55 may also have minimized study drop out. Finally, involvement of the PRP group resulted in valuable feedback on intervention content and informed intervention changes, as well as improving study procedures, in line with previous research on the benefits of public involvement in research. 60, 61 The study also has limitations. E‐therapists adherence to the intervention was examined by only one licensed clinical psychologist with adherence marked as absent/present. Future studies should develop an intervention adherence checking tool, examining both adherence and quality. 62 Participants' adherence to the intervention e.g., the MTD, was defined a priori by the research team and determined by engagement with and use of EJDeR (e.g., module completion). This definition fails to consider activities participants may have engaged in outside of the Portal. 63 Progression criteria were informed by previous experience and relevant literature. While partly informed by our previous longitudinal research with the population 6 other literature used to inform the progression criteria include a range of psychological interventions with unique methodological, procedural, and clinical uncertainties. Indeed, lack of clarity on how to set progression criteria has been identified as a challenge in the literature. 64 A 6‐month follow‐up time‐point was selected to examine the feasibility of longer‐term data collection. However, the study could have been strengthened by examining the feasibility of longer‐term follow‐up data collection e.g., 9–18 months post‐treatment. The majority of participants were female and may limit the generalizability of findings. Further, the majority of participants ($78.7\%$) had an education level higher than upper secondary school, compared to $44\%$ in the general Swedish population 65 potentially further limiting generalizability. Our sample size was informed by recommendations primarily used for pilot RCTs 54 and literature on informing sample sizes for single‐arm feasibility studies is lacking. 66 There is a possibility study objectives could have been investigated with fewer participants. However, we examined the feasibility and acceptability of an internet‐administered intervention, which could be considered technically complex (e.g., including a range of technical elements such as a tab‐based interview view, film, audio files, in‐module exercises, online homework exercises, written messages via the Portal, and video‐conferencing), with a number of intervention components (e.g., four intervention modules and e‐therapist guidance). Literature suggests feasibility studies of interventions that are technically complex and include a number of components, may require a larger sample size than interventions with minimal complexity. 66 ## Interpretation and implications for future research While we successfully recruited our target sample size with an enrolment rate of $11.0\%$, confidence intervals ranged from $8.4\%$ to $14.1\%$ and in a future pilot RCT we will continue to identify participants via additional sources such as cancer organizations and interest groups. Further, we targeted parents of children treated for cancer with a self‐reported need for psychological support. Lack of recognition of one's psychological difficulties and lack of acknowledgement for the need of support, are commonly identified barriers to seeking help. 67 Consequently, we may have failed to reach parents experiencing psychological difficulties who do not recognize or acknowledge a need for psychological support. Future research may look to identify methods to widen participation in the population and overcome potential barriers to help‐seeking, such as improving mental health literacy. 67 However, it is important to note that of the 509 parents invited via the CCR, only $20\%$ ($$n = 101$$) may be anticipated to experience at least mild symptoms of depression and/or anxiety. 6 In depression trials utilizing recruitment strategies where study invitation letters are sent to patients identified via medical records with experience of depression, a recruitment rate of $12\%$ may be anticipated. 48, 49, 51 Given study invitations were sent to all parents identified via the CCR, rather than to parents with a known history of depression and/or anxiety, our enrolment rate of $11.0\%$ may be considered as high. Despite overall recruitment success, we will strive for further improvements in the future pilot RCT, for example the use of personalized study invitation letters which resulted in improvement in recruitment rates, however small, in our embedded recruitment RCT 39 with results reported separately. 40 Future research may adopt similar strategies, including registry‐based recruitment 36; an opt‐out recruitment strategy, 41 and the use of personalized study invitation letters 40 to optimize recruitment. Our study dropout rate of $24.0\%$ bettered progression criteria of ≤$30\%$. Confidence intervals ranged from $14.9\%$ to $35.3\%$ and we aim to minimize study dropout in the forthcoming future pilot RCT by continuing to use retention strategies, including telephone reminders 59 and study newsletters. 55 In addition, assessment completion rates varied, with higher completion rates for the M.I.N.I. at each time‐point ($84.4\%$ at post‐treatment and $81.4\%$ at follow‐up), in comparison to Portal assessment completion ($65.6\%$ at post‐treatment and $67.8\%$ at follow‐up). Completion rates of weekly Portal assessments, to inform the process evaluation, were particularly low (decreasing over time from $65.7\%$ to $38.9\%$). Difficulties with assessment completion are common. 68 Less than satisfactory Portal assessment completion suggests in the future pilot RCT, we should minimize the number of online assessments used and seek to collect data over the telephone. For example, we will collect process evaluation data at three time‐points during the intervention over the telephone, rather than weekly via the Portal. Our intervention adherence rate of $47.9\%$ was slightly lower than progression criteria (≥$50\%$) and there was no evidence of harm. Results suggest the intervention may be feasible and acceptable for the population and are in line with other research suggesting internet‐administered delivery mechanisms are acceptable to parents of children on cancer treatment 22, 23 and parents of children previously treated for cancer. 25, 26 Benefits of internet‐administered delivery may relate to flexibility of use and perceptions of privacy, 21 overcoming common barriers to accessing support in the population such as guilt and putting the needs of the child before parents' own needs. 14, 15 However, results also suggest a need to adapt the intervention to improve feasibility and acceptability before progressing to the future pilot RCT. While adherence to BA was high, adherence to WM was poor, especially for fathers. Challenges regarding adherence to internet‐administered interventions are common 69, 70 and uptake within routine healthcare settings, 71 including Sweden, 72 is poor. Intervention acceptability is further explored in the embedded process evaluation, reported elsewhere, and will be used to adapt the intervention. However, adherence rates indicate a need to improve the acceptability of the intervention and there may be a need to improve the gender‐sensitivity of EJDeR, especially the WM module for fathers. Recruitment of experienced research personnel was challenging 57 delaying study set‐up. The use of paper‐based CRFs was time consuming and coordinating interviews with external personnel was resource intensive. The use of the TIC‐P (health economic outcome) was not feasible given the large amount of missing data. The Adult Service Use Schedule (AD‐SUS) developed from instruments used in similar trials 73 will be used in the future pilot RCT. Psychology program student e‐therapists did not have time to support caseloads and more experienced licensed psychologists and a CBT‐therapist supported the majority of participants. Further, psychology program student e‐therapists spent a mean time of 2.9 h per participant, per week, which is more therapist time than reported in other studies on guided internet‐administered CBT interventions. 74, 75, 76 This finding may be explained by psychology program students only supporting a mean of 2.7 parents. Consequently, they may not have gained the opportunity to develop competence in using the support protocol and a clear understanding of the intervention structure and content, or how to use the Portal. Results indicate e‐therapist training and supervision should be improved (e.g. increase length of time for training, include role‐play, and revise training material) in future research to facilitate working with the intervention more efficiently. 77 Additionally, recruiting part‐time employed e‐therapists could facilitate increased caseloads, potentially leading to increased efficiency. In summary, the following modifications to the study protocol and EJDeR are warranted before commencing a pilot RCT: [1] collection of outcome assessment data via telephone; [2] reducing the number of measures; [3] adaptation of the intervention to improve the feasibility and acceptability of EJDeR; [4] recruitment of a trial coordinator; [5] recruitment of part‐time employed e‐therapists to increase caseloads and decrease time spent on each participant; [6] use of electronic CRFs to facilitate data collection and entry; and [7] training of research team members to collect research data over the telephone. ## CONCLUSIONS Using robust methods, including a priori specified progression criteria, the use of novel recruitment strategies 34, 40, 41 and evidence‐based retention stragies, 55, 59 our findings indicate methods, study procedures, and the intervention are feasible and acceptable and progression to a pilot RCT to prepare for the design and conduct of a future superiority RCT is warranted. The EJDeR intervention represents a promising and novel solution, delivered with minimal therapist guidance to meet parents' current unmet need for psychological support. ## AUTHOR CONTRIBUTIONS Ella Thiblin: Data curation (equal); formal analysis (equal); investigation (equal); validation (equal); visualization (equal); writing – original draft (equal). Joanne Woodford: Formal analysis (equal); methodology (supporting); project administration (supporting); supervision (supporting); writing – original draft (equal). Christina Reuther: Data curation (equal); formal analysis (equal); investigation (equal); validation (equal); visualization (equal); writing – review and editing (supporting). Johan Lundgren: Supervision (supporting); writing – review and editing (supporting). Nina Lutvica: Data curation (supporting); investigation (equal); validation (supporting); writing – review and editing (supporting). Louise von Essen: Conceptualization (lead); funding acquisition (lead); methodology (lead); project administration (lead); resources (lead); supervision (lead); writing – review and editing (lead). ## FUNDING INFORMATION This work is supported by the Swedish Research Council (grant number 521‐2014‐3337/E0333701, 2018‐02578, and 2021‐00868), the Swedish Cancer Society (grant number 15 0673 and 17 0709), the Swedish Childhood Cancer Foundation (grant number PR2017‐0005), and funding via the Swedish Research Council to U‐CARE, a Strategic Research environment (Dnr 2009‐1093). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ## CONFLICTS OF INTEREST Declaration of interest: none. ## ETHICS APPROVAL STATEMENT The ENGAGE feasibility trial was approved by the Regional Ethical Review Board in Uppsala, Sweden (Dnr: $\frac{2017}{527}$) and was conducted in accordance with the Helsinki Declaration, ensuring the welfare and rights of all participants, and Good Clinical Practice (GCP) guidelines. Ethical amendment was obtained from Swedish Ethical Review Authority August 07, 2019, ref: 2019‐03083. ## PATIENT CONSENT STATEMENT Not applicable. ## PERMISSION TO REPRODUCE MATERIAL FROM OTHER SOURCES Not applicable. ## TRIAL REGISTRATION ISRCTN 57233429. ## DATA AVAILABILITY STATEMENT Data supporting feasibility objectives pertaining to recruitment and eligibility, data collection, attrition, and resources needed to complete the study and intervention are available in Zenodo at https://doi.org/10.5281/zenodo.6325611. Data stored in Zenodo supports Figure 1, Table 3 and Tables S2, S3, S7–S9. Access to the data stored in Zenodo is available upon written request from the corresponding author. Due to the nature of this research, participants of this study did not agree for their clinical data to be shared publicly, so supporting clinical data is not available and further ethical approval would be needed in order to share this data. ## References 1. 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--- title: Prevalence, characteristics and mortality of cancer patients undergoing pericardiocentesis in the United States between 2004 and 2017 authors: - Andrija Matetic - Bonnie Ky - Eric H. Yang - Phyo K. Myint - Muhammad Rashid - Shelley Zieroth - Timir K. Paul - Ayman Elbadawi - Mamas A. Mamas journal: Cancer Medicine year: 2022 pmcid: PMC10028040 doi: 10.1002/cam4.5373 license: CC BY 4.0 --- # Prevalence, characteristics and mortality of cancer patients undergoing pericardiocentesis in the United States between 2004 and 2017 ## Abstract Pericardiocentesis is most commonly performed in patients with lung, haematological and breast cancer. Cancer patients undergoing pericardiocentesis have increased mortality. There are different predictors of increased mortality in patients undergoing pericardiocentesis, including presence of metastatic disease, weight loss and coagulopathy. ### Background Pericardiocentesis is undertaken in patients with cancer for diagnostic and therapeutic purposes. However, there are limited data on the frequency, characteristics and mortality of patients with different cancers undergoing pericardiocentesis. ### Methods All hospitalisations of adult cancer patients (≥18 years) in the US National Inpatient Sample between January 2004 and December 2017 were included. The cohort was stratified by discharge code of pericardiocentesis and cancer, using the International Classification of Diseases. The prevalence of pericardiocentesis, patient characteristics, cancer types and in‐hospital all‐cause mortality were analysed between cancer patients undergoing pericardiocentesis versus not. ### Results A total of 19,773,597 weighted cancer discharges were analysed, out of which 18,847 ($0.1\%$) underwent pericardiocentesis. The most common cancer types amongst the patients receiving pericardiocentesis were lung ($51.3\%$), haematological ($15.9\%$), breast ($5.4\%$), mediastinum/heart ($3.2\%$), gastroesophageal ($2.2\%$) and female genital cancer ($1.8\%$), whilst ‘other’ cancer types were present in $20.2\%$ patients. Patients undergoing pericardiocentesis had significantly higher mortality ($15.6\%$ vs. $4.2\%$, $p \leq 0.001$) compared to their counterparts. The presence of metastatic disease (aOR 2.67 $95\%$ CI 1.79–3.97), weight loss (aOR 1.48 $95\%$ CI 1.33–1.65) and coagulopathy (aOR 3.22 $95\%$ CI 1.63–6.37) were each independently associated with higher mortality in patients who underwent pericardiocentesis. ### Conclusion Pericardiocentesis is an infrequent procedure in cancer patients and is most commonly performed in patients with lung, haematological and breast cancer. Cancer patients undergoing pericardiocentesis have increased mortality, irrespective of the underlying cancer type. ## INTRODUCTION Pericardial effusion is a common occurrence in patients with known or suspected cancer with diagnostic and therapeutic implications. It is estimated that $25\%$–$46\%$ of overall patients undergoing pericardiocentesis have malignant pericardial effusion. 1, 2, 3 Pericardial effusion can also complicate active cancer treatment. 4 Pericardial effusion varies in clinical presentation, prevalence and effusion volume amongst different cancer diagnoses, which may drive decision making around the need for pericardiocentesis. Pericardiocentesis is more complex in patients with cancer, and some patient characteristics such as metastatic status, cancer type and comorbidities have an impact on the procedural complications. 5 There are limited data around differences in the utilisation of pericardiocentesis amongst real‐world cancer populations, particularly when comparing across different cancer types, and whether there are differences in patient characteristics and clinical outcomes. Few studies reported overall worse outcomes in cancer patients undergoing pericardiocentesis compared to their non‐cancer counterparts. 2, 6 It was also suggested that lung cancer patients undergoing pericardiocentesis have the worst outcomes, 1, 5 whilst patients with haematological diseases have better outcomes compared to those with non‐haematologic malignancy. 5, 7 However, existing literature includes single‐centre or sub‐analyses with small sample sizes warranting further large‐scale studies. 1, 2, 5, 6, 7, 8 This study, therefore, aimed to determine the overall utilisation of pericardiocentesis in a real‐world national cancer population over time. It aimed to determine the most prevalent cancer types undergoing pericardiocentesis, including their characteristics and mortality. Finally, it aimed to determine the predictors of mortality amongst cancer patients undergoing pericardiocentesis. ## METHODS The National Inpatient Sample (NIS) database represents the largest healthcare database of routinely collected data in the United States (US) comprising anonymised discharge data from >7 million hospitalisations yearly. It includes data from approximately $20\%$ of inpatient hospital stays (excluding rehabilitation or long‐term acute care hospitals) from all US regions. 9 It was created by the Agency for Healthcare Research and Quality (AHRQ) under the Healthcare Cost and Utilisation Project (HCUP) to produce the US nationally representative estimates of healthcare resource utilisation, access, quality, and outcomes. 9 It is fully based on retrospective data, and starting from 1988, it obtains data through hospital discharge records from all hospitals participating in the HCUP. Collected data are being aggregated to form a national database from which retrospective research analyses can be performed. The NIS database has several advantages for large observational analyses, including anonymised data, sufficiently powered population samples, coverage of a long period of time, and a very broad capture of comorbidities. Furthermore, due to its reliance on the International classification of Diseases system, including the ninth revision (ICD‐9) and 10th revision (ICD‐10), means that there is a possibility of external validation of the study findings. 9 ## STUDY SAMPLE This study included all adult hospitalisations (≥18 years) with a cancer diagnosis between January 2004 and December 2017. The study sample was derived using the discharge diagnostic codes for ‘cancer’ (any diagnostic priority). The ICD‐9 codes were used for the initial study period (January 2004–September 2015), whilst the ICD‐10 codes were used for the remaining study period (October 2015–December 2017), as described in Table S1. The study sample was further stratified according to the discharge procedure codes for ‘pericardiocentesis’ and discharge diagnostic codes for different cancer types (any diagnostic priority for both) (Table S1). The most common cancer types undergoing pericardiocentesis were of particular interest (lung cancer, haematological cancer, breast cancer, mediastinal and heart cancer, gastroesophageal cancer, female genital cancer, and ‘other’ cancer) and were additionally investigated including their characteristics and outcomes (Table S1). The ICD‐9 and ICD‐10 coding systems were carefully used to detect the diagnoses, conditions or procedures of interest. Other variables that could be relevant to the outcomes were also captured from the NIS, including ‘weekend admission’ and hospital‐related factors (‘hospital bed size,’ ‘hospital region’ and ‘hospital location/teaching status’). ‘ Weekend admission’ variable is an indicator of whether the admission day is on the weekend and is calculated from the admission date. ‘ Hospital bed size’ variable refers to the number of short‐term acute hospital beds and is specific to the hospital's location and teaching status. 9 Economic analysis was not the focus of the study which is why hospitalisation charges were not adjusted for inflation. Cases excluded due to missing data represented $2.3\%$ ($$n = 469$$,296) of the original dataset (Figure S1). This observational study was appraised according to the Strengthening The Reporting of OBservational Studies in Epidemiology (STROBE) (Appendix A). ## OBJECTIVES/AIMS We aimed to evaluate the prevalence of pericardiocentesis and patient characteristics amongst cancer cohorts and different cancer types. We also aimed to examine the in‐hospital all‐cause mortality stratified by the utilisation of pericardiocentesis and cancer type, as well as the predictors of mortality in the pericardiocentesis cohort. ## Statistical analysis Data were expressed as numbers (percentages) for categorical data and as median (interquartile range) for continuous data. Categorical variables were analysed using a Chi‐square test, whilst continuous variables were analysed with the Kruskal–Wallis test. Binomial multivariable logistic regression analysis was conducted to determine the association of different variables with all‐cause mortality and was expressed as adjusted odds ratios (aOR) with $95\%$ confidence intervals ($95\%$ CI). The following variables were assessed due to their potential association with all‐cause mortality: Age, sex, metastatic status, weight loss, anaemias, coagulopathy, thrombocytopenia, congestive heart failure, atrial fibrillation, diabetes, arterial hypertension and chronic renal failure. All analyses were weighted using the provided discharge weights, and hierarchical multilevel modelling was used to account for the clustering/nesting of observations, as recommended by HCUP. Statistical significance was defined at a level of $p \leq 0.05.$ SPSS 25 software (IBM Corp) and Stata MP version 16.0 (StataCorp) were used for statistical analysis. ## Baseline characteristics A total of 19,773,597 weighted hospitalisations with a cancer diagnosis were included, out of which 18,847 ($0.1\%$) underwent pericardiocentesis (Figure S1). Patients undergoing pericardiocentesis were more often admitted during the weekend ($19.0\%$ vs. $10.3\%$, $p \leq 0.001$) and had a higher proportion of metastatic disease ($20.9\%$ vs. $11.1\%$, $p \leq 0.001$), as well as comorbidities such as anaemias ($32.0\%$ vs. $22.4\%$, $p \leq 0.001$), atrial fibrillation ($29.5\%$ vs. $8.8\%$, $p \leq 0.001$), congestive heart failure ($11.6\%$ vs. $5.6\%$, $p \leq 0.001$), coagulopathy ($11.2\%$ vs. $6.1\%$, $p \leq 0.001$), thrombocytopenia ($6.9\%$ vs. $4.9\%$, $p \leq 0.001$), electrolyte disorders ($43.2\%$ vs. $23.0\%$, $p \leq 0.001$) and weight loss ($19.7\%$ vs. $10.2\%$, $p \leq 0.001$) (Table 1). TABLE 1 \| Characteristics \| Cancer patients \| Cancer patients.1 \| p‐Value \| \| --- \| --- \| --- \| --- \| \| Characteristics \| Not undergoing pericardiocentesis (99.9%) \| Undergoing pericardiocentesis (0.1%) \| p‐Value \| \| Number of hospitalisations \| 19754751 \| 18847 \| \| \| Age (years), median (IQR) \| 62 (50, 73) \| 59 (50, 69) \| <0.001 \| \| Female sex, % \| 53.6 \| 52.3 \| <0.001 \| \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| <0.001 \| \| White \| 69.6 \| 68.4 \| <0.001 \| \| Black \| 14.2 \| 14.3 \| <0.001 \| \| Hispanic \| 9.2 \| 9.2 \| <0.001 \| \| Asian or Pacific Islander \| 3.2 \| 5.2 \| <0.001 \| \| Native American \| 0.4 \| 0.5 \| <0.001 \| \| Other \| 3.4 \| 2.5 \| <0.001 \| \| Weekend admission, % \| 10.3 \| 19.0 \| <0.001 \| \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| <0.001 \| \| Medicare \| 44.8 \| 37.3 \| <0.001 \| \| Medicaid \| 11.2 \| 15.8 \| <0.001 \| \| Private Insurance \| 37.5 \| 38.4 \| <0.001 \| \| Self‐pay \| 3.0 \| 4.9 \| <0.001 \| \| No charge \| 0.4 \| 0.4 \| <0.001 \| \| Other \| 3.1 \| 3.2 \| <0.001 \| \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| <0.001 \| \| 0–25th \| 26.4 \| 26.6 \| <0.001 \| \| 26th–50th \| 24.9 \| 24.9 \| <0.001 \| \| 51st–75th \| 24.6 \| 25.0 \| <0.001 \| \| 76th–100th \| 24.1 \| 23.4 \| <0.001 \| \| Diabetes Mellitus \| 19.8 \| 15.1 \| <0.001 \| \| Arterial hypertension \| 44.8 \| 38.3 \| <0.001 \| \| Anaemias \| 22.4 \| 32.0 \| <0.001 \| \| Atrial fibrillation \| 8.8 \| 29.5 \| <0.001 \| \| Rheumatoid arthritis/Collagen disease \| 1.9 \| 2.2 \| 0.014 \| \| Congestive heart failure \| 5.6 \| 11.6 \| <0.001 \| \| Valvular disease \| 3.3 \| 3.7 \| <0.001 \| \| Peripheral vascular disorders \| 3.7 \| 4.2 \| <0.001 \| \| Hypothyroidism \| 10.3 \| 9.4 \| <0.001 \| \| Chronic pulmonary disease \| 18.1 \| 31.1 \| <0.001 \| \| Coagulopathy \| 6.1 \| 11.2 \| <0.001 \| \| Thrombocytopenia \| 4.9 \| 6.9 \| <0.001 \| \| Depression \| 9.3 \| 8.8 \| <0.001 \| \| Liver disease \| 3.6 \| 3.6 \| 0.434 \| \| Chronic renal failure \| 7.6 \| 8.7 \| <0.001 \| \| Alcohol abuse \| 2.5 \| 2.8 \| <0.001 \| \| Drug abuse \| 1.4 \| 2.6 \| <0.001 \| \| Fluid and electrolyte disorders \| 23.0 \| 43.2 \| <0.001 \| \| Weight loss \| 10.2 \| 19.7 \| <0.001 \| \| Obesity \| 10.9 \| 7.3 \| <0.001 \| \| Metastatic cancer \| 11.1 \| 20.9 \| <0.001 \| \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| <0.001 \| \| Small \| 12.3 \| 10.7 \| <0.001 \| \| Medium \| 24.0 \| 19.2 \| <0.001 \| \| Large \| 63.7 \| 70.0 \| <0.001 \| \| Hospital Region, % \| Hospital Region, % \| Hospital Region, % \| <0.001 \| \| Northeast \| 21.7 \| 19.4 \| <0.001 \| \| Midwest \| 21.7 \| 25.0 \| <0.001 \| \| South \| 38.2 \| 35.7 \| <0.001 \| \| West \| 18.5 \| 19.8 \| <0.001 \| \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| <0.001 \| \| Rural \| 5.7 \| 2.6 \| <0.001 \| \| Urban non‐teaching \| 24.4 \| 20.9 \| <0.001 \| \| Urban teaching \| 69.9 \| 76.5 \| <0.001 \| ## Prevalence and characteristics of different cancer types The most common cancer types amongst the patients receiving pericardiocentesis were lung cancer ($51.3\%$), haematological cancer ($15.9\%$), breast cancer ($5.4\%$), mediastinum and heart cancer ($3.2\%$), gastroesophageal cancer ($2.2\%$) and female genital cancer ($1.8\%$), whilst ‘other’ cancer types were present in $20.2\%$ patients (Figure 1A). These findings were consistent when looking at the yearly distribution of different cancer types across the study period (Figure S2A). When looking at the proportion of patients undergoing pericardiocentesis within each cancer type, the highest proportion was observed in the mediastinum and heart cancer ($1.6\%$), followed by lung and bronchus cancer ($0.4\%$) and haematological cancer ($0.2\%$), whilst pericardiocentesis was undertaken in <$0.1\%$ of patients in other cancer types (Figure 1B). FIGURE 1: Prevalence of different cancer types in the study cohort: (A) Patients undergoing pericardiocentesis; (B) Patients not undergoing pericardiocentesis. When comparing groups based on the receipt of pericardiocentesis in the most common cancer types, patients undergoing pericardiocentesis were overall younger and had a higher proportion of metastatic disease ($p \leq 0.05$) (Table 2). The differences in major comorbidities were generally consistent with the findings in the overall cohort (Table 2). TABLE 2 \| Characteristics \| Lung cancer \| Lung cancer.1 \| Lung cancer.2 \| Haematological cancer \| Haematological cancer.1 \| Haematological cancer.2 \| Breast cancer \| Breast cancer.1 \| Breast cancer.2 \| Mediastinal and heart cancer \| Mediastinal and heart cancer.1 \| Mediastinal and heart cancer.2 \| Gastroesophageal cancer \| Gastroesophageal cancer.1 \| Gastroesophageal cancer.2 \| Female genital cancer \| Female genital cancer.1 \| Female genital cancer.2 \| ‘Other’ cancer \| ‘Other’ cancer.1 \| ‘Other’ cancer.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Characteristics \| No pericardiocentesis (99.60%) \| Pericardiocentesis (0.40%) \| p‐Value \| No pericardiocentesis (99.81%) \| Pericardiocentesis (0.19%) \| p‐Value \| No pericardiocentesis (99.91%) \| Pericardiocentesis (0.09%) \| p‐Value \| No pericardiocentesis (98.42%) \| Pericardiocentesis (1.58%) \| p‐Value \| No pericardiocentesis (99.92%) \| Pericardiocentesis (0.08%) \| p‐Value \| No pericardiocentesis (99.97%) \| Pericardiocentesis (0.03%) \| p‐Value \| No pericardiocentesis (99.97%) \| Pericardiocentesis (0.03%) \| p‐Value \| \| Number of hospitalizations \| 2335650 \| 9488 \| \| 1568973 \| 3005 \| \| 1102239 \| 999 \| \| 34695 \| 557 \| \| 510364 \| 414 \| \| 1157306 \| 332 \| \| 13045524 \| 4051 \| \| \| Age (years), median (IQR) \| 68 (60, 76) \| 62 (54, 70) \| <0.001 \| 64 (50, 75) \| 45 (25, 64) \| <0.001 \| 60 (49, 71) \| 56 (47, 65) \| <0.001 \| 58 (41, 70) \| 55 (30, 67) \| 0.002 \| 66 (57, 76) \| 59 (52, 66) \| <0.001 \| 62 (52, 71) \| 59 (51, 68) \| <0.001 \| 61 (48, 72) \| 59 (48, 70) \| <0.001 \| \| Female sex, % \| 48.3 \| 49.4 \| 0.057 \| 43.0 \| 43.1 \| 0.141 \| 99.1 \| 100.0 \| 0.033 \| 40.9 \| 42.9 \| 0.506 \| 31.0 \| 20.8 \| <0.001 \| / \| / \| / \| 49.1 \| 51.0 \| <0.001 \| \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| Race/ethnicity, % \| \| White \| 77.1 \| 69.8 \| <0.001 \| 69.2 \| 64.3 \| <0.001 \| 67.1 \| 62.8 \| <0.001 \| 65.3 \| 72.5 \| 0.001 \| 63.8 \| 65.2 \| <0.001 \| 69.6 \| 63.0 \| <0.001 \| 68.7 \| 70.0 \| <0.001 \| \| Black \| 12.7 \| 15.4 \| <0.001 \| 12.7 \| 5.1 \| <0.001 \| 16.3 \| 16.3 \| <0.001 \| 14.0 \| 15.0 \| 0.001 \| 14.9 \| 8.7 \| <0.001 \| 13.0 \| 18.5 \| <0.001 \| 14.7 \| 9.3 \| <0.001 \| \| Hispanic \| 4.7 \| 5.8 \| <0.001 \| 14.1 \| 14.1 \| <0.001 \| 9.1 \| 12.8 \| <0.001 \| 10.8 \| 10.0 \| 0.001 \| 11.6 \| 21.7 \| <0.001 \| 9.9 \| 11.1 \| <0.001 \| 9.6 \| 11.9 \| <0.001 \| \| Asian or Pacific Islander \| 2.8 \| 6.1 \| <0.001 \| 2.9 \| 3.0 \| <0.001 \| 3.6 \| 4.7 \| <0.001 \| 4.9 \| <0.1 \| 0.001 \| 5.5 \| 4.4 \| <0.001 \| 3.7 \| 3.7 \| <0.001 \| 3.2 \| 5.7 \| <0.001 \| \| Native American \| 0.4 \| 0.3 \| <0.001 \| 0.4 \| 1.0 \| <0.001 \| 0.4 \| <0.1 \| <0.001 \| 0.6 \| 2.5 \| 0.001 \| 0.5 \| <0.1 \| <0.001 \| 0.5 \| <0.1 \| <0.001 \| 0.5 \| 0.5 \| <0.001 \| \| Other \| 2.3 \| 2.5 \| <0.001 \| 3.9 \| 2.5 \| <0.001 \| 3.5 \| 3.5 \| <0.001 \| 4.3 \| <0.1 \| 0.001 \| 3.7 \| <0.1 \| <0.001 \| 3.4 \| 3.7 \| <0.001 \| 3.5 \| 2.6 \| <0.001 \| \| Weekend admission, % \| 14.8 \| 19.8 \| <0.001 \| 16.1 \| 17.3 \| <0.001 \| 7.3 \| 15.7 \| <0.001 \| 12.4 \| 14.3 \| <0.001 \| 14.2 \| 29.2 \| <0.001 \| 8.7 \| 21.4 \| <0.001 \| 8.9 \| 19.1 \| <0.001 \| \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| Primary expected payer, % \| \| Medicare \| 60.1 \| 42.4 \| <0.001 \| 46.4 \| 23.1 \| <0.001 \| 36.0 \| 28.1 \| <0.001 \| 35.7 \| 28.6 \| <0.001 \| 51.1 \| 32.7 \| <0.001 \| 43.3 \| 35.7 \| <0.001 \| 42.4 \| 41.0 \| <0.001 \| \| Medicaid \| 10.2 \| 15.9 \| <0.001 \| 12.9 \| 21.8 \| <0.001 \| 13.1 \| 16.9 \| <0.001 \| 16.4 \| 16.7 \| <0.001 \| 11.9 \| 12.8 \| <0.001 \| 12.9 \| 21.4 \| <0.001 \| 10.7 \| 9.2 \| <0.001 \| \| Private Insurance \| 23.6 \| 33.6 \| <0.001 \| 34.0 \| 45.3 \| <0.001 \| 46.1 \| 51.7 \| <0.001 \| 40.0 \| 38.1 \| <0.001 \| 30.1 \| 42.6 \| <0.001 \| 36.8 \| 25.0 \| <0.001 \| 40.3 \| 42.2 \| <0.001 \| \| Self‐pay \| 2.7 \| 4.8 \| <0.001 \| 3.0 \| 4.0 \| <0.001 \| 2.0 \| 2.3 \| <0.001 \| 4.2 \| 14.3 \| <0.001 \| 3.5 \| 4.6 \| <0.001 \| 3.6 \| 14.3 \| <0.001 \| 3.1 \| 4.8 \| <0.001 \| \| No charge \| 0.3 \| 0.6 \| <0.001 \| 0.3 \| 0.1 \| <0.001 \| 0.3 \| <0.1 \| <0.001 \| 0.3 \| <0.1 \| <0.001 \| 0.4 \| <0.1 \| <0.001 \| 0.6 \| <0.1 \| <0.001 \| 0.4 \| 0.4 \| <0.001 \| \| Other \| 3.1 \| 2.7 \| <0.001 \| 3.4 \| 5.8 \| <0.001 \| 2.5 \| 1.1 \| <0.001 \| 3.3 \| 2.4 \| <0.001 \| 2.9 \| 7.3 \| <0.001 \| 2.8 \| 3.6 \| <0.001 \| 3.2 \| 2.4 \| <0.001 \| \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| Median household income (percentile), % \| \| 0–25th \| 29.7 \| 31.0 \| <0.001 \| 25.6 \| 21.4 \| <0.001 \| 23.5 \| 21.0 \| 0.693 \| 26.4 \| 38.1 \| 0.006 \| 28.1 \| 30.4 \| 0.003 \| 26.4 \| 28.6 \| 0.007 \| 26.1 \| 19.2 \| <0.001 \| \| 26th–50th \| 26.5 \| 22.9 \| <0.001 \| 24.7 \| 26.8 \| <0.001 \| 23.0 \| 32.2 \| 0.693 \| 23.5 \| 23.8 \| 0.006 \| 24.8 \| 23.9 \| 0.003 \| 24.7 \| 28.6 \| 0.007 \| 24.9 \| 24.9 \| <0.001 \| \| 51st–75th \| 23.7 \| 24.9 \| <0.001 \| 25.1 \| 27.3 \| <0.001 \| 25.0 \| 23.0 \| 0.693 \| 24.1 \| 19.1 \| 0.006 \| 24.1 \| 16.8 \| 0.003 \| 24.9 \| 28.6 \| 0.007 \| 24.8 \| 25.7 \| <0.001 \| \| 76th–100th \| 21.0 \| 21.2 \| <0.001 \| 24.6 \| 24.5 \| <0.001 \| 28.6 \| 24.1 \| 0.693 \| 26.0 \| 19.1 \| 0.006 \| 23.0 \| 29.0 \| 0.003 \| 24.0 \| 14.3 \| 0.007 \| 24.4 \| 30.2 \| <0.001 \| \| Diabetes Mellitus \| 21.1 \| 15.0 \| <0.001 \| 19.6 \| 13.8 \| <0.001 \| 15.8 \| 12.4 \| 0.001 \| 13.1 \| 7.1 \| <0.001 \| 20.3 \| 10.4 \| <0.001 \| 18.6 \| 18.1 \| 0.808 \| 17.0 \| 13.7 \| <0.001 \| \| Arterial hypertension \| 52.1 \| 42.6 \| <0.001 \| 43.2 \| 31.7 \| <0.001 \| 40.6 \| 36.7 \| 0.013 \| 39.1 \| 28.7 \| <0.001 \| 48.8 \| 33.5 \| <0.001 \| 464 \| 38.5 \| 0.004 \| 43.8 \| 35.2 \| <0.001 \| \| Anaemias \| 22.3 \| 31.1 \| <0.001 \| 37.8 \| 32.0 \| 0.619 \| 14.0 \| 21.4 \| <0.001 \| 20.8 \| 42.9 \| <0.001 \| 32.6 \| 33.1 \| 0.819 \| 20.6 \| 41.0 \| <0.001 \| 18.8 \| 27.5 \| <0.001 \| \| Rheumatoid arthritis/Collagen disease \| 2.8 \| 2.6 \| 0.001 \| 2.3 \| 1.4 \| 0.637 \| 1.8 \| 1.2 \| 0.876 \| 2.3 \| 0.3 \| 0.098 \| 1.3 \| 2.2 \| 0.125 \| 2.0 \| 1.6 \| 0.573 \| 1.4 \| 1.8 \| 0.009 \| \| Obesity \| 5.0 \| 3.9 \| <0.001 \| 6.0 \| 7.3 \| 0.003 \| 7.6 \| 4.8 \| 0.001 \| 6.4 \| 3.5 \| 0.005 \| 5.2 \| 3.7 \| 0.163 \| 16.2 \| 9.0 \| <0.001 \| 8.0 \| 5.8 \| <0.001 \| \| Congestive heart failure \| 8.6 \| 11.1 \| <0.001 \| 8.7 \| 13.7 \| <0.001 \| 3.8 \| 9.9 \| <0.001 \| 5.6 \| 2.5 \| 0.034 \| 6.9 \| 19.8 \| <0.001 \| 4.1 \| 12.1 \| <0.001 \| 4.6 \| 12.6 \| <0.001 \| \| Atrial fibrillation \| 15.9 \| 33.0 \| <0.001 \| 11.1 \| 19.1 \| <0.0011 \| 4.4 \| 18.0 \| <0.001 \| 13.7 \| 26.2 \| <0.001 \| 12.6 \| 25.1 \| <0.001 \| 5.2 \| 16.0 \| <0.001 \| 6.4 \| 27.0 \| <0.001 \| \| Valvular disease \| 4.0 \| 4.4 \| 0.128 \| 4.1 \| 3.8 \| 0.094 \| 2.6 \| 2.6 \| 0.543 \| 3.5 \| <0.1 \| 0.001 \| 3.7 \| 3.4 \| 0.752 \| 3.1 \| <0.1 \| 0.001 \| 3.1 \| 3.5 \| 0.230 \| \| Peripheral vascular disorders \| 8.2 \| 6.1 \| <0.001 \| 3.4 \| 1.4 \| 0.027 \| 1.3 \| 1.2 \| 0.239 \| 4.2 \| 2.5 \| 0.035 \| 4.4 \| 2.4 \| 0.056 \| 1.6 \| <0.1 \| 0.021 \| 2.6 \| 3.0 \| 0.122 \| \| Hypothyroidism \| 10.8 \| 9.1 \| <0.001 \| 11.3 \| 6.1 \| <0.001 \| 12.1 \| 4.9 \| 0.021 \| 8.1 \| 15.0 \| 0.068 \| 7.4 \| 15.6 \| <0.001 \| 12.7 \| 10.8 \| 0.314 \| 8.1 \| 9.0 \| 0.044 \| \| Chronic pulmonary disease \| 49.7 \| 44.0 \| <0.001 \| 13.9 \| 13.2 \| 0.059 \| 12.3 \| 17.3 \| <0.001 \| 25.4 \| 22.5 \| 0.003 \| 17.6 \| 18.1 \| 0.813 \| 11.1 \| 12.1 \| 0.569 \| 12.9 \| 19.5 \| <0.001 \| \| Coagulopathy \| 5.2 \| 7.6 \| <0.001 \| 22.9 \| 25.0 \| 0.332 \| 3.5 \| 4.9 \| <0.001 \| 6.7 \| 10.0 \| <0.001 \| 5.4 \| 10.5 \| <0.001 \| 2.9 \| 11.8 \| <0.001 \| 3.4 \| 10.2 \| <0.001 \| \| Thrombocytopenia \| 4.4 \| 5.0 \| <0.001 \| 19.6 \| 16.0 \| <0.001 \| 2.9 \| 2.3 \| <0.001 \| 4.9 \| 7.1 \| 0.006 \| 3.7 \| 4.6 \| 0.349 \| 1.9 \| 1.5 \| 0.633 \| 2.3 \| 5.0 \| <0.001 \| \| Depression \| 11.4 \| 11.2 \| 0.014 \| 10.4 \| 3.3 \| <0.001 \| 10.9 \| 11.1 \| 0.416 \| 8.3 \| 5.0 \| 0.030 \| 7.0 \| 8.6 \| 0.213 \| 8.9 \| 3.1 \| <0.001 \| 6.9 \| 7.1 \| 0.604 \| \| Liver disease \| 2.6 \| 4.0 \| <0.001 \| 4.0 \| 4.3 \| <0.001 \| 1.9 \| 2.5 \| 0.002 \| 2.6 \| <0.1 \| 0.107 \| 3.7 \| 1.2 \| 0.007 \| 1.6 \| 4.3 \| <0.001 \| 3.0 \| 2.2 \| 0.001 \| \| Chronic renal failure \| 8.3 \| 7.7 \| 0.011 \| 13.0 \| 13.2 \| <0.001 \| 3.7 \| 3.6 \| 0.084 \| 4.5 \| 5.0 \| 0.148 \| 6.5 \| 8.6 \| 0.090 \| 4.4 \| 4.6 \| 0.798 \| 5.1 \| 8.3 \| <0.001 \| \| Alcohol abuse \| 4.1 \| 4.0 \| 0.001 \| 1.6 \| 0.9 \| 0.086 \| 0.7 \| 1.2 \| 0.916 \| 2.4 \| 0.9 \| 0.023 \| 4.6 \| 3.4 \| 0.245 \| 0.6 \| 1.6 \| 0.033 \| 2.0 \| 2.3 \| 0.170 \| \| Drug abuse \| 2.0 \| 2.6 \| 0.792 \| 1.7 \| 3.3 \| 0.001 \| 0.8 \| 2.5 \| 0.036 \| 1.8 \| 6.4 \| <0.001 \| 1.2 \| 2.4 \| 0.033 \| 0.7 \| <0.1 \| 0.129 \| 0.9 \| 1.5 \| <0.001 \| \| Fluid and electrolyte disorders \| 28.2 \| 41.0 \| <0.001 \| 38.5 \| 42.9 \| <0.001 \| 13.3 \| 46.9 \| <0.001 \| 21.4 \| 33.1 \| <0.001 \| 35.1 \| 56.5 \| <0.001 \| 19.1 \| 37.6 \| <0.001 \| 16.7 \| 41.1 \| <0.001 \| \| Weight loss \| 14.0 \| 19.4 \| <0.0011 \| 13.4 \| 15.1 \| 0.082 \| 4.7 \| 19.8 \| <0.001 \| 9.0 \| 10.0 \| 0.405 \| 24.9 \| 31.3 \| 0.003 \| 6.1 \| 10.6 \| 0.001 \| 6.2 \| 13.1 \| <0.001 \| \| Metastatic cancer \| 14.1 \| 29.0 \| <0.001 \| 3.4 \| 2.4 \| 0.719 \| 13.4 \| 17.3 \| <0.001 \| 13.8 \| 19.9 \| <0.001 \| 17.2 \| 23.7 \| 0.001 \| 8.8 \| 16.8 \| <0.001 \| 10.8 \| 19.4 \| <0.001 \| \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| Bed size of hospital, % \| \| Small \| 12.4 \| 10.2 \| <0.001 \| 12.3 \| 9.9 \| <0.001 \| 15.8 \| 11.7 \| <0.001 \| 8.5 \| 8.3 \| 0.274 \| 10.3 \| 5.7 \| 0.022 \| 9.0 \| 4.8 \| <0.001 \| 10.6 \| 8.8 \| <0.001 \| \| Medium \| 25.0 \| 19.6 \| <0.001 \| 20.7 \| 19.9 \| <0.001 \| 26.2 \| 21.6 \| <0.001 \| 20.9 \| 18.0 \| 0.274 \| 22.1 \| 23.4 \| 0.022 \| 20.2 \| 11.5 \| <0.001 \| 22.4 \| 18.0 \| <0.001 \| \| Large \| 62.7 \| 70.3 \| <0.001 \| 67.0 \| 70.2 \| <0.001 \| 57.9 \| 66.7 \| <0.001 \| 70.6 \| 73.8 \| 0.274 \| 67.6 \| 70.9 \| 0.022 \| 70.8 \| 83.7 \| <0.001 \| 67.0 \| 73.2 \| <0.001 \| \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| Hospital region, % \| \| Northeast \| 21.5 \| 19.0 \| <0.001 \| 21.7 \| 22.4 \| <0.001 \| 25.1 \| 25.5 \| <0.001 \| 21.9 \| 17.2 \| 0.014 \| 24.1 \| 23.3 \| 0.002 \| 22.1 \| 16.3 \| 0.114 \| 21.8 \| 20.5 \| <0.001 \| \| Midwest \| 23.2 \| 25.3 \| <0.001 \| 22.9 \| 25.1 \| <0.001 \| 19.3 \| 23.5 \| <0.001 \| 20.7 \| 22.0 \| 0.014 \| 21.4 \| 16.2 \| 0.002 \| 23.0 \| 27.0 \| 0.114 \| 22.0 \| 25.6 \| <0.001 \| \| South \| 41.3 \| 35.9 \| <0.001 \| 37.3 \| 31.4 \| <0.001 \| 36.5 \| 29.4 \| <0.001 \| 38.5 \| 37.4 \| 0.014 \| 35.9 \| 34.5 \| 0.002 \| 35.7 \| 36.1 \| 0.114 \| 37.5 \| 30.7 \| <0.001 \| \| West \| 14.0 \| 19.8 \| <0.001 \| 18.1 \| 21.1 \| <0.001 \| 19.2 \| 21.6 \| <0.001 \| 18.9 \| 23.4 \| 0.014 \| 18.5 \| 26.0 \| 0.002 \| 19.2 \| 20.6 \| 0.114 \| 18.7 \| 23.2 \| <0.001 \| \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| Location/teaching status of hospital, % \| \| Rural \| 7.5 \| 3.5 \| <0.001 \| 4.5 \| 1.9 \| <0.001 \| 6.5 \| 7.8 \| <0.001 \| 4.5 \| <0.1 \| <0.001 \| 7.1 \| 7.5 \| 0.954 \| 5.2 \| <0.1 \| 0.001 \| 7.4 \| 3.5 \| <0.001 \| \| Urban non‐teaching \| 29.2 \| 24.4 \| <0.001 \| 19.9 \| 10.6 \| <0.001 \| 28.2 \| 23.5 \| <0.001 \| 27.9 \| 23.3 \| <0.001 \| 32.0 \| 31.4 \| 0.954 \| 26.1 \| 26.1 \| 0.001 \| 32.6 \| 29.0 \| <0.001 \| \| Urban teaching \| 63.3 \| 72.2 \| <0.001 \| 75.6 \| 87.6 \| <0.001 \| 65.2 \| 68.6 \| <0.001 \| 67.6 \| 76.7 \| <0.001 \| 60.9 \| 61.1 \| 0.954 \| 68.7 \| 73.9 \| 0.001 \| 60.0 \| 67.5 \| <0.001 \| ## All‐cause mortality and other clinical outcomes Patients undergoing pericardiocentesis had a significantly higher all‐cause mortality ($15.6\%$ vs. $4.2\%$, $p \leq 0.001$), longer length of stay (median of 9 vs. 4 days, $p \leq 0.001$) and increased total charges (median of 71,489 vs. 33,469 United States Dollars, $p \leq 0.001$) compared to their counterparts (Table 3). These findings were consistently present across the most common cancer types (Table 4 and Figure 2). When looking at the absolute rates of mortality in patients undergoing pericardiocentesis, it was the highest in patients with gastroesophageal cancer ($25.0\%$), and the lowest in patients with heart and mediastinum cancer ($9.5\%$) (Table 4 and Figure 2). ## Sensitivity analysis based on cardiac tamponade Cardiac tamponade was present in patients undergoing pericardiocentesis across all cancer types, with the highest prevalence in breast cancer ($66.3\%$) and lowest prevalence in female genital cancer ($42.9\%$) (Figure S3). All‐cause mortality was lower in patients with cardiac tamponade undergoing pericardiocentesis across all cancer types, except in those with breast cancer ($11.9\%$ vs. $10.0\%$) and lung/bronchus cancer ($17.1\%$ vs. $13.0\%$) when compared with patients undergoing pericardiocentesis without cardiac tamponade (Figure 2). ## Predictors of all‐cause mortality The presence of metastatic disease (aOR 2.67 $95\%$ CI 1.79–3.97), weight loss (aOR 1.48 $95\%$ CI 1.33–1.65) and coagulopathy (aOR 3.22 $95\%$ CI 1.63–6.37) was independently associated with all‐cause mortality in the pericardiocentesis cohort, whilst there was no association of age, sex, anaemias, thrombocytopenia, heart failure, atrial fibrillation, diabetes mellitus, hypertension and chronic renal failure with mortality in this group ($p \leq 0.05$) (Figure 3). FIGURE 3: Predictors of all‐cause mortality in patients undergoing pericardiocentesis. ## DISCUSSION To the best of our knowledge, this is the largest cancer‐specific study to this date evaluating the prevalence, characteristics and outcomes of cancer patients undergoing pericardiocentesis. Its strengths further include a national‐level analysis and a comprehensive evaluation of the different cancer types. Several previous cohort studies evaluated cancer patients undergoing pericardiocentesis but included single‐centre analyses over a shorter period with substantially lower sample size. 1, 2, 5, 6, 7, 8 This study offers several important findings. First, it revealed that pericardiocentesis is infrequently utilised in cancer cohorts covering only a minority of patients (~$0.1\%$). Second, it is distinctively used amongst different cancer types, with the highest utilisation in the lung, haematological and breast cancer, followed by heart/mediastinum, gastroesophageal and female genital cancer. Third, this cohort has an increased prevalence of comorbidities that are considered to be higher risk in pericardiocentesis, such as anaemias, atrial fibrillation (due to anticoagulation), coagulopathy and thrombocytopenia. 5, 10, 11 Fourth, cancer patients undergoing pericardiocentesis have increased mortality compared to other cancer patients admitted to hospitals and that overall mortality rates are dependent on the underlying cancer type. Finally, we identified independent predictors of increased mortality with metastatic status, weight loss and coagulopathy. Pericardiocentesis is indicated for different diagnostic and therapeutic indications in the cancer population. Due to a strong association between cancer and pericardial effusion, it is more often undertaken than the general population and requires strict protocols to minimise the risk associated with the procedure. 11 Previous studies have shown that cancer is an underlying cause of pericardial effusion in up to $46\%$ of patients undergoing pericardiocentesis. 1, 2, 3 Pericardial effusion may be associated with cancer metastases, but also with systemic cancer effects (hypoalbuminemia, impaired lymphatic drainage) or cancer treatments (i.e., immune checkpoint inhibitor therapy). The occurrence of pericardial effusion and subsequent utilisation of pericardiocentesis differs across cancer types. The present study showed that pericardiocentesis is most utilised in lung, haematological and breast cancer, followed by heart/mediastinum, gastroesophageal and female genital cancer. This is consistent with previous reports. 2, 5, 12 All aforementioned cancer types could potentiate the development of pericardial effusion with direct or indirect mechanisms, such as serosal involvement, 13 direct extensions with local inflammation and cellular toxicity, 14 cancer‐induced cachexia and hypoalbuminemia, 15 as well as lymphatic involvement with lymphedema. 14 Furthermore, other determinants could additionally provoke pericardial effusion and increase the utilisation of pericardiocentesis, such as cancer treatment toxicity and opportunistic infections. 14 High utilisation of pericardiocentesis in these cancer types is, therefore, not surprising. One NIS‐based study investigated temporal trends and in‐hospital mortality of all‐comers undergoing pericardiocentesis over a period from 2007 to 2015. 16 In this study, around $25\%$ of patients had active cancer, and this was associated with increased in‐hospital mortality (OR 1.72; $95\%$ CI 1.6–1.85). 16 Importantly, the number of pericardiocentesis procedures increased over time, although there was no cancer‐focused analysis to evaluate specific trends. 16 Another focused analysis of 212 cancer patients undergoing pericardiocentesis at the MD Anderson Cancer Center described the feasibility of percutaneous pericardiocentesis with no procedure‐related deaths. 5 However, 1‐month ($18\%$) and 2‐year mortality rates ($61\%$) were substantially high and were associated with lung cancer, older age and severe grade 4 thrombocytopenia. 5 Lung cancer patients undergoing pericardiocentesis were previously shown to have the highest mortality compared to other cancer types, 1, 5, 17, 18 although this was not confirmed in the present study which revealed the highest mortality with gastroesophageal cancer. High recurrence (~$25\%$) and 1‐year mortality rates (~$55\%$) in cancer patients undergoing pericardiocentesis were also previously reported in a small Asian cohort study. 1 Compared to non‐cancer patients undergoing pericardiocentesis, cancer patients undergoing pericardiocentesis were shown to have significantly increased in‐hospital and 1‐year mortality. 2, 6 These findings are consistent with the present study, suggesting poor prognosis of cancer patients undergoing pericardiocentesis. The high mortality rate of cancer patients undergoing pericardiocentesis could have several potential explanations. First, pericardiocentesis is often performed in the sicker cancer population. For example, cardiac tamponade is a strong indication for therapeutic pericardiocentesis but is more often present in sicker patients with the higher risk profile. 3 Similarly, patients undergoing diagnostic pericardiocentesis such as those with undiagnosed pre‐existent cancer or those with ambiguous cancer disease (uncertain primary site) are commonly late presenters with advanced cancer stage with metastasis. 19 Therefore, it is possible that pericardiocentesis in cancer patients simply indicates sicker patients with a higher risk profile. Second, pericardiocentesis could be associated with serious complications such as arterial and cardiac injury, solid organ injury, hydropneumothorax, arrhythmias, infection and bleeding, even when performed by experts in a controlled environment. 7 For example, El Haddad et al. reported major procedural complications in five patients and minor procedural complications in 72 patients out of 212 cancer patients undergoing pericardiocentesis. 5 Although usually reversible and not associated with a fatal outcome, these complications represent a substantial burden to this high‐risk population. 5 Nevertheless, pericardiocentesis was shown to be a safe procedure in cancer patients in the hospital setting, even in those with thrombocytopenia. 5, 8 It is, therefore, most likely that other cancer‐related and patient‐related factors affect the mortality outcome, and not the procedure itself. This study distinguished different predictors of increased mortality with pericardiocentesis. Interestingly, there was no association between age and mortality in this setting, highlighting the importance of other patient risk factors such as metastatic status, frailty (weight loss) and haemostatic capacity. Metastatic status is a well‐known unfavourable prognostic factor in cancer patients undergoing pericardiocentesis. 6, 8 Weight loss is an important indicator of more advanced disease, as well as a strong measure of patient frailty. Previous studies have shown that weight loss is associated with a worse prognosis in cancer patients. 20 The present analysis detected a significant association between weight loss and all‐cause mortality which is consistent with the findings in the overall cancer cohort. 20 Coagulopathy was also associated with increased mortality in this study, highlighting the importance of secondary haemostasis for the safe performance of invasive procedures such as pericardiocentesis. Previous studies suggested that thrombocytopenia was associated with worse outcomes, 5 and it was even considered a contraindication for pericardiocentesis, 10 but other studies have not shown any association with mortality after multivariable adjustment. 8 Similarly, our study shows thrombocytopenia is not a predictor of increased mortality in cancer patients who underwent pericardiocentesis. Interestingly, patients undergoing pericardiocentesis without cardiac tamponade had even worse mortality in most cancer types. This could be potentially explained by lower effusion volume and a probably higher proportion of diagnostic indications for pericardiocentesis in this subpopulation. Additionally, due to low effusion volume in patients without cardiac tamponade the risk of cardiac, surrounding vascular and lung injury is high due to technical difficulty leading to higher mortality. This could highlight the importance of proper non‐invasive cancer assessment and utilisation of invasive procedures only in selected cases. However, the design of this study does not allow for such detailed analysis and further studies should re‐assure these speculations. Clinical implications of the study include the delineation of the most common cancer types undergoing pericardiocentesis and predictors of increased mortality. This study could potentially support usual echocardiographic assessment and cardiology follow‐up in patients with specific cancer types. Bearing in mind the observed increased mortality in the cohort undergoing pericardiocentesis, our data support increased utilisation of preventive measures (ultrasound‐guided puncture, careful preparation and planning, performance by experienced team members and close follow‐up). There are several limitations of this study. Potential coding issues associated with databases such as the NIS represent an inherent limitation of this study. It was not possible to differentiate if the pericardiocentesis procedure was done for diagnostic or therapeutic purposes, as well as the timing of cancer diagnosis (known cancer vs. newly diagnosed cancer). Furthermore, the transition between ICD‐9 and ICD‐10 systems could have affected the captured estimates. Similarly, an inadequate granularity of the ICD‐9 coding system did not allow for the detection of important subpopulations such as overall patients with pericardial effusion, or those undergoing pericardial window procedure. The observational nature of the study allows for the determination of association, but not a causal relationship. The study results are limited to the in‐hospital period and longer‐term outcomes were not assessed. NIS does not track recurrent procedures and readmissions which could be important for this population. The study was unable to assess direct procedural outcomes such are procedure‐related bleeding or other inadvertent events. The NIS does not contain data on the laboratory and detailed clinical parameters which precludes further analyses. Similarly, it was not possible to include detailed data on cancer treatment or grading some patient factors such as thrombocytopenia and anaemia (mild to severe), as well as renal failure (Stages 1–5). Finally, cancer‐related factors such as cancer activity, cancer staging, cancer duration or performance status measures (e.g., Eastern Cooperative Oncology Group Performance Status) are not available with the NIS. In conclusion, pericardiocentesis is an infrequent procedure in cancer patients that is most commonly performed in patients with lung and bronchus, haematological, breast, heart and mediastinum, gastroesophageal and female genital cancer. When performed, it is associated with substantially increased all‐cause mortality, irrespectively of the underlying cancer type. Further longitudinal studies are necessary to delineate particular differences amongst cancer types and long‐term outcomes associated with pericardiocentesis. ## AUTHOR CONTRIBUTIONS Andrija Matetic: Conceptualization (equal); formal analysis (lead); methodology (equal); software (lead); visualization (lead); writing – original draft (lead); writing – review and editing (equal). Bonnie Ky: Methodology (supporting); supervision (supporting); writing – review and editing (equal). Eric H. Yang: Methodology (supporting); supervision (supporting); writing – review and editing (equal). Phyo K. Myint: Methodology (supporting); supervision (supporting); writing – review and editing (equal). Muhammad Rashid: Methodology (supporting); resources (equal); supervision (supporting); writing – review and editing (equal). Shelley Zieroth: Methodology (supporting); supervision (supporting); writing – review and editing (equal). Timir K. Paul: Methodology (supporting); supervision (supporting); writing – review and editing (equal). Ayman Elbadawi: Methodology (supporting); supervision (supporting); writing – review and editing (equal). Mamas A. Mamas: Conceptualization (lead); methodology (lead); resources (lead); supervision (lead); writing – original draft (equal); writing – review and editing (lead). ## FUNDING INFORMATION None. ## CONFLICT OF INTEREST The authors declare that there is no conflict of interest. ## DATA AVAILABILITY STATEMENT The data underlying this article will be shared on reasonable request to the corresponding author. ## References 1. Cheong XP, Law LKP, Seow SC. Causes and prognosis of symptomatic pericardial effusions treated by pericardiocentesis in an Asian Academic Medical Centre. Singapore Med J (2020) 61 137-141. PMID: 32488274 2. Strobbe A, Adriaenssens T, Bennett J. Etiology and long‐term outcome of patients undergoing pericardiocentesis. J Am Heart Assoc (2017) 6. PMID: 29275375 3. Sánchez‐Enrique C, Nuñez‐Gil IJ, Viana‐Tejedor A. Cause and long‐term outcome of cardiac tamponade. Am J Cardiol (2016) 117 664-669. PMID: 26718232 4. Posner MR, Cohen GI, Skarin AT. Pericardial disease in patients with cancer. The differentiation of malignant from idiopathic and radiation‐induced pericarditis. Am J Med (1981) 71 407-413. PMID: 7282729 5. El Haddad D, Iliescu C, Yusuf SW. Outcomes of cancer patients undergoing percutaneous pericardiocentesis for pericardial effusion. J Am Coll Cardiol (2015) 66 1119-1128. PMID: 26337990 6. Shih CT, Lee WC, Fang HY, Wu PJ, Fang YN, Chong SZ. Outcomes of patients with and without malignancy undergoing percutaneous pericardiocentesis for pericardial effusion. J Cardiovasc Dev Dis (2021) 8. PMID: 34821703 7. Lekhakul A, Assawakawintip C, Fenstad ER. Safety and outcome of percutaneous drainage of pericardial effusions in patients with cancer. Am J Cardiol (2018) 122 1091-1094. PMID: 30064854 8. Wilson NR, Lee MT, Gill CD. Prognostic factors and overall survival after pericardiocentesis in patients with cancer and thrombocytopenia. Front Cardiovasc Med (2021) 8. PMID: 33969007 9. 9 HCUP National Inpatient Sample (NIS) . Healthcare cost and utilization project (HCUP). Agency for Healthcare Research and Quality; 2012.. Healthcare cost and utilization project (HCUP) (2012) 10. Maisch B, Seferović PM, Ristić AD. Guidelines on the diagnosis and management of pericardial diseases executive summary; the task force on the diagnosis and management of pericardial diseases of the European Society of Cardiology. Eur Heart J (2004) 25 587-610. PMID: 15120056 11. Jacob R, Palaskas NL, Lopez‐Mattei J. How to perform pericardiocentesis in cancer patients with thrombocytopenia: a single‐center experience. JACC CardioOncol (2021) 3 452-456. PMID: 34604808 12. Dragoescu EA, Liu L. Pericardial fluid cytology: an analysis of 128 specimens over a 6‐year period. Cancer Cytopathol (2013) 121 242-251. PMID: 23362233 13. Ludeman L, Shepherd NA. Serosal involvement in gastrointestinal cancer: its assessment and significance. Histopathology (2005) 47 123-131. PMID: 16045772 14. Refaat MM, Katz WE. Neoplastic pericardial effusion. Clin Cardiol (2011) 34 593-598. PMID: 21928406 15. Liu XY, Zhang X, Ruan GT. One‐year mortality in patients with cancer cachexia: association with albumin and Total protein. Cancer Manag Res (2021) 13 6775-6783. PMID: 34512017 16. Gad MM, Elgendy IY, Mahmoud AN. Temporal trends, outcomes, and predictors of mortality after pericardiocentesis in the United States. Catheter Cardiovasc Interv (2020) 95 375-386. PMID: 31705624 17. Numico G, Cristofano A, Occelli M. Prolonged drainage and intrapericardial bleomycin administration for cardiac tamponade secondary to cancer‐related pericardial effusion. Medicine (2016) 95. PMID: 27082564 18. Kim SH, Kwak MH, Park S. Clinical characteristics of malignant pericardial effusion associated with recurrence and survival. Cancer Res Treat (2010) 42 210-216. PMID: 21253323 19. Søgaard KK, Farkas DK, Ehrenstein V, Bhaskaran K, Bøtker HE, Sørensen HT. Pericarditis as a marker of occult cancer and a prognostic factor for cancer mortality. Circulation (2017) 136 996-1006. PMID: 28663234 20. Gannavarapu BS, Lau SKM, Carter K. Prevalence and survival impact of pretreatment cancer‐associated weight loss: a tool for guiding early palliative care. J Oncol Pract (2018) 14 e238-e250. PMID: 29466074
--- title: 'Gonadotropin‐releasing hormone agonist treatment and ischemic heart disease among female patients with breast cancer: A cohort study' authors: - Yi‐Sheng Chou - Chun‐Chieh Wang - Li‐Fei Hsu - Pei‐Hung Chuang - Chi‐Feng Cheng - Nai‐Hsin Li - Chu‐Chieh Chen - Chien‐Liang Chen - Yun‐Ju Lai - Yung‐Feng Yen journal: Cancer Medicine year: 2022 pmcid: PMC10028063 doi: 10.1002/cam4.5390 license: CC BY 4.0 --- # Gonadotropin‐releasing hormone agonist treatment and ischemic heart disease among female patients with breast cancer: A cohort study ## Abstract For female patients with breast cancer, the use of GnRH agonists was significantly associated with a reduced risk of IHD. After adjusting for age, treatment, and comorbidity, patients who received GnRH therapy had a significantly lower risk of developing IHD (AHR=0.5, $95\%$ CI=0.39‐0.64). GnRH agonists were significantly associated with a lower risk of incident IHD in all the subgroups, except in those with CKD or COPD, respectively. ### Background The risk of ischemic heart disease (IHD) due to the impact of gonadotropin‐releasing hormone (GnRH) agonists among female patients with breast cancer remains a controversy. ### Methods Information from the Registry for Catastrophic Illness, the National Health Insurance Research Database (NHIRD), and the Death Registry Database in Taiwan were analyzed. Female patients with breast cancer were selected from the Registry for Catastrophic Illness from January 1, 2000, to December 31, 2018. All the breast cancer patients were followed until new‐onset IHD diagnosis, death, or December 31, 2018. A Kaplan–Meier survival curve was drawn to show the difference between patients treated with and without GnRH agonists. The Cox regression analysis was used to investigate the effects of GnRH agonists and the incidence of IHD. ### Results A total of 172,850 female patients with breast cancer were recognized with a mean age of 52.6 years. Among them, 6071($3.5\%$) had received GnRH agonist therapy. Kaplan–Meier survival curves showed a significant difference between patients with and without GnRH therapy (log‐rank $p \leq 0.0001$). Patients who received GnRH therapy had a significantly decreased risk of developing IHD than those without GnRH therapy (HR = 0.18; $95\%$ CI = 0.14–0.23). After adjusting for age, treatment, and comorbidity, patients who received GnRH therapy still had a significantly lower risk of developing IHD (AHR = 0.5, $95\%$ CI = 0.39–0.64). ### Conclusion The study showed that the use of GnRH agonists for breast cancer treatment was significantly associated with a reduced risk of IHD. Further research is required to investigate the possible protective effect of GnRH on IHD. ## INTRODUCTION Breast cancer is the most common cancer among females worldwide, accounting for $25.4\%$ of total women's cancer, with more than two million newly diagnosed cases. 1 In Asia, female patients with breast cancer were younger compared with patients from Western countries. Luminal histology subtypes were also more predominate among patients in Western countries. 2 For patients with premenopausal or perimenopause endocrine positive breast cancer, gonadotropin‐releasing hormone (GnRH) agonists are increasingly administered in combination with tamoxifen 3 or cyclin‐dependent kinase $\frac{4}{6}$ inhibitor 4, 5 in the adjuvant or metastatic settings. GnRH agonists inhibit the pituitary GnRH receptors and suppress the downstream effects of follicle‐stimulating hormone (FSH) and luteinizing hormone (LH), resulting in decreased estrogen production in premenopausal ovaries. 6 Previous studies have shown diverse results regarding the effects of GnRH agonists on the cardiovascular system for hormone‐dependent cancer management. A previous animal study showed that GnRH agonists may be associated with atherosclerotic effects. 7 Several observational studies showed that GnRH agonists were related to increased cardiovascular disease risk in patients with prostate cancer. 8, 9, 10 However, a meta‐analysis of randomized trials reported no significant associations between GnRH agonists and the risk of cardiovascular disease. 11 Most evidence suggesting an association between GnRH agonists and cardiovascular disease for male patients with prostate cancer came from population‐based studies. 8, 9, 12, 13 Several meta‐analyses of observational studies disclosed that GnRH agonists were related to an increased incidence of non‐fatal cardiovascular disease. 14, 15 Whether or not GnRH agonists are associated with an excess risk of cardiovascular morbidity remains a highly controversial question. 11 To the best of our knowledge, limited literature addressing the associations between GnRH agonists and the risk of cardiovascular disease in patients with breast cancer is available. Therefore, this study intended to determine the relationship between GnRH agonists and the risk of IHD in female breast cancers. ## Data source Data from the Registry for Catastrophic Illness, the National Health Insurance Research Database (NHIRD), and the Death Registry Database in Taiwan were analyzed. The NHIRD contains healthcare data of more than $99\%$ of the population in Taiwan, including both inpatient and outpatient medical records. 16, 17 The NHIRD contained patient information such as diagnosis, drug administration, and examinations. The Institutional Review Board of TCH certified this research (no. TCHIRB‐10709107‐W). ## Study subjects Female subjects 18 years and older with a diagnosis of breast cancer between January 1, 2000, and December 31, 2018, were identified from the Registry for Catastrophic Illness (ICD‐9‐CM and ICD‐10‐CM code for female breast cancer: 174 and C50.x1x, respectively). All the cancer diagnoses recorded in the Registry of Catastrophic Illness were confirmed by pathologists. 18 The Death Registry Database in Taiwan confirmed cases of death. Study subjects were followed until new‐onset IHD diagnosis, death, or December 31, 2018. ## Outcome variables The incidence of IHD was recognized from the NHIRD. It was defined as the occurrence of more than once in inpatient medical records or more than three times in outpatient medical records (ICD‐9‐CM code, 411–414 except 414.1x and ICD‐10‐CM code I20‐I25 except for I21, I25.3, and I25.4). 19 ## Main explanatory variable Information regarding GnRH agonist prescriptions were gathered from the NHIRD. The total administered daily dose of GnRH agonists was calculated and expressed as the defined daily dose (DDD); 0.134 mg for leuprorelin and triptorelin, and 0.129 mg for goserelin, which was suggested by the Anatomical Therapeutic Chemical Classification/Defined Daily Doses (ATC/DDD) system. 20 ## Potential confounders The potential confounders were age, socioeconomic status, breast cancer therapy, including lumpectomy and radiotherapy, and comorbidities. The socioeconomic status included income level and residence. Income level was categorized as low, intermediate, and high (≤19,200; 19,201 to <40,000; ≥40,000 New Taiwan Dollars [NTD]). Residence was categorized as urban, suburban, and rural. The comorbidities were recognized by the presence of disease diagnosis recorded by the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) and ICD‐10‐CM code, including diabetes (ICD‐9‐CM:250, ICD‐10‐CM: E08‐E13), chronic kidney disease (ICD‐9‐CM: 585–586, ICD‐10‐CM: N18), hypertension (ICD‐9‐CM: 401–405, ICD‐10‐CM: I1), dyslipidemia (ICD‐9‐CM: 272.0–272.4, ICD‐10‐CM: E78.0‐E78.5), cerebrovascular disease (ICD‐9‐CM: 430–437, ICD‐10‐CM: G46.3‐G46.4, I60‐I66, I69), chronic obstructive pulmonary disease (ICD‐9‐CM: 491–492, 518.1–518.2, 770.2; ICD‐10‐CM: J41‐J44), and liver cirrhosis (ICD‐9‐CM: 491–492, 518.1–518.2, 770.2; ICD‐10‐CM: J41‐J44). Comorbidities were recognized only if the condition occurred more than once in an inpatient setting or more than three times in outpatient medical records. 21 ## STATISTICAL ANALYSIS First, the demographic data of the study subjects were shown as continuous data with mean and standard deviation (SD) or categorical data with numbers and percentages. Patients with and without GnRH agonist treatment were compared using the two‐sample t‐test and Pearson χ 2 test. The incidence of IHD was calculated using events per 1000 person‐years. Kaplan–Meier survival curves were drawn to show the difference between patients treated with and without GnRH agonists. The Cox regression analysis was used to calculate hazard ratios (HRs) and $95\%$ confidence intervals (CIs). Dose–response relations were also evaluated between GnRH agonist (as a continuous variable) and incident IHD. Death events were analyzed as competing risk events. 22 Stratified analyses were performed according to age and comorbidities in case interaction may exist. Sensitivity analysis was performed by excluding missing data of the stage of breast cancer and including cancer stage in multivariable Cox regression analysis. The data analyses were conducted using the SAS 9.4 software package (SAS Institute). ## RESULTS A total of 196,539 female patients with breast cancer were recognized from the Registry for Catastrophic Illness between January 1, 2000, and December 31, 2018. After excluding those with antecedent IHD ($$n = 22$$,687), younger than 18 years old ($$n = 15$$), and those with incomplete data ($$n = 987$$), there were 172,850 patients included in the analysis. Table 1 shows the baseline features of participants. The overall mean (SD) age was 52.6 (11.5) years, and $3.5\%$ of the subjects received treatment with GnRH agonist. The mean (SD) of the DDDs for GnRH agonists was 41.5 (6.4) among patients receiving hormone treatment. Moreover, the mean (SD) follow‐up times were 4.98 (3.80) years in patients receiving GnRH agonists and 7.19 (5.63) years in those not receiving GnRH agonists. Compared with patients not receiving GnRH agonists, those receiving GnRH agonists were younger and more likely to receive lumpectomy and radiotherapy. Moreover, patients receiving GnRH agonists had a lower proportion of comorbidities. Patients received treatment without GnRH agonists were more likely to live in rural areas and have lower incomes. TABLE 1 \| Characteristics \| Total, n = 172,850 No. (%) of subjects \| Treatment with GnRH agonists, n = 6017 \| Treatment without GnRH agonists, n = 166,833 \| p‐Value \| \| --- \| --- \| --- \| --- \| --- \| \| Age (years) \| Age (years) \| Age (years) \| Age (years) \| Age (years) \| \| Mean ± SD \| 52.56 ± 11.47 \| 41.45 ± 6.42 \| 52.96 ± 11.41 \| <0.001 \| \| 18–49 \| 75,146 (43.47) \| 5502 (91.44) \| 69,644 (41.74) \| <0.001 \| \| ≥50 \| 97,704 (56.53) \| 515 (8.56) \| 97,189 (58.26) \| \| \| Income level \| Income level \| Income level \| Income level \| Income level \| \| Low \| 18,497 (10.70) \| 316 (5.25) \| 18,181 (10.90) \| <0.001 \| \| Intermediate \| 65,788 (38.06) \| 2348 (39.02) \| 63,440 (38.03) \| <0.001 \| \| High \| 88,565 (51.24) \| 3353 (55.73) \| 85,212 (51.08) \| <0.001 \| \| Urbanization \| Urbanization \| Urbanization \| Urbanization \| Urbanization \| \| Rural \| 8942 (5.17) \| 247 (4.11) \| 8695 (5.21) \| <0.001 \| \| Suburban \| 100,028 (57.87) \| 3515 (58.42) \| 96,513 (57.85) \| <0.001 \| \| Urban \| 63,880 (36.96) \| 2255 (37.48) \| 61,625 (36.94) \| <0.001 \| \| Lumpectomy \| Lumpectomy \| Lumpectomy \| Lumpectomy \| Lumpectomy \| \| No \| 46,731 (27.04) \| 1243 (20.66) \| 45,488 (27.27) \| <0.001 \| \| Yes \| 126,119 (72.96) \| 4774 (79.34) \| 121,345 (72.73) \| <0.001 \| \| Radiotherapy \| Radiotherapy \| Radiotherapy \| Radiotherapy \| Radiotherapy \| \| No \| 151,702 (87.77) \| 4718 (78.41) \| 146,984 (88.10) \| <0.001 \| \| Yes \| 21,148 (12.23) \| 1299 (21.59) \| 19,849 (11.90) \| <0.001 \| \| Comorbidity \| Comorbidity \| Comorbidity \| Comorbidity \| Comorbidity \| \| Diabetes \| 37,657 (21.79) \| 460 (7.65) \| 37,197 (22.30) \| <0.001 \| \| Chronic kidney disease \| 7209 (4.17) \| 71 (1.18) \| 7138 (4.28) \| <0.001 \| \| Hypertension \| 62,597 (36.21) \| 710 (11.80) \| 61,887 (37.10) \| <0.001 \| \| Dyslipidemia \| 57,083 (33.02) \| 759 (12.61) \| 56,324 (33.76) \| <0.001 \| \| Cerebrovascular disease \| 14,812 (8.57) \| 115 (1.91) \| 14,697 (8.81) \| <0.001 \| \| Chronic obstructive pulmonary disease \| 17,260 (9.99) \| 293 (4.87) \| 16,967 (10.17) \| <0.001 \| \| Liver cirrhosis \| 34,802 (20.13) \| 746 (12.40) \| 34,056 (20.41) \| <0.001 \| \| Outcomes \| Outcomes \| Outcomes \| Outcomes \| Outcomes \| \| New‐onset of ischemic heart disease \| 12,605 (7.29) \| 63 (1.05) \| 12,542 (7.52) \| <0.001 \| \| Incidence of ischemic heart disease* \| 10.24 \| 2.10 \| 10.46 \| <0.001 \| \| Follow‐up years, mean ± SD \| 7.12 ± 5.59 \| 4.98 ± 3.80 \| 7.19 ± 5.63 \| <0.001 \| During the study follow‐up period, 12,605 female patients with breast cancer had a new‐onset of IHD, including 63 ($1.05\%$) patients receiving GnRH agonists and 12,542 ($7.52\%$) patients not receiving GnRH agonists. The incidence rate of IHD per 1000 person‐years was 2.10 in patients receiving GnRH agonists and 10.46 in those not receiving GnRH agonists ($p \leq 0.001$). In addition, the time to incident IHD was significantly longer in patients receiving GnRH agonists than in those not receiving GnRH agonists ($p \leq 0.001$, log‐rank test; Figure 1). FIGURE 1: Kaplan–Meier curves for time to diagnosis of incident ischemic heart disease in patients receiving and not receiving GnRH agonists. GnRH, gonadotropin‐releasing hormone. The univariable Cox proportional hazards model showed that female patients with breast cancer undergoing GnRH agonist therapy had a significantly decreased risk of incident IHD (HR: 0.18, $95\%$ CI: 0.14–0.23). After adjusting for age, sex, and comorbidities, patients using GnRH agonist therapy still had a significantly lower risk of incident IHD (AHR: 0.50; $95\%$ CI: 0.39–0.64) (Table 2). Patients with higher income levels had a lower risk of incident IHD. Other factors associated with decreased risk of incident IHD consisted of lumpectomy and radiotherapy. Moreover, risk factors of incident IHD consisted of age ≥ 50 years, diabetes, chronic kidney disease (CKD), hypertension, dyslipidemia, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), and liver cirrhosis. A significantly linear dose–response effect per DDD increase in GnRH agonists for incident IHD (AHR, 0.91; $95\%$ CI <0.84–0.98; $$p \leq 0.011$$) was also noted. Figure 2 showed the results of stratified analysis. GnRH agonists were significantly associated with a lower risk of incident IHD in all the subgroups, except in those with CKD or COPD, respectively. Sensitivity analysis was performed after adjustment for the stage of breast cancer. Patients with missing data of stage were excluded from the analysis($$n = 104$$,726). There were 68,124 participants included in multivariable Cox regression analysis. After adjusting for stage of breast cancer, the result showed that female patients with breast cancer undergoing GnRH agonist therapy had a significantly decreased risk of incident IHD (HR: 0.57, $95\%$ CI: 0.38–0.84, $$p \leq 0.004$$) (Table S1). ## DISCUSSION This study found that female patients with breast cancer receiving GnRH agonists had a lower risk of developing IHD than patients not receiving GnRH agonists. GnRH agonists bind to GnRH receptors in the pituitary gland, resulting in the secretion and initial surge of FSH and LH which stimulates the production of serum testosterone or estrogen. Subsequently, the negative feedback at the pituitary gland causes downregulation of GnRH receptors. On the contrary, no initial testosterone surge is found after administration of GnRH antagnosits. 14 The distinct impact of GnRH agonists in our study, and bilateral oophorectomy on IHD, might be partially explained by the fact that serum FSH and LH is sustainably inhibited after GnRH agonist administration but upregulated after bilateral oophorectomy. 23 Potential alternative mechanisms explaining the findings of our study were adipogenesis 24 and atherosclerosis. 25 Dysregulated fat deposits to the arterial wall cause atherosclerosis and IHD. 26 Peripheral blood mononuclear cells (PMN) and pro‐inflammatory T helper 1 lymphocytes both express GnRH receptors. The activation of these receptors is involved in the activation of PMNs, lymphocytes, and cytokine production, such as an increase in IFN‐γ, and decrease in IL‐4. 27, 28 Different effects of GnRH‐I and GnRH‐II demonstrated that GnRH‐I enhanced proliferation of PMNs and IL‐2Rγ expression, while GnRH‐II attenuated proliferation of PMNs and IL‐2Rγ expression. 29 A large population study evaluating the side effects of bilateral oophorectomy‐induced menopause on premenopausal women before age 50 without hormone replacement therapy (HRT) demonstrated a statistically significant increased risk of multimorbidity including hyperlipidemia, and diabetes mellitus. The side effects of coronary artery disease became statistically significant only in adjusted analyses restricted to females receiving oophorectomy before the age of 45. 23, 30 The deleterious effects of natural estrogen deprivation after menopause in the Study of Women's Health Across the Nation (SWAN) comprises of increased body and cardiovascular fat and alternations in body weight and waist circumference. 31, 32, 33 Association between lumpectomy and IHD risk was not yet investigated in previous studies. The procedure of lumpectomy may not be associated with pathogenesis of IHD. In this study, we tried to included detailed treatment procedure, including surgical procedure, radiotherapy, and medical treatment. The detailed surgical procedure was not available in our dataset. Further research is warranted to explore impact of lumpectomy on IHD risk. Previous studies had demonstrated that exposure of the heart to ionizing radiation during radiotherapy for breast cancer increases the subsequent rate of ischemic heart disease. 34 But the results of this study showed that radiotherapy appeared to be associated with lower risk of IHD. The detailed radiation therapy regimen including dose and area were not available in this dataset. Even radiotherapy for distal bone metastasis were included in analysis, which may lead to bias on IHD risks of radiotherapy. This study enrolled a large number of patients with breast cancer and had a long follow duration from 2000 to 2018. The diagnoses of breast cancer were confirmed by pathology reports in the Registry for Catastrophic Illness, and the diagnoses of comorbidities were confirmed by medical reports to ensure the validity of this study. Additionally, socioeconomic status and treatment strategies were included as potential confounders. Our study has several limitations. First, similarly to other retrospective population studies, patients were not randomized to both treatment groups. Patients allocated to the GnRH treatment group had significantly higher income levels, urbanization, more lumpectomy, and radiotherapy. However, these patients were younger and had fewer comorbidities including diabetes mellitus and dyslipidemia. Nonetheless, multivariate analysis demonstrated treatment with GnRH agonists as an independent predictive factor associated with lower risk of IHD. The stratified analysis also showed that GnRH agonists were significantly associated with a lower risk of IHD in all subgroups of patients. Second, we used ICD codes to identify the diagnosis of IHD in the administrative database. Although patients with less frequent visits were less likely to be diagnosed with IHD, the frequency of visits ranged from once every month to every 3 months. Patients receiving GnRH agonists usually received treatment at a one‐month interval, which made the attribution of lower risk of IHD to lower frequency of visits less likely. The generalizability of this study to other regions requires further certification because most of the study subjects were Taiwanese. Our study provides preliminary report for evaluating breast cancer treatment, considering the scarce literature currently available regarding the associations of GnRH agonists and the risk of IHD among women with breast cancer. In conclusion, our large population study is the first to report that treatment using GnRH agonists for patients with breast cancer was associated with a significantly reduced risk of IHD after adjusting for variable confounders. Furthermore, endocrine therapy for breast cancer treatment should weigh the benefits of disease‐specific survival against long‐term side effects of cardiovascular events. Patients receiving endocrine therapy should try to avoid risk factors of cardiovascular disease. Further research to delineate and confirm the causality and mechanisms is needed. ## AUTHOR CONTRIBUTIONS Yi‐Sheng Chou: Conceptualization (lead); writing – original draft (lead). Chun‐Chieh Wang: Investigation (equal). Li‐Fei Hsu: Data curation (equal); investigation (equal). Pei‐Hung Chuang: Data curation (equal); formal analysis (equal). Chi‐Feng Cheng: Investigation (equal). Nai‐Hsin Li: Conceptualization (equal); investigation (equal). Chu‐Chieh Chen: Supervision (equal); visualization (equal). Chien‐Liang Chen: Conceptualization (equal); supervision (equal). Yun‐Ju Lai: Funding acquisition (equal); investigation (equal); resources (equal); visualization (equal); writing – review and editing (equal). Yung‐Feng Yen: Conceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); writing – original draft (equal); writing – review and editing (equal). ## FUNDING INFORMATION This research is supported by Taichung Veterans General Hospital of Puli branch, Grant Number: PL‐2021002. ## CONFLICT OF INTEREST The authors declare no competing interests. ## DATA AVAILABILITY STATEMENT The datasets of the current study are available from the Health and Welfare Data Science Center of Ministry of Health and Welfare in Taiwan. ## References 1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin (2018) 68 394-424. PMID: 30207593 2. Lin CH, Chuang PY, Chiang CJ. Distinct clinicopathological features and prognosis of emerging young‐female breast cancer in an east Asian country: a nationwide cancer registry‐based study. 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--- title: Planting conditions can enhance the bioactivity of mulberry by affecting its composition authors: - Huixin Bai - Shanfeng Jiang - Jincai Liu - Ye Tian - Xiaohui Zheng - Siwang Wang - Yanhua Xie - Yao Li - Pu Jia journal: Frontiers in Plant Science year: 2023 pmcid: PMC10028076 doi: 10.3389/fpls.2023.1133062 license: CC BY 4.0 --- # Planting conditions can enhance the bioactivity of mulberry by affecting its composition ## Abstract Mulberry (Morus alba L.) has a special significance in the history of agriculture and economic plant cultivation. Mulberry has strong environmental adaptability, a wide planting range, and abundant output. It is not only an important resource for silkworm breeding but also a raw ingredient for various foods and has great potential for the development of biological resources. The bioactivities of mulberry in different planting areas are not the same, which is an obstacle to the development of mulberry. This study collected information on the planting conditions of mulberry branches in 12 planting areas, such as altitude, temperature difference, and precipitation. A comparison of the levels of 12 constituents of mulberry branches from mulberry grown in different planting areas was then made. An in vitro model was used to study the bioactivities of mulberry branches in the 12 planting areas, and mathematical analysis was used to explain the possible reasons for the differences in the composition and bioactivities of mulberry branches in different planting areas. After studying mulberry samples from 12 planting areas in China, it was found that a small temperature difference could affect the antiapoptotic effect of mulberry branch on microvascular endothelial cells by changing the levels and proportions of rutin, hyperoside, and morusin. Adequate irrigation can promote the antioxidation of the mulberry branch on microvascular endothelial cells by changing the levels and proportions of scopoletin and quercitrin. The results of the analysis of planting conditions and the levels of active constituents and their correlation with bioactivities support the improvement of mulberry planting conditions and have great significance in the rational development of mulberry resources. This is the first time that a mathematical analysis method was used to analyze the effects of planting conditions on mulberry biological activity. ## Introduction Mulberry (Morus alba L.) has a special significance in the agricultural and economic plant cultivation history of China. It is not only an important resource for silkworm breeding, but also a source of various foods and teas, and has great application potential in biological resources (Chao et al., 2021). Owing to its great adaptability to different climates, mulberry has unique advantages as a natural resource and is widely distributed across China. From Heilongjiang province in the north to Guangxi and Guangdong in the south, it spans the entire geographic latitude of China. Thus, the significant differences in planting conditions in these areas, such as altitude, precipitation, and temperature difference, have become an important factor affecting mulberry development. In addition, because mulberry trees have strong germination (Hashemi and Tabibian, 2018) and rapid regeneration after pruning, a large amount of pruning must be carried out every year to facilitate the normal growth of the trees, which produces a large number of mulberry branches and leaves. However, only the mulberry leaves and fruits, and not the branches, are fully developed and utilized. The mulberry branch is crushed to make fertilizer or simply burned (Yin et al., 2017) which is a waste of mulberry plant resources. Mulberry branches are rich in alkaloids, flavonoids, polysaccharides, etc. The active constituents of the mulberry branch, such as chlorogenic acid, mulberroside A, resveratrol, and 1-deoxynojirimycin (1-DNJ), can prevent diabetes and related complications and protect pancreatic cells from oxidative damage (Han et al., 2020; Hou et al., 2020). For example, chlorogenic acid inhibits inflammation and fat deposition in the liver by reducing energy intake and food efficiency, and it also increases the diversity of the gut microbiota, thereby improving overall metabolism. Long-term consumption of chlorogenic acid is beneficial for cardiocerebral vessels, the liver, and the metabolism (Bhandarkar et al., 2019). Morusin is a kind of flavonoid derived from mulberry that has a strong antioxidant capacity and can repress oxidative stress, thus protecting the integrity of pancreatic β-cells and reducing cell death. Morusin can also improve hyperglycemia and lipid homeostasis in type I diabetic mice induced by streptozotocin (Choi et al., 2020). Cortex Mori water extract, containing mulberroside A, reduces blood glucose, thus alleviating liver and kidney damage caused by hyperglycemia, and ameliorates diabetic endotoxemia (Xu et al., 2021). Resveratrol has been used in various anti-hyperglycemia studies because of its physiological effects of lowering levels of blood sugar, improving insulin sensitivity, and protecting pancreatic β cells (Öztürk et al., 2017; Zhu et al., 2017; Huang et al., 2020). Studies have found that scopoletin can inhibit postprandial blood glucose levels and that the mechanism of action is related to the inhibition of α-glucosidase and α-amylase activity (Jang et al., 2018). 1-deoxynojirimycin (1-DNJ) is a potent α-glucosidase inhibitor, which can suppress the increase of blood sugar before and after meals without serious side effects (Thaipitakwong et al., 2020). It can also inhibit hypercholesterolemia induced by a high-fat diet (HFD) and regulate the gut microbiota (Li et al., 2019). The mulberry branch and mulberry leaf are similar in their compositional basis and bioactivities (Bai et al., 2023), and the abundant yield of mulberry branches has obvious advantages for exploitation. However, there is no research that reveals the impact of the planting conditions of mulberry planting areas on the levels of the constituents of the mulberry branch. The lack of verification of the functional similarity of mulberry branches in different planting areas hinders the development and research of mulberry resources. Therefore, in this study, by determining fingerprint morphology and main constituent levels, the compositional similarity of mulberry branches from 12 planting areas in China was compared using the mulberry leaf as a control. A hyperglycemia and hyperlipidemia model (Bai et al., 2023) was used to study the effects of different planting areas on the mulberry branch on apoptosis and antioxidant damage in vitro. Partial least square regression (PLSR) and multiple factor analysis (MFA) were used to analyze the effects of planting condition factors on the constituents and bioactivities of mulberry branch extracts. This research provides a theoretical basis for improving mulberry planting conditions and rationally developing mulberry resources. This is the first time that the mathematical analysis method has been used to analyze the effects of planting conditions on the constituents and bioactivities of the mulberry branch. ## Instruments and reagents The mulberry branch and leaf were identified as belonging to dry branches and leaves of Morus alba L. by Ling-bian Sun, the chief pharmacist of the Air Force Medical University (Xi’an, China). The mulberry leaf was purchased from Shiquan County, Shaanxi, China. Mulberry branches were purchased from different planting areas, as shown in Table 1. Table 1 \| Planting areas \| Latitude and longitude \| Altitude (m) \| Annual temperature difference (°C) \| Total annual precipitation (mm) \| Rain concentrated (month) \| Month before harvest \| Month before harvest.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Planting areas \| Latitude and longitude \| Altitude (m) \| Annual temperature difference (°C) \| Total annual precipitation (mm) \| Rain concentrated (month) \| Temperature difference (°C) \| Precipitation (mm) \| \| S1(20180401) \| 31°42′N–39°35′N, 105°29′E–111°15′E \| 396.9 \| 28 \| 565 \| 7–9 \| 29 \| 18 \| \| S2(20180502) \| 43°25′N–53°33′N, 121°11′E–135°05′E \| 171.7 \| 42 \| 554 \| 6–8 \| 28 \| 72 \| \| S3(20180520) \| 38°43′N–43°26′N, 118°53′E–125°46′E \| 41.6 \| 37 \| 662 \| 6–8 \| 25 \| 76 \| \| S4(20180530) \| 36°01′N–42°37′N, 113°04′E–119°53′E \| 80.5 \| 30 \| 540 \| 7–9 \| 31 \| 27 \| \| S5(20180408) \| 31°42′N–39°35′N, 105°29′E–111°15′E \| 396.9 \| 28 \| 565 \| 7–9 \| 29 \| 18 \| \| S6(20180416) \| 31°23′N–36°22′N, 110°21′E–116°39′E \| 110.4 \| 28 \| 617 \| 7–9 \| 31 \| 12 \| \| S7(20180415) \| 30°45′N–35°20′N, 116°18′E–121°57′E \| 8.9 \| 26 \| 1275 \| 6–8 \| 25 \| 91 \| \| S8(20180417) \| 29°41′N–34°38′N, 114°54′E–119°37′E \| 29.8 \| 26 \| 1148 \| 6–8 \| 28 \| 93 \| \| S9(20180310) \| 27°02′N–31°11′N, 118°01′E–123°10′E \| 41.7 \| 25 \| 1639 \| 6–8 \| 28 \| 140 \| \| S10(20180408) \| 26°03′N–34°19′N, 97°21′E–108°31′E \| 505.9 \| 20 \| 980 \| 7–9 \| 20 \| 21 \| \| S11(20180413) \| 21°08′N–29°15′N, 97°31′E–106°11′E \| 1891.4 \| 12 \| 933 \| 6–9 \| 20 \| 25 \| \| S12(20180401) \| 20°54′N–26°23′N, 104°29′E–112°04′E \| 72.7 \| 16 \| 1357 \| 6–8 \| 21 \| 24 \| \| S13(20180420) \| 20°09′N–25°31′N, 109°45′E–117°20′E \| 6.6 \| 15 \| 2084 \| 5–7 \| 28 \| 130 \| The purity of all standards was ≥ $98\%$. Chlorogenic acid, cryptochlorogenic acid, rutin, hyperoside, isoquercitrin, astragalin, quercitrin, morusin, mulberroside A, resveratrol, scopoletin, 1-DNJ, and palmitic acid (PA) were purchased from Sigma Aldrich (Darmstadt, Germany). Methanol, acetonitrile (ACN), and formic acid were of chromatography grade (Thermo Fisher Scientific, Waltham, MA, USA). Ultra-pure water was obtained from a Milli-Q water purification system (Millipore, Milford, MA, USA). All other reagents were of analytical grade. Dulbecco’s Modified Eagle’s Medium (DMEM), trypsin-ethylene diamine tetraacetic acid (EDTA) solution, fetal bovine serum (FBS), and phosphate-buffered saline (PBS) were purchased from HyClone (Shanghai, China). The Cell Counting Kit-8 was purchased from EnoGene (CCK-8, Xi’an, China), fluorescein isothiocyanate (FITC) Annexin V Apoptosis Detection Kit I was purchased from Wuhan Seville Biological Technology (Wuhan City, Hubei, China), and a reactive oxygen species (ROS) assay kit was purchased from Biosharp (Guangzhou, China). ## Preparation of extracts The fresh mulberry leaf and the mulberry branch were cleaned. The mulberry leaf was dried until the moisture content was less than $10\%$ (at 30°C and $30\%$ humidity). The mulberry branch was cut into thick slices (0.2–0.5 cm in diameter) and dried until the moisture content was less than $10\%$ (at 30°C and $30\%$ humidity). The dried mulberry leaf was crushed and passed through a No 3 sieve (pore size 355 ± 13 μm). Subsequently, 300 g of mulberry branch powder from each planting area and 300 g of mulberry leaf powder were soaked in 2.4 L of $50\%$ ethanol overnight and then subjected to reflux extraction for 3 h, after which the liquid part was collected by filtration. The liquid was concentrated to 3 g/mL (i.e., 1 mL of concentrated extract was equivalent to 3 g of dried mulberry branch sample) under reduced pressure and stored at –20°C. ## Preparation of test solution Methanol was added to 0.1 mL of each extract to yield a volume of 10 mL. The suspensions were filtered through a 0.22 μm Millipore filter and used for subsequent composition analysis. ## UPLC chromatographic conditions An ultra-performance liquid chromatography (UPLC) system fitted with an LC-30AD binary pump (Shimadzu Corporation, Kyoto, Japan), an on-line vacuum degasser, an autosampler, and a column oven were used for similarity analyses. A Poroshell 120 EC-C18 column (4.6 mm × 100 mm; 2.7 μm; Agilent Technologies, Santa Clara, CA, USA) was fitted with an EC-C18 pre-column (4.6 mm × 5 mm; 2.7 μm; Agilent Technologies, Santa Clara, CA, USA). The column temperature was stabilized to 26°C and the samples were monitored at a wavelength of 320 nm. The mobile phase consisted of ACN (A) and $0.1\%$ (v/v; volume/volume) phosphoric acid in water (B). The flow rate was 0.80 mL/min and the injection volume was 20 μL. The gradient elution program was as follows: 0–3 min, $5\%$–$10\%$ A; 3–30 min, $10\%$–$15\%$ A; 30–40 min, $15\%$–$20\%$ A; 40–75 min, $20\%$–$30\%$ A; 75–80 min, $30\%$–$40\%$ A; 80–100 min, $40\%$–$55\%$ A; 100–110 min, $55\%$–$80\%$ A; 110–115 min, $80\%$–$95\%$ A; then $5\%$ ACN hold for 10 min (He et al., 2020; Polumackanycz et al., 2021; Shreelakshmi et al., 2021). ## Analysis method of UPLC-MS/MS A UPLC system fitted with an LC-30AD binary pump (Shimadzu Corporation, Japan), an on-line vacuum degasser, an autosampler, and a column oven was used for determining the levels of constituents. A Poroshell 120 EC-C18 column (4.6 × 100 mm, 2.7 μm; Agilent, USA) was fitted with an EC-C18 pre-column (4.6 × 5 mm, 2.7 μm; Agilent) and the column temperature was stabilized to 26°C. The mobile phase consisted of ACN (A) and $0.1\%$ formic acid (B), with a flow rate of 0.4 mL/min. The injection volume was 5 μL. The gradient elution program was set as follows: 0–5 min, $5\%$–$25\%$ A; 5–20 min, $25\%$ A; 20–34 min, $25\%$–$95\%$ A; then $5\%$ ACN hold for 4 min. The UPLC system was equipped with an API 4000 tandem mass spectrometer (Applied Biosystems/MDS SCIEX, USA) and an electrospray ionization (ESI) source. The quantification was performed using the multiple reactions monitoring (MRM) method. The ESI voltage of positive ions was set to 5,500 V and the ESI voltage of negative ions was set to –4,500 V. Chlorogenic acid, cryptochlorogenic acid, rutin, hyperoside, isoquercitrin, astragalin, quercitrin, morusin, mulberroside A, and resveratrol were detected in the negative ion mode. Scopoletin and 1-DNJ were detected in the positive ion mode (Hu et al., 2017; Ju et al., 2018; D'Urso et al., 2019; Negro et al., 2019; Kim et al., 2020). The mass spectrometry parameters are shown in supplementary Table S1. ## Evaluation of similarity between the mulberry branch and leaf extracts Using the operating conditions described in UPLC chromatographic conditions, the similarity between the test solutions described in Preparation of test solution was evaluated. The Similarity Evaluation System for Chromatographic Fingerprint Profiles of Chinese Medicines (2012.130723 edition, Chinese Pharmacopoeia Commission) was used to evaluate the similarity of UPLC fingerprints. ## Determination of 12 constituents of mulberry branch and leaf extracts Using the detection method described in Analysis method of UPLC-MS/MS, the level of each constituent in the test solutions described in Preparation of test solution was determined. Data acquisition and processing were performed using the Analyst 1.6.2 software [Applied Biosystems (AB Sciex)]. All results were presented as mean ± standard deviation (SD). ## Planting conditions of different planting areas For each planting area, the following information was obtained through the China Meteorological Data Sharing Service System (http://cdc.nmic.cn/home.do) (Table 1): the latitude and longitude, the altitude (m), the annual temperature difference (°C; i.e., the average value of the differences between the highest and lowest temperatures in each of the past 3 years in the planting area), total annual precipitation (mm), rain concentrated (month), the temperature difference in the month before harvest (°C; i.e., the difference between the highest and lowest temperature in the month before harvest), and the precipitation in the month before harvest (mm). The effects of the planting conditions in different planting areas on the constituents of mulberry branches were compared. ## Cell culture and establishment of in vitro model Rat brain microvascular endothelial cells (RBMECs) were obtained from BNCC (Jiangsu, China), and a cell model of hyperglycemia and hyperlipidemia was established with PA and high concentrations of glucose (Bai et al., 2021; Tyagi et al., 2021). RBMECs were cultivated with DMEM containing $10\%$ FBS, 100 U/mL streptomycin, and 100 U/mL penicillin cultured in an incubator at 37°C and $5\%$ CO2. The medium was replaced every 2 days and the cells were allowed to adhere to and grow in the culture, covering more than $80\%$ of the bottle. Sterile glucose powder was added to DMEM to prepare a high-sugar medium with a final concentration of 33 mmol/L. A medium containing 200 μmol/L PA hyperglycemia and hyperlipidemia was prepared from a 33 mmol/L high-sugar medium. The cells were cultured in the hyperglycemia and hyperlipidemia medium at 37°C and $5\%$ CO2 for 24 h to obtain an in vitro hyperglycemia and hyperlipidemia model. ## Cell viability assay The cell viability was measured using the Cell Counting Kit-8 (CCK-8) test to determine the experimental concentration of mulberry branch extracts from different areas (Pan et al., 2019). The cells were inoculated in a 96-well plate, and the number of cells per well was 5 × 103. The cells were cultured in DMEM containing $10\%$ FBS at 37°C and $5\%$ CO2. After adhering to the wall, the cells were divided into a blank control group (no cells), a normal control group, and 12 extract groups; each group comprised six replicate wells. After discarding the medium in the well, 100 μL of serum-free DMEM was added to each well of the blank control group and normal control group, and 100 μL of DMEM containing different concentrations of mulberry branch extract (concentrations of 10–7, 10–6, 10–5, 10–4, and 10–3 mg/mL of extract were prepared with serum-free DMEM) was added to the extract groups and incubated for 24 h. The original medium was aspirated, followed by the addition of 100 μL serum-free medium and 10 μL of CCK-8 reagent to each well. After culturing for 2 h in the incubator, a microplate reader (Infinite M200 Pro, TECAN, Switzerland) was used to measure the absorbance at 450 nm. The cell viability of different concentrations of mulberry branch extracts was expressed as: (the absorbance of extract group – the absorbance of blank control group) × $100\%$/(the absorbance of normal control group – the absorbance of blank control group). ## Apoptosis assay and determination of ROS level Flow cytometry was performed on a NovoCyte 2040R (ACEA, USA). Apoptosis was detected via annexin-V/propidium iodide (PI) double staining (Ma et al., 2018). The cells were inoculated in six-well plates, and the number of cells per well was 1 ×106. The cells were cultured in DMEM containing $10\%$ FBS at 37°C and $5\%$ CO2. After adhering to the wall, the cells were divided into the normal control group, hyperglycemia and hyperlipidemia model group, and extract groups, each with six replicate wells. After discarding the medium in the well, 1 mL of medium was added to each well of the normal control group, 1 mL of hyperglycemia and hyperlipidemia medium to each well of the hyperglycemia and hyperlipidemia model group, and 1 mL of medium with an extract concentration of 10–4 mg/mL prepared with hyperglycemia and hyperlipidemia medium to each well of the extract groups. The cells were cultured in an incubator for 24 h. The cells in each group were subsequently washed once with PBS and then digested with trypsin in the absence of Ethylene Diamine Tetraacetic Acid (EDTA), followed by washing twice with PBS. Thereafter, 200 μL of buffer was added to the harvested cells. Next, 5 μL of annexin-V-fluorescein isothiocyanate (FITC) was added to the cells and they were kept in the dark for 15 min at room temperature, followed by the addition of 5 μL of PI staining solution and incubation for another 5 min. Apoptosis was detected using flow cytometry. The NovoExpress software version 1.2.1 (ACEA Biosciences Inc.) was used to generate scatter plots. The number of annexin-V-fluorescein isothiocyanate (FITC)- and PI-positive cells was used to calculate the cellular apoptotic rate. Reactive oxygen detection was performed using the fluorescent probe, 2',7'-Dichlorodihydrofluorescein diacetate (DCFH-DA), which reacted with intracellular ROS, generating fluorescent products (Liu et al., 2021b). The cells were inoculated in a 12-well plate with round coverslips, and the number of cells per well was 1 × 105. The cells were cultured in DMEM containing $10\%$ FBS at 37°C and $5\%$ CO2. After adhering to the wall, the cells were divided into the normal control group, hyperglycemia and hyperlipidemia model group, and extract groups, each with six replicate wells. After discarding the medium in the well, 1 mL of medium was added to each well of the normal control group, 1 mL of hyperglycemia and hyperlipidemia medium to each well of the hyperglycemia and hyperlipidemia model group, and 1 mL of medium with an extract concentration of 10–4 mg/mL prepared with hyperglycemia and hyperlipidemia medium to each well of the extract groups. The cells were then cultured in an incubator in the dark for 24 h and then washed with a serum-free medium, followed by incubation with 10 μM DCFH-DA in a cell incubator (maintained at 37°C) in the dark for 30 min. Subsequently, the cells were washed twice with PBS to fully remove the DCFH-DA that did not enter the cells. The fluorescence intensity of the cells was detected using an inverted fluorescence microscope (Olympus IX53+DP73, Japan) at an excitation wavelength (λex) of 488 nm and an emission wavelength (λem) of 525 nm. Image J software was used to detect the fluorescence intensity of the cells in the captured cell images. One-way analysis of variance (ANOVA) in GraphPad Prism 8 software (San Diego, CA, USA) was used to analyze the data. Tukey’s test was used for comparison between groups. A p-value < 0.05 was considered statistically significant. ## Correlation analysis of the influence of planting conditions on constituents and bioactivities PLSR analysis was performed on the planting conditions described in Planting conditions of different planting areas and constituent levels described in Determination of 12 constituents of mulberry branch and leaf extracts to reveal the effects of planting conditions on constituent similarities and levels. MFA was performed on the constituent levels described in Determination of 12 constituents of mulberry branch and leaf extracts and the bioactivities described in Apoptosis assay and determination of ROS level to reveal the relationship between constituent levels and bioactivities. ROS can cause oxidative damage; therefore, antioxidation activity can be assessed by ROS-scavenging rate. The calculation methods for antiapoptotic and ROS-scavenging rates were as follows: Antiapoptotic = (the mean value of apoptosis rate in the model group – the mean of the apoptosis rate in the extract group)/(the mean value of apoptosis rate in the model group – the mean of the apoptosis rate in the normal group). ROS-scavenging rate = (the mean value of fluorescence intensity in the model group – the mean value of fluorescence intensity in the extract group) × $100\%$/(the mean value of fluorescence intensity in the model group – the mean value of fluorescence intensity in the normal group). ## The influence of planting conditions on the similarities and levels of constituents of mulberry branches The mulberry tree has low survival requirements. If sufficient light is available, it is highly adaptable to differences in temperature and soil pH, which allows for successful planting in different geographical regions. Studies have shown that the secondary metabolism of mulberry trees can undergo huge variations (Sánchez-Salcedo et al., 2016; Yang et al., 2017). These secondary metabolites are the main constituents of mulberry trees that produce bioactivities. Changes in planting conditions may be an important cause of differences in levels of constituents. Although mulberry is classified as a thermophilic plant, it can withstand temperatures of –30°C and its stem can regenerate rapidly after freezing (Blitek et al., 2022). Therefore, it is necessary to study the changes in the levels of the constituents of the mulberry branch in combination with planting conditions. The Similarity Evaluation System for Chromatographic Fingerprint Profiles of Chinese Medicines was used to obtain the similarity matrix of the mulberry branch from 12 planting areas (Table 2 and Figure 1). The planting conditions at different planting areas are listed in Table 1 (data obtained from public information on the internet and research reports). From the comparison of the numbers and retention times of chromatographic peaks, the same constituents of the mulberry leaf and the mulberry branch were found mainly in peaks 1–9, including mulberroside A, chlorogenic acid, cryptochlorogenic acid, scopoletin, rutin, isoquercitrin, hyperoside, astragalin, quercitrin, and resveratrol. The unique compound contained in the mulberry branch was peak 10, which was morusin. The above results indicate significant similarity between the mulberry leaf and branch except for branches from a few producing regions, such as Guangxi and Guangdong, which showed less similarity. The author used the established UPLC-mass spectra (MS)/MS method to determine the levels of 12 constituents of the mulberry branch and leaf (Table 3). The results revealed that the levels of morusin, mulberroside A, and 1-DNJ in the mulberry branch from almost all producing areas were higher than in the mulberry leaf, while other constituents, such as chlorogenic acid, cryptochlorogenic acid, rutin, hyperoside, isoquercitrin, astragalin, and quercitrin, were lower than in the mulberry leaf. The levels of chlorogenic acid, cryptochlorogenic acid, and isoquercitrin were the highest in S11; the levels of rutin, hyperoside, and morusin were the highest in S13; the levels of astragalin, mulberroside A, resveratrol, and 1-DNJ were the highest in S4; and the levels of quercitrin and scopoletin were the highest in S7. ## Mulberry branch extract can reduce the apoptosis rate and inhibit oxidative damage of RBMEC in a hyperglycemia and hyperlipidemia environment As shown in Figure 2A, when the extract concentration in the medium was 10–7–10–4 mg/mL, there was no significant change in the cell viability of the RBMEC ($p \leq 0.05$). Therefore, 10–4 mg/mL was chosen as the experimental concentration in vitro. The RBMECs were double stained by annexin-V-fluorescein isothiocyanate (FITC)/PI and then subjected to flow cytometry to test the protective effects of the mulberry branch extracts against the damage induced by the hyperglycemia and hyperlipidemia media. The experimental results showed that the cellular apoptotic rate of the model group ($34.43\%$) was significantly higher than that of the normal control group ($7.07\%$, $p \leq 0.05$). After treatment with mulberry branch extracts, apoptotic rates were significantly decreased ($p \leq 0.05$, relative to model group) (Figures 2B, C). Among them, the antiapoptotic effects of the S2, S3, S4, S6, S9, S10, S11, S12, and S13 groups were significantly lower than that of the mulberry leaf group ($p \leq 0.05$). Figure 2: Mulberry branch can reduce the apoptosis of rat brain microvascular endothelial cells (RBMECs) in hyperglycemia and hyperlipidemia. S1, Mulberry leaf; S2, Heilongjiang; S3, Liaoning; S4, Hebei; S5, Shanxi; S6, Henan; S7, Jiangsu; S8, Anhui; S9, Zhejiang; S10, Sichuan; S11, Yunnan; S12, Guangxi; S13, Guangdong. (A), CCK-8 assay to determine the effect of different concentrations mulberry branch extracts on the viability of cells; (B), Quantitative analysis of the apoptosis rates of RBMECs treated with different mulberry branch extracts for 24 h; (C), annexin-V/propidium iodide (PI) double staining followed by cytometry analysis was performed to evaluate the cell apoptosis of RBMEC treatment with different mulberry branch extracts for 24 h. The same letter in the two groups of data indicates no significant difference. Different letters in the two groups of data indicate a significant difference (p < 0.05). The intracellular ROS in response to the hyperglycemia and hyperlipidemia environment was significantly increased to $510\%$ that of the normal control group ($p \leq 0.05$), which eventually led to cell oxidative damage. Compared with the model, all mulberry branch extracts (10–4 mg/mL) had the ability to inhibit the intracellular production of ROS and reduce oxidative damage in the RBMECs after exposure to hyperglycemia and hyperlipidemia media, to varying degrees, and the effect was significantly stronger than in the mulberry leaf group (Figures 3A, B, $p \leq 0.05$). Among them, S3 and S8 had the most significant effects. Figure 3: Mulberry branch can reduce the apoptosis of rat brain microvascular endothelial cells (RBMECs) in hyperglycemia and hyperlipidemia. S1, Mulberry leaf; S2, Heilongjiang; S3, Liaoning; S4, Hebei; S5, Shanxi; S6, Henan; S7, Jiangsu; S8, Anhui; S9, Zhejiang; S10, Sichuan; S11, Yunnan; S12, Guangxi; S13, Guangdong. (A), Fluorescence probe DCFH-DA was used to detect the level of reactive oxygen species (ROS) generated owing to hyperglycemia and hyperlipidemia in an in vitro model after treatment with different branch extracts for 24 h; (B), Quantitative analysis of cell fluorescence intensity of different mulberry branch extracts treated for 24 h. The same letter in the two groups of data indicates no significant difference. Different letters in the two groups of data indicate a significant difference (p < 0.05). ROS is an oxidative factor that is closely related to the oxidative stress response of cells, and a hyperglycemia and hyperlipidemia environment can induce vascular endothelial cells to produce more ROS, causing oxidative damage to cells and destroying capillary structures (Rendra et al., 2019; Cao et al., 2021). As the damage intensifies, it will eventually lead to the apoptosis of vascular endothelial cells and cause microcirculatory complications in multiple chronic diseases (Chalimeswamy et al., 2021). Bai et al. found that mulberry extract had a protective effect on the increase of ROS and vascular endothelial damage caused by high sugar and high fat (Bai et al., 2021). Ranjan et al. found that mulberry extract could achieve its antioxidant effect by reducing the activities of catalase, serum glutamic oxaloacetic transaminase, and serum glutamic pyruvic transaminase in mice used as diabetes models (Ranjan et al., 2017). This research found that the mulberry branch and mulberry leaf had highly similar bioactivities, and the antioxidant and antiapoptotic effects of the mulberry branch were even greater than the mulberry leaf (Figures 2B and 3B). These results provide support for the value of developing the mulberry branch as a new economic plant resource. ## Correlation analysis of planting conditions and bioactivities PLSR was used to determine the influence of altitude, annual temperature difference, total annual precipitation, temperature differences in the month before harvest, and precipitation in the month before harvest on the levels and similarities of 12 constituents of the mulberry branch (Figures 4A, B). The results indicated that the directions of the three factors of altitude, precipitation, and temperature were broadly symmetrical but distinguishable. Among the 12 studied constituents, chlorogenic acid, cryptochlorogenic acid, isoquercitrin, and resveratrol showed a similar direction to altitude, suggesting a positive correlation. These constituents also had a weakly negative correlation with temperature difference. Therefore, the levels of these constituents were most significantly affected by altitude, indicating that mulberry branches harvested from higher altitudes tended to be rich in these constituents. In contrast, a large temperature difference tended to reduce the amount of these constituents. For example, the altitudes of S3 and S9 were almost the same, but because the temperature difference of S9 was significantly lower than that of S3, the levels of chlorogenic acid, cryptochlorogenic acid, isoquercitrin, and resveratrol were higher in S9 than in S3. This finding was consistent with the previous study by Blitek et al., in which they also showed that the levels of chlorogenic acid, cryptochlorogenic acid, isoquercitrin, and resveratrol were influenced by temperature difference and altitude (Blitek et al., 2022). In addition, the directions of these four constituents were almost perpendicular to precipitation, indicating a negligible effect of precipitation on the levels of these constituents. By applying the same analysis method, rutin, hyperoside, and morusin were found to have a strong negative correlation with temperature difference and a weak positive correlation with altitude and precipitation, revealing that temperature difference had a greater impact, and the levels of these three constituents were higher in areas with small temperature differences. For instance, S10, S11, and S13 varied greatly in altitude but had small temperature differences, and the levels of rutin, hyperoside, and morusin were similarly high among all three. Precipitation was negatively correlated with mulberroside A and 1-DNJ but had a strong positive correlation with astragalin, quercitrin, and scopoletin. The precipitation in the month before harvest had an average effect on astragalin and quercitrin, while astragalin was most significantly affected by total annual precipitation. For example, the annual precipitation in S2 and S4 was similar, but owing to more precipitation in the month before harvest in S4, the levels of astragalin and quercitrin were higher in S4 than in S2. In general, precipitation was negatively correlated with the similarity of mulberry branches in different planting areas, suggesting that it was an important influencing factor. In contrast, the altitude and temperature difference were almost vertical to the similarity, suggesting that these factors had little effect on similarity. Therefore, it is inferred that precipitation may be an important climatic factor affecting the similarities in the composition of mulberry branches. Figure 4: The correlation between the planting conditions of the planting areas, the constituents of the mulberry branch extract and the bioactivity variations were analyzed by partial least square regression (PLSR) and the “FactoMineR” software. S2, Heilongjiang; S3, Liaoning; S4, Hebei; S5, Shanxi; S6, Henan; S7, Jiangsu; S8, Anhui; S9, Zhejiang; S10, Sichuan; S11, Yunnan; S12, Guangxi; S13, Guangdong. (A) Precipitation in the month before harvest and the similarity of mulberry branches from different planting areas. Similarity: red > 0.9 (S2, S3, S4, S8, S9, S10, S11); 0.8 < orange < 0.9 (S6, S7); 0.7 < yellow < 0.8 (S5); 0.6 < green < 0.7 (S12); blue < 0.6 (S13). (B) The effects of planting conditions on the levels of constituents and the similarity of mulberry branches analyzed by PLSR: blue (planting conditions), red (constituents). The vector length of each composition represents the amount of change. The greater the change, the longer the vector length. The angle between the vector direction of the composition and the planting conditions represents the strength of the correlation. The more similar, the stronger the correlation is, and opposite direction represents negative correlation. (C) multiple factor analysis (MFA) based on reactive oxygen species (ROS) and apoptosis results in line with the constituents of mulberry branch extracts: the vector length and color of each composition in the mulberry branch extracts represent the contribution of the composition to the overall efficacy. Color of vector (contribution degree, red direction represents greater contribution value, while blue direction represents the opposite), the angle between the vector direction of composition contribution (solid line arrow), and variation in specific activity (dotted line arrow) represents the strength of the correlation: a smaller angle indicates stronger correlation. MFA was conducted to analyze the correlation between the levels of 12 constituents and the variations of two bioactivities in mulberry branch extracts from different planting areas (Figure 4C). The results showed that the 12 constituents contributed to the antiapoptotic and antioxidant activities of mulberry branches to varying degrees. Mulberroside A, chlorogenic acid, cryptochlorogenic acid, astragalin, resveratrol, isoquercitrin, and 1-DNJ had similar contributions to these two bioactivities of the mulberry branch. The sum of the contributions of these seven constituents accounted for more than half of the total efficacy. Their directions were all in the first quadrant, which was broadly perpendicular to the direction of antiapoptosis and antioxidative effect, indicating that these constituents were, in general, not related to the variation among mulberry branch extracts from different planting areas. In other words, these constituents together produced the main bioactivities but had little effect on the magnitude of bioactive differences of mulberry branch extracts from different planting areas. For example, the levels of mulberroside A, chlorogenic acid, cryptochlorogenic acid, astragalin, resveratrol, and 1-DNJ in S10 and S11 were very distinctive, but their antiapoptosis and antioxidative effects were not significantly different. Previous studies have reported on a range of bioactivities and the constituents of mulberry. Chlorogenic acid showed strong antioxidant activity at 200 mg/kg (Metwally et al., 2020). Mulberroside A in the water extract of Cortex Mori also had good antioxidant activity at 0.58 g/mL (Xu et al., 2021). A basic diet containing 500 mg/kg resveratrol could reverse the oxidative damage of hepatocytes (Liu et al., 2021a), and 5 mg/L of 1-DNJ could enhance the antioxidant activity of tilapia splenocytes (Tang et al., 2017). Similar activities were also found in cryptochlorogenic acid and isoquercitrin (Zhou, 2020). Rutin could alleviate H9C2 cell damage induced by high glucose levels by inhibiting apoptosis and endoplasmic reticulum stress (Wang et al., 2021). Hyperoside significantly inhibited apoptosis induced by high glucose levels in a dose-dependent manner (Wu et al., 2020). When the concentration of quercitrin reached 160 μg/mL, it could significantly increase the survival rate of human embryonic kidney 293 T cells (Li et al., 2020). Scopoletin prevented palmitic acid and bile acid-induced hepatocyte death by inhibiting endoplasmic reticulum stress and ROS generation and reducing the phosphorylation of JNK, one of the cell death signaling intermediates (Wu et al., 2022). These studies have shown that various constituents of the mulberry branch have clear antiapoptotic and antioxidant effects, which are the basis for the bioactivities of the mulberry branch. Mulberroside A, chlorogenic acid, cryptochlorogenic acid, astragalin, resveratrol, and 1-DNJ in the mulberry branch could act together in a combined form in the extract through various pathways, resulting in stable and basic antiapoptotic and antioxidant relatively. Therefore, it is believed that these constituents constitute the basic antiapoptotic and antioxidant effects of the mulberry branch and are the foundation of the bioactive function of the mulberry branch, but are not the elements that cause the differences in the bioactive effects of mulberry branches from different planting areas. The analysis results show that the antiapoptotic and antioxidative effects of the mulberry branch are two vectors with opposite directions. Some constituents of the mulberry branch had the same or similar directions as the antiapoptotic or antioxidant effects, indicating that these constituents were major contributors to the variations in antiapoptotic or antioxidant activities observed among mulberry branch extracts from different planting areas. Rutin, hyperoside, and morusin showed the same direction as the antiapoptotic effect but the opposite direction to the antioxidative effect, which means that these constituents were positively correlated with the variations in antiapoptosis activity among mulberry branch extracts but negatively correlated with the change in antioxidative capability. Thus, the results might suggest that these three constituents are beneficial for the antiapoptosis function of the mulberry branch but unfavorable for the antioxidant activity of the mulberry branch. Compared with the above three constituents, scopoletin and quercitrin showed almost the opposite influence on antiapoptotic and antioxidation activities. Owing to this relative affect trend, the ratio of these five constituents is probably the key factor for the differences in the bioactivity of mulberry branches from different planting areas and is perhaps more important than the levels of these constituents. Planting conditions can have a direct impact on the ratio of these key constituents. According to the analysis shown in Figure 4B, the annual temperature difference and the temperature difference in the month before harvest had a greater impact on the levels of rutin, hyperoside, and morusin. In areas where both these temperature differences were small, the amounts of rutin, hyperoside, and morusin were relatively high, e.g., in S13. In contrast, the total annual precipitation and precipitation in the month before harvest had a greater impact on the levels of quercitrin and scopoletin thus, quercitrin and scopoletin were relatively rich in areas with higher total annual precipitation and higher precipitation in the month before harvest. For example, S8 had more precipitation than S6 and so the levels of quercitrin and scopoletin in S8 were higher, which in turn affected the bioactivity of the mulberry branch in these two areas. Extracts of S8 had a certain advantage in antioxidation, while S6 was more effective in antiapoptosis. It can be seen that planting conditions have a significant impact on the content and ratio of the constituents of plants (Kreft et al., 2022), which leads to the bioactivities bias in mulberry branches from different planting areas. Thus, clarifying the relationship between planting conditions and constituents has particular significance for the utilization of bioactive resources in the mulberry branch after harvesting. ## Conclusions In this study, the correlation between planting conditions (such as altitude, temperature difference, and precipitation) in different planting areas and bioactivities in the mulberry branch was determined and analyzed. Specifically, the research found that mulberry the branch has similar constituents and bioactivities to the mulberry leaf. Comprehensive analysis suggested that the main constituents of mulberry branches in different planting areas were similar, but the bioactivities were biased. Among the 12 constituents tested in this study, seven constituents (chlorogenic acid, cryptochlorogenic acid, isoquercitrin, astragalin, mulberroside A, resveratrol, and 1-DNJ), together, probably determined the basal level of the mulberry branch’s bioactivity, which is fundamental to the potential of the mulberry branch’s bioactive resources to make mulberry an economic plant. Mulberry branches from different planting areas exhibited differences in antiapoptosis and antioxidative damage capacities. These differences were mainly caused by five key constituents (rutin, hyperoside, quercitrin, morusin, and scopoletin). Climatic factors, particularly precipitation and temperature difference, in the planting areas significantly affected the levels and ratios of these constituents. The relationship between the planting conditions, constituents, and bioactivities of the mulberry branch was analyzed by PLSR and MFA. The results showed that planting conditions with smaller temperature differences would make the antiapoptotic effect of the mulberry branch more prominent and that adequate irrigation would promote the antioxidative effect of the mulberry branch. These results not only indicate that the mulberry branch has potential use as a new economic plant part but also guide the rational utilization of mulberry branches for different medical needs, thus reducing the waste of resources. The mathematical analysis method used in this study provides data supporting the selection of planting conditions to utilize the bioactivities of mulberry. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author. ## Author contributions HB and SJ conceptualized and wrote the original draft. HB and JL carried out the experimental part under the supervision of YL. YT and XZ critically reviewed the draft. YX conducted the statistical analysis of the experimental data. SW and PJ procured research funding. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1133062/full#supplementary-material ## References 1. 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--- title: 'Gender differences in behavioral inhibitory control under evoked acute stress: An event-related potential study' authors: - Siyu Di - Chao Ma - Xiaoguang Wu - Liang Lei journal: Frontiers in Psychology year: 2023 pmcid: PMC10028078 doi: 10.3389/fpsyg.2023.1107935 license: CC BY 4.0 --- # Gender differences in behavioral inhibitory control under evoked acute stress: An event-related potential study ## Abstract ### Purpose This study investigated gender differences in behavioral inhibitory control among college students under acute stress state by using event-related potential technique. ### Methods Acute stress was evoked in 41 college students (22 males and 19 females) using the Trier Social Stress paradigm, and the neutral state was matched using out-of-speech reading, with subjects completing a two-choice Oddball task in each of the two states. In combination with the ERP technique, the area under the stress curve, reaction time, number of errors, and the difference waves between the two stimulus conditions in the frontal-central region N2 wave amplitude and the parietal-central region P3 wave amplitude were compared between the two groups of subjects in the stressful and neutral state. ### Results The results revealed that the area under the stress curve was larger under the stress condition compared to the neutral condition, and the area under the stress curve was larger in females than in males. Behavioral results showed no statistically significant differences in reaction time and number of errors between the two genders in the acute stress condition. The ERP results showed that the wave amplitudes of N2 and P3 decreased significantly in both genders in the acute stress state. The decrease in N2 amplitude was greater in females during the transition from neutral to stressful condition, while the difference in P3 amplitude was not statistically significant in both genders. ### Conclusion The findings suggest that evoked acute stress can promote behavioral inhibitory control in both genders and that females are more sensitive to acute stress state. ## 1. Introduction Behavioral inhibitory control, also known as response inhibition, is one of the core components of executive functioning (Miyake and Friedman, 2012). Behavioral inhibitory control refers to people’s inhibition of their inappropriate external behaviors under specific environment conditions, such as resisting external temptations and suppressing impulsive behaviors (Puiu et al., 2020). From a cognitive perspective, behavioral inhibitory control includes early perceptual processing, conflict awareness, and late response inhibition (Yuan et al., 2008). With better behavioral inhibition control, individuals can monitor and suppress their current or upcoming inappropriate behaviors, effectively guiding them to adopt corresponding behavioral patterns in response to changes in the environment and ultimately make correct behavioral decisions (Goldstein and Tuescher, 2007). The lack of behavioral inhibitory control often leads to a series of problems. On the one hand, reduced behavioral inhibitory control may lead individuals to uncontrolled violent behavior, delinquency, and suicidal behavior. On the other hand, weaker behavioral inhibitory control is also detrimental to the development of physical health. Some studies have found that most obese patients cannot control their diet because of their low behavioral inhibition control, which eventually leads to obesity. At the same time, as further research has been conducted, researchers have found that some disorders are also associated with behavioral inhibition control, such as attention deficit hyperactivity disorder, depression, obsessive–compulsive disorder, and schizophrenia (Hatta et al., 2001; Kelly et al., 2020; Zhao et al., 2020). Therefore, the importance of behavioral inhibitory control for individual development cannot be overstated. It has been shown that there may be gender differences in behavioral inhibitory control. As an important executive function, behavioral inhibitory control is critical to the success of both males and females in modern society and may have played a key role in the evolution of human social intelligence (Li et al., 2006). Sjoberg et al. used the Go/No-go paradigm to examine gender differences in behavioral inhibition and found that female exhibited better behavioral inhibition (Sjoberg and Cole, 2018). However, when Melynyte et al. [ 2017] used the same method for their study, they found that females were less correct and required more time for conflict monitoring and response inhibition, suggesting that females have worse behavioral inhibition. Some other researchers have argued that there are no gender differences in behavioral inhibitory control. For example, Herba et al. [ 2006] examined changes in behavior inhibitory control using the Go/No-go paradigm and found no gender differences (Yuan et al., 2010). Neuroimaging findings were similarly divergent, with Liu et al. [ 2012] finding more statistically significant activation of the left sub-parietal and striatal regions in behavioral inhibitory control processing in females, while males showed greater activation of the right sub-parietal and suprachiasmatic regions, as well as stronger anterior cingulate gyrus activation. In contrast, other studies have not found similar statistically significant lateralization features (Garavan et al., 2006; Liu et al., 2012). In summary, the available studies have not clarified whether there are gender differences in behavioral inhibitory control, presumably because they may be influenced by factors such as research paradigms and situational factors. It has been found that behavioral inhibitory control is also influenced by acute stress. Stress is a series of physiological and psychological reactions of the organism to maintain homeostasis when its internal steady state is threatened (Zhenzhen et al., 2017). Acute stress is a part of everyone’s life, and there are many sources of acute stress in life. In daily life, individuals often face various threats and challenges, such as sudden exams or interviews, various public emergencies, and so on. In the face of acute stressors, the body’s internal homeostasis is rapidly unbalanced and can trigger a series of physiological stress responses. The results of the effects of acute stress on individual behavioral inhibition remain divergent. Some studies have shown that acute stress impairs individuals’ behavioral inhibition (Jiang and Rau, 2017; Roos et al., 2017). When Jiang et al. used the Trier Social Stress Test (TSST) to study behavioral inhibitory control in individuals, they found a statistically significant rise in reaction time and an increase in P3d volatility in the stress group. However, other studies have suggested that acute stress elevates the behavioral inhibition capacity of individuals (Farbiash, 2016; Qi et al., 2017; Dierolf et al., 2018). Dierolf used the Trier Social Stress Test (TSST) paradigm to evoke different age males in an acute stress state, followed by testing the subject’s behavioral inhibitory control using the Go/No-go task, and found shorter inhibition time and smaller N2d wave amplitudes in the stress state (Dierolf et al., 2018). In summary, the direction of effect of acute stress on behavioral inhibitory control in individuals has not been clarified by existing studies. Furthermore, currently, gender differences in behavioral inhibitory control under acute stress have not been directly explored. In this study, we chose a modified two-choice Oddball paradigm to evoke behavioral inhibitory control in subjects (Yuan Jiajin et al., 2017) and combined it with the event-related potentiation technique, which is known for its high temporal resolution, to explore gender differences in behavioral inhibitory control under acute stress state. In the two-choice Oddball task, subjects are required to respond to two types of stimuli, one type is the standard stimulus with a high number of occurrences and corresponding responses. The other category is the deviant stimulus, which occurs less frequently and corresponds to fewer responses. Subjects were required to respond to both types of stimuli with keystrokes. The time difference between the responses to standard and deviant stimuli is used as a behavioral inhibition index, which effectively resolves the interference of motor contamination on the electrophysiological results existing in the Go/No-go task and the stop signal task (SST). ERP has a high temporal resolution and is often used to examine the time course of behavioral inhibitory control. Among the ERP components, the main focus is on two components, frontal-central N2 and central-parietal P3. N2 is a negative component that usually appears around 200 ms after stimulus presentation, and the maximum wave amplitude generally occurs in the prefrontal region. N2 emerges in the early stage of behavioral inhibitory control and mainly reflects conflict monitoring and conflict control. The change in N2 wave amplitude is related to conflict monitoring ability (Donkers and Boxtel, 2004; Dong et al., 2009). P3 is a positive component that usually appears around 300 ms after stimulus presentation, with the maximum wave amplitude generally appearing in the central parietal lobe. P3 emerges in the late stage of behavioral inhibitory control, mainly reflecting the inhibition process itself and related to the completion of the inhibition process. The change in P3 wave amplitude is related to the degree of cognitive effort invested (Donkers and Boxtel, 2004; Dong et al., 2009). We expected to see a moderating effect of gender on the amplitude of the N2 and P3 components, and this variation reflects the changing process of behavioral inhibitory control. It has been shown that acute stress can affect behavioral inhibitory control and that gender differences may also have an impact on behavioral inhibitory control. However, the direction of the effect of acute stress on behavioral inhibitory control in gender-specific individuals is still unclear. Investigating the gender differences in behavioral inhibitory control under acute stress can help us understand the characteristics and discrepancies in behavioral inhibitory control between the two genders in the face of stress, which in turn can help us provide targeted strategies to enhance behavioral inhibitory control. In addition, it is especially helpful to help both genders of college students to have higher behavioral inhibitory control when facing the stress in current society, dealing with various problems in life calmly, resisting temptations better, and making correct behavioral decisions. Based on existing studies, this study hypothesized that acute stress would motivate individuals to respond positively, which in turn would enhance their behavioral inhibitory control, as evidenced by a decrease in the time of behavioral inhibition and a reduction in the number of errors under acute stress. Furthermore, we hypothesized that behavioral inhibitory control is more susceptible in females than in males under acute stress state. ## 2.1. Participants A priori analysis was completed using GPower 3.1 software (effect size $f = 0.3$, α = 0.05, 1−β = 0.80, repeated measures, 2 between-group2 within-group), and calculations showed that a total of 24 subjects (12 in each group) were required. The convenience sampling method was adopted to recruit 44 university student subjects, 22 females and 22 males, through a recruitment announcement on campus. Subject selection criteria: age 18–25, right-handed, without major physical illness, no history of neurological or psychiatric disorders, no previous participation in relevant trials, non-restricted dieters, no color weakness or color blindness, body mass index in the normal range (18.5–23.9), and normal visual acuity or more positive visual acuity. Subject exclusion criteria: scores (26.1 ± 3.8) higher than 48 (moderate anxiety or higher) on the Trait Anxiety Inventory (Dai, 2014) and scores (6.7 ± 3.1) higher than 14 (moderate depression or higher) on the Beck Depression Inventory (Jackson-Koku, 2016). After the experiment, one subject failed to record all data due to an instrument error, and the other two subjects were deleted due to excessive signal noise caused by physical activity during the trial, resulting in an insufficient number of valid trials. 41 participants were actually enrolled, including 22 males and 19 females, aged 18–25 years, with a mean age of (20 ± 2) years. The study was in accordance with the Declaration of Helsinki, was reviewed and approved by the local medical ethics committee, and the subjects voluntarily participated in the experiment and signed the informed consent form. ## 2.2.1. Subjective measurement The Short State Anxiety Inventory (Marteau and Bekker, 1992) measures an individual’s state anxiety. The scale includes a total of eight entries, including sad, disgusted, angry, distracted, nervous, upset, relaxed, and calm. The scale is scored on a seven-point scale from 1 (very nonconforming) to 7 (very conforming), with the last two items scored inversely, and a higher total score represents a higher level of state anxiety. ## 2.2.2. Stress-evoking The study used the TSST paradigm to evoke an acute stress state, which consisted of two parts: free speech and mental arithmetic (Kirschbaum et al., 1993). In the acute stress state, subjects were simulated to participate in a multi-competitive recruitment event. Subjects were given 2 min to organize their language and then completed a self-presentation of about 5 min. When subjects had less than 5 min for self-presentation, each of the three main testers asked subjects about the prepared questions. The entire presentation was recorded. After completing the free speech task, the subjects were asked to complete the mental calculation task of subtracting 17 from 2023 in succession, without giving feedback if the calculation was correct and reminding the subjects to stop and start again from 2023 if the calculation was incorrect. The two states were balanced between subjects. ## 2.2.3. Task The two-choice Oddball task evoked behavioral inhibition, and the stimulus materials were the letter pictures “W” and “M.” “M” was the standard stimulus, press the “F” key; “W” was the deviant stimulus, press the “J” key. The whole test procedure was prepared by E-prime 2.0, and the stimuli were presented on a DELL 23-inch LCD monitor with a picture size of 356 pixel × 391 pixel, and the subject’s eyes were about 80 cm from the center of the screen. 280 trials were included in the test, including 200 standard stimuli and 80 deviant stimuli. First, a red “+” gaze point appears in the center of the screen for 800 ms, followed by a random blank screen for 500 ~ 1,500 ms, then a random standard stimulus/deviant stimulus with a presentation time of 1,000 ms, the subject needs to respond correctly in time, and after the keystroke ends, there will be a blank screen for 1,000 ms. The entire process used E-prime 2.0 to record response time and number of errors. See Figure 1. Figure 1: Two-choice Oddball task. ## 2.3. Procedure Participants were contacted 1 day in advance and told not to exercise and not to eat for 2 h prior to the test. Subjects were asked verbally prior to the test whether they had met the above requirements. Upon arrival at the laboratory, subjects first washed their hair and sat quietly for 20 min, then filled in their personal information and administered the 1st SSAI. Subsequently, after wearing the equipment and completing the practice trials, the 2nd SSAI was administered. Afterwards, the TSST paradigm/reading was performed for 15 min, and the 3rd test was administered. Following the stress/neutral state evocation, the Oddball trial task was completed, EEG data were recorded, and the 4th measurement was taken after the task was completed. After a 20 min rest, the 5th measurement was taken. Afterwards, TSST/reading was performed, and the 6th measurement was taken, the Oddball trial was completed, EEG data was recorded, and the 7th measurement was taken afterwards. The 8th measurement was administered after the completion of all test tasks and the end of hair washing. The 1st measurement was used as a baseline for mood, and the last measurement was used as a recovery of mood after completing the task. The results of the intermediate 6 Measurement were used to assess the status of the subject and also to calculate the area under the stress curve for both genders. See Figure 2. Figure 2: Experimental flow chart. ## 2.4. Data recording and analysis The EEG signal was collected using the Neuroscan EEG collection system. The EEG cap was a 64-conductor cap. The EEG data were collected using Curry7 software and the mean values of bilateral mastoids (M1, M2) were used as a reference. At the beginning of the experiment, the resistance between all electrodes and the scalp was less than 10kΩ. The EEG data was collected in DC mode at a sampling frequency of 1,000 Hz/conductor and was filtered online by a DC-100 Hz bandpass filter at the beginning of the experiment. After continuous data collection, the data was processed off-line using eeglab13.0. Waves below 0.05 Hz and above 30 Hz were removed by eeglab13.0. The sample rate was reduced to 500 Hz/conductor. The segmentation was performed at 200 ms before and 800 ms after the spike, with the spike occurrence as the zero point. After the segmentation, artifacts such as eye-electricity were removed using independent component analysis (Garber et al., 2011), and then extreme values with voltages greater than ±100 μV were removed. Finally, all remaining segments were superimposed and averaged to calculate the difference waves between the two stimulus conditions. According to previous studies, behavioral inhibition is mainly associated with frontal areas (Gaertner et al., 2015). Therefore, N2 values were chosen as the mean of (F1, FZ, F2) three electrode sites and P3 values were chosen as the mean of (P1, PZ, P2,) three electrode sites. In order to separate out the inhibitory control components, the ERPs under the two stimuli were subtracted (deviant-standard) to obtain the difference waves between the two stimulus conditions. 225-275 ms was chosen as the time window for N2 according to previous studies (Rueda-delgado et al., 2021). The time window of P3 was chosen as 350-500 ms (Alatorre-Cruz et al., 2021). The study used the SSAI scale score as an indicator of stress, and a repeated measures ANOVA of 2 (gender: males, females) × 2 (state: stress, neutral) was performed on reaction time and number of errors. The measures conformed to a normal distribution and were expressed as mean ± standard deviation, and the p values of all repeated measures ANOVAs were Greenhouse spherical corrected, and statistical analysis was performed using SPSS 26.0 software. ## 3.1. Subjective measurements The area under the stress curve was subjected to repeated measures ANOVA for males and females in the acute stress state and the neutral state. The results showed that the state main effect [F[1,39] = 68.96, $p \leq 0.001$, ηp2 = 0.64] was statistically significant for scores on the SSAI scale, and the area under the stress curve was larger for both genders in the acute stress state than in the neutral state. The gender main effect [F[1,39] = 0.87, $p \leq 0.05$, ηp2 = 0.02] was not statistically significant. The interaction between state and gender was statistically significant in the score [F[1,39] = 12.04, $p \leq 0.05$, ηp2 = 0.24]. The area under the stress curve for females was greater than the area under the stress state curve for males. See Table 1. Table 1 \| Unnamed: 0 \| Females \| Males \| F \| P \| ηp2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Neutral \| 20.66 ± 1.08 \| 22.21 ± 1.16 \| 0.97 \| 0.332 \| 0.02 \| \| Acute stress \| 30.59 ± 1.23 \| 26.29 ± 1.33 \| 5.66 \| 0.022 \| 0.13 \| \| F \| 74.78 \| 10.89 \| \| \| \| \| P \| <0.001 \| 0.002 \| \| \| \| \| ηp2 \| 0.66 \| 0.22 \| \| \| \| ## 3.2. Behavior results A 2 (state: stress, neutral) × 2 (gender: males, females) repeated measures ANOVA was conducted for reaction time and number of errors for the Oddball experiment, respectively. Concerning response time, the state main effect [F[1, 39] = 4.45, $p \leq 0.05$, ηp2 = 0.10] was statistically significant, with longer reaction time for both genders in the neutral state than in the stress state. The gender main effect [F[1, 39] = 4.41, $p \leq 0.05$, ηp2 = 0.10] was statistically significant, with longer reaction time for females than for males in both the neutral and stress state. The interaction of state and gender [F[1, 39] = <0.01, $p \leq 0.05$, ηp2 < 0.01] was not statistically significant. See Table 2. Table 2 \| Unnamed: 0 \| Females \| Males \| F \| P \| ηp2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Neutral \| 124.24 ± 14.31 \| 96.25 ± 13.30 \| 2.05 \| 0.16 \| 0.05 \| \| Acute stress \| 105.83 ± 7.47 \| 78.89 ± 6.94 \| 6.97 \| 0.012 \| 0.15 \| \| F \| 2.20 \| 2.25 \| \| \| \| \| P \| 0.146 \| 0.141 \| \| \| \| \| ηp2 \| 0.05 \| 0.06 \| \| \| \| Concerning the number of errors, the state main effect [F[1, 39] = 11.73, $p \leq 0.05$, ηp2 = 0.23] was statistically significant, with both genders in the stress state having fewer number of errors than in the neutral state. The gender main effect [F[1, 39] = 4.44, $p \leq 0.05$, ηp2 = 0.10] was statistically significant, with females making more errors than males in both the neutral and stress state. The interaction of state and gender [F[1, 39] = 1.93, $p \leq 0.05$, ηp2 = 0.05] was not statistically significant. See Table 3. Table 3 \| Unnamed: 0 \| Females \| Males \| F \| P \| ηp2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Neutral \| 6.14 ± 0.62 \| 5.63 ± 0.66 \| 0.31 \| 0.581 \| <0.01 \| \| Acute stress \| 5.00 ± 0.50 \| 2.95 ± 0.54 \| 7.87 \| 0.008 \| 0.17 \| \| F \| 2.24 \| 10.79 \| \| \| \| \| P \| 0.143 \| 0.002 \| \| \| \| \| ηp2 \| 0.05 \| 0.22 \| \| \| \| ## 3.3. ERP results The EEG data from the Oddball task were subjected to a 2 (state: stress, neutral) × 2 (gender: males, females) repeated measures ANOVA, and the statistics were corrected for p-values using the Greenhouse–Geisser correction. A statistical significance level of <0.05 was chosen for statistics, and ηp2 was used for statistical effect values, and Bonferroni-adjusted correlations were chosen for post hoc comparisons. ## 3.3.1. N2 (225–275 ms) The state main effect [F[1, 39] = 7.14, $p \leq 0.05$, ηp2 = 0.16] was statistically significant. The N2 wave amplitude in both males and females was smaller in the stress state than in the neutral state. The state and gender interaction [F[1, 39] = 4.28, $p \leq 0.05$, ηp2 = 0.10] was statistically significant. Post hoc tests comparing the two states revealed greater changes in the amplitude of the N2 wave in females compared to males. The gender main effect [F[1, 39] = 0.90, $$p \leq 0.40$$, ηp2 = 0.02] was not statistically significant. See Table 4 and Figure 3. ## 3.3.2. P3 (350–500 ms) The state main effect [F[1, 39] = 12.84, $p \leq 0.05$, ηp2 = 0.25] was statistically significant. The amplitude of the P3 wave in both males and females was smaller in the stress state than in the neutral state. The state and the gender interaction [F[1, 39] = 0.27, $p \leq 0.05$, ηp2 = <0.01] was not statistically significant. There was no statistically significant gender main effect [F[1, 39] = 0.04, $p \leq 0.05$, ηp2 = <0.01]. See Table 5 and Figures 4, 5. ## 4. Discussion To verify the difference in the direction of effect of acute stress on behavioral inhibition in males and females, this study combined ERP techniques to understand the effect of evoked acute stress on behavioral inhibitory control in college students of different genders at the cognitive neural level. The results of this study found that the TSST paradigm was successful in eliciting stress state in subjects. In terms of stress results, the area under the stress state curve was larger in females than in males. From the behavioral data, the reaction time of females in both neutral and stress state was longer than that of males, and the number of errors of females in both neutral and stress state were more than that of males. These results suggest that there are differences in behavioral inhibitory control between males and females, and that females have relatively lower behavioral inhibitory control. Further analysis of ERP results showed that the N2 and P3 of both genders decreased as stress level increased, indicating that the increase in stress level could enhance the behavioral inhibitory control ability of individuals. Regarding the N2 wave amplitude, it was found that the N2 wave amplitude decreased significantly in both males and females during the process from the neutral state to the stress state, while the variation in the wave amplitude was greater in females. The smaller amplitude of N2 suggests that acute stress promotes individual behavioral inhibitory control, which is consistent with previous studies (Silton et al., 2010; Clayson and Larson, 2013). For example, Rebecca reported that central frontal N2 wave amplitude was statistically significantly smaller in the emotional condition than in the neutral condition. It has been shown that larger N2 wave amplitude implies lower behavioral inhibition. For example, studies on PTSD patients have demonstrated that their low inhibition is associated with exhibiting larger N2 wave amplitude (Shu et al., 2014; Min et al., 2020), and studies on obese patients have confirmed the negative correlation between N2 wave amplitude and behavioral inhibition (Iceta et al., 2019). It is thus clear that evoked acute stress promotes behavioral inhibitory control in individuals. Furthermore, the results showed greater changes in N2 wave amplitude in females during the process from neutral to stress state. This suggests that females have weaker conflict monitoring and conflict control under acute stress state, whereas males have an advantage in this regard, which is in line with previous studies. For example, a Go/No-go study of EEG recordings found females have a lower rate of correct responses and electrophysiological analyses suggest that females require more time for conflict detection as well as more resources for response execution (Melynyte et al., 2017). One reason for this is that female is more susceptible to external influences, more sensitive to stress and less able to regulate stress than male. Neuroimaging studies have shown that a decrease in the hippocampal response is associated with adaptive stress responses, while an increase in the hippocampal response is associated with non-adaptive stress responses (Sinha et al., 2016). In a study on gender differences in neurological stress responses, it was found that females had significantly higher bilateral hippocampal responses with increased dynamics than male under stress state, suggesting that females have more nonadaptive stress responses and less stress regulation than males under stress condition. Another reason for this difference may also be emotional influences. Kelly et al. administered the Visual Analogue Rating Scales and the Profile of Mood States after TSST stress. The results showed that the females were more timid, irritable, and confused and that females showed more pronounced subjective negative experiences under the same stress state (Kelly et al., 2008). At the same time, the hippocampal response was found to be higher in females in negative emotion studies (Stevens and Hamann, 2012), reflecting female’s deficiencies in negative emotion processing, such as stress dissipation. Regarding P3 wave amplitude, it was found that P3 wave amplitude decreased significantly in both males and females during the process from neutral to stress state, and there was no significant difference in the variation of wave amplitude between females and males. A smaller amplitude of P3 suggests that acute stress promotes inhibitory control in individuals, which is consistent with previous research (Dierolf et al., 2017). It has been shown that the lower the P3 amplitude, the stronger the inhibitory control of the individual (Liu et al., 2020). Inhibitory control can be effectively improved and P3 amplitude reduced after training through inhibitory control (Melara et al., 2018). Typically, the P3 component reflects the process of assessing goals to achieve appropriate goal-directed responses (Kan et al., 2021). In the present study, there was no significant difference between females and males in the magnitude of variation in P3 wave amplitude during the process from the neutral to the stress state. This suggests that males and females under acute stress state invested approximately the same level of cognitive effort in the inhibition process itself. The reason for this may be that there is no significant gender difference between males and females at the time of late inhibition assessment and final decision making. This is in line with previous research. In a simple decision-making task, Weller et al. found no gender differences in making risky choices related to potential payoffs, with gender factors not playing a significant moderating role (Weller et al., 2010). Another reason for this result may also be due to the influence of educational background. This study selected college students as the subject group, and higher education factors may have contributed to the non-significant gender differences in decision making, which is consistent with previous studies. A behavioral study found no differences between males and females in the areas of risky decision making and inhibition in the experimental context (Kertzman et al., 2018). In previous studies, the effects of acute stress on individual behavioral inhibition have diverged, speculating that the reason may be due to differences in the experimental and stress paradigms. In this study, compared to the Go/No-go paradigm and SST paradigm used in previous studies on behavioral inhibitory control, a two-choice Oddball paradigm is adopted to evoke behavioral inhibitory control function. It helps to analyze the two behavioral indicators of reaction time and correctness, and it can effectively avoid the interference of motor contamination on the results in ERP analysis, thus improving the interpretation of behavioral results and ERP results (Yuan Jiajin et al., 2017). In the selection of stressors, the TSST paradigm, which triggers psychological tension, is chosen to reduce the direct threat to the subject’s somatic body compared to the electric shock paradigm, which acts directly on the somatic body. According to a related description in dual-competition theory, when individuals are in a high-threat environment, high threat has a processing priority that consumes limited cognitive resources first, which in turn compromises processing resources for behavioral inhibition (Lim et al., 2008). When the level of environment threat is not high, a low threat environment enhances subjects’ arousal, enhances sensory sensitivity, helps to inhibit dominant responses, and promotes subjects’ behavioral inhibition (Pessoa, 2009). The present findings also suggest some limitations and several directions for future research. First of all, only subjective emotion rating method was used to assess the stress state. Although the subjective assessment method is also a way to assess the stress state and is easy to operate, there are still individual subjective biases. Therefore, objective indicators, such as heart rate and cortisol, should be added in future studies. Secondly, only college students were selected as the subject group, and people of different ages could be invited to conduct the test in the future to improve the validity of the study results. Third, future plans could select fMRI techniques with higher resolution of neural activation data to further examine the effects of stress and gender on inhibitory function to validate and further develop the findings of this study. Finally, the present study did not fully consider the effects of other factors, such as personality traits and socioeconomic status, on the experimental results. Therefore, precise measurement and control of these variables are needed in future studies to avoid ambiguity in the interpretation of experimental results on the one hand, and to extend relevant research findings on the other. ## 5. Conclusion In summary, evoked acute stress promoted behavioral inhibitory control in both males and females, and females were more sensitive to the stressful situation. In particular, acute stress reduced response inhibition time and response error rate, and decreased N2 and P3 wave amplitudes in college students of both genders. The change in N2 amplitude was greater in females when switching from neutral to stress state. Therefore, it is suggested that when individuals have sufficient cognitive resources, they should moderately increase tension to increase the level of physiological arousal and help improve behavioral inhibition, especially for the female group. A detailed examination of the acute stress and gender effects in behavioral inhibition processing and their interaction effects is beneficial to better understand the neural mechanisms of inhibition function. In the future, based on a deeper understanding of gender differences in inhibitory function, the development of gender-specific educational and neuropsychological intervention procedures can be explored to enhance behavioral inhibition more efficiently in both genders. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by the First Affiliated Hospital of Shihezi University, Shihezi University. The patients/participants provided their written informed consent to participate in this study. ## Author contributions SD designed the experiment, collected data, and prepared the manuscript. XW collected the data and made data analysis. CM corrected the whole language of the manuscript and made final approval. LL gave technique supports and valuable suggestions in experiment designing. All authors contributed to the article and approved the submitted version. ## Funding This research was supported by the Graduate Education Innovation Program of the Xinjiang Uygur Autonomous Region, China (XJ2022G097). ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Alatorre-Cruz G. C., Downs H., Hagood D., Sorensen S. T., Williams D. K., Larson-Prior L.. Effect of obesity on inhibitory control in preadolescents during stop-signal task. 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--- title: 'Extended and replicated white matter changes in obesity: Voxel-based and region of interest meta-analyses of diffusion tensor imaging studies' authors: - Lorielle M. F. Dietze - Sean R. McWhinney - Joaquim Radua - Tomas Hajek journal: Frontiers in Nutrition year: 2023 pmcid: PMC10028081 doi: 10.3389/fnut.2023.1108360 license: CC BY 4.0 --- # Extended and replicated white matter changes in obesity: Voxel-based and region of interest meta-analyses of diffusion tensor imaging studies ## Abstract ### Introduction Obesity has become a global public health issue, which impacts general health and the brain. Associations between obesity and white matter microstructure measured using diffusion tensor imaging have been under reviewed, despite a relatively large number of individual studies. Our objective was to determine the association between obesity and white matter microstructure in a large general population sample. ### Methods We analyzed location of brain white matter changes in obesity using the Anisotropic Effect Size Seed-based d Mapping (AES-SDM) method in a voxel-based meta-analysis, with validation in a region of interest (ROI) effect size meta-analysis. Our sample included 21 742 individuals from 51 studies. ### Results The voxel-based spatial meta-analysis demonstrated reduced fractional anisotropy (FA) with obesity in the genu and splenium of the corpus callosum, middle cerebellar peduncles, anterior thalamic radiation, cortico-spinal projections, and cerebellum. The ROI effect size meta-analysis replicated associations between obesity and lower FA in the genu and splenium of the corpus callosum, middle cerebellar peduncles. Effect size of obesity related brain changes was small to medium. ### Discussion Our findings demonstrate obesity related brain white matter changes are localized rather than diffuse. Better understanding the brain correlates of obesity could help identify risk factors, and targets for prevention or treatment of brain changes. ## Introduction Obesity has become a major public health issue due to its extremely high prevalence. For instance, $63.1\%$ of the population in Canada and $52\%$ of adult population worldwide can be classified as overweight or obese [1, 2]. Obesity is an established health risk factor for hypertension, type 2 diabetes, cardiovascular disorders, metabolic syndrome, and cancer among others (3–8) and is associated with massive health care costs and human suffering. It is less well recognized that the brain is one of the targets of obesity related damage [9, 10], and consequently, obesity is also associated with a range of brain disorders, including psychiatric [11], and neurodegenerative disorders/dementia (12–14). It is important to map and better understand the brain correlates of obesity. Such studies could help identify risk factors for brain alterations, targets, and possibly new mechanisms of action for prevention or treatment of brain changes and associated cognitive or mental health outcomes. For instance, deterring obesity could prevent about $3\%$ of depressive disorders, deterring maternal pre-pregnancy obesity would prevent about $9\%$ of ADHD, and deterring maternal overweight pre/during pregnancy would prevent about $6\%$ of both ADHD and autism spectrum disorders [15]. Preliminary evidence suggests that weight loss is associated with improvements in brain structure [16]. Obesity is associated with diffuse changes in gray matter, including the medial prefrontal cortex, temporal pole, precentral gyrus, inferior parietal cortex and the cerebellum [17]. The associations between obesity and brain gray matter are supported by several large studies (18–20). Some studies have documented an association between obesity and white matter integrity, but these findings are much less diffuse, less replicated, and under reviewed. A previous narrative review mentions links with obesity and alterations of white matter integrity in the genu, body, and splenium of the corpus callosum, fornix, cingulum, corona radiata, corticospinal tracts, uncinate fasciculus, and cerebellar peduncles [21]. However, considering the heterogeneity of these findings, a meta-analysis is needed to quantitatively review the available evidence. Whole brain DTI studies can be analyzed using spatial meta-analysis, which identifies the most replicated location of obesity related alterations [22, 23]. Region of interest studies can be used in a traditional effect size meta-analysis to establish the regional extent of obesity related alterations [24]. Furthermore, focusing on replications among the two meta-analyses minimizes the risk of false positives [25]. A single previous spatial meta-analysis investigated the location of obesity related white matter alterations. However, this study had restrictive inclusion criteria and contained only one half of the available studies in the literature [26]. Furthermore, regions of interest studies were not included in this meta-analysis, thus representing less than a third of the reviewed literature. There are no prior effect size meta-analyses quantifying the extent of obesity related alterations and no studies combining a spatial and effect size meta-analysis. Thus, we present the first study that analyzes the location of white matter changes in obesity using a voxel-based spatial meta-analysis and then validates the results and estimates the extent of obesity-related alterations with a region of interest (ROI) effect size meta-analysis. This approach allowed us to review the entire available literature on this topic, maximize sample size, minimize false negatives and by focusing on replications to also minimize the risk of false positives. ## Search strategy We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [27] and performed a systematic search of articles until January 15, 2023 in PubMed database,1 using the following keywords: [1] “obesity AND white matter”; and [2] “obesity AND anisotropy.” We excluded animal studies and also searched references of downloaded articles and previous reviews (see Figure 1; Tables 1 and 2) and meta-analyses [26] for additional studies. Figure 1: Study selection flow chart. Template adapted from Page et al. (27). TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2 ## Eligibility criteria Only studies that included either whole brain analyses using voxel-based morphometry (VBM) or tract based spatial statistics (TBSS) were used for the voxel-based spatial meta-analysis, while studies providing numerical estimates of effect sizes of fractional anisotropy (FA) from region of interest (ROI) analyses were used for the ROI effect size meta-analysis. We originally planned to additionally look at mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) but there were too few studies to analyze. We included studies which analyzed associations between FA and a measure of obesity, including body mass index (BMI), waist-to-hip ratio, waist circumference, body composition (total fat mass or body fat percentage), and body fat distribution as measured by abdominal MRI scans. Some studies compared groups of people with overweight or obesity and normal weight individuals, while others investigated an association between an obesity-related measurement (e.g., BMI, waist circumference, waist-to-hip ratio, total body fat mass, percentage of body fat mass, visceral adipose tissue) as a continuous variable and diffusion tensor imaging (DTI) measures. We jointly analyzed results from both types of analyses. Our objective was to maximize the scope of literature included in the analyses, and so we set minimum exclusion criteria. Exclusion criteria were set to studies that did not age-match participants, did not test for association between obesity and DTI measures, did not provide numeric results, or did not respond to email requests for data availability. All the whole brain studies provided corrected results. The initial search identified 443 studies, and two raters, TH and LD independently screened the list of titles and abstracts for inclusion, see Figure 1 for an overview of the study selection. ## Spatial voxel-based meta-analysis We used the Anisotropic Effect Size Seed-based d Mapping (AES-SDM) method2 [22, 23] for the spatial voxel-based meta-analysis and extracted peak coordinates (x, y, z) and corresponding t-statistics from each study. If the study included a different measure of effect size like a value of p or z-score, these were converted to t-statistics using the SDM software. We contacted the authors of studies that met our inclusion criteria for additional information. With this strategy, we obtained one full t-map [28], and used it jointly with the peak coordinates and t-values from other studies. Inclusion of t-maps improves precision of the results [22]. See Table 1 for additional information about each study. The AES-SDM method uses the peak coordinates and effect size from individual studies, to recreate, for each study, a map of the effect sizes of the statistical associations, and then conduct a standard random-effects variance-weighted meta-analysis in each voxel. This version uses anisotropic kernels, which assign different values to surrounding voxels of a peak coordinate based on spatial correlations between them [23]. We assessed potential publication bias via AES-SDM software and used jack-knife analysis to determine the robustness of the results after removing individual studies from the meta-analysis. We preprocessed all data files with the TBSS template based on the FA skeleton [29] included in AES-SDM as it allows combination of VBM and TBSS studies [30]. We set all statistical parameters as recommended (anisotropy 1.0, FWHM 20, mask TBSS, voxels 2 mm) and performed a 500-permutation randomization. After calculating meta-analytic means, we applied a combined threshold ($p \leq 0.001$, peak threshold > 1) as suggested by Radua et al. [ 22] but more conservative and discarded clusters comprising fewer than 15 voxels. This method showed an adequate sensitivity and an excellent control of false positives [31]. Unlike other methods of coordinate based meta-analyses, the AES-SDM allows for inclusion of studies which showed no statistically significant results and models the relative increases and decreases in the same map. We used FSLeyes software3 to visualize effect sizes by overlaying our results with brain and FA skeleton templates [32]. See Table 1. for a description of the studies included in the meta-analysis. ## Effect size ROI meta-analysis To validate the spatial voxel-based meta-analysis results we conducted an ROI effect size meta-analysis investigating ROI from the spatial meta-analysis and regions most explored in the literature (see Supplementary Table S1 online for a full list of ROI). We manually recorded Cohen’s d (standardized mean difference) effect size measures from individual studies [33]. When studies included a different measure of effect size, this was converted to Cohen’s d by standard formula [24]. We also recorded other relevant information like confidence intervals and measures of significance, along with sample descriptive statistics, see Table 2. We used Comprehensive Meta-Analysis Software (CMA) version 3.3.0704 to conduct the ROI meta-analysis. The CMA software can convert different individual study information including effect size, variability, and significance variables of both categorical and continuous studies into one meta-analysis. We performed analyses for regions of interest that had four or more viable studies. Only three studies investigated the anterior thalamic radiation, but we analyzed this region to test for replication of the spatial meta-analysis results. To assess whether results may be driven by a few studies reporting very large effect sizes, we repeated the analyses excluding studies with effect sizes > 1. We Assessed potential publication bias using R 4.0.3 and the metaphor package (v3.0-2; https://www.metafor-project.org/doku.php/metafor). See Table 2 for a description of the studies used in this meta-analysis. ## Study characteristics We included 30 studies in the voxel-based meta-analysis, and 21 in the ROI meta-analysis, see Tables 1 and 2. The spatial meta-analysis included 5,237 participants 8–92 years old, while there were 16,505 participants in the effect size meta-analysis (age range 6–95 years). Most studies reported a negative correlation between FA and obesity, i.e., decreased FA in overweight or obese individuals, in both the voxel-based (23 out of 30 studies) and ROI datasets (17 out of 21 studies). Obesity was mainly measured using BMI, or adjusted BMI measures, but other weight measures were not excluded. See Supplementary Tables S2 and S3 online for detailed description of the included studies. ## Spatial voxel-based meta-analysis results The AES-SDM meta-analysis indicated that obesity measures were related to reduced FA values in several white matter regions, including the right and left genu of the corpus callosum (MNI = 22, 32, 12; MNI = −18, 30, 16), left splenium of the corpus callosum (MNI = −8, −28, 16), middle cerebellar peduncles (MNI = −38, −56, −38), anterior thalamic radiation (MNI = 14, −26, 12), right cortico-spinal projections (MNI = 4, −30, −24), and the left cerebellum (MNI = −16, −60, −40) see Table 3; Figure 2 for details. We did not find any region with increased FA in obesity. Jack-knife analysis reliably reproduced each cluster except the left cerebellum, which was present only in 8 out of 30 iterations (see Table 3). We did a subgroup analysis in adults where the corpus callosum (MNI = 22, 32, 12; $p \leq 0.001$), and middle cerebellar peduncles (MNI = −24, −66, −36; $p \leq 0.001$) clusters were replicated. No potential publication bias was detected except for the left cerebellum, where the Egger test was statistically significant ($$p \leq 0.042$$; see Table 3). ## Region of interest meta-analysis results and validation of spatial meta-analysis findings Relative to controls, obese individuals had lower FA in some of the same regions as those identified in the spatial voxel-based meta-analysis, including the genu of the corpus callosum (Cohen’s d = −0.263, $95\%$ Confidence Interval –0.423 to –0.103, $$p \leq 0.001$$; see Figure 3A), splenium of the corpus callosum (Cohen’s d = −0.380, $95\%$ Confidence Interval –0.560 to –0.200, $p \leq 0.001$; see Figure 3B), and middle cerebellar peduncles (Cohen’s d = −0.157, $95\%$ Confidence Interval-0.174 to-0.139, $p \leq 0.001$; see Figure 3C). We found two other ROIs, which showed significantly lower FA in obese individuals as compared to controls of a healthy weight and were not represented in the spatial voxel-based meta-analysis. These included the superior longitudinal fasciculus (Cohen’s d = −0.135, $95\%$ Confidence Interval –0.227 to –0.044, $$p \leq 0.004$$; see Figure 3D), and the fornix which was not significant after removal of a study with a large effect size. Some regions which showed significant associations in spatial meta-analysis, including body of the corpus callosum, uncinate fasciculus, cingulum, parahippocampal cingulum, cortico-spinal tracts, anterior thalamic radiation, showed comparable FA values between people with overweight and obesity and controls in the ROI studies. We did not detect potential publication bias in any region except the splenium of the corpus callosum where the test for funnel plot asymmetry was significant (z = −2.175, $$p \leq 0.03$$). Figure 3: Associations between obesity and FA in the region of interest effect size meta-analyses. Forest plots of the significant findings from the effect size meta-analysis within the regions of (A) genu of the corpus callosum, (B) splenium of the corpus callosum, (C) middle cerebellar peduncles, and (D) superior longitudinal fasciculus. ## Discussion In this study, we conducted a spatial voxel-based meta-analysis on obesity and DTI from 30 individual studies to determine which white matter tracts were most frequently associated with obesity. We further validated the spatial voxel-based meta-analysis results in a ROI effect size meta-analysis of an additional 21 studies. The spatial meta-analysis suggested that overweight and obesity were associated with lower FA in the left and right genu of the corpus callosum, left splenium, middle cerebellar peduncles, anterior thalamic radiation, right cortico-spinal projections, and the left cerebellum. However, the left cerebellum changes were only reproduced in $\frac{8}{30}$ iterations in the jack-knife analysis (see Table 3). The effect size meta-analysis results replicated the negative associations between overweight and obesity and FA in the genu and splenium of the corpus callosum, as well as the middle cerebellar peduncles. Additionally, we found that the superior longitudinal fasciculus, which was not represented in the spatial meta-analysis results, also showed reduced FA in people with overweight and obesity. The effect size of overweight and obesity related changes in these regions ranged from –0.135 to –0.38 (see Figure 3). A recent spatial meta-analysis also investigated the association between obesity and white matter microstructure, however there were some differences between the present study and the previous meta-analysis. Our meta-analysis contained 30 studies, while the previous study contained 16 studies due to restrictive exclusion criteria limiting studies including children or individuals with psychiatric conditions [26]. Obesity in the developed world typically starts early in childhood or adolescence [34, 35]. Also, many patients who are overweight or obese will have co-morbid conditions such as anxiety, depression, diabetes, or metabolic syndrome (3, 4, 8–10), and excluding these individuals is not representative of the general population. In addition, the previous study performed a spatial voxel-based meta-analysis, whereas for the first time we performed a ROI effect size meta-analysis for validation of the spatial voxel-based meta-analysis results. The ROI effect size meta-analysis contained an additional 21 studies with a range of regions including a breakdown of the corpus callosum and other regions not shown in the spatial analysis, with a combined total of 16,505 participants. We replicated the results of the previous spatial meta-analysis, finding reduced FA values for the genu of the corpus callosum in people with overweight or obesity as compared to healthy weight individuals, and expanded these findings to the splenium of the corpus callosum, and middle cerebellar peduncles which were both validated in the ROI effect size meta-analysis. If we overlay the white matter changes found in this study with the obesity related gray matter changes from the previous meta-analysis, they fall in three distinct networks [17]. The corticospinal tract from our spatial voxel-based meta-analysis, passes through the middle cerebellar peduncles as a part of the corticopontocerebellar pathway, [36] which may be linked to reduced exercise or mobility in obesity [37, 38]. The splenium of the corpus callosum is implicated in the default mode network [39], which is involved at rest in self-referential processes [40]. We also found significant association between obesity and reduced FA in the genu of the corpus callosum, which has afferent projections to the prefrontal cortex [41, 42] and is a part of the executive control network [17]. The default mode network and the executive control network may be implicated in the cognitive deficits in obesity (37, 38, 43–45). We cannot infer causality from cross-sectional studies. Reduced FA in specific regions of the brain may either predispose an individual to obesity, or the changes could be a consequence of obesity. The spatial location of changes could help us infer the direction of association. If obesity damages the brain, we would expect to find diffuse alterations. The white matter correlates of obesity in this meta-analysis were not diffuse, but rather localized to only a few tracts and a few specific networks. This may indicate that white matter changes in specific circuits involving executive functioning or default mode network predispose to obesity. Evidence of reduced FA generally suggests water diffusion is isotropic, or the fiber bundles are less organized [46], but there are many other theories including a decrease in myelination of axons in white matter [47, 48] or edema, although this is unclear and mean diffusivity is a better predictor [49, 50]. The mechanisms through which obesity contributes to these changes are unknown, and maybe multifold instead of a singular mechanism, but some plausible candidates include systemic inflammation [51], the overactivation of microglia [52, 53], stress [54], microvascular changes, insulin resistance, hyperglycemia, plasticity related to lower mobility, or genetic predisposition to brain changes which increase the risk of obesity. The present study was a starting point to establish the location of brain changes in overweight and obesity and associated effect sizes of these differences. Due to the cross-sectional nature of this study, we cannot determine causality between brain changes and obesity or the underlying mechanisms. However, to determine causality, future studies should either employ a longitudinal design or a mendelian randomization study. An alternative is studying obesity in animals due to the ability to control their environment throughout the lifespan. As with any meta-analysis, we are limited by the information provided in published studies and cannot control the reporting of results including those that might be negative and not published. We were not able to analyze MD, AD, and RD associations with overweight and obesity as these measures were reported only in a minority of studies. Of the 79 studies originally read to determine suitability for inclusion in this study, many articles did not meet the criteria for inclusion by not reporting the respective statistics, including the peak coordinates, t-values, or sample sizes. It is very important for future studies to maintain a high standard for reporting results and follow reporting guidelines [55, 56]. There is an argument after seeing much variability in the pre-processing and processing of DTI data around the world for a standardizing pipeline as in the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium studies [57], which would remove some methodological variability. The methods of scanning had heterogeneity due to different magnet strengths, although many were 3.0 T, and different MRI manufacturers. Our meta-analyses contained varying ages from children to the elderly. Although FA is not reliable when comparing across wide age spans, this was less of a problem for this study as the studies were age matched, containing children with overweight or obesity comparing FA with children of a healthy weight, and other studies with older adults comparing similar age ranges. Most studies in the present meta-analyses primarily used BMI as a measure of obesity, however, a few studies used body composition, waist circumference, or visceral adipose tissue. It would be preferable to compare measures of obesity using the same metric, but we are limited to the methodology provided in previous studies. The clusters we found were small, but the highly localized nature of the findings could have interesting implications. We provided some plausible explanations for the results of these meta-analyses, but the underlying mechanisms of obesity, and inferring causality for specific FA differences are questions that cannot be answered via a cross-sectional design as in this study, as this would require a prospective study to look at changes over time. A strength of this study is our replication and extension of the previous meta-analysis [26] results indicating association between overweight and obesity and lower FA in the genu of the corpus callosum in a larger sampling of studies, which was also extended to the middle cerebellar peduncles and splenium of the corpus callosum in our voxel-based meta-analysis and validated in the ROI effect size meta-analysis. Our combination of spatial voxel-based and ROI effect size meta-analysis allowed us to obtain information about location and extent of any changes, as well as to check for replication among different sets of studies. The replication of exploratory findings in the spatial voxel-based meta-analysis by ROI effect size meta-analysis suggested that some of our findings were robust and not false positive. To conclude, we found replicated associations of lower FA in the genu and splenium of the corpus callosum as well as the middle cerebellar peduncles with overweight and obesity. The extent of these obesity related alterations was a small to medium effect, but the main findings were highly replicated across different studies and meta-analyses. Since we currently do not know the mechanism behind brain changes in overweight and obesity, future studies should determine whether lower FA in these regions are a consequence or cause of obesity. Ultimately, we would need prospective longitudinal designs to clarify this question. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author. ## Author contributions LD and TH were responsible for designing the search strategy for relevant literature, identifying relevant articles, and screening articles based on title and abstract. LD was responsible for assessing articles for eligibility, extracting, and analyzing the data, interpreting results, creating the figures and tables, and writing the manuscript. SM was responsible for assisting in the data analysis. JR was responsible for assisting in the data analysis and interpretation. TH and JR contributed to the manuscript and provided feedback. All authors contributed to the article and approved the submitted version. ## Funding This study was supported by funding from the Canadian Institutes of Health Research (142255 and 180449). ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1108360/full#supplementary-material ## References 1. Overweight and obese adults, 2018. 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--- title: A plant-produced SARS-CoV-2 spike protein elicits heterologous immunity in hamsters authors: - Emmanuel Margolin - Georgia Schäfer - Joel D. Allen - Sophette Gers - Jeremy Woodward - Andrew D. Sutherland - Melissa Blumenthal - Ann Meyers - Megan L. Shaw - Wolfgang Preiser - Richard Strasser - Max Crispin - Anna-Lise Williamson - Edward P. Rybicki - Ros Chapman journal: Frontiers in Plant Science year: 2023 pmcid: PMC10028082 doi: 10.3389/fpls.2023.1146234 license: CC BY 4.0 --- # A plant-produced SARS-CoV-2 spike protein elicits heterologous immunity in hamsters ## Abstract Molecular farming of vaccines has been heralded as a cheap, safe and scalable production platform. In reality, however, differences in the plant biosynthetic machinery, compared to mammalian cells, can complicate the production of viral glycoproteins. Remodelling the secretory pathway presents an opportunity to support key post-translational modifications, and to tailor aspects of glycosylation and glycosylation-directed folding. In this study, we applied an integrated host and glyco-engineering approach, NXS/T Generation™, to produce a SARS-CoV-2 prefusion spike trimer in Nicotiana benthamiana as a model antigen from an emerging virus. The size exclusion-purified protein exhibited a characteristic prefusion structure when viewed by transmission electron microscopy, and this was indistinguishable from the equivalent mammalian cell-produced antigen. The plant-produced protein was decorated with under-processed oligomannose N-glycans and exhibited a site occupancy that was comparable to the equivalent protein produced in mammalian cell culture. Complex-type glycans were almost entirely absent from the plant-derived material, which contrasted against the predominantly mature, complex glycans that were observed on the mammalian cell culture-derived protein. The plant-derived antigen elicited neutralizing antibodies against both the matched Wuhan and heterologous Delta SARS-CoV-2 variants in immunized hamsters, although titres were lower than those induced by the comparator mammalian antigen. Animals vaccinated with the plant-derived antigen exhibited reduced viral loads following challenge, as well as significant protection from SARS-CoV-2 disease as evidenced by reduced lung pathology, lower viral loads and protection from weight loss. Nonetheless, animals immunized with the mammalian cell-culture-derived protein were better protected in this challenge model suggesting that more faithfully reproducing the native glycoprotein structure and associated glycosylation of the antigen may be desirable. ## Introduction The increasing incidence of viral outbreaks highlights the need for pandemic preparedness and the importance of investing in infrastructure development for vaccine manufacturing (Krammer, 2020). This is particularly relevant in low-income countries, such as those in Africa, where the capacity for end-to-end vaccine manufacturing is limited and where vaccines are almost exclusively sourced from wealthier countries (Margolin et al., 2020a). Accordingly, there is a clear need to establish sustainable and self-sufficient manufacturing sites in these vulnerable regions. However, in most cases the costs remain prohibitive – especially where manufacturing processes are reliant on mammalian cell culture systems which are especially expensive. Molecular farming, the production of proteins in plants, has risen to prominence in recent years following efficacy reports of plant-made vaccines against influenza (Ward et al., 2020) and SARS-CoV-2 (Hager et al., 2022), and the therapeutic treatment of Ebola virus infection with plant-produced antibodies (Group et al., 2016). The use of plants as pharmaceutical bioreactors offers several advantages that lend themselves towards implementation in developing countries, including most notably lower infrastructure requirements(Fischer and Buyel, 2020) and potentially lower production costs (Rybicki, 2009; Murad et al., 2020). Furthermore, large-scale transient protein production in plants can be completed within weeks without the need to generate stable cell lines, which is time consuming and comparatively slower (D'Aoust et al., 2010). This presents an obvious advantage for responding to pandemic outbreaks where speed and scale are critical. Lastly, protein-based drugs typically require a less stringent cold-chain than other vaccine modalities, such as mRNA, which is an important consideration for resource-limited countries (Pambudi et al., 2022). Given these advantages it is unsurprising that several plant-made vaccines against SARS-CoV-2 are at various stages of clinical development. The most advanced candidate is Medicago Inc.’s virus-like particle (VLP) vaccine which was approved for use in Canada in February 2022, and which demonstrated $69.5\%$ efficacy against symptomatic disease and $78.8\%$ efficacy against moderate-to-severe disease (Hager et al., 2022). These VLPs are comprised of a chimaeric spike where the native transmembrane and cytoplasmic tail regions have been replaced with the equivalent domains from influenza H5 haemagglutinin (Ward et al., 2021). This modification improves the formation of VLPs presenting the spike, which bud from the host cell without the need for any additional accessory proteins (Ward et al., 2021). Other noteworthy plant-derived candidates in clinical testing include a recombinant SARS-CoV-2 receptor-binding domain (RBD) antigen from Baiya Phytopharm (Phase 1, NCT05197712) and a RBD protein conjugated to a tobacco mosaic virus scaffold (Royal et al., 2021) from Kentucky BioProcessing, Inc. (Phase $\frac{1}{2}$, NCT04473690). Similar success has also been described in preclinical studies from academic groups who have reported expression and immunogenicity of RBD antigens (Maharjan et al., 2021; Mamedov et al., 2021; Mardanova et al., 2021; Shin et al., 2021; Siriwattananon et al., 2021a; Siriwattananon et al., 2021b) and a full-length spike ectodomain that was produced by co-expression of human calreticulin (Margolin et al., 2022b). More recently, A SARS-CoV-2 VLP composed exclusively of the native spike has also been described (Jung et al., 2022). Historically the production of complex glycoproteins in their native conformations, and particularly envelope viral glycoproteins, has posed a considerable challenge for molecular farming (Margolin et al., 2018), as plant-derived glycosylation is distinct from that of mammalian cells (Strasser et al., 2014; Strasser, 2016), and the glycoproteins from enveloped viruses typically have extensive disulfide bonding and a consequent significant dependence on host chaperones (Alonzi et al., 2017). In many cases viral envelope glycoproteins only accumulate at low levels in plants (Kang et al., 2018) (Margolin et al., 2018), and the recombinant products may be poorly folded and aberrantly glycosylated (Strasser et al., 2014) (Margolin et al., 2021a; Margolin et al., 2022a). These observations can largely be attributed to inadequacies in the plant glycosylation-directed folding pathways, which do not always adequately support the high levels of glycosylation (Margolin et al., 2021a), chaperone-mediated folding and processing (Wilbers et al., 2016; Margolin et al., 2020c; Margolin et al., 2022b) that are required by many viral glycoproteins. Furthermore, plant-produced glycoproteins often display unique features in their glycosylation including lower glycan occupancy compared to mammalian hosts, elevated oligomannose-type N-glycans, plant-specific complex N-glycans and unwanted N-glycan processing events (Strasser, 2016; Shin et al., 2017; Castilho et al., 2018; Margolin et al., 2021a). In order to address these challenges and exploit the advantages inherent in plant-based protein production, there has been an increasing drive to develop novel host engineering approaches to support the folding and glycosylation requirements of complex viral glycoproteins (Margolin et al., 2020b; Margolin et al., 2020d). Constraints in the host chaperone machinery can be addressed by over-expression of chaperones to support critical folding events (Margolin et al., 2020c; Margolin et al., 2021b; Rosenberg et al., 2022), and recent evidence suggests that a combinatorial approach may confer additional benefit (Rosenberg et al., 2022). This approach typically results in increased glycoprotein accumulation and reduced ER stress, as the toxicity associated with the accumulation of misfolded protein is alleviated. Impaired proteolytic maturation can similarly be addressed by transient host engineering, and the co-expression of the protease furin has been shown to support efficient maturation of prototype viral glycoproteins which would not otherwise be properly cleaved in plants (Margolin et al., 2020c; Margolin et al., 2022b). The plant glycosylation machinery also imposes a bottleneck for the production of many viral glycoproteins, and aberrant glycosylation has been implicated in protein misfolding and aggregation (Margolin et al., 2021a; Margolin et al., 2022a). Foreign glyco-epitopes have been associated with hypersensitive reactions and some plant glycoproteins are allergens (Altmann, 2007; Arnold and Misbah, 2008); however, plant-specific glycosylation has been shown to be safe in volunteers immunized with plant-derived VLPs (Ward et al., 2014; Ward et al., 2020; Ward et al., 2021). These can similarly be addressed by remodelling the cellular machinery for glycosylation in the plant host – by the in situ provision of heterologous machinery where the plant cell glycosylation machinery is limiting and by the elimination of plant enzymes which impart expression system-dependent modifications (Margolin et al., 2020b; Margolin et al., 2020d) (Shin et al., 2017). We previously developed a combinatorial host engineering platform, NXS/T Generation™, to produce well-folded viral glycoproteins in N. benthamiana with improved glycosylation (Margolin et al., 2022a). The expression technology revolves around the transient expression of the lectin binding chaperones calnexin or calreticulin (Protein Origami™) to support protein folding (Margolin et al., 2020c), which is combined with a series of glyco-engineering strategies to remodel the plant glycosylation machinery. These involve the co-expression of a single subunit oligosaccharyltransferase from Leishmania major (LmSTT3D) to enhance glycan occupancy (Castilho et al., 2018), an RNA interference construct to ablate truncation of glycans by an endogenous β-N-acetylhexosaminidase (Shin et al., 2017), and the use of a mutant strain of N. benthamiana ΔXF where the activity of α1,3-fucosyltransferase and β1,2-xylosyltransferase have been suppressed to prevent the formation of plant-specific complex glycans (Strasser et al., 2008). The resulting antigens are referred to as “glycan-enhanced” (GE) as they represent a notable improvement over the glycosylation of the protein when produced in the absence of this integrated host and glyco-engineering approach. Specifically, the GE proteins comprise of increased glycan occupancy and negligible undesired plant-specific modifications, which are associated with a concomitant improvement in protein structure, folding and oligomerization (Margolin et al., 2022a). The NXS/T Generation™ platform was recently used to produce a soluble trimeric HIV envelope gp140 vaccine and the resulting GE antigen elicited largely equivalent immune responses in rabbits compared to the same antigen produced in mammalian cells (Margolin et al., 2022a). Encouraged by these observations, we initiated studies to investigate the broad applicability of this host-engineering platform to other pandemic and emerging viruses, including SARS-CoV-2. Given that direct comparisons between plant-produced and mammalian cell-produced viral glycoproteins are generally lacking, we sought to establish how closely the GE antigens resemble the equivalent protein when produced in mammalian cells, both in terms of glycosylation and immunogenicity. Previous work by our group and others has demonstrated that the co-expression of human calreticulin substantially improved the yields of a soluble SARS-CoV-2 spike ectodomain (Song et al., 2022; Margolin et al., 2022b), warranting integration of this approach with the glyco-engineering strategies that comprise the NXS/T Generation™ Platform. Therefore, in the present study we have built on this work by producing an improved spike antigen (HexaPro) using the NXS/T Generation™ platform. The resulting GE spike was then compared to the equivalent material produced in mammalian cells with regard to its structure and glycosylation. Finally, the vaccines were evaluated for their ability to elicit immunity against both the homologous SARS-CoV-2 wildtype (Wuhan) strain and the heterologous Delta variant, and to protect against viral challenge with the heterologous Delta variant (B.1.617.2). ## Gene design and expression constructs for protein production A human-codon optimized variant of the HexaPro spike antigen was synthesized by GenScript using the sequence reported by Hsieh et al., which contains the following mutations F817P, A892P, A899P, A942, K986P & V987P and the furin cleavage site was replaced with a short linker sequence GSAS from amino acid 682 to 685 (PDB 6XKL). The antigen also contains a synthetic C-terminal foldon trimerization motif, an HRV3C protease recognition sequence, a twin Strep-tag and an octa-histidine sequence as reflected in the original manuscript (Hsieh et al., 2020). Synthetic HindIII and AgeI sites were incorporated at the 5’ end of the gene. EcoRI and XhoI sites were added to the 3’ end of the gene coding sequence. A Kozak sequence (CCACC) was incorporated into the sequence immediately upstream of the start codon of the gene. The gene was cloned into pMEx for mammalian cell expression (van Diepen et al., 2018) and pEAQ-HT for plant expression (Sainsbury et al., 2009) using HindIII and EcoRI or AgeI and XhoI, respectively. The recombinant pEAQ-HT plasmid was transformed into A. tumefaciens AGL1 by electroporation. Recombinant A. tumefaciens strains encoding LmSTT3D, human CRT and HEXO3RNAi have been described previously (Margolin et al., 2022a). ## Production of SARS-CoV-2 HexaPro spike in plants N. benthamiana ΔXF plants were propagated in flat trays at 25°C ($55\%$ humidity) under a controlled 16-hour light/8-hour dark photocycle, as described previously (Margolin et al., 2019). Recombinant spike was produced in N. benthamiana ΔXF by transient co-expression of the antigen with CRT, LmSTT3D and HEXO3RNAi, using A. tumefaciens-mediated infiltration to deliver the DNA coding sequences to the plant cells (Margolin et al., 2022a). The plant biomass was harvested 4 days post agroinfiltration and then homogenized in 2 buffer volumes of tris-buffered saline [pH 7.8], supplemented with $1\%$ Depol 40 (Biocatalysts) and EDTA-free protease inhibitor (Roche). The resulting homogenate was incubated on an orbital shaker, at 4°C, for 1 hour and then filtered through Miracloth (Millipore Sigma) to remove insoluble plant debris. The pH was adjusted to 7.8 and the homogenate was clarified at 17000×g for 30 minutes. The sample was then filtered using a 0.45 µm Stericup-GP vacuum driven filter (Merck Millipore). The recombinant glycoprotein was captured using Galanthus nivalis lectin and trimeric spike was isolated by gel filtration, as described previously (Margolin et al., 2019). ## Production of SARS-CoV-2 HexaPro in mammalian suspension cells FreeStyle™ HEK293F cells (Invitrogen) were grown in sterile polycarbonate Erlenmeyer flasks on an orbital shaking platform set to 125 rpm. The cells were maintained at a density of 1-3×106 cells/ml at 37°C, with $8\%$ CO2. The cultures were passaged every 3-4 days, at a seeding density of 3×105 cells/ml, using fresh FreeStyle™ 293 Expression Medium (Invitrogen). Spike protein was transiently expressed by transfecting cells, at a density of 1×106 cells/ml, with 1 µg/ml of plasmid DNA. Polyethylenimine was used for transfections at a 3:1 ratio of transfection reagent:DNA. The culture media was harvested 5 days post-transfection and clarified by centrifugation at 2500×g, for 30 minutes. The clarified media was then filtered using a 0.45 µm Stericup-GP device (Merck Millipore). Spike trimers were purified as described for the plant-produced material. ## Polyacrylamide gel electrophoresis and immunoblotting Purified protein was resolved under denaturing conditions by SDS-PAGE as described previously (Margolin et al., 2019), and then immunoblotted using polyclonal mouse anti-His antibody (Serotech, MCA1396) at a 1:2000 dilution (Margolin et al., 2022b). Proteins were also resolved under native conditions using the BN-PAGE system followed by staining with BioSafe Coomassie G250 as previously reported (van Diepen et al., 2019). ## Negative stain electron microscopy and image processing Samples were pipetted onto glow-discharged (30 s in air) carbon-coated copper grids, washed three times in dH2O and stained with $2\%$ uranyl acetate. For each sample ~30 images were collected using SerialEM at 2.2 Å/pixel using a Tecnai F20 transmission electron microscope fitted with a DE16 camera (Direct Electron, San Diego, CA USA) operated at 200 kV at an electron dose of ~50 e/Å2 and a defocus of −1.5 μm. Relion 3.1 (Scheres, 2012) was used for image processing and 3D reconstruction: briefly ~1 000 particles were manually picked and used to generate 2D class averages for reference-based picking yielding ~4000 particles. This particle set was refined using 2D classification and used to generate a de novo initial model using stochastic gradient descent (Punjani et al. 2017). Following refinement (final resolution: ~20 Å), UCSF Chimera (Pettersen et al., 2004) was used for three-dimensional visualization and rendering. ## Site-specific N-glycan analysis of purified spike Aliquots of spike protein were denatured for 1 h in 50 mM Tris/HCl, pH 8.0 containing 6 M of urea and 5 mM dithiothreitol (DTT). Next, spike proteins were alkylated by adding 20 mM iodoacetamide (IAA) and incubated for 1 h in the dark, followed by a 1 h incubation with 20 mM DTT to eliminate residual IAA. The alkylated spike proteins were buffer exchanged into 50 mM Tris/HCl, pH 8.0 using Vivaspin columns (3 kDa) and digested separately overnight using trypsin, chymotrypsin (Mass Spectrometry Grade, Promega), or alpha lytic protease (Sigma Aldrich) at a ratio of 1:30 (w/w). The next day, the peptides were dried and extracted using C18 Zip-tip (MerckMillipore). The peptides were dried again, resuspended in $0.1\%$ formic acid and analyzed by nanoLC-ESI MS with an Ultimate 3000 HPLC (Thermo Fisher Scientific) system coupled to an Orbitrap Eclipse mass spectrometer (Thermo Fisher Scientific) using stepped higher energy collision-induced dissociation (HCD) fragmentation. Peptides were separated using an EasySpray PepMap RSLC C18 column (75 µm × 75 cm). A trapping column (PepMap 100 C18 3 μM, 75 μM × 2 cm) was used in line with the liquid chromatography (LC) before separation with the analytical column. The LC conditions were as follows: 275 min linear gradient consisting of $0\%$–$32\%$ acetonitrile in $0.1\%$ formic acid over 240 min followed by 35 minutes of $80\%$ acetonitrile in $0.1\%$ formic acid. The flow rate was set to 300 nl/min. The spray voltage was set to 2.5 kV and the temperature of the heated capillary was set to 40°C. The ion transfer tube temperature was set to 275°C. The scan range was 375 − 1500 m/z. Stepped HCD collision energy was set to $15\%$, $25\%$, and $45\%$, and the MS2 for each energy was combined. Precursor and fragment detection were performed using an Orbitrap at a resolution MS1= 120,000, MS2 = 30,000. The AGC target for MS1 was set to standard and injection time set to auto, which involves the system setting the two parameters to maximize sensitivity while maintaining cycle time. Full LC and mass spectrometry (MS) methodology can be extracted from the appropriate Raw file using XCalibur FreeStyle software or upon request. Glycopeptide fragmentation data were extracted from the raw file using Byos (Version 3.5; Protein Metrics Inc.). The glycopeptide fragmentation data were evaluated manually for each glycopeptide; the peptide was scored as true-positive when the correct b and y fragment ions were observed along with oxonium ions corresponding to the glycan identified. The MS data was searched using the Protein Metrics 309 N-glycan library with sulfated glycans added manually combined with the Protein metrics 57 Plant N-linked glycan library. The relative amounts of each glycan at each site, as well as the unoccupied proportion were determined by comparing the extracted chromatographic areas for different glycotypes with an identical peptide sequence. All charge states for a single glycopeptide were summed. The precursor mass tolerance was set at 4 and 10 p.p.m. for fragments. A $1\%$ false discovery rate was applied. The relative amounts of each glycan at each site as well as the unoccupied proportion were determined by comparing the extracted ion chromatographic areas for different glycopeptides with an identical peptide sequence. Glycans were categorized according to the composition detected. Chimera X v1.3 was used to visualize and represent the glycosylation of the S protein (Pettersen et al., 2021), using the.pdb file produced by (Zuzic et al., 2022), using S_full_domgly as the template. HexNAc[2]Hex(10+) was defined as M9Glc, HexNAc[2]Hex(9–4) was classified as oligomannose-type. Any of these structures containing a fucose were categorized as FM (fucosylated mannose). HexNAc[3]Hex[5-6]X was classified as Hybrid with HexNAc[3]Hex[5-6]Fuc[1]X classified as Fhybrid. Complex-type N-glycans were classified according to the number of HexNAc subunits and the presence or absence of fucosylation. As this fragmentation method did not provide linkage information compositional isomers were grouped, so for example a triantennary glycan contained HexNAc 5 but so did a biantennary glycan with a bisect. Core glycans refer to truncated structures smaller than M3. Any compositions containing a monosaccharide corresponding to a pentose (e.g., Xylose) were classified in the pentose category. Likewise, any glycan composition detected containing at least one fucose or sialic acid were assigned as “Fucose” and “NeuAc,” respectively. ## Isolation, propagation and titration of SARS-CoV-2, delta variant All work involving live SARS-CoV-2 was performed inside an accredited Biosafety Level 3 facility in accordance with the safety regulations regarding risk level 3 pathogens (WHO, 2021). SARS-CoV-2-positive patient samples were obtained from the National Health Laboratory Service (NHLS), Tygerberg, Cape Town, South Africa, and the lineage was confirmed to be SARS-CoV-2 Delta at Stellenbosch University (SU) as part of the Network for Genomic Surveillance in South Africa (NGS-SA) initiative (Msomi et al., 2020). Vero E6 cells were maintained in Dulbecco’s modified Eagle medium (DMEM) containing sodium pyruvate and L-glutamine (PAN Biotech, Aidenbach, Germany) with $10\%$ foetal bovine serum (Gibco, Texas, USA) and $1\%$ each of non-essential amino acids (Lonza, Basel, Switzerland), amphotericin B (Gibco, Texas, USA) and penicillin/streptomycin (PAN Biotech, Aidenbach, Germany). Vero E6 cells were grown at 37°C and $5\%$ CO2 and were passaged every 3-4 days. For virus isolation, Vero E6 cells were seeded at 3.5x105 cells/ml in a 6-well plate 18-24 hrs before infection. After one wash with 1xDPBS (PAN Biotech, Aidenbach, Germany) the cells were inoculated with patient sample that was diluted 1:5 in DMEM. The inoculum was removed after one hour incubation at room temperature, and the cells were washed once with 1xDPBS before the addition of post-infection media (DMEM, $2\%$FBS, $1\%$ each of non-essential amino acids, amphotericin B, penicillin/streptomycin). The cells were incubated at 37°C with $5\%$ CO2 and monitored daily for 3-7 days or until >$90\%$ cytopathic effect (CPE) was observed. The cell culture supernatant was then harvested and used to infect freshly seeded Vero E6 cells to produce a second passage stock of the virus, and then a third passage stock. The third passage stock was sequence confirmed at SU using Oxford Nanopore Technology, as described previously (Maponga et al., 2022), to ensure no mutations had been introduced during passaging of the virus. The viral RNA load was quantified using a quantitative real time PCR assay specific for the E gene, as described by Corman et al. ( Corman et al., 2020), and the infectious virus titer was determined using a standard plaque assay on Vero E6 cells. ## Hamster vaccination and intranasal infection Immunization and challenge experiments were carried out using 6–9-week-old male or female Syrian Golden Hamsters (Mesocricetus auratus). All experimental procedures were conducted in the Research Animal Facility at the University of Cape Town, in accordance with AEC 021_005. Animal immunizations took place under BSL-2 conditions, whereas viral challenge experiments were confined to the BSL-3 laboratory using the IsoRAT900 Biocontainment system. Prior to challenge experiments, the Delta variant of SARS-CoV-2 was tested to determine the optimal inoculum for infection. Groups of 5 hamsters were inoculated intranasally with 104 PFU or 105 PFU virus or an equivalent volume of PBS. The body weights of the animals were monitored daily, and oropharyngeal swabs were collected prior to challenge and on days 3 and 6 post infection. The experiment was terminated on day 14 post-infection and organs were harvested for histological investigation. The vaccine challenge study was conducted using 5 hamsters per experimental group. Animals were immunized intramuscularly with 5 µg of protein, formulated 1:1 in Alhydrogel® adjuvant (van Diepen et al., 2018; Margolin et al., 2022b), on days 0 and 28. The experimental control group was immunized with an equivalent volume of PBS. Blood was drawn on day 0, 14 and 46. Vaccinated animals were transferred to the IsoRAT900 biocontainment system in the BSL3 laboratory on day 46 and were intranasally infected with 104 PFU of the Delta variant of SARS-CoV-2 on day 49. Oropharyngeal swabs were collected on days 46 (prior to infection), 52 (3 days post infection) and 55 (5 days post infection). The weight of the hamsters was recorded prior to both vaccination and infection, and then monitored daily following infection, until the experimental endpoint. The experiment was terminated on day 55. Lungs were collected in $10\%$ buffered formalin. ## Neutralization assay Neutralizing antibodies against wild type/Wuhan and Delta viruses were quantified at weeks 0, 14 and 46 using a pseudovirus neutralization assay, as described previously (Margolin et al., 2022b). SARS-CoV-2 pseudovirions were generated in HEK-293T cells by co-transfection of plasmids pNL4-3.Luc. R-.E- (aidsreagent #3418) and pcDNA3.3-SARS-CoV-2-spike Δ18 (Wuhan strain) (Rogers et al., 2020) or pcDNA3.3-SARS-CoV-2-spike Δ18 – Delta (Delta variant), respectively. The latter was derived from plasmid pcDNA3.3-SARS-CoV-2-spike Δ18 (Wuhan strain) by side-directed mutagenesis using the QuikChange Lightning Multi Site-Directed Mutagenesis Kit (Agilent) together with the primers T19R (agccagtgtgtgaacctgaggaccaggaccc), R158G (aacaagtcctggatggagtctggggtctactcc), L452R (aggcaactacaactaccgctacagactgttcagga), T478K (atttaccaggctggcagcaaaccatgtaatggagt), D614G (gctgtgctctaccagggtgtgaactgtactgag), P681R (acccagaccaacagcagaaggagggcaaggtc), D950N (ccctgggcaaactccaaaatgtggtgaaccagaa), 156_157_del_F (acaacaagtcctggatggagtctagggtctactcc), and 156_157_del_R (ggagtagaccctagactccatccaggacttgttgt). Neutralization titres were reflected as the half maximal inhibitory dilution for each plasma sample. ## Determination of SARS-CoV-2 viral loads SARS-CoV-2 viral loads were determined from nasal swabs sampled in 500µl QVL Lysis Buffer containing 10µg/ml carrier RNA (Poly A) (Omega Bio-Tek). MS2 Phage Control (TaqMan™ 2019-nCoV Control Kit v2 (Applied Biosystems)) was added to each sample prior to RNA extraction as an internal positive control. RNA isolation was performed using the E.Z.N.A.® Viral RNA Kit (Omega Bio-Tek). This was followed by multiplex qRT-PCR of the isolated RNA on a QuantStudio™ 7 Flex Real-Time PCR System (Thermo Fisher) using the TaqMan™ 2019-nCoV Control Kit v2 (Applied Biosystems) including serial dilutions of known SARS-CoV-2 viral RNA ranging from 1x104 copies/µl to 1x100 copies/µl. Viral loads were calculated based on the standard curve and expressed as copies/µl. ## Histopathology The lungs were sectioned and stained routinely with H&E for histopathology. Tissue was evaluated by light microscopy for evidence of necrosis/inflammation and/or repair/fibrosis. Lesions in the lung were graded as either absent [0], minimal [1], mild [2], moderate [3], marked [4] or severe [5]. The severity of histopathology was graded based on observations of 1) lympho-plasmacytic infiltration, 2) bronchiolitis/peribronchiolitis, alveolitis and bullous emphysema, 3) vasculitis/perivasculitis and 4) atelectasis. ## Statistical analyses All statistical analyses were conducted using GraphPad Prism 9. Statistical comparisons between groups over time were analysed using a two-way Anova test whereas statistical comparisons between two groups at a single time point were made using a multiple unpaired t-test. A p value of <0.05 was considered as the threshold of significance for all statistical tests. The half maximal inhibitor dilution (ID50) of sera samples was determined using a non-linear regression. ## Production of a glycan-enhanced SARS-CoV-2 spike in plants The SARS-CoV-2 spike protein is a typical trimeric class 1 viral fusion protein which is initially synthesised as a single chain precursor of ~1300 amino acids that is cleaved by host proteases, such as furin, into two subunits, S1 and S2 (Duan et al., 2020; Zhang et al., 2021; Yan et al., 2022). The cleavage is essential for cell fusion and viral infectivity (Yan et al., 2022). The mature glycoprotein, on the viral surface, binds to the angiotensin-converting enzyme 2 (ACE2) receptor on the host cell membrane. This complex is then cleaved by the type 2 transmembrane protease TMPRSS2, triggering rearrangement of the protein from a metastable prefusion conformation to a stable postfusion conformation, activating the spike protein and enabling entry into the host cell (Duan et al., 2020; Zhang et al., 2021; Yan et al., 2022). In order to produce the SARS-CoV-2 spike antigen in plants, we implemented an integrated suite of approaches that were previously developed to transiently remodel the plant secretory pathway to address host constraints in glycosylation and glycosylation-directed folding (NXS/T Generation™) (Margolin et al., 2022a). We applied this platform to produce a spike construct (HexaPro) that had been engineered to stabilize the antigen in the prefusion conformation by the incorporation of 6 proline mutations (Hsieh et al., 2020). Following affinity-chromatography and gel filtration (Figure 1A), aggregated material (fractions 31-35) was discarded (Figure 1B) and a homogenous population of well-ordered trimers (fractions 36-40) was recovered (Figure 1C). Coomassie-stained BN-PAGE gels verified the gel filtration peaks comprised of aggregates and trimers respectively (Figures 1D, E) and Western blotting confirmed the identity of the purified material with expected products observed at ~180 kDa (Figure 1F). Two-dimensional class averages and three-dimensional reconstruction of the purified spike trimers yielded a typical “kite-like” structure, which projected from a narrow base that would be proximal to the membrane in the context of the native virus (Figure 1G). When viewed from above the reconstruction depicted characteristic 3-fold symmetry and was consistent with published accounts for the antigen (Hsieh et al., 2020). The HexaPro spike was also produced in FreeStyle HEK293F mammalian cells as a control. The protein yielded equivalent structures that were consistent with previously published accounts (Hsieh et al., 2020), and which were indistinguishable from the plant-derived spike (Figure S1). Figure 1: Production of a soluble, stabilized spike in plants. (A) Gel filtration profile of affinity-captured spike. Peak 1, fractions 31-35 = aggregates; Peak 2, fractions 36-40 = trimers. (B) Negative stain electron microscopy of fractions 31-35 derived from peak 1 (aggregates) in A. Large circles indicate large, clumps of aggregated spike protein. (C) Negative stain electron microscopy of fractions 36-40 derived from peak 2 (trimers) in A. Small circles indicate spike trimers. (D) Coomassie-stained BN-PAGE of fractions 31-35, peak 1 (aggregates) in A. (E) Coomassie-stained BN-PAGE of fractions 36-40, peak 2 (trimers) from A. (F) Western blotting of fractions 31-35 (lane 1) and 36-40 (lane 2) from peaks 1 and 2 respectively in A using polyclonal mouse anti-His antibody [Serotech, MCA1396]. (G) Three-dimensional reconstruction and class averages of purified trimeric spike protein (fractions 36-40) from electron micrographs shown in (C). ## Site-specific glycan analysis of spike produced using the NXS/T generation™ system The site-specific N-glycosylation of the plant-produced antigen was determined using a previously developed analytical pipeline (Figures 2A, S2 and Table S1) (Margolin et al., 2021a). The protein was decorated almost exclusively with oligomannose-type N-glycans, which exhibited low levels of glycan processing. The N-glycosylation of the control mammalian protein was similarly determined (Figures 2B, S3 and Table S2). The mammalian cell-produced spike contained large amounts of complex N-glycans, as well as incompletely processed oligomannose glycans at several N-glycosylation sites (N61, N122, N165, N234, N603, N616, N709, N717, N801, N1074). Minor populations of core glycans were also observed at $\frac{3}{22}$ sites in the plant-produced protein (N61, N331 and N616) which were absent when the protein was produced in HEK293F cells. These structures comprise of Hex[3]HexNAc[2] or smaller structures which arise from enzymatic cleavage of the glycan. The N-glycan site occupancy between the 2 systems was largely comparable (Figure 3 and Table S3). Partial unoccupied sites were observed at N234 ($4\%$), N1074 ($34\%$), N1098 ($1\%$) and N1194 ($61\%$) in the plant-produced protein (Table S1). Figure 2: Site-specific N-glycosylation of plant and HEK293F-produced spike. (A) Fully glycosylated model of the SARS-CoV-2 spike glycoprotein in a native-like prefusion conformation, generated previously by (Zuzic et al., 2022). The individual N-glycan sites were colored according to the site-specific abundance of oligomannose-type glycans determined by LC-MS for spike protein produced using glycan enhanced N.benthamiana cells. (B) Model of the SARS-CoV-2 S protein produced in an identical manner to panel A except colored according to LC-MS analysis of spike protein produced by transient transfection of HEK293F cells. Figure 3: The change in site-specific N-glycan occupancy of plant-produced and mammalian cell-derived spike. (A) Model of the SARS-CoV-2 S glycoprotein in the native-like prefusion conformation generated by Zuzic et al., (2022). Individual N-glycan sites are colored according to the percentage point change in glycan occupancy of plant-derived protein relative to the comparator HEK293F-produced protein and therefore positive and negative values represent a relative increase or decrease in the glycan occupancy each sequon. (B) Bar graph depicting the percentage point changes depicted in panel A. A negative value indicates lower glycan occupancy in the plant-produced spike protein. Sites that could not be resolved are denoted with an asterisk. ## Immunogenicity of plant-produced spike in hamsters Hamsters were immunized with 5 µg of purified trimer, formulated in Alhydrogel®, to compare the immunogenicity of the vaccine when produced in plants and mammalian cells (Figure 4A). The vaccines both elicited neutralizing antibodies against the matched wild type virus, and against the heterologous Delta virus in all immunized animals after the second injection (Figures 4B, C, respectively). With the exception of a single animal in the plant group, all hamsters developed detectable neutralizing antibodies against the Wuhan virus after a single immunization (Figure 4B). Similarly, $\frac{4}{5}$ animals in each group developed neutralizing antibodies against the Delta virus after a single immunization (Figure 4C). The mammalian cell-derived protein induced significantly higher mean titers of neutralizing antibodies against both viral isolates when compared to the plant protein. Irrespective of the producing system, heterologous neutralizing antibody titres were lower than the titres observed against the vaccine-matched virus (Figure 4C). Figure 4: Immunogenicity of spike trimers in hamsters when produced in plants and mammalian cells. (A) Immunization schematic depicting the timing of immunizations and sampling during the experiment. (B) Neutralizing antibody titres (ID50) against the matched wildtype (Wuhan) viral isolate. (C) Neutralizing antibody titres (ID50) against heterologous Delta virus. Pre-bleed= day -2, first bleed = day 14, final bleed = day 46. The hamsters were then challenged with the heterologous Delta virus to emulate the world scenario at the time of the experiment where the approved vaccines were based on the original wild type virus and the predominating variant was Delta. Following challenge, a slight decrease in body weight was witnessed in all groups over the first 2 days with no appreciable differences observed between vaccinated and unvaccinated animals. Both vaccinated groups rapidly recovered the lost weight, whereas the weights of the unvaccinated hamsters continued to decline until the experimental endpoint when a slight increase was observed. Both vaccines afforded significant protection against weight loss compared to the control group which demonstrated protracted weight loss and slower recovery ($p \leq 0.05$) (Figure 5A). Animals immunized with the mammalian cell-produced antigen demonstrated a trend towards faster recovery of the lost weight, although this was not statistically significant ($p \leq 0.05$). Figure 5: Vaccine-mediated protection against heterologous challenge. (A) Change in weight following experimental challenge. (B) Grading of lung pathology following challenge. (C) Representative images of histopathology findings in lung sections. (D) Viral load following challenge. Pathology of lung tissue following challenge was graded based on the observed severity which was reflected as a numerical score of 0-5 where 0 indicates that no significant histological changes were observed and 5 indicates severe changes (Figures 5B, S4). Moderate to marked microscopic lesions were observed in all unvaccinated animals. In contrast, the majority of animals from both vaccinated groups showed only mild or minimal changes on histopathology (Figure 5C). A single animal that was vaccinated with the mammalian cell-produced protein demonstrated complete protection from any observable changes on histopathology. Both vaccines resulted in lower viral loads following challenge when compared to the control group, and this was observed at both time points when viral load was assayed (Figure 5D). Animals vaccinated with the mammalian cell produced spike had a trend towards lower viral loads than animals immunized with the plant-produced spike, although this was only significant at the final time point. ## Discussion Remodelling the host cell factory has shown promise as a novel paradigm to produce increasingly complex biopharmaceuticals in plants (Frigerio et al., 2022; Ruocco and Strasser, 2022; Ganesan et al., 2023). These approaches enable intrinsic constraints in the host machinery to be addressed resulting in increased accumulation, enhanced folding, improved glycosylation and even tailor-made post-translational modifications (Margolin et al., 2020d). This has important implications for vaccine development and accordingly plant host engineering has shown promise in the production of novel vaccine antigens (Margolin et al., 2020c; Rosenberg et al., 2022; Song et al., 2022; Margolin et al., 2022a; Margolin et al., 2022b; Ruocco and Strasser, 2022; Margolin et al., 2020d) including those from enveloped viruses which historically have posed a challenge for plant-based production. Following a series of studies to identify constraints in glycosylation-directed folding pathways in plants (Margolin et al., 2020c; Margolin et al., 2021a; Margolin et al., 2022b), we previously developed an integrated approach, NXS/T Generation™, to support improved viral glycoprotein production in N. benthamiana. In a proof-of-concept study, we recently used this technology platform to produce a soluble HIV envelope gp140 trimer, which elicited comparable immune responses to the cognate mammalian protein in a rabbit immunogenicity model (Margolin et al., 2022a). In the present study, we applied this approach to the prototype spike glycoprotein from SARS-CoV-2 and compared the resulting protein to the antigen when it was produced in mammalian cells. This work serves as part of a larger initiative to evaluate the broader applicability of the NXS/T Generation™ technology. Following production in plants using this platform, the proteins observed by NSTEM were consistent with the prefusion trimer of the SARS-CoV-2 spike (Hsieh et al., 2020), and these were indistinguishable with what was observed for the mammalian control protein. However, despite the apparent similarities, juxtaposition of the site-specific N-glycosylation of the two proteins revealed substantial differences. Whilst the plant-derived protein contained an abundance of poorly processed oligomannose structures, the mammalian cell-derived control displayed a high degree of N-glycan processing with an abundance of mature complex glycans. Other studies have also reported reduced mannose processing in plants for viral glycoproteins (Dobrica et al., 2021; Margolin et al., 2021a), although the extent to which processing was arrested here remains a surprise. Interestingly the N-glycosylation pattern observed for the plant-produced spike in this study contrasts with a recent report describing the production of virus-like particles bearing the full-length protein in wild type N. benthamiana (Balieu et al., 2022). In the study conducted by Balieu et al., highly processed mature N-glycans were observed, including typical plant-derived complex glycans with Lewis A epitopes, contrasting against the abundance of immature oligomannose N-glycans that we observed. Although the use of N. benthamiana ΔXF as an expression host in the present study would preclude the formation of typical plant complex N-glycans carrying β1,2-xylose and core α1,3-fucose, it should not undermine normal glycan processing along the secretory pathway, and therefore the predominance of unprocessed oligomannose-type N-glycans is puzzling. It is worth noting that the spike protein in this study was soluble and therefore would not have the same association with the endomembrane system as would be expected for a full-length antigen. In the case of the HIV Env glycoprotein, membrane-bound Env has been reported to exhibit enhanced glycan processing and maturation compared to the cognate soluble protein, and this is thought to result from improved association with the membrane-bound host processing machinery (Allen, 2021). Although this could be a contributory factor, it seems unlikely to account for the dramatic retardation of glycan maturation that we observed. Although decreased glycan processing was also observed in our previous work with HIV Env using this production platform, this was less apparent as the extensive glycosylation of the glycoprotein sterically hinders glycan-processing leading to an already elevated oligomannose content (Pritchard et al., 2015; Margolin et al., 2022a). More encouragingly, the site-specific glycan occupancy of the plant-derived material was similar to that of the mammalian protein and the extensive under glycosylation that we previously observed for viral glycoproteins produced without the NXS/T Generation™ platform (Margolin et al., 2021a) was not seen here. This can be attributed to the co-expression of Leishmania LmSTT3D which has been demonstrated to improve glycan occupancy of a broad range of substrates in plants (Castilho et al., 2018; Margolin et al., 2022a). The impact of impaired glycan maturation on vaccine immunogenicity is largely speculative and probably depends on the antigen in question. In the previous study with HIV, elevated high-mannose glycans did not appear to negatively impact the immunogenicity of the plant-produced vaccine – although it is acknowledged that the sample size was small (Margolin et al., 2022a) and that the antigen naturally displays increased mannose content (Pritchard et al., 2015). Accordingly, it is less clear how this would impact immunogenicity in the context of an antigen, which would typically have complex glycans as the predominating species, such as the SARS-CoV-2 spike (Chawla et al., 2022). However, robust immune responses have been generated to SARS-CoV-2 spike proteins derived from a wide range of different sources with different glycosylation profiles (Chawla et al., 2022). Although the plant-produced antigen was immunogenic, hamsters immunized with the vaccine developed significantly lower titres of neutralizing antibodies than animals that were immunized with the mammalian cell-produced protein. This is not unprecedented and these observations mirror studies with influenza virus where haemagglutinin antigens containing high-mannose glycans elicited lower haemagglutinin inhibition antibody titres than equivalent antigens containing typical mammalian-type complex glycans (de Vries et al., 2012). Nonetheless, the plant-produced vaccine still elicited cross-neutralizing antibodies against the Delta variant of SARS-CoV-2. Following heterologous challenge, both plant-produced and mammalian cell-derived vaccines conferred significant protection from disease. The initial loss of weight indicates that the hamsters were not protected from infection but were able to clear the virus, probably via a T cell response. This protected against lung pathology. We had previously reported strong T cell responses to this plant made vaccine in mice (Margolin et al., 2021b). However, the mammalian-cell produced antigen elicited superior protection across multiple parameters including weight change, viral load and lung pathology. This can potentially be attributed to the observed differences in glycosylation. It has been shown that exposure of mannose residues can promote protein turnover, which would potentially result in reduced half-life of the plant-produced protein (Yang et al., 2015). This would in turn result in reduced antigenic stimulation compared to the mammalian cell-derived product, which contains significantly reduced mannose by comparison. In the context of HIV Env, it has also been shown that the abundance of mannose residues impairs immune responses from dendritic cells (Shan et al., 2007) and that enzymatic elimination of these mannose structures can increase the immunogenicity of an Env-derived gp120 antigen (Banerjee et al., 2009). It is unclear if a similar phenomenon is occurring here and further work is required to delineate the mechanism by which the glycosylation impairs the immune response. Lastly, although the differences in immunogenicity have been attributed to the differential glycosylation patterns from the expression hosts, we cannot discount expression-system dependent differences in folding which would also influence vaccine immunogenicity. We also note the limited resolution afforded by negative stain electron microscopy, and cryo-electron microscopy would be required to determine whether meaningful structural differences exist. In conclusion, this study highlights the utility of the NXS/T generation™ platform for remodelling the plant secretory pathway to produce complex viral glycoproteins in plants. Nonetheless, further refinement of the technology could greatly enhance vaccine immunogenicity and this could potentially even be used to generate vaccines with tailor-made glycosylation. Finally, this work highlights the influence of glycosylation in vaccine immunogenicity and reinforces this as an important consideration for production of important vaccine antigens by molecular farming. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding authors. ## Ethics statement The animal study was reviewed and approved by Animal Ethics Committee of the University of Cape Town, South Africa. ## Author contributions EM conceptualized the study with input from A-LW, RC and ER. Protein production was conducted by EM. JW carried out the negative stain electron microscopy and image processing. JA completed the site-specific N-glycan analysis. AS propagated and titrated the SARS-CoV-2 virus, under the supervision of MS and WP. RC managed the hamster experiment. Immunogenicity assays were conducted by EM and GS. MB cloned the Delta variant of the Spike protein for production of the pseudovirions used for neutralization assays. GS determined the viral loads and performed the neutralization assays. SG carried out the histopathology. EM drafted the manuscript. All authors contributed to data analysis and reviewed the final manuscript before submission. Funding for the project was obtained by RC, EM, MS, WP and A-LW. All authors contributed to the article and approved the submitted version. ## Conflict of interest EM, AM, RC, A-LW, RS and ER have filed patent applications describing approaches to improve production of glycoproteins in plants. These include WO $\frac{2018}{069878}$ A1, WO $\frac{2018}{220595}$ A1, PA174002/PCT and PA176498_P. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1146234/full#supplementary-material ## References 1. Allen J.. The glycan shields of HIV-1 and SARS-CoV-2 spike proteins and their differential importance in vaccine design. Doctor philosophy Univ. Southampton (2021) 2. Alonzi D. S., Scott K. A., Dwek R. A., Zitzmann N.. 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--- title: 'Whole-body vibration as a passive alternative to exercise after myocardial damage in middle-aged female rats: Effects on the heart, the brain, and behavior' authors: - Kata Tóth - Tamás Oroszi - Csaba Nyakas - Eddy A. van der Zee - Regien G. Schoemaker journal: Frontiers in Aging Neuroscience year: 2023 pmcid: PMC10028093 doi: 10.3389/fnagi.2023.1034474 license: CC BY 4.0 --- # Whole-body vibration as a passive alternative to exercise after myocardial damage in middle-aged female rats: Effects on the heart, the brain, and behavior ## Abstract ### Background Females with cardiovascular disease seem more vulnerable to develop concomitant mental problems, such as depression and cognitive decline. Although exercise is shown beneficial in cardiovascular disease as well as in mental functions, these patients may be incapable or unmotivated to perform exercise. Whole body vibration (WBV) could provide a passive alternative to exercise. Aim of the present study was to compare WBV to exercise after isoproterenol (ISO)-induced myocardial damage in female rats, regarding effects on heart, brain and behavior. ### Methods One week after ISO (70 mg/kg s.c., on 2 consecutive days) or saline injections, 12 months old female rats were assigned to WBV (10 minutes daily), treadmill running (30 minutes daily) or pseudo intervention for 5 weeks. During the last 10 days, behavioral tests were performed regarding depressive-like behavior, cognitive function, and motor performance. Rats were sacrificed, brains and hearts were dissected for (immuno)histochemistry. ### Results Significant ISO-induced cardiac collagen deposition (0.67 ± 0.10 vs 0.18 ± $0.03\%$) was absent after running (0.45 ± 0.26 vs 0.46 ± $0.08\%$), but not after WBV (0.83 ± 0.12 vs 0.41 ± $0.05\%$). However, WBV as well as running significantly reduced hippocampal (CA3) collagen content in ISO-treated rats. Significant regional differences in hippocampal microglia activity and brain derived neurotrophic factor (BDNF) expression were observed. Significant ISO-induced CA1 microglia activation was reduced after WBV as well as running, while opposite effects were observed in the CA3; significant reduction after ISO that was restored by WBV and running. Both WBV and running reversed the ISO-induced increased BDNF expression in the CA1, Dentate gyrus and Hilus, but not in the CA3 area. Whereas running had no significant effect on behavior in the ISO-treated rats, WBV may be associated with short-term spatial memory in the novel location recognition test. ### Conclusion Although the female rats did not show the anticipated depressive-like behavior or cognitive decline after ISO, our data indicated regional effects on neuroinflammation and BDNF expression in the hippocampus, that were merely normalized by both WBV and exercise. Therefore, apart from the potential concern about the lack of cardiac collagen reduction, WBV may provide a relevant alternative for physical exercise. ## 1. Introduction Many patients with cardiovascular disease may also experience mental disorders, including depression and cognitive impairment. These mental disorders are often overlooked or regarded as “natural” responses to a life-threatening condition. However, mental disorders can be associated with increased morbidity and mortality (Gharacholou et al., 2011; Meijer et al., 2013). Moreover, female patients seemed more vulnerable to developing heart failure-associated depression (Gottlieb et al., 2004; Eastwood et al., 2012) and cognitive decline (Ghanbari et al., 2013) than male patients. Although the worsening of cardiovascular prognosis by comorbid depression is well recognized (Nabi et al., 2010), anti-depressant treatment may alleviate depressive symptoms but does not improve cardiovascular prognosis (Thombs et al., 2008). In female patients, it may even deteriorate cardiovascular prognosis (Krantz et al., 2009). A rationale for therapy of this comorbidity is hampered by the lack of understanding of the heart–brain interaction and the potential difference in male and female patients. Extensive evidence points to a role of a derailed (neuro)inflammatory response to cardiac damage, as a mechanism underlying the cardiovascular disease-depression association (Liu et al., 2013; Angermann and Ertl, 2018). However, the efficacy of anti-inflammatory therapy (Kosmas et al., 2019) seems rather poor. A lot of knowledge about the heart–brain interaction comes from animal studies. Heart failure induced by coronary artery ligation was associated with cognitive impairment (Hovens et al., 2016), as well as depressive-like behavior in rodents (Schoemaker and Smits, 1994; Wang et al., 2013; Frey et al., 2014). This behavior could be affected by cardiovascular-directed treatment (Schoemaker et al., 1996) and by treatment targeted at the brain and behavior (Grippo et al., 2003, 2006; Bah et al., 2011a,b; Ito et al., 2013). The isoproterenol (ISO)-induced cardiac damage model is often used as a way to induce focal cardiac damage and could be preferred above the coronary artery ligation model, as the former does not require brain-changing thoracic surgery (Hovens et al., 2016; Toth et al., 2021). In the ISO model, the effects of treatment on cardiovascular aspects are extensively studied (Nichtova et al., 2012; Ma et al., 2015; Alemasi et al., 2019), but studies on behavioral consequences are limited. Reduced exploratory behavior (Tkachenko et al., 2018) and declined sucrose preference after ISO (Hu et al., 2020) suggest depressive-like behavior, while the cognitive decline was also observed (Ravindran et al., 2020). Concomitant cardio and neuroprotective effects have been obtained in this model with Corvitin (Tkachenko et al., 2018), sodium thiosulfate (Ravindran et al., 2020), and traditional Chinese medicine Kai-Xin-San (Hu et al., 2020). However, these effects were only studied in young male rats, leaving potentially different effects in female rats unrevealed. Sex dimorphism in the response to ISO was already recognized in the 70s (Wexler et al., 1974; Wexler and Greenberg, 1979), and supported by our group (Toth et al., 2022b). Exercise is generally acknowledged for its beneficial effects on physical as well as mental conditions in health and disease (Pedersen and Saltin, 2015). Recently, we showed that exercise training after ISO-induced cardiac damage could reverse the anxiety-like behavior in male rats (Toth et al., 2021), but not in female rats (Toth et al., 2022a). Physical exercise before ISO prevented cardiac fibrosis and the upregulation of pro-inflammatory cytokines (Ma et al., 2015; Alemasi et al., 2019), but exercise after ISO seemed to deteriorate the cardiac damage (Azamian Jazi et al., 2017). However, not all patients are capable and/or motivated to perform physical exercise. For them, a passive form of exercise, such as whole-body vibration, could provide an alternative (Runge et al., 2000; Zhang et al., 2014). Whole-body vibration (WBV) is a passive mechanical stimulation on a vibrating platform (van Heuvelen et al., 2021). In addition to increased muscle strength (Annino et al., 2017) and aerobic fitness (Zhang et al., 2014), WBV was associated with improved wound healing (Wano et al., 2021) and a reduced inflammatory phenotype (Weinheimer-Haus et al., 2014). Moreover, we recently showed that WBV improved motor performance, spatial memory, and anxiety-like behavior in aged rats (Oroszi et al., 2022a). However, these effects seemed more pronounced in male than in female rats. In a study of middle-aged female rats, treated with WBV from 1 to 30 days after mid-cerebral ischemia-reperfusion, decreased inflammasome activation (caspase-1 and IL1-beta) and increased brain-derived neurotrophic factor (BDNF) expression were observed, concomitant with infarct reduction (Raval et al., 2018). Taken together, female patients with cardiovascular disease seemed more prone to develop mental disorders; beneficial effects of exercise training on behavior were observed in male rats with ISO-induced myocardial infarction (Toth et al., 2021), but not in female rats; WBV has indicated a passive alternative to exercise. Therefore, the present study aimed to explore WBV as an alternative to physical exercise in female rats after ISO-induced myocardial infarction, regarding its effects on the heart, the brain, and behavior. ## 2.1.1. Animals Animals were housed in groups of two or three in cages of 304220 cm with sawdust as bedding in the conventional animal facility of the University of Physical Education, Hungary, in a room with 22 ± 2°C and humidity of 50 ± $10\%$. The light was provided from 6 a.m. to 6 p.m. CEST. Experiments were performed approximately between 9 a.m. and 5 p.m. Standard rodent chow (LT/R, Innovo Ltd., Gödöllo, Hungary) and tap water were provided ad libitum. All methods were performed in accordance with the ARRIVE guidelines. Experimental animals and procedures were approved by the local animal committee of the University of Physical Education, Budapest, Hungary. ## 2.1.2. Pilot Exposure time–effect relation of WBV Before starting the main study on WBV as an alternative to exercise, a pilot exposure time–effect study was performed in order to find the optimal WBV exposure time per session for our female rats. For that, 28 female Wistar rats were collected from the breeding colony of the University of Sports Science, Hungary. Rats were randomly divided into four experimental groups, receiving 5 weeks (one time a day and 5 days a week) of treatment with either pseudo-WBV (0 min), or 5, 10, or 20 min of WBV per day. At the end of these 5 weeks, effects on behavior were evaluated, regarding open field (OF) exploration, short-term memory in the novel object/novel location recognition tests (NOR/NLR), and motor performance in the balance beam and grip hanging tests. For details about the WBV and behavioral testing, refer to Section 2 of the main study. ## 2.1.3. Main study Sixty-four middle-aged (on average 12 months old) female Wistar rats were obtained from the breeding colony of the University of Sports Science, Hungary. Rats were randomized to six experimental groups: running ISO/saline, WBV ISO/saline, and as control pseudo-ISO/saline, and were subjected to the protocol presented in Figure 1. For that, rats were treated with ISO to induce heart lesions or received saline injections. After 1 week of recovery, rats were either subjected to 5 weeks of WBV or treadmill running or received pseudo-treatment (sedentary). Exploratory behavior, cognitive performance, and motor function were assessed during the last 10 days of the training period. After completion of all tests, animals were killed, and heart and brain tissues were collected for further analyses of cardiac collagen, brain collagen, neuroinflammation, and neuronal function. This experimental protocol is similar to the one described in detail in our previous studies (Toth et al., 2021; Oroszi et al., 2022a). Figure 1: Experimental protocol. Rats (n = 64) were subdivided into six experimental groups. Part of the rats (n = 25) received two saline injections and the other part (n = 39) received two ISO injections, both 24 h apart. One week later, rats were subdivided into a sedentary group, receiving the pseudo-intervention, an exercise intervention group (treadmill running), and a whole-body vibration intervention group, for 5 weeks. In the last 10 days of this period, behavioral tests were performed, and rats were subsequently killed. ## 2.2.1. Cardiac damage Acute cardiac damage was induced by isoproterenol hydrochloride (ISO; C11H7NO3·HCl: ISO), a non-selective β-adrenoceptor agonist that mimics the histological, physical, and endocrinological events of human myocardial infarction presumably by myocardial hyperactivity-induced ischemia and energy depletion (Wexler et al., 1968). Rats were injected subcutaneously with ISO (Sigma Aldrich) in a dose of 70 mg/kg dissolved in 1 ml/kg saline (Toth et al., 2021, 2022a,b). Control animals received 1 ml/kg saline. Both groups received two injections with 24 h in between, according to the protocol described by Ravindran et al. [ 2020]. ## 2.2.2. Whole-body vibration Rats received a single vibration session of 10 min, five times per week, for 5 consecutive weeks, using a vibration platform (MarodyneLiV—low-intensity vibration; BTT 129 Health GmbH, Germany), as described in detail elsewhere (Oroszi et al., 2022a). The platform ensures constant vibration exposure with a frequency of 30 Hz and an amplitude of 50–200 microns. Rats were placed in empty individual cages on the platform (the same shape as the individual home cage, but without bedding). WBV took place in an adjacent room with the same climate conditions as the housing room. ## 2.2.3. Treadmill running Saline- and ISO-treated groups were assigned to a treadmill running protocol on a six-lane rat treadmill (Tartonik Elektronika, Italy) with individual lanes of 125413 cm, as described in detail previously (Toth et al., 2021). The training program lasted for 5 weeks, five times per week on each weekday. On the 1st week of the training program, rats were habituated to running: on the 1st day, rats started with 10 min of running with a maximal speed of 10 m/min, which was gradually increased to 30 min and a maximal speed of 18 m/min (moderate intensity; ~$65\%$ of VO2max; Hoydal et al., 2007) by the 5th day. For the following 4 weeks, each running session lasted 30 min at 18 m/min. ## 2.2.4. Pseudo-WBV/running Rats that received pseudo-treatment served as sedentary controls for both WBV and exercise. Pseudo-treatment consisted of either 10 min on the turned-off vibrating platform or 30 min on the turned-off treadmill, on alternate days, for 5 weeks, 5 days a week. ## 2.3.1. General Effects on behavior were tested during the last 10 days of the protocol (refer to Figure 1). Open field behavior (OF) was used to assess anxiety/depressive-like behavior (Schoemaker and Smits, 1994). Short-term memory was tested in the novel object recognition (NOR) and the novel location recognition test (NLR), as a measure for cognitive effects (Hovens et al., 2014b). Effects on motor performance were obtained in the balance beam test for motor coordination (Song et al., 2006) and in the grip strength test (Shear et al., 1998) for effects on muscle strength. Tests have been described in detail in our previous study (Oroszi et al., 2022a). All tests were recorded with a digital video camera (Canon Legria HFR106, Canon Inc., Tokyo, Japan) and stored on a memory card for later offline analyses. ## 2.3.2. Open field An open-field exploration test was performed to assess exploratory and anxiety-related behavior, as we described earlier (Toth et al., 2021). A round-shaped arena (diameter of 80 cm) was divided into an inner circle (diameter of 48 cm; center area) and an outer annulus (wall area). Initially, the area was divided into three concentric circles: wall, middle, and center (each 16 cm wide). However, as the rats hardly moved into the so-defined center area, this center area plus the middle area were taken together as “çenter” in our measurements. Animals were placed in the arena and allowed to explore for 5 min. After each animal, the arena was cleaned with $70\%$ ethanol to remove smell cues. Time spent in the center and the wall area, as well as the number of visits to the center, were obtained using Eline software (University Groningen, the Netherlands). The total number of crossings between the initially defined three areas was used as an estimate for locomotion activity and exploration. ## 2.3.3. Novel object and novel location recognition To assess short-term visual memory, which depends primarily on prefrontal cortex function, a novel object recognition test (NOR) was performed, while short-term spatial memory, associated with hippocampal function, was assessed in the novel location recognition test (NLR), as described previously (Hovens et al., 2014b). After habituation to the test environment, the test battery consisted of three phases, each lasting 3 min, with 1 min in-between: For habituation, the animal was placed in the test box and allowed 3 min to explore the set-up; then the rat was presented two identical objects and allowed to explore those for 3 min. Subsequently, objects were removed and after 1 min placed back, but one of them in a different location than in the previous phase (NLR), followed by exploration for 3 min. Finally, after being removed for 1 min, the objects were presented again, but now one of the two identical objects was replaced by a different object and put in the same location as in the preceding phase (NOR). Between the phases, the objects were removed and cleaned with $70\%$ ethanol to remove smell cues. After each animal, the test box and objects were also cleaned with $70\%$ ethanol. Time spent exploring the objects was measured using Eline software (the University of Groningen, the Netherlands). Preference for the novel location or the novel object was calculated by dividing the time spent exploring the novel location or novel object by the time spent exploring both objects. Preference of $50\%$ indicated chance level = no recognition. Results from rats that did not explore the objects or only one of them were excluded from further analyses. ## 2.3.4. Balance beam test A balance beam test was conducted two times on separate days to analyze motor coordination (Oroszi et al., 2022a). A 150-cm long and 2-cm wide wooden slat was positioned horizontally at 1 m above the floor, and on one end connected to the home cage of the rat, as a target. On the first day, the rats were trained to walk across the suspended beam, at which the rats performed three trials [50 cm, 100 cm, and one full test (150 cm)]. After these three training trials, the rats performed three full test trials. On the second day, the rats performed also full three test trials. The average latency to reach the home cage was used as a measure of performance on the balance beam. Animals who were unable and/or unwilling to perform the test procedure were excluded from the final statistical analysis. ## 2.3.5. Grip strength test The grip strength test was performed two times on separate days and included three trials per day (Oroszi et al., 2022a). Animals were held by their trunk and were guided to get grip on a suspended wood beam (2 mm in diameter, 35 cm in length, and 50 cm above the surface of the table) by their forepaws. Time until drop-off was recorded. During the three trials, the animals were rotated to offer time for recovery between the trials. The average of the six trials over the 2 days was utilized for statistical analysis. ## 2.4. Tissue collection and processing At the end of the experiment, rats were anesthetized with $6\%$ sodium pentobarbital solution and injected intraperitoneally (2 ml/kg). Rats were transcardially perfused with heparinized (1 ml/l) $0.9\%$ saline until the liver turned pale. The right gastrocnemius and soleus muscles as well as the heart and the brain were dissected and weighed. The brain and the heart tissues were immersion fixated in $4\%$ buffered formaldehyde freshly depolymerized with paraformaldehyde. After 4 days, tissues were washed in 0.01 M phosphate-buffered saline (PBS), dehydrated using a $30\%$ sucrose solution, and subsequently quickly frozen in liquid nitrogen and stored at −80°C until 25 μm coronal sections were cut using a microtome. Heart sections and three brain sections were placed on glass immediately after cutting and processed for histochemical staining. The remaining brain sections were stored free-floating in 0.01 M (PBS) containing $0.1\%$ sodium azide at 4°C till further processing for (immuno)histochemistry. ## 2.5.1. Microglia To visualize microglia, immunohistochemical staining of ionized calcium binding adaptor molecule 1 (IBA-1) was performed, as described in detail previously (Hovens et al., 2014a). In brief, after incubation for 3 days with 1:2,500 rabbit-anti IBA-1 (Wako, Neuss, Germany) in $2\%$ bovine serum albumin, $0.1\%$ triton X-100 at 4°C, followed by a 1 h incubation with 1:500 goat-anti-rabbit secondary antibody (Jackson, Wet Grove, USA) at room temperature, sections were then incubated for 2 h with avidin–biotin–peroxidase complex (Vectastain ABC kit, Vector, Burlingame, USA) at room temperature. Labeling was visualized by using a 0.075 mg/mL diaminobenzidine (DAB) solution activated with $0.1\%$ H2O2. Sections were mounted onto glass slides and photographs were taken from the dorsal hippocampus (hippocampus) at 200 times magnification (Toth et al., 2022b). Microglia morphology was analyzed in the different areas of the dorsal hippocampus: CA1, CA3, dentate gyrus (DG), and hilus, according to our previous publication (Hovens et al., 2014a), regarding coverage, density, cell size, cell body area, and processes area. Microglia activity was calculated as cell body area/total cell size (Hovens et al., 2014a). ## 2.5.2. Brain-derived neurotrophic factor For brain function, brain slices were stained for brain-derived neurotrophic factor (BDNF), as described previously (Hovens et al., 2014b). In brief, sections were blocked for 1 h with $5\%$ normal goat serum, then incubated with 1:1,000 rabbit-anti BDNF antibody (Alomone Labs, Israel) in $1\%$ BSA, followed by incubation with 1:5,000 goat-antirabbit secondary antibody (Jackson, Wet Grove, USA). Photographs were taken at 50× magnification (Toth et al., 2022b) from the different areas of the dorsal hippocampus, CA1, CA3, DG, and hilus, and BDNF expression was obtained as corrected optical density (Image-J) compared to an underlying reference area (Hovens et al., 2014b). ## 2.5.3. Collagen In the heart, the percentage of collagen was used to measure cardiac damage. For that, 25 μm thick transverse slices at mid-ventricular and apex levels of the heart were stained with Sirius red (Sigma, Aldrich) and Fast green as counterstaining (Hovens et al., 2016). Color pictures were taken and enlarged to cover the complete left ventricle in the image analyses screen. Image analysis (Image Pro Plus, USA) was used to measure the collagen-positive (red) area and was expressed as a percentage of the total left ventricular tissue area. Since WBV may also affect collagen in other organs than the heart, a similar Sirius red/Fast green stain was performed on three brain sections that were immediately placed on glass, using the same procedure as was used for the cardiac sections. Because of a positive collagen signal observed in the hippocampus in this pilot, for a subgroup of rats randomly selected from the experimental groups, free-floating sections were stained with Sirius red (without Fast green), after thoroughly washing (two times daily for 4–5 days) to remove the azide. Photographs were taken from the granular layers of the dorsal hippocampus (CA1, CA2, CA3, DG, and hilus; 100× magnification), and collagen expression was obtained as corrected optical density, compared to the underlying reference area (Image-J). ## 2.6. Data analyses Data are presented as mean ± $95\%$ confidence interval (CI; figures) and standard error of the mean (SEM; tables). Results more than two times the standard deviation of its group were considered outliers and were excluded before analyses (maximally one per experimental group). Results were compared using a two-way analysis of variance (ANOVA) with a least square difference (LSD) post-hoc test, with saline/ISO and sedentary/runner/whole-body vibration as factors. Association between selected parameters was measured with Pearson linear correlation. For the novel object/novel location recognition tests, outcomes were also tested against chance level (=$50\%$), using a single sample t-test. A p-value of < 0.05 was considered statistically significant and is denoted as . Potentially relevant tendencies ($p \leq 0.1$) were mentioned as well. ## 3.1. WBV exposure time–effect pilot To obtain insight into the effects of different exposure times of WBV in order to find an effective protocol to compare with the active exercise in our study, a pilot exposure time—effect study on behavior was performed. The results are presented in Figure 2. We tested daily for 5, 10, or 20 min against pseudo-treatment (=0 min). Five and ten minutes of WBV per day had similar effects, whereas the effects of 20 min per day appeared deviant. Daily 5 or 10 min of WBV significantly reduced anxiety-like behavior in the OF, without effects on short-term memory and balance beam performance, while muscle strength measured as grip hanging was slightly improved. From these results, we chose 10 min of WBV per day to use in our main study. Figure 2:* (A, B) Results from the open field test. (C, D) Short-term memory in the NOR and NLR test, respectively. (E, F) Effects on motor function. Results from the pilot exposure time-effect study on the different whole-body vibration (WBV) session times (n = 5–8 per group). Rats were subjected to 5 weeks daily (5 days a week) WBV for 0 (pseudo), 5, 10, or 20 min. Behavioral responses were obtained according to descriptions in the methods section. Significant effect (p < 0.05).* ## 3.2.1. General Mortality in the ISO group was $26\%$ (10 out of 39 rats), whereas none of the saline-treated rats died. At the start of the experiment, rats weighed on average 249 ± 3 g, with no differences between the experimental groups. Before killing, the rat's body weight was, on average, 251 ± 3 g. Body weights and organ weights corrected for body weight are presented in Table 1. Neither body weight nor relative organ weights were different between the experimental groups. Table 1 \| Unnamed: 0 \| Sed saline \| Sed ISO \| Run saline \| Run ISO \| WBV saline \| WBV ISO \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| n \| 8 \| 9 \| 8 \| 10 \| 9 \| 10 \| \| Body weight (g) \| 253 ± 7 \| 247 ± 4 \| 258 ± 12 \| 254 ± 7 \| 250 ± 10 \| 245 ± 7 \| \| Heart weight (%bw) \| 0.38 ± 0.02 \| 0.40 ± 0.01 \| 0.40 ± 0.02 \| 0.39 ± 0.02 \| 0.38 ± 0.01 \| 0.40 ± 0.01 \| \| Brain weight (%bw) \| 0.78 ± 0.02 \| 0.79 ± 0.02 \| 0.75 ± 0.03 \| 0.77 ± 0.02 \| 0.79 ± 0.03 \| 0.79 ± 0.01 \| \| Soleus weight (%bw) \| 0.084 ± 0.004 \| 0.081 ± 0.003 \| 0.080 ± 0.003 \| 0.076 ± 0.003 \| 0.081 ± 0.002 \| 0.083 ± 0.005 \| \| Right gastronemicus weight (%bw) \| 0.58 ± 0.01 \| 0.60 ± 0.01 \| 0.59 ± 0.02 \| 0.60 ± 0.01 \| 0.60 ± 0.01 \| 0.58 ± 0.02 \| ## 3.2.2. Cardiac collagen The percentage of collagen in the left ventricle measured at mid-ventricular and apex levels was used as a measure of cardiac damage (Figure 3). Two-way ANOVA revealed a significantly elevated collagen percentage after ISO compared to saline-treated rats at both ventricular levels (middle: $$p \leq 0.003$$; apex: $$p \leq 0.002$$), and a significant intervention effect ($$p \leq 0.040$$) and interaction effect ($$p \leq 0.024$$) only at the mid-ventricular level. Post-hoc analyses indicated that the fibrotic effect of ISO, seen in the sedentary rats (middle: $$p \leq 0.008$$; apex: $$p \leq 0.001$$), was absent after running, but not after WBV. In fact, WBV in ISO rats tended to even further increase collagen (mid-ventricular level; $$p \leq 0.052$$). However, the effect of running may at least in part be attributed to increased collagen in the control saline-treated rats, since running ($$p \leq 0.030$$), and to a lower extent WBV (ns), by itself already increased cardiac collagen. Figure 3: Presentation of midventricular and apex sections stained for Sirius red (red) and Fast green (green), from sedentary saline [(A, C), respectively] and isoproterenol whole-body vibration-treated rats [(B, D), respectively], showing increased Sirius red positive areas after ISO treatment. Lower panels: actual measurements of the percentage of collagen in the left ventricle as Sirius Red positive area at mid-ventricular (E) and apex level (F), in saline or isoproterenol (ISO)-treated rats, under sedentary conditions, or after 5 weeks of exercise training (running) or whole-body vibration (vibration). Significant difference between indicated groups (p < 0.05).* ## 3.2.3. Hippocampal collagen Figure 4A shows photographs of the dorsal hippocampus (dentate gyrus), stained according to the same protocol as had been used for cardiac sections; Sirius red/Fast green. The photograph showed that in addition to the expected positive staining (red) of blood vessel walls and the fibrous tissue of the choroid plexus, the granular layers (containing mostly the neuronal soma) in the hippocampus also stained positive for collagen. The Sirius red/Fast green staining protocol for sections collected directly on the glass, however, could not be used for free-floating sored sections, potentially due to the presence of azide in the storage solution. Therefore, in order to visualize collagen in the hippocampus in the sections from our experimental groups, 4–5 days of daily washing appeared to be necessary to obtain a sufficient signal-to-noise ratio, and quantification was obtained from optical density (Figures 4B, C). The results of the measurement were shown in the lower part of the figure. While the hippocampus (Figure 4I) overall did not show statistically significant differences between the experimental groups regarding collagen, effects seemed to differ locally. In the CA1 (Figure 4D), DG (Figure 4G), and hilus areas (Figure 3H), no differences were observed between groups. However, in the CA2 area (Figure 4E), the saline-treated WBV rats appeared to show consistently increased collagen expression, whereas, in all other groups, virtually no expression was observed. Although this did not result in significant differences between groups in the CA2, differences reached statistical significance ($$p \leq 0.011$$) in the CA3 area (Figure 4F). Surprisingly, running ($$p \leq 0.007$$) as well as WBV ($$p \leq 0.019$$) caused a significant decline of collagen in ISO-treated, compared to saline-treated, rats. Moreover, running after ISO significantly declined collagen expression ($$p \leq 0.023$$) compared to sedentary ISO rats. Figure 4: (A) Example of hippocampal dentate gyrus stained with Sirius red (red) and Fast green (green), showing positive staining for collagen (red) in blood vessel walls, fibrous tissue of the choroid plexus, and the granular layer of the dentate gyrus, indicating the presence of collagen in brain tissue (50× magnification). (B) A typical example of a black and white photograph of a section stained with Sirius red only (magnification 20×). (C) Detail of (B) as was used to quantify gray values (100 × magnification). (D–H) Actual measurements of optical density in the different areas of the hippocampus, as well as for the whole hippocampus (I) in saline- or isoproterenol (ISO)-treated rats, under sedentary conditions (n = 4 and n = 5, respectively), or after 5 weeks of exercise training (running; n = 4 each) or whole-body vibration (vibration; n = 5 each). Significant difference between indicated groups (p < 0.05).* ## 3.2.4. Hippocampal microglia activity and BDNF expression Effects on neuroinflammation were obtained from morphological changes in microglia represented by increased cell body-to-cell size ratio and microglia activity (Figures 5A, C). No significant differences due to either ISO treatment or running/WBV interventions were observed in the overall hippocampal microglia activity score. Similarly, no effects of ISO or intervention were observed on average hippocampal BDNF expression (Figure 5D). However, within the hippocampus, the different regions responded differently as described in Sections 3.2.5 and 3.2.6, respectively, and illustrated in Figures 6, 7. Figure 5: Typical pictures of IBA-1—(A) (200× magnification) and BDNF staining (B) (50× magnification) from the CA1 region of the hippocampus. Mean hippocampal microglia activity (C) and BDNF expression (D) in saline- or isoproterenol (ISO)-treated rats, under sedentary conditions (n = 8 and n = 9, respectively), or after 5 weeks of exercise training (running; n = 6–8 and n = 8, respectively) or whole-body vibration (vibration; n = 8–9 and n = 9, respectively). Figure 6: (A) CA1, (B) CA3, (C) DG, and (D) Hilus. Measurement of microglia activity as cell body/cell size in the different areas of the hippocampus in saline- or isoproterenol (ISO)-treated rats, under sedentary conditions (n = 8 and n = 9, respectively), or after 5 weeks of exercise training (running; n = 6 and n = 8, respectively) or whole-body vibration (vibration; n = 8 and n = 10, respectively). Significant difference between indicated groups (p < 0.05).* Figure 7: Expression of brain-derived neurotrophic factor (BDNF) in the different areas of the hippocampus in saline- or isoproterenol (ISO)-treated rats, under sedentary conditions (n = 7 and n =9, respectively), or after 5 weeks of exercise training (running; n = 8 each) or whole-body vibration (vibration; n = 9 each). Significant difference between indicated groups in post-hoc analyses (p < 0.05).* ## 3.2.5. Hippocampal neuroinflammation Analyzing the different areas within the hippocampus (Figure 6), two-way ANOVA revealed significant differences between groups in the CA1 ($$p \leq 0.010$$), CA3 ($$p \leq 0.013$$), and DG ($$p \leq 0.003$$), but not in the hilus area. In the CA1 ($$p \leq 0.022$$), CA3 ($$p \leq 0.011$$), and DG ($$p \leq 0.001$$), differences could be attributed to a significant effect of the intervention (sedentary, running, or WBV); in the CA3 area, this effect appeared on top of a significant ISO/saline effect ($$p \leq 0.023$$). Post-hoc testing revealed a significant increase in microglia activity due to ISO in the CA1 ($$p \leq 0.011$$), no effect in the DG, and a significant decline in activity in the CA3 ($$p \leq 0.010$$). In both the CA1 and CA3 areas, effects of ISO were normalized by WBV as well as running exercise (CA1: $$p \leq 0.001$$ and $$p \leq 0.007$$, for WBV and running exercise, respectively; CA3: $$p \leq 0.050$$ and $$p \leq 0.007$$, for WBV and running exercise, respectively). In the DG, WBV seemed to activate microglia in saline ($$p \leq 0.050$$) as well as ISO-treated rats ($$p \leq 0.014$$). Since the microglia activity (cell body to total cell size ratio) resulted from a calculation based on measured morphological features, effects on the underlying parameters are presented in Table 2. The opposite responses of the CA1 and CA3 microglia activity to ISO, as described in Figure 6, seemed reflected in the underlying morphological parameters. In the CA1, total cell size declined after ISO, due to loss of processes area with a (ns) increase in cell body size, while running, and to a lesser extent WBV, increased cell size by increasing processes size. In the CA3, cell size as well as processes tended to increase ($p \leq 0.1$), while cell bodies declined in size, resulting in reduced microglia activity after ISO in this area. WBV and exercise after ISO seemed to normalize these parameters. The DG and hilus microglia were not so much altered after ISO. WBV, but not exercise, seemed to reduce cell size and process size, resulting in higher microglia activity (Figure 6). Table 2 \| Experimental group/hippocampal area \| Sedentary saline \| Sedentary ISO \| Runner saline \| Runner ISO \| Vibration saline \| Vibration ISO \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| n \| 8 \| 9 \| 6 \| 8 \| 8 \| 10 \| \| CA1 \| CA1 \| CA1 \| CA1 \| CA1 \| CA1 \| CA1 \| \| Density (#/area) \| 63.6 ± 3.1 \| 57.9 ± 4.1 \| 65.0 ± 3.5 \| 67.4 ± 5.4 \| 58.1 ± 5.3 \| 55.6 ± 4.2 \| \| Coverage (%) \| 4.4 ± 0.5 \| 3.4 ± 0.2 \| 5.1 ± 0.6 \| 3.9 ± 0.3 \| 4.2 ± 0.6 \| 3.5 ± 0.3 \| \| Cell size (px) \| 760 ± 59 \| 605 ± 41 # \| 899 ± 81 * \| 702 ± 35 # \| 717 ± 28 + \| 686 ± 24 \| \| Cell body size (px) \| 88 ± 7 \| 97 ± 9 \| 86 ± 4 \| 78 ± 6 \| 88 ± 6 \| 68 ± 5 * # \| \| Processes size (px) \| 685 ± 61 \| 530 ± 42 # \| 822 ± 85 \| 630 ± 35 # \| 636 ± 27 + \| 607 ± 25 \| \| CA3 \| CA3 \| CA3 \| CA3 \| CA3 \| CA3 \| CA3 \| \| Density (#/area) \| 68.9 ± 3.0 \| 69.2 ± 4.1 \| 56.3 ± 3.2 * \| 67.3 ± 2.7 # \| 59.7 ± 3.0 \| 59.0 ± 3.6 * \| \| Coverage (%) \| 3.7 ± 0.2 \| 3.8 ± 0.3 \| 3.2 ± 0.2 \| 4.0 ± 0.2 # \| 3.2 ± 0.2 \| 3.7 ± 0.2 # \| \| Cell size (px) \| 629 ± 16 \| 688 ± 28 \| 596 ± 23 \| 592 ± 23 * \| 627 ± 26 \| 570 ± 17 * \| \| Cell body size (px) \| 95 ± 4 \| 82 ± 2 # \| 98 ± 4 \| 91 ± 4 \| 93 ± 4 \| 81 ± 4 # \| \| Processes size (px) \| 542 ± 15 \| 602 ± 23 \| 516 ± 23 \| 523 ± 21 * \| 541 ± 29 \| 513 ± 19 * \| \| DG \| DG \| DG \| DG \| DG \| DG \| DG \| \| Density (#/area) \| 70.3 ± 3.5 \| 68.8 ± 4.9 \| 66.3 ± 3.2 \| 66.4 ± 3.0 \| 64.7 ± 4.5 \| 61.3 ± 4.1 \| \| Coverage (%) \| 3.9 ± 0.1 \| 3.8 ± 0.2 \| 3.2 ± 0.3 * \| 3.6 ± 0.2 \| 3.0 ± 0.2 * \| 3.3 ± 0.2 \| \| Cell size (px) \| 629 ± 17 \| 598 ± 14 \| 627 ± 14 \| 650 ± 7 \| 599 ± 26 \| 548 ± 20 # + \| \| Cell body size (px) \| 87 ± 4 \| 100 ± 7 \| 87 ± 5 \| 93 ± 2 \| 97 ± 5 \| 98 ± 5 \| \| Processes size (px) \| 554 ± 17 \| 532 ± 22 \| 540 ± 13 \| 541 ± 26 \| 504 ± 26 \| 470 ± 20 # + \| \| Hilus \| Hilus \| Hilus \| Hilus \| Hilus \| Hilus \| Hilus \| \| Density (#/area) \| 56.8 ± 3.1 \| 56.0 ± 4.4 \| 46.6 ± 2.8 \| 57.1 ± 4.1 \| 37.9 ± 3.3 * \| 48.6 ± 3.1 # \| \| Coverage (%) \| 4.3 ± 0.3 \| 4.6 ± 0.3 \| 3.9 ± 0.3 \| 4.3 ± 0.3 \| 4.5 ± 0.4 \| 4.6 ± 0.3 \| \| Cell size (px) \| 512 ± 21 \| 491 ± 28 \| 545 ± 29 \| 483 ± 26 \| 506 ± 27 \| 482 ± 20 \| \| Cell body size (px) \| 99 ± 5 \| 94 ± 6 \| 106 ± 6 \| 90 ± 5 \| 99 ± 6 \| 94 ± 5 \| \| Processes size (px) \| 420 ± 23 \| 378 ± 13 \| 425 ± 35 \| 388 ± 27 \| 411 ± 24 \| 400 ± 23 \| ## 3.2.6. Hippocampal BDNF Brain-derived neurotrophic factor expression was used as a measure of neuronal function (Figures 5B, D, 7). Although overall hippocampal BDNF expression appeared not significantly different between groups (Figure 5D), results presented in Figure 7 suggested regional differences within the hippocampus. Two-way ANOVA showed significant differences in the CA1 area ($$p \leq 0.030$$), reflected in a significant effect of interventions ($$p \leq 0.025$$), which resulted in ISO causing a significantly increased BDNF expression ($$p \leq 0.043$$), that was reversed by running exercise ($$p \leq 0.003$$) as well as by WBV ($$p \leq 0.013$$) in post-hoc testing (Figure 7). Similar effects were observed in the DG and hilus areas; DG: a trend toward differences between groups ($$p \leq 0.071$$), resulting in significantly increased expression after ISO ($$p \leq 0.050$$), which was reversed by both running exercise ($$p \leq 0.043$$) as well as WBV ($$p \leq 0.005$$); hilus: significant differences between groups (two-way ANOVA: $$p \leq 0.020$$), with a trend toward effects of interventions ($$p \leq 0.055$$) and significant interactions between effects of ISO and interventions ($$p \leq 0.034$$). ISO significantly increased BDNF expression ($$p \leq 0.018$$), which was reversed by running exercise ($$p \leq 0.003$$) as well as WBV ($$p \leq 0.002$$). In the CA3 area, no significant differences were observed. No correlations were observed between microglia activity and BDNF expression in the overall hippocampal data, nor in any of the hippocampal areas. Neither microglia activity nor BDNF expressions were significantly associated with collagen expression in the specific hippocampal areas. ## 3.2.7. Behavior Levels of exploration and anxiety were obtained from behavior in the OF. Regarding exploration, a tendency for differences in locomotion activity between the groups was observed ($$p \leq 0.07$$), resulting from a significant effect of interventions ($$p \leq 0.012$$). Post-hoc analyses indicated that WBV rats displayed reduced locomotion (sedentary saline: 26 ± 2; sedentary ISO: 28 ± 2; sedentary running: 25 ± 4; running ISO: 27 ± 2; sedentary WBV: 19 ± 2; WBV ISO: 21 ± 3 crossings per 5 min). No differences between groups, regarding ISO/saline, interventions, or interactions, were observed for the time rats spent in the relatively safe area and the wall. For the number of center visits, ANOVA revealed a tendency for differences between groups ($$p \leq 0.10$$), which could be attributed to a significant effect of the intervention ($$p \leq 0.020$$), as no effect of ISO vs. saline nor interactions were observed. ISO rats with WBV showed a reduction in center visits ($$p \leq 0.025$$; Figures 8A, B). Effects on cognition were measured as short-term memory in the NOR and NLR tests (Figures 8C, D). All rat groups could recognize the novel object in the NOR, as they all performed significantly above the chance level. However, no difference in performance between the groups was observed. Similarly, in the NLR, no differences between groups were seen, but here only saline runners and WBV ISO rats performed above the chance level. Motor performance was obtained from the balance beam test and the grip hanging test, presented in Figures 8E, F. Balance beam performance appeared similar in all groups. In the grip hanging test, the saline-treated runners stood out, as they seem to perform better than all other groups. No significant associations between behavioral parameters and microglia activity or BDNF expression were observed. Figure 8: Exploration in the open field (n = 8–10 per group) was measured as time spent along the wall (A) and the number of center visits (B), cognitive performance measured in the novel object- [(C); n = 6–10 per group] and the novel location recognition [(D); n = 8–10 per group] tests, and motor performance measured by the time rats needed to cross the balance beam [(E); n = 7–10 per group] and the time they could keep their grip on the hanging bar (F; n = 6–9 per group) in saline- or isoproterenol (ISO)-treated rats, under sedentary conditions, or after 5 weeks of exercise training (running) or whole-body vibration (vibration). Significant difference between indicated groups (p < 0.05); #significantly above chance level (dashed line = 50% = chance level).* ## 4.1. General Myocardial infarction is often associated with mental disorders, including depression and cognitive decline, with female patients being more susceptible than male patients. Exercise training may provide a rational therapeutic approach for this comorbidity, due to its known beneficial effects on physical as well as mental conditions. However, physical exercise may not be appreciated shortly after myocardial infarction. WBV could provide a passive alternative to exercise in this condition (Alam et al., 2018). The present study aimed to explore WBV compared to physical exercise in female rats after ISO-induced myocardial damage. Exercise, but not WBV, reversed cardiac damage. Surprisingly, collagen was also expressed in the hippocampus and a reduced expression was found after exercise as well as after WBV in the hippocampal CA3 of ISO-treated rats. Effects of ISO on microglia activity varied from increased (CA1), no difference (DG and hilus), to decreased (CA3). Both exercise and WBV normalized the effects of ISO. The ISO-induced elevated BDNF expression was no longer present after exercise or WBV. These WBV effects were associated with less locomotion and lower interest in the center area of the OF, but preserved short-term spatial memory in ISO-treated rats; an effect that was not observed after exercise. On the contrary, exercise, but not WBV, seemed to improve grip strength. Results indicate that apart from the lack of effect on cardiac collagen, the effects of WBV appeared quite comparable to exercise, indicating that WBV may provide a valuable alternative for patients who cannot perform physical exercise. However, more research on the optimal WBV protocol is necessary. ## 4.2.1. General In the present study, WBV was evaluated as an alternative to physical exercise after myocardial damage, as WBV and physical exercise share effects on the body, such as improved muscle strength (Annino et al., 2017, 2021; Beaudart et al., 2017), bone density (Slatkovska et al., 2010; Benedetti et al., 2018), and wound healing (Zhou et al., 2016; Wano et al., 2021), as well as on the brain, including neurotrophic factors (Raval et al., 2018; Mee-Inta et al., 2019) and cognitive improvement (Keijser et al., 2017; Boerema et al., 2018; Cardoso et al., 2022). As reviewed by Alam et al. [ 2018], WBV is regarded as a neuromuscular training method, that could be used as an alternative to conventional training. Moreover, WBV was shown to reduce brain damage and brain inflammatory markers, with increased BDNF and improved functional activity after transient brain ischemia in middle-aged female rats (Raval et al., 2018). ## 4.2.2. Effects on the heart As anticipated from our previous study in female rats (Toth et al., 2022a), ISO increased cardiac collagen levels. Although exercise by itself may slightly increase cardiac collagen in saline-treated rats, potentially by increasing fibroblast growth factor 21 (Ma et al., 2021), the ISO-induced cardiac fibrosis was completely reversed. WBV, however, seemed to even exaggerate ISO-induced cardiac fibrosis. In contrast, WBV was reported to increase tolerance to ischemia-reperfusion injury by reducing infarct size in rats (Shekarforoush and Naghii, 2019). On the contrary, in patients, although the significant improvement was seen after the standard exercise rehabilitation program, no extra effects of WBV were observed (Nowak-Lis et al., 2022). Both studies, however, were performed only on male subjects. In our previous studies, we showed different responses to ISO in male and female rats, whereas in male rats, ISO increased cardiac collagen, but no effect of exercise was observed (Toth et al., 2021), in female rats of the same age, exercise significantly reduced the ISO-induced cardiac fibrosis (Toth et al., 2022a). Hence, in our middle-aged female rats, exercise, but not WBV, was capable of reversing cardiac damage due to ISO treatment. ## 4.2.3.1. Hippocampal collagen In addition to the expected expression in brain blood vessel walls and meninges, collagen expression was observed in the granular layers of the hippocampus, where it may reflect extracellular matrix components. The presence of a neuronal cell surface feature, called perineuronal net (PNN), is consistent with a brain extracellular matrix (Bonneh-Barkay and Wiley, 2009). This PNN mainly covers the cell body and dendrites, is usually associated with neuroprotection, and plays an important role in learning, memory, and information processing in health and disease (Krishnawamy et al., 2021). More specifically, it may affect synaptic morphology and function. Loss of PNN is often observed in neurodegenerative diseases, as reviewed by Bonneh-Barkay and Wiley (Bonneh-Barkay and Wiley, 2009). Moreover, PNNs around hippocampal interneurons can resist destruction by activated microglia (Schuppel et al., 2002). Although WBV often has been associated with increased collagen in the peripheral body, to the best of our knowledge, no literature is available for effects on brain (hippocampal) collagen. In the present study, no effects of ISO treatment or subsequent intervention with running or WBV were observed in the hippocampal CA1, DG, and hilus areas. However, although not statistically significant, WBV caused a consistent increase in collagen expression in the CA2 in saline-treated rats, but not in ISO-treated rats. In the hippocampal CA2 area, the PNN is known to play a role in restricting synaptic plasticity (Carstens et al., 2016). In the CA3 area, running as well as WBV decreased collagen expression in ISO treated rats. If indeed collagen levels might reflect PNN and restrict synaptic plasticity, as described for the CA2 (Carstens et al., 2016), the reduction of collagen by exercise and WBV in ISO-treated rats may then be speculated as an improvement of synaptic plasticity. Alternatively, it may still point to the loss of neuroprotection (Krishnawamy et al., 2021). ## 4.2.3.2. Neuroinflammation Exercise (Petersen and Pedersen, 2005) and WBV (Jawed et al., 2020; Sanni et al., 2022) are associated with anti-inflammatory effects. This anti-inflammatory property may extend to neuroinflammation (Mee-Inta et al., 2019; Chen et al., 2022; Oroszi et al., 2022a). Based on the literature, WBV may affect neuroinflammation in female rats (Raval et al., 2018) as well as in male rats (Oroszi et al., 2022a). However, neither in male (Toth et al., 2021) nor in female rats (Toth et al., 2022a), exercise affected microglia activity after ISO. In the present study, overall hippocampal microglia activity was neither affected by ISO, nor by the interventions. However, the areas within the hippocampus showed regional differences; whereas in the CA1, indeed microglia activation was observed after ISO, and was completely reversed by exercise as well as WBV, in the CA3 area, ISO caused a decline of microglia activity, which was also completely reversed by exercise and WBV. While both the CA1 and CA3 areas are involved in learning and memory (Stevenson et al., 2020), the CA3 area is rather specifically involved in pattern completion (Stevenson et al., 2020). We did not perform behavioral testing to specifically explore the potential role of the CA3. For the CA1, the outcome of the NLR was not found to correlate with microglia activity. Therefore, a direct relationship between neuroinflammation and behavior could not be established here. Alternatively, different time courses for the different parameters, as seen before (Hovens et al., 2014b), may provide a potential explanation for the observed differences in effects in CA1 and CA3. ## 4.2.3.3. Neuronal function (BDNF) Exercise training is usually associated with increased brain BDNF expression (Sleiman et al., 2016; El Hayek et al., 2019). Exercise-induced increased expression of BDNF and double cortin positive cells were observed in the ischemic hippocampus after stroke in rats (Luo et al., 2019; Cheng et al., 2020). WBV was shown to increase BDNF levels in the peri-infarct regions after brain ischemia-reperfusion in middle-aged female rats (Raval et al., 2018). Furthermore, WBV was demonstrated to reverse the decreased level of BDNF in the CA1 area of the hippocampus induced by chronic restrain stress in male rats (Peng et al., 2021). In male rats, ISO had no effects on hippocampal BDNF expression 6 weeks later, but running exercise significantly increased BDNF expression in the CA1 and hilus areas of the hippocampus (Toth et al., 2021). In contrast, in female rats, exercise after ISO seemed to decline BDNF expression in the CA1 area (Toth et al., 2022a). Accordingly, in the present study, the ISO-induced increases in BDNF in the sedentary rats were completely reversed by exercise as well as by WBV. Since most of the results of exercise-induced increases in BDNF expression were obtained in male subjects, the deviant results in female rats in the present study may well be attributed to sex dimorphism (Toth et al., 2022b). ## 4.2.4.1. Depression/anxiety Although we anticipated cardiac damage-induced anxiety/depressive-like behavior in the ISO-treated rats (Tkachenko et al., 2018; Hu et al., 2020), in agreement with our previous study on middle-aged female rats (Toth et al., 2022a), ISO with and without exercise training had no effect on behavior in the OF. Similarly, at 24 months of age, no effect of ISO was observed in OF behavior in female rats, but anxiety-associated effects were seen in male rats (Toth et al., 2022b). However, although the lower number of center visits of the ISO-treated WBV rats in the present study may point to a higher level of anxiety after WBV, no effect on wall time, as a measure for depressive-like behavior, was observed. The isolated reduction of center visits may therefore rather reflect the previously described reduction of arousal after WBV (Boerema et al., 2018), which would be further supported by the reduced OF locomotion of these rats (Oroszi et al., 2022a). Taken together, the results of the different studies indicated that neither ISO nor exercise affected OF behavior in female rats, but the effects of WBV on OF behavior remained inconclusive. ## 4.2.4.2. Cognition The pilot exposure time–effect study did not show significant effects of either WBV schedule on short-term memory in the NOR and NLR tests. Similarly, in the main study, no effect of WBV (nor exercise) was observed in the results of the NOR. However, similar to our previous study on middle-aged female rats (Toth et al., 2022a), saline-treated exercise rats performed above chance level in the NLR, an effect that was not seen in the saline WBV rats. Whereas, exercise could not overcome poor performance in rats after ISO, WBV after ISO seemed to improve performance to above chance level. ## 4.2.4.3. Motor performance No effects of ISO were observed on motor performance, tested either on the balance beam or in the grip hanging test. Exercise is generally accepted to improve motor performance (Hubner and Voelcker-Rehage, 2017) but actual effects may depend on the exercise protocol. Exercise by itself seemed to improve grip hanging, with large variation, however, but had no effect after ISO. Muscle weight was not increased by either exercise or WBV, indicating no significant effect of training. Several literature studies indicate a positive effect of WBV on muscle strength and motor coordination, although often in combination with regular physical training (Kawanabe et al., 2007; Annino et al., 2017, 2021). In the present study, no significant effect of WBV was seen on balance beam performance. Accordingly, in our pilot exposure time–effect study, no significant effects were observed on motor performance. However, 5 min of WBV already seemed to double grip hanging, without further increase with longer WBV exposure times. Studies in male mice showed that 5 min, but not 30 min, daily WBV improved motor performance (Keijser et al., 2017), and 5 min, but not 20 min, WBV improved rearing in the OF and grip hanging in male rats (Oroszi et al., 2022a). Moreover, in a recent study of our group in aged male and female rats (Oroszi et al., 2022b), behavioral effects of 10 min daily WBV appeared rather mild in female rats compared to male rats. Therefore, apart from male mice vs. female rats, the effects of WBV on motor performance may largely depend on the treatment protocol. ## 4.3. Limitations The ISO model was used to avoid surgery-induced brain and behavioral changes that were observed after coronary artery ligation (Hovens et al., 2016). However, we are well aware that this provides a model for the consequences of cardiac damage, rather than its etiology of it. To compare the effects of WBV to exercise after ISO, the exercise protocol was based on our previous studies in male and female rats (Toth et al., 2021, 2022a) and the WBV protocol on our exposure time response pilot. Cardiac function measurements in the female rat study suggested a reduced cardiac performance after exercise, which may suggest a too-severe exercise protocol for these females. It raises the question of what parameter(s) should be optimal for the comparison of the two rather different interventions. As discussed in our previous study in female rats (Toth et al., 2022a), interventions started 1 week after ISO treatment, when inflammatory processes are merely complete (Alemasi et al., 2019). Accordingly, ISO induced cardiac damage, but that did not result in major effects on behavior 6 weeks later, leaving limited scope for improvement by either exercise or WBV. Exercise, but not WBV, reversed cardiac damage, but indeed neither did affect behavior. Nevertheless, in the present study, clear effects on local brain parameters were observed, providing a new potential entrée for treatment, specifically regarding the CA3 area and its associated functions, as this area stood out in the measured brain parameters. ## 5. Conclusion The study aimed to explore WBV after ISO as an alternative to exercise, on the heart, the brain, and behavior in female rats. Although the female rats did not show the anticipated depressive-like behavior or cognitive decline after ISO, our data indicated regional effects on neuroinflammation and BDNF expression in the hippocampus, which were merely normalized by both WBV and exercise. Furthermore, collagen expression was observed in the granular layers of the hippocampus and appeared regionally specific and sensitive to exercise as well as WBV in ISO-treated rats. Therefore, apart from the potential concern about the lack of cardiac collagen reduction, WBV may provide a relevant alternative to physical exercise and be of help to (female) subjects that cannot or are not motivated to perform the exercise. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. ## Ethics statement The experiments were conducted under the general license for animal experiments of the Laboratory of Physical Education, University of Budapest, Hungary. ## Author contributions KT: design of the study, acquisition of data, analyses and interpretation of data, and substantively revised the manuscript. TO: performing behavioral studies, acquisition of data, and substantive revision of the manuscript. CN: conception, design of the study, and interpretation of data. EZ: interpretation of data and substantively revised the manuscript. RS: conception, design of the study, analyses and interpretation of data, drafting of the manuscript, and substantive revision of the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor RT is currently organizing a Research Topic with the author EZ. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: A population‐based study of factors associated with systemic treatment in advanced prostate cancer decedents authors: - Jennifer Leigh - Danial Qureshi - Ewa Sucha - Roshanak Mahdavi - Igal Kushnir - Luke T. Lavallée - Dominick Bosse - Colleen Webber - Peter Tanuseputro - Michael Ong journal: Cancer Medicine year: 2022 pmcid: PMC10028120 doi: 10.1002/cam4.5401 license: CC BY 4.0 --- # A population‐based study of factors associated with systemic treatment in advanced prostate cancer decedents ## Abstract Patients receiving care at Regional Cancer Centers were substantially more likely to receive advanced prostate cancer treatments that affect overall survival. In contrast, geography, income, and stage at presentation were not important factors. Physicians caring for patients with advanced prostate cancer should consider referral to specialized treatment centers. ### Introduction Life‐prolonging therapies (LPTs) are rapidly evolving for the treatment of advanced prostate cancer, although factors associated with real‐world uptake are not well characterized. ### Methods In this cohort of prostate‐cancer decedents, we analyzed factors associated with LPT access. Population‐level databases from Ontario, Canada identified patients 65 years or older with prostate cancer receiving androgen deprivation therapy and who died of prostate cancer between 2013 and 2017. Univariate and multivariable analyses assessed the association between baseline characteristics and receipt of LPT in the 2 years prior to death. ### Results Of 3575 patients who died of prostate cancer, $40.4\%$ ($$n = 1443$$) received LPT, which comprised abiraterone ($66.3\%$), docetaxel ($50.3\%$), enzalutamide ($17.2\%$), radium‐223 ($10.0\%$), and/or cabazitaxel ($3.5\%$). Use of LPT increased by year of death (2013: $22.7\%$, 2014: $31.8\%$, 2015: $41.8\%$, 2016: $49.1\%$, and 2017: $57.9\%$, $p \leq 0.0001$), driven by uptake of all agents except docetaxel. Adjusted odds of use were higher for patients seen at Regional Cancer Centers (OR: 1.8, $95\%$ CI: 1.5–2.1) and who received prior prostate‐directed therapy (OR: 1.3, $95\%$ CI: 1.0–1.5), but lower with advanced age (≥85: OR: 0.54, $95\%$ CI:0.39–0.75), increased chronic conditions (≥6: OR: 0.62, $95\%$ CI: 0.43–0.92), and long‐term care residency (OR: 0.38, $95\%$ CI: 0.17–0.89). Income, stage at presentation, and distance to the cancer center were not associated with LPT uptake. ### Conclusion In this cohort of prostate cancer‐decedents, real‐world uptake of novel prostate cancer therapies occurred at substantially higher rates for patients receiving care at Regional Cancer Centers, reinforcing the potential benefits for treatment access for patients referred to specialist centers. ## INTRODUCTION Prostate cancer accounts for nearly $20\%$ of all new cancer diagnoses in men. 1 Although a majority are diagnosed at an early stage and with favorable prognosis, 1, 2 a subset develops resistant disease following prostate‐directed treatments and subsequent androgen deprivation therapy (ADT). A further minority have aggressive behavior and present with “de novo” metastases at outset, requiring ADT for initial treatment. Together, these cohorts of ADT‐resistant prostate cancers account for $10\%$ of all male cancer‐related deaths. 1 Fortunately, for those with metastatic disease, systemic treatment has been rapidly evolving, with a number of ground‐breaking life‐prolonging therapies (LPT) that increase survival and quality of life of patients with metastatic castration‐resistant (mCRPC) and hormone‐sensitive prostate cancer. 3, 4, 5, 6, 7, 8, 9 LPTs now approved for use in metastatic prostate cancer on the basis of improved survival, progression‐free, and symptomatic outcomes include docetaxel and cabazitaxel chemotherapy, androgen‐receptor axis therapies (ARATs), including abiraterone acetate and enzalutamide, and radiopharmaceuticals including radium‐223, 10, 11 although the field continues to move rapidly with new treatments emerging. It is clear, however, from existing literature that approval of systemic treatments does not automatically lead to real‐world practice uptake. 12 Frequently cited barriers include patient factors such as comorbidity, functional status, and increased age; provider belief in treatment efficacy and patient fitness; and logistical factors including the ability to consult a medical oncologist. 13, 14, 15, 16, 17, 18, 19 For prostate cancer, the introduction of ARATs is expected to increase access to LPT given excellent survival outcomes, easier tolerability than chemotherapy, high quality of life data, and oral routes of administration. 10, 11 Despite these characteristics, the real‐world uptake and barriers to access of ARATs and other LPTs are poorly understood. In this study, we describe the use of LPT in Ontario prostate cancer decedents between 2013 and 2017, including factors associated with treatment receipt, and prescription trends over time. This time frame was chosen to encompass the periods after Health Canada approvals for abiraterone and enzalutamide in 2011 and 2013, respectively, as well as a time frame with data available for cause‐related mortality. We examine patient‐ and disease‐related factors associated with LPT receipt with the objective of understanding whether real‐world use of LPT is influenced by traditional barriers such as geographical distance of care, socioeconomic status, age, and comorbidity. ## Identification of decedents We conducted a retrospective cohort study using population‐based administrative databases held at ICES (formerly the Institute for Clinical Evaluative Sciences) to identify all patients 65 years or older at the study index date in Ontario, Canada with a diagnosis of prostate cancer, who received ADT or had a bilateral orchiectomy after diagnosis and before the index date, and who died between 2013 and 2017 of prostate cancer. The study index date was defined as the date 2 years prior to the patient's death. A 2‐year look‐back period prior to death and therefore from the study index date was analyzed to determine LPT receipt in relation to patient and disease factors. The age 65 cutoff was selected as that is when patients become eligible for Ontario Drug Benefit (ODB), meaning there would be no funding restriction for access to therapy. The year 2017 was selected for data cutoff as at the time of this study cause of death data was not available past 2017. We used the STROBE cohort checklist when writing our report. 20 ## Data sources These datasets were linked using unique coded identifiers and analyzed at ICES. The databases utilized and descriptions of the information they provided can be found in Table S1. Use of the data in this project is authorized under section 45 of Ontario's Personal Health Information Protection Act (PHIPA) and does not require review by a Research Ethics Board. ## Characteristics of interest Patient characteristics including age, area‐level income quintile, Rurality Index of Ontario (RIO), count of chronic disease, Charlson Comorbidity Index Score (CCI), home care registration, straight‐line distance to the cancer center, and long‐term care (LTC) residency were collected using previously validated methods at study index data. 21, 22, 23, 24, 25, 26, 27, 28 Physician involvement, number of visits by specialty, Regional Cancer Center registration, primary care rostering, and LPT receipt were all continuous variables that were examined within the 2 years between study index date and death. Receipt of radiotherapy or prostatectomy was examined at any time between diagnosis and death. The type of oncologist involved in care and the number of visits to oncologic specialists was identified using physician billing data from the Ontario Health Insurance Plan Database (OHIP). Rostering to a primary care physician was determined using the Client Agency Program Enrolment database. TNM stage and year of diagnosis were captured from the Ontario Cancer Registry, and prostate specific antigen at ADT initiation was captured from the Ontario Laboratories Information System. The drug codes utilized can be found in Tables S1 and S2. ## Outcomes of interest The primary outcome was the receipt of LPT in the last 2 years of life. Agents considered to be LPT were abiraterone, enzalutamide, docetaxel, cabazitaxel, and radium‐223, and their use within the look‐back period was captured using the ODB and New Drug Funding Program databases. A patient was considered to have received LPT if they had at least one prescription for any of the above‐listed drugs. A secondary outcome was to examine prescribing trends over time of each agent and determine whether certain agents are prescribed more frequently than others. ## Statistical analysis Descriptive statistics were used to describe patient and disease characteristics as categorized by LPT receipt. Categorical variables are reported as the number (n) and proportion (%) of patients, and continuous variables are reported as mean and standard deviation (SD). Factors associated with LPT receipt were examined using univariate and multivariable logistic regression models. Variables adjusted for included age, area‐level income quintile, distance to cancer center, count of chronic diseases, involvement of a medical, radiation, or uro oncologist, regional cancer center registration, LTC residency, stage at diagnosis, year of diagnosis, and prior prostate‐directed therapy. Statistical significance was defined as p ≤ 0.05. All analyses were conducted using SAS Enterprise Guide 7.1 (SAS Institute Inc.). ## Receipt of therapy There were 1443 of 3575 patients ($40.4\%$) who received LPT during the lookback period. Type of LPT received included abiraterone ($$n = 957$$, $66.3\%$), docetaxel ($$n = 726$$, $50.3\%$), enzalutamide ($$n = 248$$, $17.2\%$), radium‐223 ($$n = 145$$, $10.0\%$), and cabazitaxel ($$n = 50$$, $3.5\%$). There were 1157 of these patients ($80.2\%$) registered at a Regional Cancer Center. Prostate‐directed therapy was received by 918 ($25.7\%$) patients overall, with 759 ($21.2\%$) receiving prostate radiotherapy, 270 ($7.6\%$) who underwent prostatectomy, and 111 ($3.1\%$) who received both. There were 1746 patients ($48.8\%$) who received palliative radiotherapy to the bone, and 313 ($8.8\%$) in other regions. ## Cohort characteristics A total of 3575 prostate cancer patients who received ADT were identified to have prostate cancer‐related death between 2013 and 2017 (Table 1). In the lowest age bracket of 65–69, 143 ($58.9\%$) received LPT, compared with 221 ($22.3\%$) receiving LPT who were 85 years or older. The proportion receiving LPT was similar for patients living in the most urban centers ($$n = 905$$, $37.8\%$) and most rural ($$n = 17$$, $40.5\%$), and for those in the highest ($$n = 295$$, $41.2\%$) and lowest ($$n = 264$$, $38.4\%$) income brackets. The mean distance to the cancer center was 38.1 km for those who received LPT, and 35.4 km for patients that did not. There were 532 ($14.9\%$) patients who had a CCI of ≤2, of whom 163 ($30.6\%$) received LPT and 2389 ($66.8\%$) who had a CCI of ≥5, of whom 1064 ($44.5\%$) received LPT. A minority were long‐term care residents ($$n = 90$$, $2.52\%$), with 7 ($7.8\%$) receiving LPT, and/or home care recipients ($$n = 593$$, $16.6\%$), with 202 ($34.1\%$) receiving LPT. TABLE 1 \| Characteristic \| Description \| No Life‐Prolonging Therapy n (%) (n = 2132, 59.6%) \| Life‐Prolonging Therapy n (%) (n = 1443, 40.4%) \| Overall (n = 3575) \| \| --- \| --- \| --- \| --- \| --- \| \| Age \| 65–69 \| 100 (41.2) \| 143 (58.9) \| 243 \| \| Age \| 70–74 \| 269 (41.3) \| 383 (58.7) \| 652 \| \| Age \| 75–79 \| 406 (53.5) \| 353 (46.5) \| 759 \| \| Age \| 80–84 \| 585 (63.0) \| 343 (37.0) \| 928 \| \| Age \| 85+ \| 772 (77.7) \| 221 (22.3) \| 993 \| \| Area‐level income quintile \| 1 (lowest) \| 424 (61.6) \| 264 (38.4) \| 688 \| \| Area‐level income quintile \| 2 \| 441 (61.0) \| 282 (39.0) \| 723 \| \| Area‐level income quintile \| 3 \| 423 (58.6) \| 299 (41.4) \| 722 \| \| Area‐level income quintile \| 4 \| 416 (58.1) \| 300 (41.9) \| 716 \| \| Area‐level income quintile \| 5 (highest) \| 420 (58.7) \| 295 (41.2) \| 715 \| \| Rurality Index for Ontario \| 0 to 9 (most urban) \| 1328 (59.5) \| 905 (37.8) \| 2233 \| \| Rurality Index for Ontario \| 10 to 30 \| 399 (60.6) \| 259 (40.5) \| 658 \| \| Rurality Index for Ontario \| 31 to 45 \| 230 (58.7) \| 162 (41.3) \| 392 \| \| Rurality Index for Ontario \| 46 to 55 \| 55 (56.7) \| 42 (43.3) \| 97 \| \| Rurality Index for Ontario \| 56 to 75 \| 72 (62.1) \| 44 (37.9) \| 116 \| \| Rurality Index for Ontario \| 76 to 100 (least urban) \| 25 (59.5) \| 17 (40.5) \| 42 \| \| Distance to Cancer Center (km) \| Mean (SD) \| 35.4 (56.2) \| 38.1 (55.6) \| 36.8 (55.9) \| \| Charlson Comorbidity Index Score \| ≤2 \| 369 (69.4) \| 163 (30.6) \| 532 \| \| Charlson Comorbidity Index Score \| 3–4 \| 178 (78.8) \| 48 (21.2) \| 226 \| \| Charlson Comorbidity Index Score \| ≥5 \| 1325 (55.5) \| 1064 (44.5) \| 2389 \| \| Count of Chronic Diseases \| Mean (SD) \| 3.49 (1.94) \| 3.09 (1.76) \| 3.33 (1.88) \| \| Physicians involved in their cancer care \| Medical Oncologist \| 1006 (43.5) \| 1306 (56.5) \| 2312 \| \| Physicians involved in their cancer care \| Radiation Oncologist \| 1220 (51.7) \| 1140 (48.3) \| 2360 \| \| Physicians involved in their cancer care \| Urologist \| 1793 (60.9) \| 1152 (39.1) \| 2945 \| \| Mean number of visits by physician specialty (SD) \| Medical Oncologist \| 9.67 (15.2) \| 27.9 (26.9) \| 20.0 (24.3) \| \| Mean number of visits by physician specialty (SD) \| Radiation Oncologist \| 5.42 (5.02) \| 6.83 (6.00) \| 6.09 (5.56) \| \| Mean number of visits by physician specialty (SD) \| Urologist \| 9.91 (9.22) \| 10.2 (7.97) \| 10.0 (8.75) \| \| Mean number of visits by physician specialty (SD) \| Family Physician \| 10.8 (19.3) \| 12.9 (21.9) \| 11.7 (20.4) \| \| Rostered to primary care \| Yes \| 1759 (58.7) \| 1238 (41.3) \| 2997 \| \| Rostered to primary care \| No \| 373 (64.5) \| 205 (35.5) \| 578 \| \| Consultation at regional cancer center \| Yes \| 1146 (49.8) \| 1157 (50.2) \| 2303 \| \| Consultation at regional cancer center \| No \| 986 (77.5) \| 286 (22.5) \| 1272 \| \| Long‐Term Care Resident \| Yes \| 83 (92.2) \| 7 (7.78) \| 90 \| \| Long‐Term Care Resident \| No \| 2049 (58.8) \| 1436 (41.2) \| 3485 \| \| Home care involvement \| Yes \| 391 (65.9) \| 202 (34.1) \| 593 \| \| Home care involvement \| No \| 1741 (58.3) \| 1241 (41.6) \| 2982 \| Provider care included a medical oncologist ($$n = 2312$$, $64.6\%$), radiation oncologist ($$n = 2360$$, $66.0\%$), and urologist ($$n = 2945$$, $82.4\%$), and LPT use was $56.5\%$, $48.3\%$, and $39.1\%$, respectively. For patients receiving LPT, the mean number of encounters were 27.9 medical oncology, 6.83 radiation oncology, and 10.2 urology visits. For patients not receiving LPT, mean number of encounters were 9.67 medical oncology, 5.42 radiation oncology, and 9.91 urology visits. Nine hundred and ninety‐seven ($83.8\%$) were rostered to primary care, of which 1238 ($41.3\%$) received LPT. Among 578 ($16.2\%$) not rostered to primary care, 205 ($35.5\%$) received LPT. Among 2303 ($64.4\%$) patients registered at a Regional Cancer Center, 1157 ($50.2\%$) received LPT. Among 1272 ($35.6\%$) not registered, 286 ($22.5\%$) received LPT. Disease‐specific characteristics are outlined in Table 2. There were 909 patients ($25.4\%$) diagnosed between 2002 and 2006, 1215 ($34.0\%$) between 2007 and 2011, and 1451 ($40.6\%$) between 2012 and 2016. LPT use by initial AJCC 6th edition stage: stage I/II/III 985 ($27.5\%$), stage IV 1311 ($36.7\%$), and stage missing 1279 ($35.8\%$). LPT use in those with prostate‐directed therapy was $\frac{500}{918}$ ($54.5\%$) and $\frac{943}{2657}$ ($35.5\%$) in those who did not receive prostate‐directed therapy. TABLE 2 \| Characteristic \| Description \| No Life‐Prolonging therapy n (%) (n = 2132) \| Life‐Prolonging therapy n (%) (n = 1443) \| Overall (n = 3575) \| \| --- \| --- \| --- \| --- \| --- \| \| Stage at diagnosis \| I/II/III \| 568 (57.7) \| 417 (42.3) \| 985 \| \| Stage at diagnosis \| IV \| 770 (58.7) \| 541 (41.3) \| 1311 \| \| Stage at diagnosis \| Missing \| 794 (62.1) \| 485 (37.9) \| 1279 \| \| M category at diagnosis \| M0 \| 483 (59.9) \| 324 (40.1) \| 483 \| \| M category at diagnosis \| M1a \| 24 (57.1) \| 18 (42.9) \| 42 \| \| M category at diagnosis \| M1b \| 536 (60.4) \| 351 (39.6) \| 351 \| \| M category at diagnosis \| M1c \| 87 (63.0) \| 51 (37.0) \| 51 \| \| M category at diagnosis \| Missing \| 1002 (58.9) \| 699 (41.1) \| 699 \| \| Year of diagnosis \| 2002–2006 \| 534 (58.7) \| 375 (41.3) \| 909 \| \| Year of diagnosis \| 2007–2011 \| 664 (54.6) \| 551 (45.4) \| 1215 \| \| Year of diagnosis \| 2012–2016 \| 934 (64.4) \| 517 (35.6) \| 1451 \| \| PSA at first ADT initiation (ng/ml) \| <10 \| 116 (49.4) \| 119 (50.7) \| 235 \| \| PSA at first ADT initiation (ng/ml) \| 11–19 \| 85 (53.5) \| 74 (46.5) \| 159 \| \| PSA at first ADT initiation (ng/ml) \| 20–99 \| 240 (55.6) \| 192 (44.4) \| 432 \| \| PSA at first ADT initiation (ng/ml) \| 100–1000 \| 233 (59.1) \| 161 (40.9) \| 394 \| \| PSA at first ADT initiation (ng/ml) \| >1000 \| 74 (61.7) \| 46 (38.3) \| 120 \| \| PSA at first ADT initiation (ng/ml) \| Missing \| 1384 (61.9) \| 851 (38.1) \| 2235 \| \| Prostatectomy prior to death \| Yes \| 93 (34.4) \| 177 (65.6) \| 270 \| \| Prostatectomy prior to death \| No \| 2039 (61.7) \| 1266 (38.3) \| 3305 \| \| Radiotherapy to the prostate prior to death \| Yes \| 359 (47.3) \| 400 (52.7) \| 759 \| \| Radiotherapy to the prostate prior to death \| No \| 1773 (61.7) \| 1043 (38.3) \| 2816 \| \| Prostatectomy or radiotherapy to prostate prior to death \| Yes \| 418 (45.5) \| 500 (54.5) \| 918 \| \| Prostatectomy or radiotherapy to prostate prior to death \| No \| 1714 (64.5) \| 943 (35.5) \| 2657 \| \| Radiotherapy to bone prior to death \| Yes \| 823 (47.1) \| 923 (52.9) \| 1746 \| \| Radiotherapy to bone prior to death \| No \| 1309 (71.6) \| 520 (28.4) \| 1829 \| \| Radiotherapy to other body parts \| Yes \| 149 (47.6) \| 164 (52.4) \| 313 \| \| Radiotherapy to other body parts \| No \| 1983 (60.8) \| 1279 (39.2) \| 3262 \| ## Univariate and multivariable relationships between LPT receipt and population characteristics Univariate analysis revealed patients had higher odds of LPT use if they had Regional Cancer Center registration (OR 3.5 [$95\%$ CI 3.0–4.1]), receipt of prostate‐directed therapy (OR 2.2 [$95\%$ CI 1.9–2.5]), radiation oncologist involvement (OR 2.8 [$95\%$ CI 2.4–3.3]), and medical oncologist involvement (OR 11 [$95\%$ CI 8.8–12], Table 3). Odds of LPT use were higher in younger age groups compared with those 85 years or older (65–69: OR 5.0 [$95\%$ CI 3.7–6.7], 70–74: OR 5.0 [$95\%$ CI 4.0–6.2], 75–79: OR 3.0 [$95\%$ CI 2.5–3.7], 80–84: OR 2.0 [$95\%$CI 1.7–2.5]). Odds of LPT use decreased in patients with ≥3 chronic conditions (3–5: OR 0.69 [$95\%$ CI 0.51–0.94], ≥6: OR 0.42 [$95\%$ CI 0.30–0.61]), and long‐term care residency (OR 0.12 [$95\%$ CI 0.055–0.26]). There were no differences in odds of LPT use with income quintile, rurality index, distance to the nearest cancer center, or TNM stage at diagnosis. TABLE 3 \| Characteristics \| Unnamed: 1 \| Univariate Odds ratio (95% CI) \| Univariate p value \| Multivariable Odds ratio (95% CI) \| Multivariable value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Age \| 65–69 \| 5.0 (3.7–6.7) \| <0.0001 \| Reference \| \| \| Age \| 70–74 \| 5.0 (4.0–6.2) \| <0.0001 \| 1.3 (0.93–1.8) \| 0.13 \| \| Age \| 75–79 \| 3.0 (2.5–3.7) \| <0.0001 \| 0.91 (0.66–1.2) \| 0.56 \| \| Age \| 80–84 \| 2.0 (1.7–2.5) \| <0.0001 \| 0.73 (0.53–1.0) \| 0.055 \| \| Age \| 85+ \| Reference \| \| 0.54 (0.39–0.75) \| 0.0003 \| \| Income \| Quartile 1 (Lowest) \| 0.89 (0.72–1.1) \| 0.27 \| 0.88 (0.69–1.1) \| 0.32 \| \| Income \| Quartile 2 \| 0.91 (0.74–1.1) \| 0.38 \| 0.91 (0.71–1.2) \| 0.42 \| \| Income \| Quartile 3 \| 1.0 (0.82–1.2) \| 0.95 \| 1.0 (0.80–1.3) \| 0.85 \| \| Income \| Quartile 4 \| 1.0 (0.83–1.3) \| 0.81 \| 0.97 (0.76–1.24) \| 0.81 \| \| Income \| Quartile 5 (Highest) \| Reference \| \| Reference \| \| \| Count of Chronic Diseases \| 0 \| Reference \| \| Reference \| \| \| Count of Chronic Diseases \| 1–2 \| 0.80 (0.58–1.1) \| 0.17 \| 0.88 (0.61–1.3) \| 0.49 \| \| Count of Chronic Diseases \| 3–5 \| 0.69 (0.51–0.94) \| 0.019 \| 0.86 (0.60–1.2) \| 0.43 \| \| Count of Chronic Diseases \| 6+ \| 0.42 (0.30–0.61) \| <0.0001 \| 0.62 (0.41–0.94) \| 0.024 \| \| Physician Involved in their cancer care \| Medical Oncologist \| 11 (8.8–12) \| <0.0001 \| 7.6 (6.2–9.3) \| <0.0001 \| \| Physician Involved in their cancer care \| Radiation Oncologist \| 2.8 (2.4–3.3) \| <0.0001 \| 1.2 (0.98–1.5) \| 0.067 \| \| Physician Involved in their cancer care \| Urologist \| 0.75 (0.63–0.89) \| 0.001 \| 0.84 (0.68–1.0) \| 0.10 \| \| Consultation at Regional Cancer Center \| Yes \| 3.5 (3.0–4.1) \| <0.0001 \| 1.8 (1.5–2.1) \| <0.0001 \| \| Consultation at Regional Cancer Center \| No \| Reference \| \| Reference \| \| \| Distance to Cancer Center (km) \| Mean \| 1.0 (1.0–1.0) \| 0.27 \| 1.0 (0.99–1.0) \| 0.16 \| \| Long‐Term Care Resident \| Yes \| 0.12 (0.055–0.26) \| <0.0001 \| 0.39 (0.17–0.89) \| 0.026 \| \| Long‐Term Care Resident \| No \| Reference \| \| Reference \| \| \| Stage at Diagnosis \| Stage I/II/III \| Reference \| \| Reference \| \| \| Stage at Diagnosis \| Stage IV \| 0.96 (0.81–1.3) \| 0.61 \| 0.94 (0.76–1.2) \| 0.62 \| \| Prostatectomy or Radiotherapy to the prostate prior to death \| Yes \| 2.2 (1.9–2.5) \| <0.0001 \| 1.3 (1.0–1.5) \| 0.0011 \| \| Prostatectomy or Radiotherapy to the prostate prior to death \| No \| Reference \| \| Reference \| \| Multivariable analysis revealed higher odds of LPT use for patients with Regional Cancer Center registration (OR 1.8 [$95\%$ CI 1.5–2.1]) and receipt of prostate‐directed therapy (OR 1.3 [$95\%$ CI 1.0–1.5], Table 3). Lower odds of LPT use were observed in those 85 or older (OR 0.54 [0.39–0.75]), a higher number of co‐morbidities (≥6: OR 0.62 [$95\%$ CI 0.41–0.94]), and long‐term care residency (OR 0.39 [$95\%$ CI 0.17–0.89]). Odds of LPT use were not associated with distance to the nearest cancer center, income‐level quintile, and TMN stage at diagnosis. ## Prescribing trends over time The proportion of patients receiving LPT significantly increased by year of death (2013:$22.7\%$, 2014:$31.8\%$, 2015:$41.8\%$, 2016:$49.1\%$, 2017:$57.9\%$, $p \leq 0.0001$, Figure 1). Of patients who received LPT, the use of abiraterone ($p \leq 0.0001$), enzalutamide ($p \leq 0.0001$), cabazitaxel ($p \leq 0.0001$), and radium‐223 ($p \leq 0.0001$) each increased in utilization by year of death, whereas docetaxel prescriptions were largely unchanged (Figures 2 and 3). FIGURE 1: Proportion of patients receiving any Life‐Prolonging Therapy (LPT) prior to their death. Represented as % of all patients in our cohort who received any LPT prior to their death (black), % of patients who were registered at a Regional Cancer Center and received any LPT (red), and % of patients whose care occurred outside a Regional Cancer Center and received LPT (gray). Data are stratified by year of death. FIGURE 2: Uptake of Life‐Prolonging Therapy (LPT) is driven by novel agents. Represented as the percentage of receipt of each therapy for all patients in the cohort that received any type of LPT prior to death, stratified by year of death. Those who did not receive LPT were excluded. The following agents are included: abiraterone (black circle), enzalutamide (red circle), docetaxel (green triangle), cabazitaxel (pink triangle), and radium‐223 (gray square). FIGURE 3: Uptake of life‐prolonging therapy (LPT) at Regional Cancer Centers is brisker and to a greater percentage of patients than at non‐Regional Cancer Center sites. This represents the percentage of all patients in the cohort who received each type of LPT, stratified by year of death. The types of LPT included are abiraterone (top left), enzalutamide (top right), docetaxel (middle left), cabazitaxel (middle right), and radium‐223 (bottom left). The red circle represents uptake for patients who were registered for care at a Regional Cancer Center, and the black circle represent uptake for patients who were not registered at a Regional Cancer Center. There were 2303 patients ($64.4\%$) registered at a Regional Cancer Center. Registration increased by year of death (2013: $61.0\%$; 2014: $61.8\%$; 2015: $63.8\%$; 2016: $65.9\%$; and 2017: $70.3\%$, $$p \leq 0.0001$$). Uptake of LPT increased over time for registered patients (2013: $31.4\%$; 2014: $43.5\%$; 2015: $49.6\%$; 2016: $58.3\%$; and 2017: $67.0\%$, $p \leq 0.0001$, Figure 1), and for patients not registered at a Regional Cancer Center (2013: $9.2\%$; 2014: $12.9\%$; 2015: $28.1\%$; 2016: $31.3\%$; and 2017: $36.4\%$; $p \leq 0.0001$). Figure 3 outlines the uptake of each agent by Regional Cancer Center registration. ## DISCUSSION We demonstrate in a large population database of prostate cancer decedents after ADT treatment that a majority ($59.6\%$) do not receive additional LPT in the 2 years preceding death. Income and rurality were not associated with LPT receipt, whereas those who were registered at Regional Cancer Centers, received prostate‐directed therapy, or were younger, less comorbid, and did not register at long‐term care had higher use of LPT. We also observe substantial increases in LPT use over time, which is largely driven by ARATs and to a lesser degree radium‐223. Although these trends were influenced by the timing of Health Canada and provincial approvals, ARATs that were available “post‐docetaxel” initially and then “pre‐docetaxel” in 2014–2015 had vastly higher uptake compared with cabazitaxel despite similar availability, with its approval postdocetaxel in 2011. 29, 30, 31, 32, 33 Evolving indications for ARATs and docetaxel for metastatic hormone‐sensitive disease will clearly impact the 40–$50\%$ of patients in this cohort who may have benefitted from earlier access to LPT. 34, 35, 36, 37, 38 Regional Cancer Center registration was a major factor associated with LPT use, which is consistent with prior data suggesting that specialist cancer center care leads to improved outcomes and therapy access. 39 Specialist centers generally have greater drug familiarity through early access in clinical trials, specialized practices, and drug navigation services that promote the ability to access novel LPT. 40 In our cohort, uptake of ARATs was brisk whether or not patients were registered at a Regional Cancer Center, whereas it was substantially higher at Regional Cancer Centers for radium‐223 and cabazitaxel, reflecting a larger capacity for the introduction of these specialized therapies. Additionally, uptake of all drugs occurred at higher rates overall at Regional Cancer Centers. This suggests that knowledge translation of clinical trial findings occurs at a fast pace at Regional Cancer Centers, which is a strength that can be potentially utilized to assist with quicker adoption of these therapies across the province through tools like educational sessions. Prior prostate‐directed therapy was also associated with increased LPT use. Patients with initially localized disease have improved prognoses compared to those with de novo metastatic disease, and therefore have a longer window to receive LPT. Prostate radiotherapy may also confer a survival advantage for low‐volume metastatic disease. 34, 35, 36 The small proportion of patients who received localized treatment in our cohort speaks to the highly controllable state of most localized prostate cancer and the importance of identifying potentially lethal diseases within this population. Factors negatively associated with LPT use included older age, increasing co‐morbidities, and long‐term care registration. Patients with increasing age and comorbidity have been shown to receive less systemic therapy in cancer care. 18, 41 The reasoning behind this is multifactorial, including greater risk of harm in frail patients, patient goals of care including increasing preferences placed on quality of life and minimizing toxicities, and lack of evidence generalizability as they are often excluded from trials. 41 Although evidence suggests that ARATs are well tolerated in fit elderly patients and can promote or preserve the quality of life, 41 our study still observes a downward trend for LPT use with increasing age. Other important factors in treatment decisions such as the social support elderly patients have could not be assessed. Interestingly, rurality, distance to cancer center, and income were not associated with LPT use. While the fact that free drug access in our cohort may have influenced the role that income played, it is widely understood that the impact of socioeconomic status is complex and has a larger influence on healthcare access than the ability to afford a medication alone. Existing literature focusing on the impact of these factors is heterogeneous, indicating that barriers to accessing cancer care are specific to both treatment modality and disease site. 15, 17, 18 Study limitations include reliance on data available in the administrative databases, which lack details that play into the treatment decision‐making process including patient preferences, treatment indication, and line of therapy. Patients under 65 were excluded as they are not automatically covered under ODB for oral drugs. Additionally, receipt of any therapies discontinued for patients prior to the last 2 years of their life would not have been captured. Oncology drugs following Health Canada approval are also often initially funded by private insurance or drug company programs, which could not be captured. Last, we did not restrict our cohort to those with a formal definition of mCRPC as our primary objective was to identify marginalized patients who did not receive LPT, and marginalized patients would be less likely to fulfill formal definitions within the administrative data set. A sensitivity analysis in formally defined mCRPC was largely concordant (Tables S3 and S4). Future studies examining LPT receipt in formally defined mCRPC, younger patients, and immigrant patients will be important in obtaining a more thorough understanding of LPT usage in lethal prostate cancer. In conclusion, we performed a population‐based study of prostate cancer decedents receiving androgen deprivation therapy and found that patients who are seen at specialist cancer centers, receive prostate‐directed treatment, are younger, less comorbid, and not long‐term care residents have higher odds of receiving LPT. Amongst these, a key modifiable factor is caring for patients at a Regional Cancer Center. We identified that substantial gains have been made in delivery over time, driven largely by the introduction of abiraterone, enzalutamide, and radium‐223. Despite this, a substantial proportion of patients do not yet access LPT. In this cohort with universal access to healthcare and drug benefit, there were no differences detected on the basis of income, remoteness, or rurality for use of LPT. Future directions for research and policy should consider models of care that improve patient access to specialist multidisciplinary cancer center consultation as well as education outreach from these centers as LPT continues to rapidly evolve to encompass more novel treatments and earlier disease states. ## AUTHOR CONTRIBUTIONS Michael Ong, Peter Tanuseputro, Jennifer Leigh: Conceptualization (equal); data curation (equal); formal analysis (equal); writing – original draft (equal); Dominick Bosse, Peter Tanuseputro, Igal Kushnir, Luke Lavallee, Danial Quereshi: writing – review and editing (equal). Ewa Sucha: Formal analysis (equal). Roshanak Mahdavi: Formal analysis (equal). ## FUNDING INFORMATION This work was supported by funding from the Genitourinary Medical Oncologists of Canada (GUMOC) Astellas Research Grant Program. This work was also supported by ICES (formerly the Institute for Clinical Evaluative Sciences), which is an independent, nonprofit research institute funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long‐Term Care (MLTC). ## CONFLICTS OF INTEREST LTL is an advisory board participant for Sanofi, AbbVie, Janssen, Bayer, and Knight. DB has received an honorarium for advisory boards or speaker fees from Bayer, Janssen, Ipsen, Amgen, Pfizer, AstraZeneca, BMS, and AbbVie. MO has been a consultant to and received honoraria from Janssen and Bayer. All other authors have no conflicts to disclose. ## ETHICAL APPROVAL Use of the data in this project is authorized under section 45 of Ontario's Personal Health Information Protection Act (PHIPA) and does not require review by a Research Ethics Board. ## DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. ## References 1. Cancer Care Ontario. (2018) 2. Fradet Y, Klotz L, Trachtenberg J, Zlotta A. The burden of prostate cancer in Canada. Can Urol Assoc J (2009) 3 S92-S100 3. Kirby M, Hirst C, Crawford ED. Characterising the castration‐resistant prostate cancer population: A systematic review. Int J Clin Pract (2011) 65 1180-1192. PMID: 21995694 4. Kantoff PW, Higano CS, Shore ND. Sipuleucel‐T immunotherapy for castration‐resistant prostate cancer. New Eng J Med (2010) 363 411-422. PMID: 20818862 5. Bono JS, Logothetis CJ, Molina A, Fizazi K. 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--- title: 'RNA‐seq and ATAC‐seq analysis of CD163 + macrophage‐induced progestin‐insensitive endometrial cancer cells' authors: - Lulu Wang - Qiaoying Lv - Pengfei Wu - Shuhan Luo - Sijia Liu - Xiaojun Chen - Xuezhen Luo journal: Cancer Medicine year: 2022 pmcid: PMC10028121 doi: 10.1002/cam4.5396 license: CC BY 4.0 --- # RNA‐seq and ATAC‐seq analysis of CD163 + macrophage‐induced progestin‐insensitive endometrial cancer cells ## Abstract Infiltrating CD163+ macrophages were correlated with progestin insensitivity in endometrial cancer. CD163+ macrophages antagonize PR signaling, driving EC cell unresponsiveness to progestins. An ECM‐related mechanism might be involved in CD163+ macrophage‐induced progestin insensitivity. ### Background Progestins are used as fertility‐sparing regimens for young patients with stage 1A endometrioid endometrial cancer (EEC) and atypical endometrial hyperplasia (AEH). CD163+ macrophages promote estrogen‐dependent EEC development, but whether they induce progestin insensitivity remains unclear. This study aimed to investigate the possible effects of CD163+ macrophages on progestin response in AEH/EEC patients. ### Methods The number of infiltrating CD163+ macrophages in progestin‐insensitive and ‐sensitive endometrial lesions was compared. The effects of CD163+ macrophages on progestin responses and progesterone receptor (PR) expression in EC cells were evaluated in vitro. ATAC‐seq and RNA‐seq were combined to identify molecular/biological changes induced by CD163+ macrophages in progestin‐insensitive EC cells. ### Results Increased CD163+ macrophage infiltration was significantly associated with progestin insensitivity and longer treatment durations in AEH/EEC patients. Additionally, the number of CD163+ macrophages was negatively correlated with PR expression in AEH/EEC tissues. Furthermore, the CD163+ macrophage‐mediated microenvironment and secreted cytokines downregulated PR expression and impaired the response of EC cells to medroxyprogesterone acetate (MPA). RNA‐seq analysis demonstrated that CD163+ macrophages antagonized PR signaling by blocking or even reversing MPA‐regulated differential gene expression. Based on RNA‐seq and ATAC‐seq analyses, extracellular matrix (ECM) signaling and ECM‐related transcription factors, FOXF2, POU1F1, and RUNX1were identified to potentially be involved in CD163+ macrophage‐induced progestin insensitivity in endometrial cancer patients. ### Conclusions We identified CD163+ macrophages as an important mediator of progestin desensitization and an unfavorable factor for the efficacy of fertility‐preserving treatment in AEH/EEC patients. ## INTRODUCTION Endometrial carcinoma (EC) is one of the most common gynecologic malignancies, and its incidence is increasing in younger patients. Epidemiological evidence revealed that approximately $7.1\%$ of EC patients were between 20 and 44 years old at the time of diagnosis, and $70\%$–$88\%$ of them had not completed childbearing. 1 Because the occurrence of EC is strongly related to prolonged estrogen exposure without progestin protection, synthetic progestins have been administered clinically as fertility‐sparing agents for young patients with atypical endometrial hyperplasia (AEH) and early endometrioid cancer (EEC). Complete response (CR) rates for progestin therapy in EEC patients range from $72.9\%$ to $95.3\%$, depending on the drugs and regimens used. 2 However, approximately $30\%$ of EEC patients fail to respond to progestins or exhibit a temporary or partial response. 3, 4 Moreover, the molecular mechanisms underlying progestin insensitivity are poorly understood. The progesterone receptor (PR) is the primary target of progestins, and the efficacy of progestin treatment in endometrial cancer is mainly mediated by the PR signaling pathway. 5 It has been widely reported that progestin insensitivity has a strong relationship with the loss of expression, downregulation, or dysfunction of PR. 6 Therefore, identifying environmental factors that affect the protein expression of PR and desensitize EC cells to progestin is important. Macrophages, an important component of chronic inflammation, are classified into the pro‐inflammatory M1 phenotype and the anti‐inflammatory M2 phenotype. A previous study reported that the obesity‐related tumor microenvironment recruited macrophages and promoted M2 polarization through the COX‐2/PEG2 pathway in prostate cancer. 7 Similarly, several studies demonstrated increased CD163+ macrophage infiltration with higher pathological grades in abnormal endometrial hyperplasic lesions. We previously demonstrated that cytokines secreted by CD163+ macrophages (M2‐like macrophages) induced ERα expression through epigenetic modulation of the ESR1 gene promoter and stabilized ERα through de‐ubiquitination of ERα protein. 8, 9 However, whether CD163+ macrophages induce progestin insensitivity remains unclear. In the present study, we compared the number of infiltrating CD163+ macrophages in progestin‐insensitive and progestin‐sensitive AEH/EC patients. In addition, the regulatory effects of CD163+ macrophages on progestin sensitivity and PR expression in EC cells were investigated. Furthermore, ATAC‐seq and RNA‐seq analyses were integrated to identify molecular/biological changes induced by CD163+ macrophages in progestin‐insensitive EC cells. Our study demonstrated reduced sensitivity of EC cells to progestin treatment in the CD163+ macrophage‐induced chronic inflammatory state. CD163+ macrophages and cytokines, such as IL10 and TGFβ, downregulated PR protein expression. RNA‐seq analysis revealed that the CD163+ macrophage environment antagonized PR signaling via blocking or even reversing MPA‐regulated differential gene expression. By integrating ATAC‐seq and RNA‐seq analyses, extracellular matrix‐related signaling was identified as an important mechanism potentially involved in CD163+ macrophage‐induced progestin insensitivity in EC cells. ## Ethics statement This study complied with the principles of the Declaration of Helsinki and was approved by the Medical Ethics Committee of the Obstetrics and Gynecology Hospital of Fudan University (approval No.2021–131). All patients signed an informed consent form before participating in the study. ## Patients and specimens A total of 22 endometrial tissues were collected from AEH or EEC patients who received fertility‐preserving treatment at the Obstetrics and Gynecology Hospital of Fudan University between January 2017 and August 2019. All patients were pathologically diagnosed by endometrial biopsy through dilation and curettage under a hysteroscope. Pathologic diagnosis was independently confirmed by two experienced gynecological pathologists, according to the World Health Organization pathological classification [2014]. If their opinions differed, a seminar was held in the pathology department to determine the final diagnosis. The inclusion and exclusion criteria for fertility‐sparing treatment followed National Comprehensive Cancer Network guidelines. 10, 11 The inclusion criteria were as follows: [1] histologically‐proven AEH or well‐differentiated EEC G1 without myometrial invasion; [2] no signs of suspicious extrauterine involvement on enhanced magnetic resonance imaging, enhanced computed tomography or ultrasound; [3] patients younger than 45 years old; [4] strong willingness to preserve fertility; [5] no contraindications for progestin treatment or pregnancy; [6] not pregnant; and [7] good compliance for treatment. Written informed consent was obtained from all patients before initiating treatment. The exclusion criteria were as follows: [1] use of local or systematic progestins with more than 1 month before hysteroscopic evaluation; [2] recurrent AEH or EEC; [3] evidence of myometrial invasion; and [4] loss of follow‐up. All endometrial tissues in this study were obtained before fertility‐preserving treatment or within 1 month of treatment. We defined progestin insensitivity as meeting one of the following criteria 11: [1] presented progressive disease at any time during conservative treatment, [2] maintained stable disease after 7 months of first‐line treatment, or [3] did not achieve CR after 10 months of first‐line treatment. The conditions above indicated progestin insensitive (PIS), whereas other conditions were considered progestin sensitive (PS). The clinical characteristics of all enrolled patients are shown in Table 1. TABLE 1 \| Variables \| Total \| Sensitivity \| Insensitivity \| p‐value* \| \| --- \| --- \| --- \| --- \| --- \| \| No. of patients \| 22 \| 13 \| 9 \| — \| \| Age at diagnosis (year) \| 30.5 (20–39) \| 31 (23–39) \| 29 (20–34) \| 0.158 \| \| BMI (kg/m2) \| 28.08 (20.07–45.17) \| 28.13 (20.07–45.17) \| 27.82 (22.68–30.47) \| 0.815 \| \| HOMA‐IR index \| 3.17 (0.19–22.8) \| 3.23 (0.19–22.8) \| 2.93 (1.56–10.51) \| 0.717 \| \| MS \| 10 \| 6 \| 4 \| 1.000 \| \| Hypertension \| 5 \| 3 \| 2 \| 1.000 \| \| Diabetes mellitus \| 1 \| 0 \| 1 \| 0.409 \| \| Nulliparous \| 17 \| 10 \| 7 \| 1.000 \| \| Histologyat diagnosis \| \| \| \| 1.000 \| \| AEH \| 8 \| 5 \| 3 \| — \| \| EEC \| 14 \| 8 \| 6 \| — \| \| Progestin therapy \| \| \| \| 0.423 \| \| MA \| 7 \| 4 \| 3 \| — \| \| MA + MET \| 6 \| 5 \| 1 \| — \| \| LNG‐IUS \| 3 \| 2 \| 1 \| — \| \| MA + LNG‐IUS \| 6 \| 2 \| 4 \| — \| ## Cell lines and cell culture The human EC cell line Ishikawa was kindly provided by Dr. Yu Yinhua (MD Anderson Cancer Center), and the human EC cell line ECC‐1 was purchased from American Type Culture Collection (Manassas, VA, USA). The human monocyte cell line (THP‐1) was kindly provided by the Stem Cell Bank, Chinese Academy of Sciences. All cell lines were authenticated by short tandem repeat profiling and were routinely tested for no mycoplasma contamination. ECC‐1 and THP‐1 cells were cultured in RPMI 1640 medium, and Ishikawa cells were cultured in DMEM/F12 medium, both supplemented with $10\%$ fetal bovine serum (FBS), $1\%$ penicillin, and streptomycin. All cells were maintained at 37°C in a humidified incubator containing $5\%$ CO2. ## Preparation of human peripheral blood monocyte‐derived macrophages First, peripheral blood mononuclear cells (PBMCs) were isolated from the buffy coats of healthy donors by density gradient centrifugation with lymphocyte isolation solution (Serumwerk Bernburg AG). Then, CD14+ cells were isolated from mononuclear cells by CD14 immunomagnetic beads through positive magnetic selection (Miltenyi Biotec). CD14+ monocytes were cultured in RPMI 1640 with $10\%$ FBS and in 6‐well flat‐bottom culture plates at 5 × 105 cells/mL. ## Drug intervention The following drugs were used for cell interventions as indicated: phorbol‐12‐myristate‐13‐acetate (PMA) (Sigma Aldrich, 793,416), recombinant human IL4 (Sigma Aldrich, srp3093), recombinant human IL13 (Sigma Aldrich, srp3274), recombinant human IL10 (Novoprotein, CX04), recombinant human TGFβ (Novoprotein, CA59), recombinant human IL10 neutralizing antibody (Biolegend, 501,427), recombinant human TGFβ neutralizing antibody (Biolegend, 947,303), and medroxyprogesterone acetate (MPA) (Selleck, NSC‐26386). Vehicles included dimethyl sulfoxide (DMSO), phosphate buffer saline (PBS), or PBS supplemented with $0.1\%$ BSA. THP‐1 cells and PBMCs were first treated with 100 ng/mL PMA for 24 h, respectively, to generate M0 macrophages, which are differentiated but unpolarized macrophages. Then, M0 cells were treated with IL4 (20 ng/mL)/IL13 (20 ng/mL) for 48 h to induce CD163+ macrophage polarization. The cell culture supernatant was collected as conditioned medium (CM) after culturing THP‐1 or PBMC‐derived CD163+ macrophages for 24 h and used in further studies as indicated. Prior to MPA or CM treatment, cells were cultured in phenol red‐free medium supplemented with $10\%$ charcoal‐stripped FBS. ## Enzyme‐linked immunosorbent assay (ELISA) The levels of IL10 and TGFβ in cell culture supernatants of THP‐1 cells and CD163+ macrophages were measured by an ELISA kit (R&D Systems) according to the manufacturer's instructions. ## Flow cytometry Macrophages were incubated with fluorescent‐tagged antibodies for flow cytometry as follows: anti‐Human CD163‐PE, anti‐Human IL10‐APC, and anti‐Human TGFβ‐BV421 (all Biolegend, USA). Then, macrophages were detected by flow cytometry (Beckman). ECC‐1 and Ishikawa cells (300,000 cells/well) were seeded in 6‐well plates and allowed to adhere to culture plates overnight. Cells were then treated with MPA, CM, and IL10/TGFβ for 48 h. The cells were then trypsinized with EDTA‐free trypsin and washed twice with cold PBS. The cells were re‐suspended in 100 μl of binding buffer and stained with 1 μl of annexin V‐FITC and 1 μl of PI working solution for 15 min at room temperature in the dark (Dojindo, Japan). Finally, the cell apoptotic level was determined using a flow cytometer (Beckman). As negative controls, isotype‐matched antibodies with corresponding fluorescent labels were used. ## Real‐time quantitative PCR (RT‐qPCR) Total RNAs were extracted using an RNA Purification Kit (EZBioscience, B0004). After removing genomic DNAs with a DNA remover, total RNAs were reverse transcribed into cDNAs using the Reverse Transcription Kit (EZBioscience, A0010GQ). cDNA amplification was performed using the TB Green® Premix Ex Taq™ II (TaKaRa, RR820A). The 2−ΔΔCt method was used to calculate gene expression levels relative to GAPDH. Primers were listed in Table S1. ## Western blotting analysis Western blotting analysis was conducted as previously described. 12 The following primary antibodies were used: PR (Santa Cruz Biotechnology, sc‐166,169), cyclin D1 (Cell Signaling Technology, E3P5S), p21 (Cell Signaling Technology, 12D1), p27 (Cell Signaling Technology, D69C12), and β‐actin (Huabio, M1210). ## Cell viability assay ECC‐1 and Ishikawa cells (3000 cells/well) were seeded in 96‐well plates, allowed to adhere overnight, and treated with MPA, CM, or cytokines for 48 h. Cell viability was detected using Cell Counting Kit‐8 (CCK‐8) in accordance with the manufacturer's instructions (DOJINDO, Japan). ## Immunohistochemical (IHC) staining IHC staining was performed as previously described. 13 Primary antibodies used in IHC staining included CD163 (Abcam, ab182422) and PR (Abcam, ab32085). Semi‐quantitative optical analysis was performed as previously described. 14 ## ATAC‐seq and RNA‐Seq analyses Ishikawa cells were harvested from 6‐cm dishes in 1 ml Trizol (Invitrogen, Carlsbad, CA, USA) in accordance with the manual after a 24 h induction with 20 μM MPA or DMSO in the presence or absence of CM. RNAs were subjected to RNA‐Seq analysis with a BGISEQ‐500 system by Beijing Institute (BGI), China. RNA integrity was examined using a NanoDrop spectrophotometer (Thermo Fisher) and Bioanalyzer 2100 (Agilent). RNAs were sheared and reverse transcribed into cDNAs using random primers for library construction. Subsequently, sequencing was performed using the prepared library. 15 All generated raw sequencing reads were filtered to obtain clean reads stored in the FASTQ format. Bowtie2 and HISAT were applied to align the clean reads to the reference gene and genome, respectively. 16, 17 The expression level (FPKM) of genes was calculated by RSEM. 18 Read counts for each gene were determined using the SubRead package. 19 Normalization and differential expression analysis were performed using DeSeq2. 20 To investigate chromatin accessibility, Ishikawa cells in 10‐cm dishes were collected after 24 h of treatment with MPA or DMSO in the presence or absence of CM. ATAC‐seq was performed as described previously. 21 Libraries were pooled in equimolar ratios with barcodes and sequenced on the BGISEQ‐500 platform (BGI‐Shenzhen, China). For RNA‐seq analysis, genes with \|Log2 Fold change\| > 0.5849 and adjusted $p \leq 0.05$ were selected as differentially expressed genes (DEGs). For ATAC‐seq analysis, opening or closing peaks with \|Log2 Fold change\| > 0.5849 and non‐adjusted $p \leq 0.05$ were selected according to MAnorm analysis. Raw and processed data are available from the corresponding author on reasonable request. ## Bioinformatic analysis To investigate the biological relationship between PGR (PR coding gene) and M1/M2‐like macrophage‐secreted cytokine genes, Ingenuity Pathway Analysis software (IPA, Ingenuity System; http://www.ingenuity.com) was used for the analysis of functional biological networks. Network analysis was performed by uploading gene IDs into IPA, carefully ensuring that each gene was uniquely and accurately recognized by the software. Gene interaction networks were then generated automatically (i.e., independent of investigators). Pathways and networks were ranked according to the number of molecules with a cutoff value ($p \leq 0.05$) for significantly enriched pathways/networks involving PGR, and cytokine genes were identified using the “Compare” module in IPA. The bioinformatics analyses used in integration analysis of ATAC‐Seq and RNA‐Seq included REACTOME pathways, transcription factor (TF) prediction, and Motif enrichment. [ 1] Based on overlapping DEGs by ATAC‐Seq and RNA‐Seq integrated analysis, top ten REACTOME pathways were enriched, and DEGs in the pathways potentially regulating progestin insensitivity were first screened out; [2] potential TFs that regulate the expression of the overlapping DEGs were enriched by HOMER Software, and DEGs‐encoding TFs with p value <0.05 were screened out; and [3] Motif enrichment was performed to identify important TFs by using homer peak analysis software. We employed deepTools 22 to plot the heatmap of the read coverage. Briefly, the sorted bam files were used to calculate the scores per genome regions by computeMatrix with scale‐regions mode, and the heatmap was generated based on the score matrix by plotHeatmap with default options. ## Statistics analysis Statistical analyses were performed using SPSS statistical software (version 23.0, IBM). All experiments were repeated at least three times. Student's t‐test, one‐way or two‐way ANOVA, and Spearman's correlation analysis were used for further statistical analyses. The clinical characteristics of patients were analyzed using the Mann–Whitney U test, chi‐square test, or Fisher's exact test as appropriate. Statistical significance was determined as $p \leq 0.05$ in two‐sided tests. ## Increased number of infiltrating CD163 + macrophages in AEH and EEC patients with progestin insensitivity To determine whether CD163+ macrophages regulate progestin sensitivity in patients with AEH/EEC, we first analyzed the relationship between CD163+ macrophage infiltration and progestin sensitivity. CD163 was used to identify M2‐like macrophages in AEH or EEC specimens with PS ($$n = 13$$) and PIS ($$n = 9$$). The clinical characteristics of PS and PIS groups were comparable (Table 1). IHC staining analysis revealed that CD163 was mainly expressed in tumor stromal regions in AEH/EEC tissues (Figure 1A). The number of CD163+ macrophages in AEH/EEC specimens was counted and analyzed by semi‐quantitative optical analysis. We found that the number of infiltrating CD163+ macrophages in the tumor stroma of patients with PIS was significantly higher than that in the PS group (Figure 1B). Furthermore, infiltrating CD163+ macrophages increased gradually with the time to achieve CR (Figure 1C). The increased number of infiltrating CD163+ macrophages indicated their important roles in mediating progestin insensitivity in AEH/EEC patients. FIGURE 1: Increased number of infiltrating CD163+ macrophages in AEH/EEC tissues of patients with progestin insensitivity. (A) Representative images of IHC staining for CD163 in AEH and EEC tissues before progestin therapy in progestin‐sensitive or progestin‐insensitive groups. Scale bar: 50 μm. (B) The quantification of CD163+ macrophages per mm2 was analyzed in PS or PIS AEH and EEC tissues. (C) The number of CD163+ macrophages per mm2 was compared in AEH and EEC tissues according to the time to achieve CR. Statistical analysis was conducted using two‐tailed Student's t‐test and ANOVA. PIS, progestin insensitive; PS, progestin sensitive; CR, complete response. ns, not significant; p < 0.05; p < 0.01; p < 0.001 compared with the control. ## CD163 + macrophages decreased the sensitivity of EC cells to progestin therapy Based on the clinical evidence above, we assumed that infiltrating CD163+ macrophages might contribute to progestin sensitivity in AEH/EEC. Accordingly, CD163+ macrophages were successfully induced from THP‐1 cells (Figure S1) and PBMCs, respectively (Figure S2B). Macrophages were terminally differentiated after treatment with PMA and IL4/IL13 and showed higher levels of M2 type macrophage markers, including CD163, IL10, and TGFβ. First, we co‐cultured EC cells with different amounts of CD163+ macrophage‐derived CM diluted with normal medium (NM). As shown in Figure S2A, cells cultured with diluted CM (CM:NM = 1:2) showed the similar cell viability compared with cells treated with complete NM. Therefore, diluted CM (CM:NM = 1:2) was applied for the following study. We next explored the response of EC cells to MPA in the presence or absence of CM. We found that CM stimulated the proliferation of ECC‐1 and Ishikawa cells, whereas the inhibitory rate of MPA was significantly decreased in Ishikawa cells (59.18 ± $0.7975\%$ vs. 30.67 ± $2.050\%$, $p \leq 0.0001$) and ECC‐1 (70.03 ± $0.2963\%$ vs. 30.29 ± $0.9188\%$, $p \leq 0.0001$) (Figure 2A,B). PBMC‐derived CD163+ macrophages were employed to further confirm our findings. CM from CD163+ macrophages could induce a significant decline of the inhibitory rate of MPA in Ishikawa (31.20 ± $0.6673\%$vs. 57.20 ± $0.2406\%$, $p \leq 0.0001$) and ECC‐1 (22.20 ± $1.477\%$ vs. 68.39 ± $0.2782\%$, $p \leq 0.0001$) cells compared with CM from M0 macrophages (Figure S2C,D). Progestins induce the cell cycle arrest of EC cells by downregulating cyclin D1 and upregulating p21 and p27 expression. 23 Cyclin D1 is an essential factor for cell cycle G1/S transition, whereas p21 and p27 bind to cyclin‐CDK complexes and induce cell cycle arrest. 24 As shown in Figure 2C,D, MPA treatment alone significantly decreased cyclin D1 expression and increased p21 and p27 expression in EC cells. CM of CD163+ macrophages generated from THP‐1 cells significantly diminished the MPA‐induced cell cycle inhibition. Consistent results were obtained when EC cells treated with CM of CD163+ macrophages generated from PBMCs (Figure S2E,F). Another important indicator of effective progestin treatment is an increase in apoptotic EC cells. We next evaluated whether CM affects progestin‐induced EC cell apoptosis using an annexin‐V and PI assay. Compared with the MPA alone group, the proportion of early and late apoptotic cells was significantly decreased in the MPA and CM combination group of ECC‐1 cells (20.78 ± $0.5485\%$ vs. 11.94 ± $1.075\%$, $p \leq 0.0001$) and Ishikawa cells (20.78 ± $0.5485\%$ vs. 11.94 ± $1.075\%$, $p \leq 0.0001$) (Figure 2D). Collectively, these results demonstrated that CD163+ macrophages decreased the progestin sensitivity of EC. FIGURE 2: CD163+ macrophages decreased the sensitivity of EC cells to progestin therapy. (A, B) CD163+ macrophages decreased the MPA‐mediated inhibitory effect on EC cell proliferation. Ishikawa (A) and ECC‐1 (B) cells were cultured together with the indicated dilutions of conditional medium (CM) derived from macrophages and/or MPA for 48 h. Cell viability was evaluated with CCK‐8 test (left), and the inhibitory rate was calculated (right). (C, D) CD163+ macrophages inhibited MPA‐mediated downregulation of cyclin D1 protein and upregulation of p21 and p27 protein in the cell cycle pathway. Endometrial cancer cells were treated with CM and/or MPA for 24 h, and the expression of cell cycle proteins was analyzed by Western blotting analysis. (E, F) CD163+ macrophages decreased MPA‐induced apoptotic effects on EC cells. EC cells were cultured with CM and/or MPA for 48 h, and the apoptotic rate was determined by flow cytometry. All values are presented as the mean ± SD. ns, not significant; p < 0.05; p < 0.01; p < 0.001 compared with the control. ## CD163 + macrophages and secreted cytokines decreased PR protein in EC cells It is well established that the effects of progestin on the endometrium are mediated by interactions with the PR and PR‐mediated signaling pathway. 25 We asked whether PR expression was downregulated in CD163+ macrophage‐induced PIS EC cells. First, IHC staining of serial endometrial tissue sections showed increased infiltrating CD163+ macrophages in the stroma and decreased PR expression levels in adjacent epithelial and stroma regions (Figure 3A). We further analyzed the correlation between the number of CD163+ macrophages and epithelial PR expression using Pearson's correlation test, and the results demonstrated that the number of CD163+ macrophages was significantly negatively correlated with the PR IHC score (Figure 3B). To determine the PR expression status after treatment with CM from CD163+ macrophages, we analyzed the changes in PR expression in EC cells. Real‐time PCR showed that treatment with CM derived from CD163+ macrophages decreased the PR mRNA level in a dose‐dependent manner (Figure 3C). Similarly, Western blotting analysis demonstrated that CM from both THP‐1 and PBMC‐derived CD163+ macrophages reduced PRA and PRB protein expression in ECC‐1 and Ishikawa cells dose‐dependently (Figure 3D and Figure S2G). This suggested that PR downregulation was an important event for the CD163+ macrophage‐induced desensitization of EC cells to progestins. FIGURE 3: CD163+ macrophages and secreted cytokines decreased PR protein in EC cells. (A) Representative IHC images for CD163 (Figure 1A) and PR expression in AEH and EEC samples before progestin therapy in progestin‐sensitive (PS) or progestin‐insensitive (PIS) groups. Scale bar: 50 μm. Sequential tissue slices from each patient in Figure 1A were further immunostained for PR marker, except for one patient in PS group without enough tissue specimens. (B) The staining intensity of PR was scored by semi‐quantitative optical analysis. Correlation between the number of CD163+ macrophages per mm2 and PR score was analyzed by Spearman's correlation. (C) CM derived from CD163+ macrophages inhibited the transcriptional level of PR in endometrial cancer cells in a dose‐dependent manner. Ishikawa and ECC‐1 cells were treated with the indicated dilutions of CM from CD163+ macrophages for 24 h. Relative PR mRNA level was analyzed by real‐time PCR. (D) CM derived from CD163+ macrophages downregulated the protein expression of PR in EC cells in a dose‐dependent manner. Ishikawa and ECC‐1 cells were treated with the indicated dilution CM from CD163+ macrophages for 24 h and then harvested for PR protein analysis by Western blotting. (E) IL10 and TGFβ inhibit the PR transcriptional level in Ishikawa and ECC‐1 cells. Endometrial cancer cells were incubated with IL10 and TGFβ for 24 h and then harvested for analyzing the PR mRNA level by real‐time PCR. (F) IL10 and TGFβ downregulated PR expression in Ishikawa and ECC‐1 cells. Endometrial cancer cells were incubated with IL10 and TGFβ for 48 h and then harvested for analyzing PR protein by Western blotting. (G, H) IL10 and TGFβ neutralizing antibody (NA) antagonized the PR downregulation effects induced by CM from CD163+ macrophages. RT‐qPCR and Western blot were used to detect PR mRNA and protein expression levels in EC cells treated with or without CM from CD163+ macrophages or IL10/TGFβ‐NA. I. IL10 and TGFβ‐NA reversed the progestin insensitivity induced by CM in ECC‐1 cells. ECC‐1 cells were cultured together with CM and/or MPA in the presence or absence of IL10/TGFβ‐NA for 48 h before the CCK‐8 test. All values are presented as mean ± SD. ns, not significant; p < 0.05; p < 0.01; p < 0.001 compared with the control. Our above findings showed that CM derived from CD163+ macrophages induced progestin insensitivity and downregulated PR expression. We predicted that cytokines secreted by CD163+ macrophages might be involved in the CM‐mediated progestin insensitivity. To test this, IPA was used to determine whether a potential regulating network existed between CD163+ macrophage‐related cytokine genes and PGR. As shown in Figure S3A, the IPA network illustrated possible molecular interactions among several M1/M2 cytokines and PGR, and IL10 and TGFβ were identified as potential cytokines regulating PGR in EC cells. ELISA further confirmed that THP‐1‐derived CD163+ macrophages exhibited a significantly increased secretion of IL10 and TGFβ than the THP‐1 cells (Figure S2H). Next, IL10 or TGFβ could inhibit PGR transcription and PR protein expression in EC cells (Figure 3E,F). Furthermore, CM‐induced PR downregulation effects could be antagonized by IL10/TGFβ neutralizing antibodies in EC cells (Figure 3G,H). IL10/TGFβ neutralizing antibodies could also reverse the progestin insensitivity induced by CM in ECC‐1 cells (Figure 3I). Taken together, these findings indicate that CD163+ macrophages decreased PR expression and desensitized EC cells to progestins. ## CD163 + macrophages antagonized PR signaling, driving EC cell unresponsiveness to progestins The data above showed that CD163+ macrophages desensitized EC cells to progestins, but the molecular/biological profile changes in EC cells remain unclear. Next, RNA‐seq analysis of Con, MPA, CM, and MPA_CM treatment groups was performed. To confirm the quality of RNA‐seq data, Pearson's correlation was calculated based on the read counts (Figure S4A), and principal component analysis was performed according to the amount of data variability (Figure S4B). Both analyses showed clustered samples by group. The mRNA expression profiles were presented in Figure S4C. The numbers of upregulated and downregulated DEGs were calculated according to the Con or MPA group (Figure S4D). To determine how CD163+ macrophage‐derived CM affects MPA‐regulated DEGs, an expression heat map of 1180 MPA‐upregulated and ‐downregulated DEGs in all samples was generated (Figure 4A). Here, the MPA_CM group tended to show decreased MPA‐upregulated DEGs and increased MPA‐downregulated DEGs compared with the MPA group. Among 744 MPA‐upregulated DEGs, the expression of $42.5\%$ was reversed, $51.6\%$ remained unaffected, and only $5.9\%$ were increased in the MPA_CM group (Figure 4B). Similarly, among 436 MPA‐downregulated DEGs, the expression of $37.8\%$ was reversed, $58.0\%$ remained unaffected, and only $4.1\%$ were decreased in the MPA_CM group (Figure 4C). Taken together, we concluded that CM derived from CD163+ macrophages was likely to antagonize PR signaling in EC cells by blocking or even reversing the expression of MPA‐regulated DEGs. FIGURE 4: CD163+ macrophages antagonize PR signaling and drive EC cell unresponsiveness to progestins. (A) Heat map of 1180 MPA‐upregulated and ‐downregulated DEGs in Con, MPA, CM, and MPA_CM groups. (B) In the MPA_CM group, 316 (42.5%) MPA‐upregulated DEGs were reversed, 384 (51.6%) were unaffected, and only 44 (5.9%) were increased compared with the MPA group. (C) In the MPA_CM group, 165 (37.8%) MPA‐downregulated DEGs were reversed, 253 (58.0%) were unaffected, and only 18 (4.1%) were increased compared with the MPA group. ## CD163 + macrophage‐induced profile alterations determined by RNA‐seq and ATAC‐seq To further explore the mechanism by which CD163+ macrophages antagonize PR signaling in EC cells, DEGs from CM versus Con and MPA_CM versus MPA were merged and 211 overlapping upregulated DEGs and 124 overlapping downregulated DEGs were generated (Figure 5A). To understand the functional alterations in the 335 dysregulated DEGs, gene ontology (GO) terms were generated, and the top ten terms were shown in Figure 5B–D. The GO terms for the biological process (BP) category included extracellular matrix organization, positive regulation of cell migration, and cell adhesion (Figure 5B). Similarly, the GO terms for the cellular component (CC) category were significantly enriched in extracellular space, extracellular exosome, plasma membrane, integral component of plasma membrane, cell surface, external side of plasma membrane, extracellular region, proteinaceous extracellular matrix, endoplasmic reticulum lumen, and extracellular matrix (Figure 5C). In addition, the molecular function category included protein binding, fibronectin binding, heparin binding, receptor binding, cytokine activity, protease binding, integrin binding, semaphorin receptor binding, extracellular matrix binding, and chemorepellent activity (Figure 5D). Furthermore, KEGG pathway analysis was performed on these overlapping DEGs, and the enriched pathways included ECM‐receptor interaction, TNF signaling pathway, focal adhesion, PI3K‐AKT signaling pathway, cytokine‐cytokine receptor interaction, pathway in cancer, NF‐kappa B signaling pathway, cell adhesion molecules, and others (Figure 5E). Based on the GO and KEGG analyses, extracellular matrix‐related signaling was significantly enriched in CD163+ macrophage‐induced EC cells. Taken together, these findings suggested that extracellular matrix (ECM) related mechanisms might be involved in the CD163+ macrophage‐mediated desensitization of EC cells to progestin. FIGURE 5: CD163+ macrophage‐induced profile alterations determined by RNA‐seq and ATAC‐seq. (A) Overlapping upregulated (211) and downregulated (124) DEGs between CM versus Con and MPA_CM versus MPA from RNAseq analysis. (B–D) GO term analysis of 335 overlapping DEGs and top ten GO terms for BP, CC, and MF. Black represents a significant term with p < 0.05. (E) KEGG annotation was generated, and the top twenty pathways were listed. Red represents a significant term with p < 0.05. (F) Overlapping upregulated (29) and downregulated (19) DEGs between CM versus Con and MPA_CM versus MPA from the integrated analysis of RNA‐seq and ATAC‐seq. (G) Candidate transcription factors were generated from the overlapping DEGs in (F) using homer peak analysis. To accurately obtain transcriptional regulatory sequence information based on chromatin accessibility, ATAC‐seq was performed (Figure S5A). The M‐A plot after normalization showed opening and closing peaks in genes relative to the corresponding control (Figure S5B). Then, the opening genes in ATAC‐seq and upregulated DEGs in RNA‐seq were overlapped. Similarly, the closing genes in ATAC‐seq and downregulated DEGs in RNA‐seq were overlapped. The number of overlapped genes was shown in Figure S5C. The targeted DEGs with chromatin open or closed regions were obtained from merged CM versus Con and MPA_CM versus MPA groups based on the integrated analysis of ATAC‐seq and RNA‐seq (Figure 5F). Based on the targeted DEGs, 10 candidate transcription factors, including PAX4, GATA1, MYOD1, PITX2, FOXF2, POU1F1, RUNX1, SRF, TBP, and CBFA2T3, were identified by homer peak analysis (Figure 5F). Within them, FOXF2, POU1F1, and RUNX1 are also involved in regulating ECM‐related signaling, which is consistent with GO and KEGG pathway enrichment analyses. In summary, through RNA and ATAC‐seq analysis, ECM signaling and ECM‐related transcription factors, FOXF2, POU1F1, and RUNX1, were identified to be the critical molecular factors underlying IL10/TGFβ induced progestin desensitization. ## DISCUSSION In this study, we first found that the increased number of infiltrating CD163+ macrophages was significantly associated with progestin insensitivity and prolonged the treatment duration needed to achieve CR in AEH/EEC patients. The CD163+ macrophage number was negatively correlated with PR protein expression in human AEH/EEC tissues. Further investigation confirmed that CD163+ macrophage‐derived CM and secreted cytokines, including IL10 and TGFβ, decreased the MPA response and PR protein expression level in EC cells. RNA‐seq analysis demonstrated that CD163+ macrophages antagonized PR signaling by blocking or reversing the expression of MPA‐regulated DEGs, thereby promoting EC cell unresponsiveness to progestins. Based on GO and KEGG pathway enrichment analyses of DEGs and the integrated analysis of RNA‐seq and ATAC‐seq data, extracellular matrix‐related signaling was identified to potentially be involved in CD163+ macrophage‐induced progestin insensitivity in EC cells. Our study identified CD163+ macrophages as an important mediator of progestin desensitization and an unfavorable factor for the efficacy of fertility‐preserving treatment in AEH/EEC patients. Increased infiltrating CD163+ macrophages might be a manifestation of chronic inflammation. Recently, clinical trials revealed a strong association between chronic inflammatory status and progestin insensitivity in AEH patients who received progestin‐based fertility‐sparing treatment. Yang et al. reported that obesity and insulin resistance were associated with lower cumulative CR rates and longer treatment times to achieve CR, indicating declined progestin sensitivity in AEH patients. 10, 26 In obese subjects, adipose tissue expansion is associated with increased secretion of several inflammatory cytokines, such as IL6, IL8, and MCP‐1. 27, 28 These inflammatory cytokines, especially MCP‐1, actively recruit circulating monocytes to tissues, which later differentiate into anti‐inflammatory or pro‐inflammatory macrophages. 29 Moreover, insulin resistance‐related factors regulate the immune microenvironment during the very early stages of endometrial hyperplasic lesions. Therefore, factors derived from obesity‐ or insulin resistance‐induced chronic inflammation might affect the recruitment and polarization of macrophages in AEH/EC patients. PR status is an important factor that largely reflects the progestin response rate. Factors that interfere with PR signaling, including decreased active PR protein or even loss of PR expression, PR‐related cofactor dysfunctions, or abnormal signaling activation (e.g., TGFɑ/EGF, TGF‐EGFR, PI3K/AKT signaling, and other pathways), desensitize EC cells to progestins. In this study, we found that CD163+ macrophages impaired the progestin response in EC cells. Here, we highlight that the number of infiltrating CD163+ macrophages was negatively correlated with PR protein in AEH/EC tissues, and CM derived from CD163+ macrophages decreased the protein expression of PR. These findings suggested that decreased PR expression was an important event in the microenvironment of CD163+ macrophages. An extracellular matrix‐related mechanism might regulate the progestin response. Based on the results of GO, KEGG, and transcription factor predictions, extracellular matrix‐related signaling was significantly enriched in CD163+ macrophage‐induced PIS EC cells. The extracellular matrix is composed of a variety of fibrillar components and non‐fibrillar molecules, which form complex networks that actively communicate with cells through binding to cell surface receptors and/or matrix effectors. Previous studies showed that Ishikawa cells aggregated to form glandular‐appearing tubular and spherical structures when plated on basement membrane extract, and the response of Ishikawa cells to progestin was increased in the presence of CM from stromal cells. Moreover, early decidualization in canines is accompanied by extracellular matrix remodeling in vivo. We postulate that abnormal extracellular matrix remodeling might induce progestin insensitivity, but further studies are needed. The analysis of chromatin accessibility via ATAC‐seq not only identifies regulatory regions for transcription but also infers transcription factor activity within them. To assess the relationship between chromatin accessibility and gene expression, correlation analysis of peaks at the promoter or distal regions and genome‐wide gene expression in endometrial cancer cells cultured in the presence or absence of CM was conducted (Figure 5G). Among these transcription factor candidates enriched at the regions of altered accessibility, POU1F1 is known to regulate several genes involved in pituitary development and hormone expression. In addition to the well‐known effects on growth hormone and prolactin gene transcription, it has been demonstrated that CD163+ macrophages induce POU1F1 expression in breast cancer cells. The overexpression of POU1F1 in cancer cells increases CXCL12 chemokine secretion, which promotes monocyte recruitment to the tumor microenvironment and macrophage transformation into CD163+ macrophages. 30, 31 POU1F1 activation may possibly correlate with cancer cells and M2‐like macrophage polarization and promote CD163+ macrophage‐induced progestin insensitivity through a positive feedback mechanism. DEG enrichment emphasized that modulation of the extracellular matrix played an important role in CD163+ macrophage‐induced progestin insensitivity in endometrial cancer. Several studies have previously reported that candidate transcription factors, including MYOD1, FOXF2, and RUNX1, transcriptionally regulate the expression of ECM genes and ECM remodeling, 32, 33, 34 suggesting that these transcription factors are potentially involved in progestin insensitivity. Additionally, Bone'y‐Montoya et al. reported that FOXF2 could directly bind to distal PR gene regions, 35 indicating that FOXF2 may be a key regulator of PR gene expression. These effects of candidate transcription factors require further investigations. The effects of M2‐like macrophages on PR expression seem to be diverse in different tumors. Lindsten et al. demonstrated that CM derived from M2‐like macrophages suppressed expression of estrogen receptor alpha (ERα), but not PR in breast cancer. 36 The discrepancy between the study by Lindsten et al. and our findings may be attributed to the heterogeneity of tumor cells and macrophages. Even though breast and endometrial cancer are both hormone‐dependent, emerging evidence supports distinct roles of PR in the pathogenesis of these two cancers. It has been demonstrated that PR signaling has diverse effects on reproductive tissues. In the breast, progestin acts in concert with estrogen to promote proliferative and pro‐survival gene programs. In sharp contrast, progestin inhibits estrogen‐driven growth in endometrium and protects endometrium from neoplastic transformation. Based on the heterogenous nature of PR, the influence of M2‐like macrophages on PR expression may differ between endometrial and breast cancers. Besides, M2‐like macrophages, including M2a, M2b, M2c, and M2d, may also have varied influences on PR expression on breast and endometrial tissues. 37 Although these M2 subsets share some markers and immunosuppressive functions, different subsets are induced by different mechanisms and have diverse physiological functions. Recent studies have found significant differences among macrophages from distinct tumors. It has been demonstrated that the percentage of M2c‐like macrophages was significantly higher in advanced (stages II and III) breast cancer. 38 However, the predominant M2 subset in hormone receptor‐positive breast cancer and endometrial cancer remain unclear. The detailed landscape of M2‐like macrophages must be deciphered with the integration of new technologies, such as multiplexed immunohistochemistry (mIHC) and single‐cell RNA‐seq (scRNA‐seq), for analyzing which M2‐like macrophage subsets regulated PR signaling in breast and endometrial cancer. Our study has limitations that should be noted. First, the mechanism underlying decreased PR expression in the CD163+ macrophage microenvironment remains unknown. Second, how CD163+ macrophages induced progestin sensitivity in EC cells was not definitively answered. Whether extracellular matrix‐related mechanisms are involved in CD163+ macrophage‐induced progestin insensitivity needs further investigation. In summary, our study revealed that a high CD163+ macrophage number was significantly associated with progestin insensitivity in AEH/EEC patients. By utilizing in vitro experiments, we suggested that IL10/TGFβ derived from CD163+ macrophages desensitized EC cells to progestin by downregulating PR mRNA and protein expression, and through RNA and ATAC‐seq analysis, ECM signaling and ECM‐related transcription factors, FOXF2, POU1F1, and RUNX1, were identified to be the critical molecular factors underlying IL10/TGFβ induced progestin desensitization. Our study highlighted CD163+ macrophages as an important mediator of progestin desensitization and an unfavorable factor for the efficacy of fertility‐preserving treatment in AEH/EEC patients. ## AUTHOR CONTRIBUTIONS Lulu Wang: Conceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); visualization (equal); writing – original draft (equal). Qiaoying Lv: Conceptualization (equal); data curation (equal); formal analysis (equal); writing – review and editing (equal). Pengfei Wu: Data curation (equal); methodology (equal); supervision (equal). Shuhan Luo: Data curation (equal); validation (equal). Sijia Liu: Data curation (equal); supervision (equal). Xiaojun Chen: Conceptualization (equal); funding acquisition (equal); supervision (equal); writing – review and editing (equal). Xuezhen Luo: Conceptualization (equal); funding acquisition (lead); resources (equal); supervision (equal); writing – review and editing (equal). ## FUNDING INFORMATION This research was funded by Natural Science Foundation of Shanghai, grant number 18ZR1405300 and Shanghai Medical Centre of Key Programs for Female Reproductive Diseases, grant number 2017ZZ010616. ## CONFLICTS OF INTEREST No potential conflicts of interest are disclosed. ## DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from the corresponding author upon reasonable request. ## References 1. Wang Y, Yang JX. Fertility‐preserving treatment in women with early endometrial cancer: the Chinese experience. Cancer Manag Res (2018) 10 6803-6813. PMID: 30584372 2. Fan Z, Li H, Hu R, Liu Y, Liu X, Gu L. Fertility‐preserving treatment in young women with grade 1 presumed stage IA endometrial adenocarcinoma: a meta‐analysis. Int J Gynecol Cancer (2018) 28 385-393. PMID: 29266019 3. 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--- title: Prevention of lymphoedema after axillary clearance by external compression sleeves PLACE randomised trial results. Effects of high BMI authors: - Nigel J. Bundred - Emma Barrett - Chriss Todd - Julie Morris - Donna Watterson - Arnie Purushotham - Katie Riches - Abigail Evans - Anthony Skene - Vaughan Keeley journal: Cancer Medicine year: 2022 pmcid: PMC10028125 doi: 10.1002/cam4.5378 license: CC BY 4.0 --- # Prevention of lymphoedema after axillary clearance by external compression sleeves PLACE randomised trial results. Effects of high BMI ## Abstract Around $25\%$ of women undergoing Axillary Clearance (ANC) develop lymphedema (LE). Intervention with a compression garment is recommended to prevent LE but no randomised evidence exists to support this strategy. A Randomised trial of external arm compression garments after axillary node clearance in women developing early arm swelling failed to find an effect of the intervention in preventing lymphoedema. ### Methods A randomised trial tested standard management versus application of graduated compression garments (20‐24 mmHg) to affected arm, for 1 year. Women with node positive breast cancer ($$n = 1300$$) undergoing ANC consented to arm volume measurements and those developing a 4–$9\%$ relative arm volume increase (RAVI) (subclinical LE) within 9 months post‐surgery were randomised. Primary outcome was proportion of patients developing LE (RAVI > $10\%$) by 24‐months in each group. Secondary endpoints included Quality of life in each group. ### Results In total 143 patients were randomised (74 no sleeve: 69 compression sleeve) between October 2010 and November 2015. The lymphoedema rate at 24 months in the ‘no sleeve’ group was at $41\%$, similar to the ‘sleeve’ group ($30\%$: $$p \leq 0.32$$). Thirtytwo patients randomised to the ‘no sleeve’ group had a sleeve applied within 24 months. Body Mass Index (BMI) at randomisation predicted LE at any time point HR 1.04 (CI 1.01–1.08; $$p \leq 0.01$$). Patients with obesity (BMI > 30) had higher rates of LE in both groups ($46\%$) compared to those with BMI < 30 ($24\%$). No difference between patients was found in either group in changes in QoL. Compression sleeves applied after development of LE improved QoL scores (FACT‐B $$p \leq 0.007$$:TOI $$p \leq 0.042$$). ### Conclusion Early intervention with External Compression garments does not prevent clinical LE, particularly in women with a high BMI > 30. The use of prophylactic garments in subclinical LE (RAVI < $9\%$) is unwarranted. ## INTRODUCTION As survival for breast cancer has improved with better treatments, management of the long‐ term complications that reduce patient's quality of life (QoL) is increasingly important for patient care. 1, 2, 3, 4, 5 Breast cancer related arm lymphoedema (LE) is swelling of the arm after surgery or radiotherapy to the axilla. 1, 3, 5, 6, 7, 8 LE is a progressive condition with initial fluid accumulating in the interstitial space of the subcutaneous tissue, followed by chronic inflammation, which leads to fibrotic thickening of skin and dermis. 7 LE causes physical and psychosocial morbidity, with altered body image and recurrent infections of the arm (cellulitis) leading to progression of lymphoedema by further damage to lymph vessels. 3, 5, 7 Increases in the relative ipsilateral arm volume (versus the contralateral arm) of more than $10\%$ is accepted criteria for the diagnosis of lymphoedema. 3, 5, 7, 8, 9 Most patients develop LE within 12–24 months of surgery and it is claimed early intervention after surgery may benefit patients. 3, 5, 6, 7 Both a meta‐analysis and a large prospective UK study found high Body Mass Index (BMI) predicted development of lymphedema after axillary node clearance (dissection) surgery. 6, 7 High BMI after development of LE also predicted earlier progression of lymphoedema. 7 It is unknown whether BMI interferes with the ability of garment sleeves to provide sufficient compression to the arm. A small US army cohort study of 43 women (in a group of patients with early 3–$9\%$ RAVI arm swelling) treated with arm sleeves for a median 4.4 months claimed early intervention of the compression arm sleeves prevented the development of chronic lymphoedema. 10 This study asserted that early intervention prevented further arm swelling and QoL improved. 10 Nearly all the patients in this study had a normal BMI. Both in the United States in the National Lymphoedema Network Guidelines 11 and in the International Lymphoedema Framework guidelines, 9 surveillance strategies to identify patients developing lymphoedema after surgery have been introduced based on the Stout‐Giegich data, compared to the paradigm that addressed LE once it had developed. Compression garments, which reduce the amount of interstitial fluid, are graduated with the greatest compression at the distal end and the least compression at the proximal end thus covering the entire area of oedema. 1, 9, 11 The evidence base for these treatments in established LE is poor quality, with only three single centre randomised studies involving 150 patients with lymphoedema, none of which involved the same interventions. 12 Reductions in arm swelling of 4–$24\%$ were found in studies of established lymphoedema. 12 Early arm swelling predicts development of lymphoedema. 7 Arm swelling of 4–$9\%$ is not usually clinically apparent unless arm measurements have been made pre‐operatively, but has been shown to predict an increased risk of lymphoedema. 7, 10, 11 A trial of Manual Lymphatic Drainage (MLD) compared to arm exercises in women with early arm swelling after axillary surgery found that $28\%$ control and $24\%$ MLD patients developed LE (defined as Relative Arm Volume Increase (RAVI) > $10\%$) and concluded manual lymphatic drainage did not prevent LE development. 13 Currently there is no large randomised trial evidence to support the value of compression garments in preventing lymphoedema after ANC. We tested the hypothesis whether early intervention in a group of patients with increased lymphoedema risk (RAVI 4–$9\%$ arm swelling) using a compression garment and supportive treatment compared to supportive treatment alone (written advice, arm elevation exercises and massage) reduces the subsequent development of lymphoedema and improves QoL in patients presenting through a surveillance programme following surgery who had developed a RAVI of 4–$9\%$. ## METHODS As part of another prospective study (BEA) 7 which compared bioimpedance spectroscopy with limb volume measurements in the early detection of LE after breast cancer treatment, women scheduled to undergo ANC gave consent to have baseline and follow‐up arm volume measurements by Perometer at 1, 3, 6 and 9 months after surgery in nine UK centres (http://isrctn.com/ISRCTN48880939). Perometer (http://www.pero‐system.de/wirueber_e.htm) arm volume measurements are reproducible, validated and have a low inter‐test variation in both normal volume human arms and lymphoedema arms. 7, 14, 15 From these, participants developing RAVI between 4–$9\%$ within 9 months of surgery were recruited into this trial (PLACE) and subsequently randomised to compare intervention with a compression garment or standard management (Figure 1 Consort diagram). FIGURE 1: PLACE Trial Consort diagram. In the Flow diagram, the percentage Lymphoedema at each timepoint excludes the patients that dropped out, whereas in the main paper the percentage developing Lymphoedema is ITT so includes all patients Inclusion criteria: women aged 18–90 years with early breast cancer (no metastasis), who had undergone ANC. Exclusion criteria: women with inoperable breast cancer (T4 category or distant metastasis), previous axillary radiotherapy or clearance prior to consent, past history of breast/chest wall radiotherapy prior to entering the trial (radiotherapy to chest wall or breast as part of treatment after clearance was allowed). Intervention: application of graduated compression garments (Sigvaris:20‐‐24 mmHg) to the affected arm for 12 months (applied by trained lymphoedema practioners to ensure good fit) together with written advice on elevation, exercises and self‐massage. Standard management: written advice on elevation, exercises and self‐massage. Ethical approval for both the BEA and PLACE trial was granted by the ethics review board (REC 10/H$\frac{1003}{35}$). All patients had a prestudy Body Mass Index (BMI) recorded and had BMI (weight) measurements repeated at each clinic visit to examine the potential relationship between high BMI and reduced compression sleeve efficiency. ## Outcome measures The primary outcome was proportion of participants developing lymphoedema (defined by RAVI > $10\%$ from pre‐operative measurement) by 24‐months post‐randomisation in each arm as assessed by time to lymphoedema. Arm volume was measured at baseline, 1, 3, 6, 9 and 12 months then 6 monthly thereafter to 2 years, followed by annual measurements to 5 years after trial entry using a perometer. ## Secondary outcomes Quality of Life (measured by Trial Outcome Index[TOI] and FACT‐B + 4) at 12, 18 & 24‐months post‐surgery, The FACT‐B + 4 is a validated forty item cancer specific instrument which has 4 additional arm morbidity questions relevant to axillary surgery. 4, 7 The TOI health score is derived from FACT‐B subscale scores. Quality of Life assessments: Quality of life questionnaires (TOI and FACT‐B + 4) and standard health utility measures were administered 6 monthly for 2 years then yearly to 5 years. bIncidence of cellulitis,cIncidence of moderate lymphoedema (RAVI > $20\%$) by 24‐months.dEffect of BMI on lymphoedema incidence Participants in the intervention group were fitted by a lymphoedema practitioner with a graduated compression garment (Sigvaris:CE, 20–24 mmHg round knit) which covered the whole arm from the wrist to the upper arm and was worn daily for 1 year after which it was discontinued. And 4 garments were provided to each participant to last for the year. All patients were reviewed 6 monthly. The patients in the control group, whose arm swelling increased to RAVI > $10\%$ (lymphoedema) were considered to have failed control management and an appropriate compression sleeve provided as treatment by a lymphoedema therapist. Those in the intervention group whose RAVI increased to >10 were also recorded as having lymphoedema and their further 1 treatment delivered out of trial by lymphoedema nurses. 9, 10, 11 The trial sought to change patient practice by empowering women to use arm sleeves to manage their own arm swelling, rather than consult lymphoedema nurses. ## Statistical analysis The sample size calculation was based on a two‐tailed two‐sample chi‐square test comparing the proportion of patients developing lymphoedema within 24‐months post‐randomisation, with a 1:1 treatment allocation ratio. Estimating that $45\%$ of patients develop lymphoedema, to detect a $20\%$ difference (i.e. $45\%$ vs. $25\%$) in lymphoedema rate by 24‐months between the two treatment groups with $90\%$ power and $5\%$ significance level, requires 120 patients in each group but was increased to 135 per group to allow for dropouts. Descriptive statistics are presented as Mean (SD) or Median (IQR), and as number (%) for continuous and categorical variables, respectively, unless otherwise stated. The analyses involved two‐tailed two‐sample tests with $5\%$ significance level, performed on an intention‐to‐treat basis using statistical software R version 4.0.2. The primary analysis, incidence of lymphoedema within 24‐months post‐surgery, was assessed by chi‐square test. Quality of Life, measured by FACT‐B + 4 and TOI, was assessed by t‐test. Incidence of infection during follow‐up, and incidence of moderate/severe lymphoedema within 24‐months were assessed by Fisher's Exact test. Survival analysis was performed for time‐to‐lymphoedema, involving Kaplan–Meier curves and Cox regression. Additional exploratory analyses were performed for the primary outcome, to investigate the effect of BMI, and a per‐protocol analysis to take into account of protocol deviations. ## RESULTS From the 1229 participants screened in the BEA study, 414 developed a 4–$9\%$ RAVI within 9 months of ANC, of which 125 (Median Age 55 years) were recruited and randomised into the PLACE Trial (see Figure 1 CONSORT diagram). Due to slow recruitment, the PLACE study was opened to 5 more centres and a further 18 participants were recruited from a further 458 screened, thus 43 women (69 to the intervention group and 74 to the control group) entered the trial between 2011 and November 2015. With continued low rate per month recruitment, the Independent Data Monitoring Committee (IDMC) advised that, even if recuitment of 200 patients was achieved, the outcome of the study was unlikely to alter and accordingly the trial was closed to new participants. Follow‐up of patients in the trial continued until at least 2 years after trial entry (August 2018). Median follow‐up was 43 months for patients in the intervention group, and 41 months in the control group. Both groups were well matched for Body Mass Index (BMI), age, dominant arm, side of operation, smoking history, type of surgery and radiotherapy treatments. All patients underwent axillary clearance, 73 patients underwent mastectomy as surgical treatment and 70 patients breast conservation. Radiotherapy was given to Regional Nodes in 28 patients randomised to no sleeve and 27 to the sleeve application arm. Median follow‐up is 42 months (range 27.2–53.8) (see Table 1). TABLE 1 \| Unnamed: 0 \| Unnamed: 1 \| No sleeve (n = 74) \| Sleeve (n = 69) \| \| --- \| --- \| --- \| --- \| \| Age at randomisation \| \| 55.5 (33.5, 89.9) \| 55.8 (32.0, 86.9) \| \| Body Mass Index (BMI) (pre op) \| \| N = 72 27.8 (17.2, 45.3) \| N = 67 28.7 (16.9, 60.9) \| \| BMI (at PLACE entry) \| \| 26.9 (18.0, 47.1) \| 28.3 (16.9, 50.5) \| \| Smoking history \| Never \| 40 \| 35 \| \| Smoking history \| Ex \| 20 \| 25 \| \| Smoking history \| Current \| 5 \| 9 \| \| Follow‐up (months from randomisation) \| \| N = 72 41 (27.2, 53.8) \| N = 69 43 (35, 53) \| \| Tumour site \| Upper Outer Quadrant \| 34 \| 37 \| \| Tumour site \| Upper Inner quadrant \| 9 \| 7 \| \| Tumour site \| Lower Outer Quad \| 9 \| 2 \| \| Tumour site \| Lower Inner quadrant \| 6 \| 2 \| \| Tumour site \| Central areolar \| 5 \| 10 \| \| Tumour site \| Other \| 11 \| 11 \| \| Side \| Right:Left \| 29:36 \| 23:41 \| \| Dominant hand \| Right;Left \| 69;5 \| 64:5 \| \| Grade \| 1 \| 4 (5) \| 4 (6) \| \| Grade \| 2 \| 32 (43) \| 31 (46) \| \| Grade \| 3 \| 36 (49) \| 29 (43) \| \| Grade \| Ungraded \| 2 (3) \| 3 (4) \| \| Type of surgery \| ANC \| 13 \| 15 \| \| Type of surgery \| WLocal Excision +ANC \| 23 \| 17 \| \| Type of surgery \| Mastectomy+ANC \| 36 \| 34 \| \| Type of surgery \| Other \| 2 \| 3 \| \| Radiotherapy postop \| Yes \| 64 \| 60 \| \| Dose (cGy) \| \| N = 64 4005 (3960, 5605) \| N = 60 4005 (1068, 6010) \| \| # Fractions \| \| N = 64 15 (15, 25) \| N = 60 15 (4, 30) \| \| Site of radiotherapy \| Breast \| 29 \| 25 \| \| Site of radiotherapy \| Breast+SupraclavFossa \| 22 \| 22 \| \| Site of radiotherapy \| Breast+Axilla \| 3 \| 2 \| \| Site of radiotherapy \| Breast+SCF + Axilla \| 3 \| 3 \| \| Site of radiotherapy \| Other \| 7 \| 8 \| \| Adjuv chemotherapy \| Yes \| 61 (82) \| 59 (86) \| \| Number of nodes \| Involved \| 2 (1–6.8) \| 3 (1–6) \| \| Number of nodes \| Removed \| 16 (13–22) \| 17 (11–22) \| \| HER2 \| Negative \| 60 (81) \| 50 (72) \| \| HER2 \| Amplified or 3+ \| 14 (18.9) \| 19 (27.5) \| \| Receptor status \| ER positive \| 55 (75) \| 58 (84) \| \| Receptor status \| PR positive \| 32 (62) \| 29 (59) \| \| RAVI Difference (at PLACE entry) \| \| 5.9 (4.1, 10.3) \| 5.9 (4, 8.5) \| \| Time to lymphoedema (months) \| \| 4.5 (2.0–17.5) \| 9.9 (4.7–14.8) \| ## Primary outcome‐lymphoedema development There was no difference in the proportion of patients who developed lymphoedema within 12 or 24 months in the No sleeve group (18 ($26\%$) and 25 ($41\%$) respectively) compared to the Sleeve group (11 ($17\%$) and 18 ($30\%$ respectively:$$p \leq 0.32$$)). Twentyone percent [13] of patients randomised to No sleeve and $13\%$ [8] in the Sleeve group were lost to follow‐up by 24‐months. Twentyeight ($41\%$) and 20 ($30\%$) of patients developed lymphoedema within 5‐years, in the No sleeve and Sleeve groups, respectively ($$p \leq 0.32$$). For the subgroup who developed lymphoedema, Median (IQR) time to lymphoedema was 4.5 months (1, 17.5) for patients in the No sleeve arm, and 9.9 months [6, 15] for patients randomised to Sleeve. The Kaplan–Meier time‐to‐lymphoedema curves did not differ between groups in the Intention to treat and perprotocol analyses ($$p \leq 0.21$$ and $$p \leq 0.12$$: Figure 2A,B). FIGURE 2: (A) Lymphoedema Free Survival at 60 months. (B) Per protocol analysis of Lymphoedema Free Survival. (C) Functional (FWB) and Emotional Well‐being (EWB) changes between Groups FACT‐B. Median and interquartile range are shown. (D) Emotional Well‐being (EWB) changes between the groups. Median and interquartile range are shown A total of 33 patients randomised to the ‘no sleeve’ group had a sleeve applied within 24 months;18 received the sleeve after measuring RAVI > $9\%$, 1 was lost to follow‐up and the remaining 13 patients had a sleeve applied before measuring RAVI > $9\%$. In the per‐protocol analysis, 13 patients randomised to No sleeve were included as having received a Sleeve, with time‐to‐lymphoedema measured from the date the Sleeve was applied. Thirtyseven percent of the No sleeve patients ($\frac{22}{60}$) and $27\%$ ($\frac{22}{81}$) of the per protocol sleeve patients developed lymphoedema within 24‐months. Similarly, $40\%$ in the No sleeve group and $30\%$ in the Sleeve group developed lymphoedema within 5‐years (Table 2). TABLE 2 \| Unnamed: 0 \| No Sleeve \| Sleeve \| p‐value \| \| --- \| --- \| --- \| --- \| \| \| n = 59 \| n = 75 \| p‐value \| \| Lymphoedema \| Lymphoedema \| Lymphoedema \| Lymphoedema \| \| Within 2 years \| \| \| \| \| Yes \| 22 (37) \| 22 (27) \| 0.23 \| \| Within 5 years \| \| \| \| \| Yes \| 24 (40) \| 24 (30) \| 0.2 \| Body mass index (BMI) greater than 25 was present in $66\%$ patients recruited to the trial. During the trial, the average change in BMI was a gain of 0.14 kg/m2 (IQR −4.43–3.87) with only 1 patient in each arm reducing their BMI to less than 30 and four in the no sleeve arm and 6 in the compression sleeve groups increasing their BMI over 30.BMI did not reduce from presurgery to randomisation despite advice on diet and exercise routinely provided to the patients in the study (Table 1). ## Quality of life outcomes Changes in Quality of Life (FACT‐B and TOI [Trial Outcome index]) from pre‐surgery to 12, 18 or 24 months post‐surgery did not differ between the two treatment groups (Table 3). At 12‐months, median (IQR) change in FACT‐B + 4 score was 0.5 points (−7, 9) in the No sleeve group, and 5 points (−5, 12) in the Sleeve group ($$p \leq 0.36$$) For TOI, median change was 3.5 points (−3, 10) in the No sleeve group and 4 points (−2, 13.5) in the Sleeve group ($$p \leq 0.33$$). TABLE 3 \| Variable \| Change from pre‐surgery a , EMM (95% CI) \| Change from pre‐surgery a , EMM (95% CI).1 \| Change from pre‐surgery a , EMM (95% CI).2 \| Change from pre‐surgery a , EMM (95% CI).3 \| p‐value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variable \| n \| No sleeve \| n \| Sleeve \| p‐value \| \| FACT‐B at 12 months \| 43 \| 2.60 (−1.66, 6.85) \| 46 \| 3.44 (−0.67, 7.56) \| 0.78 \| \| TOI at 12 months \| 46 \| 0.83 (−2.25, 3.91) \| 48 \| −0.11 (−3.12, 2.91) \| 0.67 \| \| ARM at 12 months \| 43 \| −3.67 (−4.93, −2.40) \| 43 \| −4.04 (−5.30, −2.77) \| 0.68 \| \| FACT‐B at 18 months \| 49 \| −3.25 (−7.41, 0.92) \| 47 \| 1.88 (−2.37, 6.14) \| 0.091 \| \| TOI at 18 months \| 51 \| −3.09 (−6.18, 0.001) \| 49 \| −0.30 (−3.46, 2.85) \| 0.21 \| \| ARM at 18 months \| 42 \| −3.50 (−4.78, −2.21) \| 45 \| −3.33 (−4.57, −2.09) \| 0.85 \| \| FACT‐B at 24 months \| 44 \| −0.34 (−4.84, 4.16) \| 39 \| 4.80 (0.01, 9.58) \| 0.12 \| \| TOI at 24 months \| 45 \| −0.43 (−3.72, 2.86) \| 41 \| 1.14 (−2.30, 4.58) \| 0.51 \| \| ARM at 24 months \| 38 \| −1.73 (−3.21, −0.25) \| 35 \| −3.22 (−4.76, −1.68) \| 0.17 \| Changes in Functional Well Being scores in FACT‐B (No sleeve FWB ‐1 (−4.74, 0), versus Sleeve FWB: 6 [2, 7]) were significant ($$p \leq 0.007$$: Non‐parametric Wilcoxon/Mann Whitney U test: Figure 2C) Emotional Well Being changes (No sleeve −2 (−5, 1.75)) compared to Sleeve arm −5 (−6, −1) was not significant ($$p \leq 0.24$$). Compression sleeves applied after development of Lymphoedema produced a short term improvement in QoL scores at 12 months (Table 4:FACT‐B $$p \leq 0.007$$:TOI $$p \leq 0.042$$). Which disappeared after 18 and 24 months. TABLE 4 \| Variable \| Change from pre‐surgery a , EMM (95% CI) \| Change from pre‐surgery a , EMM (95% CI).1 \| Change from pre‐surgery a , EMM (95% CI).2 \| Change from pre‐surgery a , EMM (95% CI).3 \| p‐value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Variable \| n \| No sleeve \| n \| Sleeve \| p‐value \| \| FACT‐B at 12 months \| 11 \| 2.95 (−1.66, 7.56) \| 14 \| 11.80 (7.71, 15.88) \| 0.007 \| \| TOI at 12 months \| 12 \| 0.41 (−4.13, 4.95) \| 14 \| 6.84 (2.63, 11.04) \| 0.042 \| \| ARM at 12 months \| 12 \| −3.17 (−5.26, −1.08) \| 13 \| −3.92 (−5.93, −1.91) \| 0.60 \| \| FACT‐B at 18 months \| 12 \| −2.96 (−10.82, 4.89) \| 14 \| 4.69 (−2.58, 11.96) \| 0.15 \| \| TOI at 18 months \| 13 \| −3.82 (−9.84, 2.20) \| 14 \| 2.18 (−3.62, 7.98) \| 0.15 \| \| ARM at 18 months \| 11 \| −2.44 (−4.32, −0.55) \| 14 \| −3.58 (−5.26, −1.91) \| 0.36 \| \| FACT‐B at 24 months \| 13 \| 0.30 (−8.04, 8.63) \| 13 \| 4.71 (−3.63, 13.04) \| 0.46 \| \| TOI at 24 months \| 13 \| −0.38 (−6.19, 5.43) \| 13 \| 1.90 (−3.91, 7.71) \| 0.58 \| \| ARM at 24 months \| 11 \| −2.10 (−4.87, 0.68) \| 12 \| −4.41 (−7.07, −1.75) \| 0.22 \| ## Incidence of cellulitis Twelve ($16\%$) patients in the no sleeve arm and 5 ($7\%$) patients in the sleeve arm developed cellulitis of the affected arm during follow‐up ($$p \leq 0.12$$). ## Incidence of moderate lymphoedema (RAVI > 20%) by 24‐months No difference was found in the proportion of patients who developed moderate lymphoedema (RAVI > $20\%$) within 24‐months; $5\%$ ($\frac{4}{74}$) in the No sleeve group, and $9\%$ ($\frac{6}{69}$) in the Sleeve group ($$p \leq 0.66$$). ## Effect of BMI on lymphoedema incidence Body Mass Index (BMI) assessed as a continuous value at randomisation predicted lymphoedema at any time point HR 1.04 (CI 1.01–1.08; $$p \leq 0.02$$). $\frac{25}{74}$ = $35\%$ of patients in the No sleeve group, and $\frac{26}{69}$ = $41\%$ in the Sleeve group had obesity (BMI > 30) at randomisation. Of these patients with obesity, 13 ($57\%$) in the No sleeve group and 10 ($39\%$) in the Sleeve group developed lymphoedema by 24‐months. Of the patients with BMI < 30, 11 ($26\%$) in the No sleeve group and 8 ($21\%$) in the Sleeve group developed lymphoedema. No difference was found in the two treatment groups (HR = 0.69 [0.39, 1.23], $$p \leq 0.21$$) for time to development of lymphoedema, and the difference remained non‐significant after adjusting for BMI at randomisation (HR = 0.61 [0.34–1.1], $$p \leq 0.1$$). During the PLACE trial, the average change in BMI was a gain of 0.14 kg/m2 (IQR −4.43–3.87) with only 1 patient in each arm reducing their BMI to less than 30 and four in the no sleeve arm and 6 in the compression sleeve groups increasing their BMI over 30. BMI did not reduce from presurgery to randomisation despite advice on diet and exercise routinely provided to the patients in the study (Table 1). ## DISCUSSION External Compression Garment application to the arm has been used as treatment for established arm LE for decades 1, 7, 9, 11 despite the lack of evidence for the efficacy for compression therapy based on single centre studies.12 Following the 16 claim that sleeve application in patients with early arm volume increases, prevented progression of arm swelling to LE, 16 most international lymphoedema guidelines 9, 11 have advised baseline arm volume or other measurements before surgery and intervention with compression sleeves if arm swelling (RAVI > $4\%$) occurs. This requires considerable time in patient outpatient visits and health economic costs. The PLACE trial was designed to test the efficacy of such a strategy. Essentially, the reduction in lymphoedema with a compression garment should be considered as a percent of the control rate in a high risk population. Within 12 months using the sleeve, $17\%$ of patients developed lymphoedema compared to $26\%$ with best supportive care and $30\%$ participants developed lymphoedema at 2 years in the intervention group. There was no evidence of benefit from surveillance and early application of a compression sleeve in subclinical LE in preventing clinical LE. Neither was a benefit of early intervention found either in terms of preventing lymphoedema progression to moderate LE or its infective complications. It could be argued that early intervention may treat subclinical / mild clinical lymphoedema and therefore after 1 year in the intervention group we should see a difference compared with the control group which could disappear after the compression garment is discontinued. However, this was not the result found (see Figure 2A,B). One limitation of the study is that we did not have an accurate record of adherence by participants to wearing the garment in the intervention group, despite the provision of diaries. Another limitation may be the different rate of loss to follow‐up by 24 months in the 2 groups ($21\%$ in the controls and $13\%$ in the intervention group). In this study, no benefit of early intervention was found either in terms of preventing lymphoedema progression to moderate LE or its infective complications. In the BEA study, high BMI was also associated with progression of lymphoedema after application of a compression sleeve. 7 The poorer response to compression in obese patients is well recognised in lymphoedema clinics. However, it should be recognised that although those who developed lymphoedema were followed up in the study, the treatment of their lymphoedema was provided by lymphoedema therapists outside the study and not standardised. These findings will potentially apply to other cancers requiring axillary clearance (ie melanoma) causing lymphoedema following surgery. Although PLACE patient recruitment was lower than planned, the similar rate of lymphoedema development in both trial arms indicates that there is no preventative effect of Compression Sleeves on Lymphoedema development compared to standard conservative management, particularly in overweight and obese patients. Notably, the small differences in lymphoedema rates was largely seen in the women with a normal BMI. Women with a normal BMI represent a minority of the cancer population. We had previously reported that in 271 nonrandomised BEA patients developed LE in $24\%$ patients by 24 months despite sleeve application. Older Age, BMI > 30 and the number of metastatic nodes at axillary clearance predicted progression to Lymphoedema in those BEA patients. 7 In the BEA study, high BMI was also associated with progression of lymphoedema after application of a compression sleeve. 7 A small controlled trial randomised 45 women (23 in the compression group and 22 to the control) to light compression sleeves (15–21 mmHg) worn daily immediately after breast cancer surgery, yet two years later, 17 3 out of 20 patients from the compression group were still wearing their garments and 6 out of 21 from the control group had arm lymphoedema defined by an increased volume greater than $10\%$ compared with preoperative values. There was no difference between the change in arm volume from preoperative values between the groups after two years, findings similar to our trial. 17, 18 Stuiver et al found no evidence that class 2 compression stockings prevented lower limb lymphoedema in a trial of 85 patients undergoing groin dissection for cancer and they argued alternative prevention strategies were required. 19 Following the 16 claim that sleeve application in patients with early arm volume increases, prevent progression of arm swelling to LE, 16 most international lymphoedema guidelines 9, 11 have advised baseline arm volume or other measurements before surgery and intervention with compression sleeves if arm swelling (RAVI > $4\%$) occurs. In the ALMANAC Trial of those patients developing 4–$9\%$ arm swelling by 6 months postsurgery, $30\%$ improved spontaneously with standard management. 2, 4 Many of these minor changes in arm swelling would not have been detected without preoperative measurements of both arms. In the BEA study $43\%$ women mentioned arm swelling when asked at 6 months but only $10.5\%$ had developed LE on measurement. 7 Thus, 4–$9\%$ increase in arm swelling is usually clinically undetectable and asymptomatic. To screen such women thereby increasing patient anxiety while treating ephemeral arm changes is inappropriate and wasteful of health resources. The lack of evidence of the effects of surveillance or compression sleeves on preventing LE raises doubts about the National Lymphoedema Network guideline recommendations. Higher Lymphoedema risks for both overweight (OR 2) and obese (OR 3) patients after both sentinel 20 and axillary node surgery 6, 7, 13, 21 have been reported. We found BMI > 25 was associated with no treatment benefit from arm sleeve compression. Metaanalysis of Compression Sleeve Therapies in Lymphoedema 1, 12 found any effect size was likely to be small, so it was not surprising we found little effect of the compression sleeve on preventing lymphoedema. Weight Gain after Breast Cancer Surgery is common and due to the effects of chemotherapy and radiotherapy causing fatigue and steroid therapy during chemotherapy. Exercise and diet regimes reduce weight gain on adjuvant endocrine therapy but not chemotherapy 22 Fluctuations in weight are reported to increase the risk of Lymphoedema. 23 Weight loss has been found to reduce lymphoedema in a pilot study of overweight breast cancer survivors. 23, 24 Two recent studies have shown upper ‐body exercise reduces lymphoedema flare‐ups and symptoms, possibly due to increased muscle function and vascular flow. 24, 25 Additionally weightlifting has been found to reduce arm volume and lymphoedema when wearing a compression sleeve. 26 The use of compression sleeves did not reduce progression of mild to moderate lymphoedema by five years after surgery nor did it reduce arm infections in the PLACE trial. However $80\%$ of our patients underwent adjuvant chemotherapy and we have found that chemotherapy and particularly the corticosteroids prescribed during chemotherapy mitigate against any weight loss in the first year after surgery which may partly explain why chemotherapy increases the risk of lymphoedema. 7 However the exercise interventions used in these studies above did not affect body weight 22, 23, 26, 27 suggesting body weight loss, regimens, upper body exercises and compression sleeves may require to be tested in combination with exercise regimens as a management strategy for preventing lymphoedema. The purpose of any screening intervention to prevent disease is to identify patients who will benefit from an intervention and prevent the disease being screened for subsequently developing. Early Intervention with compression sleeves did not prevent lymphoedema development. The lack of preventative interventions suggests that screening for lymphoedema should not be recommended for all patients after axillary node clearance. ## AUTHOR CONTRIBUTIONS Emma Barrett: Data curation (equal); formal analysis (equal); methodology (equal); software (equal); writing – review and editing (equal). Chriss Todd: Data curation (equal); formal analysis (equal); funding acquisition (equal); methodology (equal); project administration (equal); writing – original draft (equal); writing – review and editing (equal). Donna Watterson: Data curation (equal); project administration (equal); software (equal); writing – review and editing (equal). Julie Morris: Formal analysis (equal); methodology (equal); resources (equal); writing – original draft (equal). Arnie Purushotham: Project administration (equal); resources (equal); supervision (equal); writing – original draft (equal); writing – review and editing (equal). Katie Riches: Methodology (equal); project administration (equal); writing – original draft (equal). Abigail Evans: Project administration (equal); writing – original draft (equal); writing – review and editing (equal). Anthony Skene: Investigation (equal); project administration (equal); supervision (equal); writing – original draft (equal); writing – review and editing (equal). Vaughan Keeley: Conceptualization (equal); investigation (equal); methodology (equal); project administration (equal); supervision (equal); writing – original draft (equal); writing – review and editing (equal). ## FUNDING INFORMATION The trial was funded by the UK National Institute for Health Research (NIHR) Programme Grant for Applied Research (RP‐PG‐0608‐10168), held by Professor Bundred (Chief Investigator). Funding NIHR Programme grant. Protocol no: 2008/NJB/0503.dy. EudraCT no: 2008–001500‐22. ## CONFLICT OF INTEREST There are no conflicts of interest. ## ETHICS APPROVAL AND CONSENT TO PARTICIPATE The study was performed in accordance with the Declaration of Helsinki. The ethics was approved by the South Birmingham Research Ethics Committee. The participants all consented to take part in the study. ## CONSENT FOR PUBLICATION All authors have provided their consent for publication. ## DATA AVAILABILITY STATEMENT The data and material are all available through writing to Manchester CTU (formerly MAHSC‐CTU). ## References 1. Preston NJ, Seers K, Mortimer PS. Physical therapies for reducing and controlling lymphoedema of the limbs. Cochrane Database Syst Rev (2004.0) 4. DOI: 10.1002/14651858.CD003141.pub2 2. Mansel RE, Fallowfield L, Kissin M, Goyal A, Newcombe RG, Dixon JM. Randomized multicenter trial of sentinel node biopsy versus standard axillary treatment in operable breast cancer: the ALMANAC trial. J Natl Cancer Inst (2006.0) 98 599-609. PMID: 16670385 3. Hayes S, diSipio T, Rye S. Prevalence and prognostic significance of secondary lymphoedema following breast cancer. Lymphat Res Biol (2011.0) 9 135-141. PMID: 22066743 4. Fleissig A, Fallowfield LJ, Langridge CI. Post‐operative, arm morbidity and quality of life. Results of the ALMANAC randomised trial comparing sentinel node biopsy with standard axillary treatment in the management of patients with early breast cancer. Breast Cancer Res Treat (2006.0) 95 279-293. PMID: 16163445 5. Engel J, Kerr J, Schlesinger‐Raab A, Sauer H, Hölzel D. Axilla surgery severely affects quality of life: results of a 5‐year prospective study in breast cancer patients. Breast Cancer Res Treat (2003.0) 79 47-45. PMID: 12779081 6. DiSipio T, Rye Newman B. And Hayes incidence of unilateral arm lymphedema after breast cancer:a systematic review and meta analysis. Lancet Oncology (2013.0) 14 500-515. PMID: 23540561 7. Bundred N, Foden P, Todd CMJ, Watterson D, Purushotham A. 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--- title: 'Relationship Between Childhood Abuse and Body Mass Index in Young Adulthood: Mediated by Depression and Anxiety?' authors: - Leonie K. Elsenburg - Aart C. Liefbroer - Annelies E. van Eeden - Hans W. Hoek - Albertine J. Oldehinkel - Nynke Smidt journal: Child Maltreatment year: 2022 pmcid: PMC10028135 doi: 10.1177/10775595221092946 license: CC BY 4.0 --- # Relationship Between Childhood Abuse and Body Mass Index in Young Adulthood: Mediated by Depression and Anxiety? ## Abstract We examined whether childhood abuse is related to body mass index (BMI) in young adults and whether this relationship is mediated by depression and anxiety. Data are from the Dutch longitudinal cohort study TRAILS (nfemales = 836, nmales = 719). At wave 4, childhood sexual, physical and verbal abuse, and lifetime major depressive disorder (MDD) and generalized anxiety disorder (GAD) were assessed. BMI was measured at wave 4 and 5 (mean age = $\frac{19.2}{22.4}$ years). Sex-stratified structural equation models were estimated. Females who had experienced sexual abuse had a higher BMI at wave 4 ($B = 0.97$, $95\%$CI = [−0.01,1.96]) and a higher increase in BMI between wave 4 and 5 ($B = 0.52$, $95\%$CI = [0.04,1.01]) than females who had not experienced sexual abuse. Additionally, MDD and BMI at wave 4 were related in females ($B = 1.35$, $95\%$CI = [0.52,2.18]). MDD mediated the relationship between sexual abuse and BMI at wave 4 in females. In addition, sexual abuse moderated the relationship between MDD and BMI at wave 4. The relationship was stronger among females who had experienced sexual abuse than among females who had not. Prevention of BMI changes among females who experienced sexual abuse may thus be warranted, particularly when they developed MDD. MDD treatment, such as abuse-focused psychotherapy, may aid this prevention. ## Introduction Childhood maltreatment, i.e., childhood sexual, physical and verbal abuse and neglect, has been associated with obesity in adulthood (Danese & Tan, 2014; Hemmingsson et al., 2014). In addition, childhood abuse has been related to cardiovascular disease and type 2 diabetes (Basu et al., 2017). Potential pathways via which childhood maltreatment could affect obesity and related conditions are alterations in health behaviors, biological factors, such as stress hormones, and mental health (Suglia et al., 2018). Depression is a mental health disorder that has been related to obesity (Mannan et al., 2016; Mühlig et al., 2016). While a relationship between childhood maltreatment and obesity in adulthood has been established, it is unclear when this relationship comes to expression. It could become apparent during the transition to adulthood (Schneiderman et al., 2015), as a result of individuals gaining autonomy over their health behaviors in this life period (Viner et al., 2015). Given that obesity is a risk factor for cardiovascular disease and type 2 diabetes, obesity could serve as an early marker for risk of cardiovascular disease and type 2 diabetes following childhood maltreatment (Suglia et al., 2018). Therefore, the first aim of this study was to investigate whether there is a relationship between childhood sexual, physical and verbal abuse and body mass index (BMI) in young adulthood. A study into the possible mechanisms linking childhood abuse to adulthood obesity identified major depressive disorder (MDD) and generalized anxiety disorder (GAD) symptoms as possible mediator and suppressor, respectively, of the relationship between physical abuse and BMI (Francis et al., 2015). When examined in separate and sex-stratified models, only the suppressor effect of GAD symptoms was identified among females. GAD symptoms acted as a suppressor of the relationship, as there was a positive direct effect between physical abuse and BMI while the indirect effect via GAD symptoms was negative (MacKinnon et al., 2000). Another study found that childhood physical abuse, but not childhood sexual abuse, was positively related to BMI via depressive symptoms in females (Dedert et al., 2010). These results demonstrate several things. Firstly, MDD and GAD may mediate and suppress the relationship between childhood abuse and BMI. Secondly, childhood abuse may be linked to increases as well as decreases in BMI. Thirdly, mediation of the relationship between abuse and BMI may depend on the type of abuse studied. Finally, the relationships may be sex-specific. Therefore, the second aim of this study was to assess whether MDD and GAD mediated identified relationships between childhood sexual, physical, and verbal abuse and BMI among young adult females and males. Childhood abuse may not only be related to changes in BMI via MDD and GAD, but may also serve as a moderator of the relationship between MDD/GAD and BMI (Salas et al., 2019; Stunkard et al., 2003). In individuals who experienced childhood abuse and who suffer from depression, alterations in biological reactions and in the body’s biology appear to be different than in individuals who suffer from depression, but experienced no childhood abuse (Danese et al., 2008; Heim et al., 2008a; Vythilingam et al., 2002). This suggests that the biology of depression is different according to whether individuals experienced childhood abuse or not (Heim et al., 2008b). It is shown that childhood abuse may influence the structure of the brain (McCrory et al., 2010). Depression and BMI are assumed to be related via unhealthy behaviors and/or biological mechanisms (Penninx, 2017). Biological mechanisms that are suggested to play a role in the relationship are systems that are involved in the stress response or that are influenced by the stress response, such as the autonomic nervous system, the HPA-axis and immuno-inflammatory reactions (Penninx, 2017). Given that depression and BMI may be related via biological mechanisms and the biology of depression may be influenced by childhood abuse, the relationship between MDD and BMI may also be different between individuals who experienced childhood abuse and individuals who did not (Penninx et al., 2013). Therefore, the third aim of this study was to examine moderation of the relationship between MDD/GAD and BMI by childhood abuse when mediation was examined. ## Methods Data are from the TRacking Adolescents' Individual Lives Survey (TRAILS), a prospective cohort study of Dutch adolescents and young adults (Huisman et al., 2008; Oldehinkel et al., 2015). The TRAILS study was approved by the Central Committee on Research Involving Human Subjects (Dutch CCMO). From two municipalities in the North of the Netherlands children born between 1 October 1989 and 30 September 1990 were recruited and from three other municipalities in this area children born between 1 October 1990 and 30 September 1991 were recruited ($$n = 3483$$). Baseline data collection took place at schools. Written informed consent was obtained from parents and adolescents. Exclusion criteria were being in a primary school that did not agree to participate, having no parental or child consent, having a severe physical illness or mental retardation and not having a Dutch, Turkish or Moroccan speaking parent or parent surrogate ($$n = 548$$). At baseline, 2230 children were included ($76.0\%$ of eligible children in participating schools) with a mean age of 11 years. For the current study, data of wave 4 (October 2008 to September 2010) and wave 5 (April 2012 to November 2013) were used. Participants were aged between 18–21 years and between 21–23 years at these waves. ## Childhood Abuse A questionnaire, developed by TRAILS, was used at wave 4 to collect information on sexual, physical and verbal abuse before the age of 16 years. Participants answered five questions on sexual and verbal abuse and six on physical abuse (see Supplementary Material, Table S1). Response options to questions on sexual abuse were ‘never’, ‘yes, once’ and ‘yes, more than once’. Sexual abuse was categorized into ‘no reported occurrence’ [0] and ‘one or more reported occurrences’ [1] due to the low frequency of reporting sexual abuse. Response options to questions on physical and verbal abuse ranged from ‘never’ [0] to ‘very often’ [4]. For both types of abuse, the mean of the answers was taken for participants who provided answers to all questions concerning that abuse type. At wave 4, 1714 children filled out the questionnaire, 1644 participants provided complete information on sexual and verbal abuse and 1640 children provided complete information on physical abuse. ## Anthropometric Measurements Trained research assistants performed anthropometric measurements at wave 4 and 5. Weight was measured with calibrated scales (Seca 876, Hamburg, Germany and Besthome EB813-SL) and height was measured with stadiometers/measuring tapes (Seca $\frac{201}{222}$) with participants dressed in light clothes. BMI was calculated as weight divided by height squared (kg/m2). BMI measurements of 1574 participants were taken at wave 4 and of 1444 participants at wave 5. ## Major Depressive Disorder and Generalized Anxiety Disorder At wave 4, the occurrence of major depressive disorder (MDD) and generalized anxiety disorder (GAD) was assessed with the Composite International Diagnostic Interview (CIDI) version 3.0 (Kessler & Üstün, 2004; Ormel et al., 2015). The CIDI version 3.0 is a structured diagnostic interview to assess mental disorders according to the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM–IV, American Psychiatric Association, 2000). It was administered in person by a trained lay interviewer. Age of occurrence was also assessed. In the current study, participants were classified according to whether or not they had a lifetime MDD or GAD diagnosis. In total, 1584 participants participated in the CIDI. ## Covariates Information on covariates was reported at wave 1. Parents reported on [1] mothers’ and [2] fathers’ education (in five categories from elementary to University education), [3] mothers’ and [4] fathers’ occupation (according to the International Standard Classification of Occupations (ISCO) (Ganzeboom & Treiman, 1996) and [5] household income. Scores on these indicators were standardized and averaged as a measure of socio-economic status (SES) (Amone-P’Olak et al., 2009). Parents also reported on their child’s ethnicity. Very few participants in the sample were non-Dutch (i.e., one or both parents born outside the Netherlands), therefore ethnicity was coded as either Dutch or non-Dutch. Age of the child was recorded at every measurement occasion. The associations of the covariates with childhood abuse, MDD and GAD and BMI were slightly different in the different models, i.e., the models including the three different types of abuse and the models with and without MDD and GAD. Among females, non-Dutch ethnicity seemed associated with a higher odds of sexual abuse and MDD, and with higher physical abuse and verbal abuse compared with Dutch ethnicity. However, the standard errors of the associations with sexual abuse and MDD were large and the associations were not statistically significant in all different models. Among females, SES was negatively associated with both verbal abuse and BMI at wave 4. Among males, non-Dutch ethnicity compared with Dutch ethnicity was associated with a higher odds of sexual abuse, higher verbal abuse and lower BMI at wave 4. The effect estimate of the direct association of ethnicity with BMI at wave 5 was also indicative of negative association when comparing non-Dutch ethnicity to Dutch ethnicity, but the standard error was large and the association was not statistically significant in all models. Non-Dutch ethnicity also seemed associated with higher physical abuse compared with Dutch ethnicity, but the association was not statistically significant. Among males, SES was negatively associated with physical abuse and BMI at wave 4, although the effect estimate of the association with physical abuse was small. The effect estimate of the direct association between SES and BMI at wave 5 was also negative, but the association was not statistically significant. SES further seemed associated with lower odds of sexual abuse, but the association was not statistically significant. The results generally suggest an association between non-Dutch ethnicity and higher odds of sexual abuse and higher physical and verbal abuse among both females and males, and some negative associations between SES and abuse experience. However, both ethnicity and SES were generally not related to MDD and GAD, and mainly SES was associated with lower BMI. This suggests that individuals from non-Dutch ethnicity generally experience more abuse, or are more likely to report abuse, while they are not more likely to experience MDD and GAD, or have a higher BMI, independent of abuse experience. It is important to emphasize that this cohort is from the North of the Netherlands where a relatively small share of the population is of non-Dutch ethnicity. We would have potentially identified more differences if we had been able to make comparisons between specific ethnic groups. In this study, we did not examine whether the associations under study differ between individuals of Dutch and non-Dutch ethnicity. If the associations under study are the same for individuals from different ethnic groups, the suggested prevention efforts would, at a minimum, likely need to be culturally sensitive, and take into account the potentially different context in which the abuse occurred (Martsolf & Draucker, 2005). ## Statistical Analysis Structural equation modeling was used. In the analysis, we included the variables in the temporal order in which they were measured and we expected them to have occurred (Figure 1). Childhood abuse that occurred before age 16 years was measured at wave 4. Lifetime MDD and GAD diagnosis were also measured at wave 4. BMI was measured at wave 4 (18–21 years) and wave 5 (21–23 years).Figure 1.A. The studied relationship between abuse <16 years (assessed at wave 4, 18–21 years) and body mass index (BMI) at wave 4, wave 5 (21–23 years) and between wave 4 and wave 5. Abuse and BMI are regressed on parental socio-economic status (SES) and ethnicity. BMI at wave 4 is regressed on age at wave 4 and BMI at wave 5 is regressed on age at wave 5 and BMI at wave 4. Age at wave 5 is regressed on age at wave 4. B. Models including clinical diagnosis of MDD and GAD before wave 4 as mediators of the relationship between abuse and BMI at wave 4 and 5 are tested in a second step. MDD and GAD are additionally regressed on SES and ethnicity in these models. In a first step, the relationship between sexual, physical and verbal abuse and BMI at wave 4 and 5 was assessed using linear regression (Figure 1, paths 1a, 1b and 1c). This was done in separate models for each type of abuse. To adjust for possible confounding, paths from SES and ethnicity to the abuse and BMI variables and paths from age at wave 4 to BMI at wave 4, age at wave 5 to BMI at wave 5 and age at wave 4 to age at wave 5 were added. In a second step, the associations between abuse and MDD/GAD and between MDD/GAD and BMI were tested using logistic and linear regression, respectively. Further, mediation of the identified associations between abuse and BMI by MDD and GAD was examined. MDD and GAD were added concurrently to the three models developed in the first step. To adjust for potential confounding, paths from SES and ethnicity to MDD and GAD were added. To assess mediation, we examined the indirect relationships between abuse and BMI via MDD and GAD. When testing indirect effects, we additionally examined whether the association between MDD/GAD and BMI was moderated by abuse experience. This was done as moderation of the relationship between MDD/GAD and BMI by childhood abuse can be expected and because indirect effect estimates may be incorrect in the presence of exposure-mediator interaction (Danese et al., 2008; Heim et al., 2008a; Valeri & VanderWeele, 2013; Vythilingam et al., 2002). To test moderation, BMI at wave 4 and 5 were additionally regressed on the interaction term of abuse and MDD/GAD. Analyses were stratified according to sex, as sex differences have been identified in the relationship between depression and BMI and in mediation of the relationship between abuse and BMI by GAD (Anderson et al., 2006; Francis et al., 2015; Mannan et al., 2016; Mühlig et al., 2016; Richardson et al., 2003). As post-hoc analyses showed sex differences, stratification was justified. As a sensitivity analysis, participants diagnosed with MDD and GAD before age 16 years only were excluded from the analyses. This was done as the temporal order of the experience of abuse and the diagnosis of MDD/GAD was unclear for these individuals. Please note that, whereas including the early onsets might lead to an overestimation of the associations under study, excluding them likely results in an underestimation. In a second sensitivity analysis, identified associations of variables with BMI at wave 4 were additionally adjusted for BMI at wave 1. This was done to examine to what extent associations of abuse and MDD/GAD with BMI at wave 4 may be overestimated because of preexisting differences in BMI at wave 1. However, this sensitivity analysis likely results in an underestimation of the associations under study as abuse and MDD/GAD may have occurred before wave 1 and have influenced BMI at wave 1. BMI at wave 1 was standardized based on age- and sex-specific reference curves of the International Obesity Task Force (IOTF), as BMI in childhood is dependent upon age and sex (Cole & Lobstein, 2012). BMI at wave 4 was regressed on abuse, BMI at wave 1, SES, ethnicity and age at wave 4. Additionally, paths from SES and ethnicity to abuse, from SES, ethnicity and age at wave 1 to BMI at wave 1 and from age at wave 1 to age at wave 4 were added. BMI at wave 5 was not incorporated in these models. MDD and GAD were added to these models in the same manner as in the main analysis. Analyses were performed in MPlus version 7.3. Except for the mediation analysis, all analyses were performed using maximum likelihood estimation with robust standard errors (MLR). In these models, full information maximum likelihood was used to handle missing data. For the mediation analysis, weighted least squares (WLSMV) was used, as mediation by a binary variable cannot be tested using MLR. Standard errors of the indirect effect were obtained via bootstrapping (5000 iterations). As level of significance $p \leq .05$ was used. ## Results The characteristics of the study sample (nfemales = 836, nmales = 719) are described in Table 1.Table 1.Descriptive statistics of the study sample. FemalesMalesn (%) a Mean (SD)n (%) a Mean (SD) Age wave 4 (years) 19.17 (0.58)19.23 (0.57) Age wave 5 (years) 22.39 (0.60)22.45 (0.59) Sexual abuse 111 (13.7)32 (4.7) Physical abuse b 340 (42.1)0.19 (0.35)267 (39.6)0.18 (0.33) Verbal abuse b 615 (75.8)0.79 (0.80)514 (76.3)0.70 (0.69) Diagnosis MDD 167 (20.1)67 (9.4) Diagnosis GAD 43 (5.2)13 (1.8) BMI wave 4 (kg/m 2) c 23.11 (3.99)22.51 (3.75) BMI wave 5 (kg/m 2) c 23.70 (4.33)23.49 (3.68) Parental SES 0.06 (0.76)0.10 (0.78) Ethnicity Dutch744 (89.0)646 (89.9) Non-Dutch92 (11.0)73 (10.2)MDD = major depressive disorder, GAD = generalized anxiety disorder, BMI = body mass index, SES = socio-economic status. The number of participants with valid data on a given variable ranges between 725-836 for females and 605-719 for males.aPercentages shown are percentages of the number of participants with valid data on a given variable.bThe categories ‘1-2 times’ to ‘very often’ have been combined here to provide the number and percentage of individuals reporting an occurrence of physical and verbal abuse. In the analyses, physical and verbal abuse were included as continuous variables.cMean (SD) of the participants with BMI measurements at both wave 4 and 5: females wave 4 = 23.03 (3.87), females wave 5 = 23.71 (4.33), males wave 4 = 22.53 (3.72), males wave 5 = 23.49 (3.69). ## Childhood Abuse and BMI Associations between childhood abuse and BMI are shown in Table 2 (see Figure 1(A) for a figure of the assessed associations).Table 2.Direct, indirect and total associations between sexual, physical and verbal abuse and body mass index (BMI) at wave 4 and wave 5.Direct a Indirect b Total c BMI wave 4BMI wave 5BMI wave 5BMI wave 5B($95\%$ CI)B($95\%$ CI)B($95\%$ CI)B($95\%$ CI) Females Sexual abuse0.97(−0.01–1.96)0.52(0.04–1.01)0.93(−0.02–1.88)1.46(0.36–2.55) Physical abuse0.08(−0.72–0.88)0.36(−0.11–0.84)0.08(−0.70–0.85)0.44(−0.50–1.38) Verbal abuse0.06(−0.31–0.42)−0.14(−0.34–0.05)0.05(−0.30–0.41)−0.09(−0.47–0.29) Males Sexual abuse0.79(−0.81–2.39)−0.27(−0.96–0.41)0.68(−0.69–2.05)0.41(−0.97–1.78) Physical abuse−0.71(−1.60–0.18)0.33(−0.10–0.76)−0.61(−1.38–0.16)−0.28(−1.19–0.64) Verbal abuse−0.14(−0.56–0.29)0.13(−0.08–0.33)−0.12(−0.48–0.25)0.01(−0.41–0.43)Females: $$n = 836$$, males: $$n = 719$.$ BMI = body mass index. Sexual abuse is a dichotomized variable (no/yes). Associations are assessed in separate models per abuse type. All associations are adjusted for parental socio-economic status and ethnicity. Additionally, BMI at wave 4 and BMI at wave 5 are regressed on age at wave 4 and age at wave 5, respectively, and BMI at wave 5 is regressed on BMI at wave 4.aThe direct associations with BMI are path 1a (BMI wave 4) and path 1b (BMI wave 5) in Figure 1.bThis indirect association is the association between abuse and BMI at wave 5 through BMI at wave 4 (Figure 1, path 1a1c).cTotal associations between abuse and BMI at wave 5 are equal to the sum of the direct and the indirect associations with BMI at wave 5. In females, experience of sexual abuse versus no experience of sexual abuse was related to a higher BMI at wave 4 (Figure 1, path 1a: $B = 0.97$, $95\%$CI = [−0.01,1.96]), to a higher increase in BMI between wave 4 and wave 5 (Figure 1, path 1b: $B = 0.52$, $95\%$CI = [0.04,1.01]) and to a higher BMI at wave 5 (Figure 1, paths 1a1c + 1b: $B = 1.46$, $95\%$CI = [0.36,2.55]). For females who experienced sexual abuse, the mean BMI at wave 4 was 23.96 (SD = 4.65) and the mean BMI at wave 5 was 24.95 (SD = 5.04). For females who did not experience sexual abuse, the mean BMI at wave 4 was 22.97 (SD = 3.78) and the mean BMI at wave 5 was 23.53 (SD = 4.20). There was no clear evidence for a relationship between physical abuse or verbal abuse and BMI at wave 4, wave 5 and between wave 4 and 5 in females. In males, there was no clear evidence for any relationship between sexual, physical or verbal abuse and BMI at wave 4, wave 5 and between wave 4 and 5. ## Childhood Abuse and MDD/GAD Associations between childhood abuse and MDD and GAD are shown in Table 3 (Figure 1, path 2).Table 3.Direct associations between sexual, physical and verbal abuse and major depressive disorder (MDD) and generalized anxiety disorder (GAD) and between MDD and GAD and body mass index (BMI) at wave 4 and 5.Abuse and MDD/GAD a MDD/GAD and BMI wave 4 b MDD/GAD and BMI wave 5 c B($95\%$CI)B($95\%$CI)B($95\%$CI) Females Sexual abuse MDD 1.10(0.66–1.54)1.35(0.52–2.18)−0.09(−0.50–0.32) GAD 1.29(0.59–1.98)−0.72(−1.88–0.45)0.21(−0.90–1.33) Physical abuse MDD 0.79(0.32–1.26)1.46(0.61–2.31)−0.05(−0.46–0.35) GAD 0.81(0.26–1.36)−0.57(−1.71–0.56)0.27(−0.82–1.35) Verbal abuse MDD 0.53(0.32–0.74)1.47(0.61–2.33)0.02(−0.39–0.42) GAD 0.75(0.43–1.07)−0.57(−1.72–0.58)0.41(−0.69–1.51) Males Sexual abuse MDD 1.59(0.77–2.40)−0.34(−1.36–0.69)0.15(−0.37–0.67) GAD 2.26(0.94–3.57)−1.10(−2.84–0.65)−0.63(−1.74–0.48) Physical abuse MDD 1.06(0.47–1.65)−0.12(−1.10–0.86)0.07(−0.45–0.60) GAD 1.00(0.22–1.78)−0.76(−2.53–1.01)−0.74(−1.87–0.40) Verbal abuse MDD 0.88(0.55–1.22)−0.17(−1.21–0.86)0.06(−0.49–0.61) GAD 0.37(−0.55–1.29)−0.84(−2.63–0.94)−0.69(−1.84–0.46)Females: $$n = 836$$, males: $$n = 719$.$ MDD = major depressive disorder, GAD = generalized anxiety disorder, BMI = body mass index. Mediation of the relationship between abuse and BMI is assessed in separate models per abuse type (i.e. sexual, physical and verbal abuse), MDD and GAD are assessed concurrently in these models. Sexual abuse is a dichotomized variable (no/yes). All associations are adjusted for parental socio-economic status and ethnicity. Additionally, BMI at wave 4 and 5 are regressed on age at wave 4 and 5, respectively, and BMI at wave 5 is regressed on BMI at wave 4.aFigure 1, path 2: examined using logistic regression, the coefficient describes change in log odds of MDD and GAD with every unit increase in abuse.bFigure 1, path 3a.cFigure 1, path 3b. In females, all types of abuse were associated with increased odds of MDD and increased odds of GAD. In males, all types of abuse were associated with increased odds of MDD and sexual and physical abuse were associated with increased odds of GAD. No statistically significant association was identified between verbal abuse and GAD in males ($B = 0.37$, $95\%$CI = [−0.55,1.29]). ## MDD/GAD and BMI The estimates of the associations between MDD/GAD and BMI at wave 4 and 5, as identified in the three different models including the three different types of abuse, can be found in Table 3 (Figure 1, path 3a and path 3b). There was a relationship between MDD and BMI at wave 4 in females ($B = 1.35$–1.47, $95\%$CI = [0.52, 2.18–0.61, 2.33]). There was no clear evidence for a direct association between MDD and BMI at wave 5 or for an association between GAD and BMI at wave 4 or 5. In males, no statistically significant associations between MDD and GAD and BMI at wave 4 or 5 were identified. ## Mediation and Moderation Analyses We limited our mediation analysis to the relationship between sexual abuse and BMI among females. We found evidence for an indirect relationship between sexual abuse and BMI at wave 4 via MDD ($$p \leq .015$$). However, we also found evidence for moderation of the relationship between MDD and BMI at wave 4 by sexual abuse in females. The relationship was stronger among females who had experienced sexual abuse ($$n = 111$$, $B = 3.40$, $95\%$CI = [1.57, 5.22]), than among females who had not ($$n = 698$$, $B = 0.62$, $95\%$CI = [−0.27, 1.51]). There was no clear evidence for an indirect relationship between sexual abuse and BMI at wave 5 via MDD or GAD – other than via BMI at wave 4. There was also no clear evidence for moderation of the direct relationships between MDD/GAD and BMI at wave 5 by sexual abuse. We identified mediation of the relationship between sexual abuse and BMI at the end of adolescence by diagnosis of MDD in females. Previous research identified mediation of the relationship between childhood physical abuse and BMI by MDD symptoms and identified GAD symptoms as a suppressor (Francis et al., 2015). Differences in study results could be caused by the fact that in the previous study participants were recorded cases of abuse and MDD and GAD symptoms were measured instead of MDD and GAD diagnoses. However, both our and the previous study suggest that a higher BMI in individuals who experienced abuse compared to individuals who did not experience abuse may, partially, be prevented by preventing MDD development or by preventing BMI gain in those who developed MDD. We also found evidence for moderation of the relationship between MDD and BMI by sexual abuse in females. Possibly, MDD and BMI are more strongly related in individuals who experienced childhood abuse as biological alterations in response to childhood abuse are at the root of both conditions. This idea is in line with research showing biological differences between depressed patients who did experience childhood abuse and depressed patients who did not (Danese et al., 2008; Heim et al., 2008a; Vythilingam et al., 2002). In addition, studies have shown that the clinical course of and treatment success in MDD is influenced by adverse childhood experiences (Heim et al., 2004; Nanni et al., 2012; Nelson et al., 2017). This study also suggests that MDD treatment needs to be informed by childhood sexual abuse. ## Sensitivity Analysis In the first sensitivity analysis, participants with a diagnosis of MDD and GAD before age 16 years, but not after age 16 years, were excluded. Therefore, the sample size for this analysis was smaller (nfemales = 787, nmales = 705). Differences with the main analysis were that the association between sexual abuse and BMI at wave 4 in females was attenuated ($B = 0.62$, $95\%$CI = [−0.31,1.56]). Further, the relationship between physical abuse and GAD in males was attenuated ($B = 0.75$, $95\%$CI = [−0.10,1.59]). Evidence for an indirect relationship between sexual abuse and BMI at wave 4 via MDD in females also became weaker ($p \leq .10$). Unlike in the main analysis, we found evidence for moderation of the relationships between GAD and BMI at both wave 4 and 5 by sexual abuse among females. We identified a negative association between GAD and BMI at wave 4 among females who had experienced sexual abuse ($$n = 95$$, B = −2.15, $95\%$CI = [−4.12,−0.19]), but not among females who had not experienced sexual abuse ($$n = 667$$, $B = 0.67$, $95\%$CI = [−1.05,2.39]). This same pattern was visible for the association between GAD and BMI at wave 5, although the association was significant neither among females who had experienced sexual abuse ($$n = 95$$, B = −1.25, $95\%$CI = [−3.00,0.50]) nor among females who had not ($$n = 667$$, $B = 1.04$, $95\%$CI = [−0.62,2.69]). In the second sensitivity analysis, identified associations with BMI at wave 4 were additionally adjusted for BMI at wave 1. Sample sizes were larger than in the main analysis as individuals who participated at wave 1, but not at wave 4, were included in these analyses. Differences with the main analysis were that the association between sexual abuse and BMI at wave 4 in females was attenuated ($$n = 1106$$, $B = 0.48$, $95\%$CI = [−0.20,1.17]). In addition, the effect estimate of the association between MDD and BMI at wave 4 was about half the size as in the main analysis ($$n = 1106$$, $B = 0.66$, $95\%$CI = [0.06,1.27]). Evidence for moderation of the relationship between MDD and BMI at wave 4 by sexual abuse also became weaker ($$p \leq .04$$). However, as in the main analysis, the relationship was stronger among females who had experienced sexual abuse ($$n = 118$$, $B = 1.82$, $95\%$CI = [0.60,3.04]), than among females who had not ($$n = 771$$, $B = 0.21$, $95\%$CI = [−0.47,0.88]). ## Discussion In this study, we examined the relationship between childhood abuse and BMI in young adulthood, and mediation of this relationship by major depressive disorder (MDD) and generalized anxiety disorder (GAD). Of the three types of abuse we distinguished (i.e. sexual, physical and verbal abuse), only sexual abuse in females was related to higher BMI at the end of adolescence and a higher increase in BMI in young adulthood. MDD mediated the relationship between sexual abuse and BMI at the end of adolescence in females. Sexual abuse also moderated the relationship between MDD and BMI at the end of adolescence. The relationship between MDD and BMI at the end of adolescence was particularly present among females who experienced sexual abuse. ## Childhood Abuse and Body Mass Index The associations between sexual abuse and BMI in females are in line with a study identifying higher increases in BMI between childhood and young adulthood among females who did experience sexual abuse as opposed to females who did not (Noll et al., 2007). The transition to adulthood seems to be a crucial period for the emergence of changes in BMI development following sexual abuse in females. No statistically significant associations were identified between sexual abuse and BMI in males, but this could be due to a lack of power. Further, we found no evidence for an association between physical and verbal abuse and BMI in females or males. This suggests that the relationships between both physical and verbal abuse and BMI, which are identified in adults (Danese & Tan, 2014; Hemmingsson et al., 2014), have not come to expression yet in young adulthood. ## Childhood Abuse and Major Depressive Disorder and Generalized Anxiety Disorder In line with earlier studies, strong associations were found between childhood abuse and diagnosis of MDD and GAD (Fernandes & Osório, 2015; Infurna et al., 2016; Li et al., 2016; Lindert et al., 2014; Mandelli et al., 2015; Norman et al., 2012). No statistically significant association was identified between verbal abuse and GAD in males. It is possible that males do not become anxious, or at least do not develop GAD, following verbal abuse (Fernandes & Osório, 2015). It is also possible that the association is simply not identified due to a lack of power. ## Major Depressive Disorder and Generalized Anxiety Disorder and Body Mass Index Associations were identified between diagnosis of MDD and BMI at the end of adolescence in females. Interestingly, this association was not identified in males. Several studies found a stronger relationship between depression and subsequent obesity for females than males (Korczak et al., 2013; Mannan et al., 2016; Mühlig et al., 2016). Possibly, females with MDD are more prone than males with MDD to display unhealthy behaviors (Camilleri et al., 2014). In addition, differences between males and females could be the result of biological differences (Mannan et al., 2016). No statistically significant associations were found in this study between GAD and BMI. Previously, a meta-analysis revealed moderate evidence for a positive cross-sectional association between anxiety disorders and obesity (Gariepy et al., 2010). However, none of the included studies assessed the relationship between GAD and BMI in young adulthood. A study at the end of young adulthood identified a negative association between GAD and BMI (Francis et al., 2015). Results of the limited number of studies into the relationship thus point into different directions, while studies are difficult to compare due to between study differences. ## Prevention and Intervention Efforts Successful prevention and intervention efforts for this population likely need to be multifaceted (Britto et al., 2017). Prevention of childhood abuse is most effective if it starts early in life (Britto et al., 2017). Prevention programs focusing on parenting and caregiving and led by professionals visiting the home seem to hold promise in this regard (Britto et al., 2017). Parenting programs, focusing on enhancing knowledge about parenting, building parenting skills, enhancing competency and parent support, seem to be successful as a primary, secondary and tertiary prevention of childhood abuse (Chen & Chan, 2016). When considering interventions to reduce the adverse consequences of childhood abuse, it seems crucial that MDD treatment in individuals who experienced adverse childhood experiences includes psychotherapy (Heim et al., 2004; Nemeroff et al., 2003). Abuse-focused psychotherapy may reduce depression among adults who experienced childhood sexual abuse (Martsolf & Draucker, 2005) and trauma-focused cognitive behavioral therapy seems promising when it comes to reducing MDD symptoms in preschool age children who experienced trauma (Cummings et al., 2012). ## Strengths and Limitations A strength of this study is the use of longitudinal data. We used detailed information on the occurrence of abuse before age 16, MDD and GAD before the end of adolescence and BMI at the end of adolescence and in young adulthood, allowing us to test a temporal relationship between these variables. Moreover, diagnosis of MDD/GAD was assessed with a structured diagnostic interview and BMI was determined using objective height and weight measurements obtained by trained research assistants. Finally, we assessed moderation of the relationship between MDD and BMI by childhood sexual abuse. This highlighted that there is indeed moderation of the relationship, which may hold important implications from a prevention and intervention perspective. A limitation of the current study is that participants reported childhood abuse retrospectively using a self-report questionnaire. However, the alternative of examining official cases of childhood abuse carries the downside of only including the subset of abuse cases that comes to professional attention (Gilbert et al., 2009). Another limitation is that the questionnaire used was developed by TRAILS. This was done, because none of the existing questionnaires at the time was considered fully appropriate for use in the TRAILS sample in terms of item content or number of items. A third limitation is that we did not adjust for all potential confounders of the relationships under study. However, adjusting for biological factors, alcohol and drug abuse, and other mental health conditions, would have likely resulted in overadjustment of the relationships in this study (Colman et al., 2012; Penninx, 2017). A fourth limitation is that we could not specifically adjust for BMI before the occurrence of MDD. However, when adjusting the association between MDD diagnosis and BMI at the end of adolescence for early adolescent BMI, a relationship between MDD diagnosis and BMI at the end of adolescence was still identified. However, we cannot be sure that MDD is affecting BMI. There likely is a reciprocal relationship between MDD and BMI and there also may be third factors – that could be a consequence of childhood abuse – that are able to influence MDD and GAD occurrence and BMI, such as the potential confounders mentioned above (MacKinnon et al., 2007). This requires us to be cautious in our interpretation of the results. The association between MDD and BMI may partly be due to BMI affecting MDD, or third factors affecting both conditions. Another limitation is the non-random nonresponse at the fourth wave of TRAILS (Ormel et al., 2015, 2017). For example, nonresponders at wave 4 more often had low socio-economic status compared to responders (Nederhof et al., 2012). Finally, the fact that the CIDI is to be applied by trained lay interviewers instead of clinical professionals could be a limitation (Ormel et al., 2015). ## Conclusion In this study, a relationship was identified between sexual abuse and BMI among females, specifically in young adulthood. This implies that young adulthood is a crucial life phase for the development of obesity after the experience of sexual abuse in females, and that, when prevention of sexual abuse has failed, interventions to prevent obesity development following sexual abuse should be planned before the end of adolescence. Sexual abuse in males and other forms of abuse in females and males were not related to BMI this early in adulthood. In contrast, the occurrence of MDD and GAD in individuals who experienced childhood abuse was already elevated before the end of adolescence. 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--- title: The impact of responsible gambling framing on people with lived experience of gambling harm authors: - Sarah Marko - Samantha L. Thomas - Hannah Pitt - Mike Daube journal: Frontiers in Sociology year: 2023 pmcid: PMC10028136 doi: 10.3389/fsoc.2023.1074773 license: CC BY 4.0 --- # The impact of responsible gambling framing on people with lived experience of gambling harm ## Abstract ### Background The framing of health issues influences how people think about and respond to these topics. Gambling has largely been framed as an issue of personal responsibility, with the gambling industry, governments and some researchers promoting responsible gambling strategies as a way to address gambling harm. While there is evidence that the internalization of personal responsibility can negatively impact gamblers, this study aimed to explore how people who have experienced gambling harm interpret and apply personal responsibility frames and ‘gamble responsibly' messages in their lives. ### Methods In-depth semi-structured interviews were conducted via Zoom and telephone with 15 gamblers who had been harmed by their own gambling and six affected others who had been harmed by someone else's gambling. This study was informed by public health and critical qualitative approaches to inquiry. The data were analyzed using reflexive thematic analysis. ### Results Four themes were constructed from the data. First, gamblers and affected others generally conceptualized gambling and gambling harm as being the responsibility of the individual because it was perceived as the outcome of individual behavior. Second, they attempted to apply responsibility to their own experience either as gamblers who tried to stop or reduce their gambling, or affected others who felt responsible for helping the gambler in their lives. Third, gamblers and affected others were negatively impacted when it was perceived the gambler could not ‘control' their gambling or had not done enough to take responsibility. Finally, gamblers and affected others recommended responsible gambling strategies be reframed to be more effective at addressing gambling harm. ### Conclusion This study provides evidence further supporting research demonstrating that personal responsibility frames may have unintended or negative consequences for gamblers and affected others. It underscores the need to reframe public messages about gambling away from responsible gambling, and toward research-based messages that can complement broader legislative changes and other measures to protect individuals. ## 1. Introduction Message framing is recognized as having a powerful influence on how health and social issues are defined (Entman, 1993). Framing influences how different population groups perceive the causes and consequences of complex health and social issues, and can impact on the range of policy solutions that are used to respond (Koon et al., 2016). As people construct meaning and knowledge from information in the world around them, framing can be used strategically to influence how people and policy makers think about and respond to health and social issues (Burr, 2015; Molder et al., 2021). There has been a specific focus in the academic literature on how harmful commodity industries, such as the tobacco and alcohol industries, have used personal responsibility framing about the consumption of their products (and associated harms) in ways that serve to protect their interests and reputation (Friedman et al., 2015; Maani Hessari and Petticrew, 2018; Maani et al., 2022). For example, prevention programs developed by the tobacco industry promoted and improved the industry's image and interests rather than changing smoking behaviors (Henriksen et al., 2006). Similarly, campaigns developed by the alcohol industry have been found to be ambiguous and the meanings of the messages interpreted in multiple ways by community members, with some interpreting their messages as promoting alcohol consumption (Jones et al., 2017). The World Health Organization [2017] has stated that careful consideration is needed about how health issues are framed because messages must be clear, relevant and empowering in order to be effective. More recently, researchers have criticized the strategies used by the gambling industry, governments, and some researchers to frame gambling as an issue that is largely associated with personal responsibility (van Schalkwyk et al., 2019). This framing and the associated responsible gambling paradigm positions gambling as a recreational activity that the majority of the adult population can choose to engage with in a responsible way without experiencing harm (Hancock and Smith, 2017a; Orford, 2019). This responsible gambling framing has been increasingly criticized by commentators from the public health community for creating the perception that gambling problems only occur if individuals misuse products and engage in irresponsible behavior (Hancock and Smith, 2017a; Miller and Thomas, 2018; Francis and Livingstone, 2021). Researchers have argued that this approach implies that there is a ‘right' way to gamble which is fun and controlled (the ‘responsible gambler'), and a wrong way to gamble which is uncontrolled and harmful (the ‘problem gambler' or ‘gambling addict') (van Schalkwyk et al., 2022:2). Responsible gambling also assumes that individuals make rational and informed decisions about how they use gambling products (Francis and Livingstone, 2021). Researchers have argued that such messages have limited impact because the focus on individuals as decision-makers overlooks the role of the gambling industry and governments in the creation of harmful gambling (Marko et al., 2022). Research conducted in Australia (Marko et al., 2022), Canada (Savard et al., 2022) and Sweden (Samuelsson and Cisneros Örnberg, 2022) shows that gamblers largely view gambling as an issue of individual responsibility. This internalizing of personal responsibility among gamblers is a concern because it contributes to the problematization and stigmatization of people who experience problems with gambling (Alexius, 2017; Miller and Thomas, 2018). This is because it creates an “overly simplistic” narrative of the causes of harm that does not accurately reflect the factors that influence how people make decisions about their behavior (Hodgins, 2021:876-877). Furthermore, qualitative research has shown that people who experience problems with gambling do not perceive responsible gambling messages as being effective at changing an individual's behavior (Miller and Thomas, 2018). Given the criticisms of responsible gambling framing, there has been a shift within the gambling industry and some government organizations toward safer gambling messages which promote strategies to keep gamblers safe while they gamble (Davies et al., 2022). As the focus remains on individuals, there is no evidence for any meaningful difference between these and responsible gambling messages. Research into the effectiveness of ‘responsible' or ‘safer' messages has largely focused on pop-up messages that aim to disrupt individual gambling behaviors. A recent systematic review found that while such messages may have some short-term impacts on behavior, there is limited evidence for sustained behavior change (Bjørseth et al., 2021). These messages may also be overwhelmed by the omnipresent commercial messages about gambling through advertising and marketing and does not address the harms experienced by others such as families and social networks (Victorian Auditor-General's Office, 2021). However, responsible or safer gambling frames still dominate the messages that are given about strategies to minimize gambling harm. This has been referred to by Alexius (2017:472) as “direct consumer responsibilization.” For example, recent industry harm minimization campaigns have encouraged gamblers to “Take a sec before you bet” (Sportsbet, 2021), “Take time to think” (Betting and Gaming Council, 2022), and “Have a game plan” (American Gaming Association, 2022). Similar messages which promote responsible gambling are communicated in public education campaigns that are run by governments even when these are framed as public health campaigns (Cassidy, 2020, p. 93). Examples of taglines from such campaigns include “Embrace moderation” (Alberta Gaming, Liquor and Cannabis, 2021), “Stop gambling in time!” ( The Brussels Times, 2021), and “You've got the power” (Department of Justice Community Safety, 2015) [which bears a striking resemblance to the widely criticized “I've got the power” school-based education program about smoking sponsored by Philip Morris in the late 1990s (Chapman, 2001)]. Recent reports have criticized the impact and effectiveness of these messages in public health approaches to preventing gambling harm. For example, van Schalkwyk et al. ( 2021a) found that the gambling industry's claims about the effectiveness of their “When the fun stops, stop” campaign were not backed by evidence, and that the framing used in the campaign may create the perception that gambling harm is a problem for a minority of gamblers rather than a broader public health issue. There are, however, four clear gaps in the current literature. The first is that there is limited research that explores how gamblers may attempt to apply information about responsible gambling to their own lives. Second, there is limited understanding of the alternative frames that gamblers think could be used in public messages about gambling. Third, while there is some evidence that affected others view gambling harm as the result of individual behavior (McCarthy et al., 2022c), no research to date has specifically explored how responsible gambling frames may impact on the perceptions held by people who have been directly impacted by someone else's gambling (‘affected others'). Finally, Alexius [2017] suggested that the members of a gambler's social network may reinforce that the gambler is responsible for harm if they have been taught that gambling is a personal responsibility. However, there has been limited research regarding how affected others view responsible gambling messages and how they apply these in their lives. In order to address these gaps, this study drew on data collected as part of a broader qualitative study that sought to understand how people who have been harmed by gambling conceptualize gambling risk and harm. The aim of the current study was to explore how people who have experienced gambling harm interpret and apply personal responsibility frames and ‘gamble responsibly' messages in their lives. The study was guided by four research questions: ## 2.1. Approach to inquiry This study used both public health and critical qualitative approaches to inquiry. A public health approach recognizes that a range of individual, socio-cultural, environmental, commercial and political factors contribute to gambling harm, and that this harm can impact individuals, their families, and communities (Goyder et al., 2020). This approach recognizes that gambling harm disproportionately impacts vulnerable communities, and that the gambling industry exploits these vulnerabilities (Rae and Fell, 2022). In order for solutions to be equitable, Rae and Fell [2022] argue that strategies cannot focus on clinical or individual interventions but on the broader drivers of harm. A critical qualitative approach to inquiry was also used as it aligns with the public health approach and seeks to advance social justice by studying power and inequality (Charmaz, 2017). Going beyond interpretation, critical researchers identify opportunities to advocate for change to address inequalities, inequities, and injustices, and focus on the powerful institutions, public policies, and discourses that contribute to these issues (Denzin, 2017). These approaches were appropriate given the unequal power wielded by the gambling industry and governments in framing gambling and influencing strategies to address harm, as compared to those impacted by gambling harm (Hancock and Smith, 2017b; van Schalkwyk et al., 2021b). Experts by Experience (EbyE) have been recognized as valuable stakeholders who have unique perspectives due to their lived experience of gambling harm, and who should be included in considerations about gambling research, education and treatment (Nyemcsok et al., 2021). The authors formed an EbyE Advisory Group of four individuals who were either former gamblers or affected others, and who provided feedback on the project. This included ensuring the interview was respectful and reflected the language used by people with lived experience of harm. Members of the EbyE Advisory Group were compensated for their time with a $50 grocery voucher. ## 2.2. Recruitment and data collection The study aimed to recruit 15-20 adult participants, which was similar to previous critical qualitative inquiries in relation to gambling (McCarthy et al., 2021; Nyemcsok et al., 2022b). To be included in the broader study, participants needed to be adults who lived in Australia and had experienced housing-related harm as a result of their own current or past gambling (‘gamblers') or someone else's gambling (‘affected others'). Gamblers were included as they are the target audience for responsible gambling programs, and affected others were included because to our knowledge there had been limited research into their perspectives despite their being impacted by gambling harm (Goodwin et al., 2017). A range of convenience, purposive and snowball sampling methods were used to recruit participants. The members of the EbyE Advisory Group and other people with lived experience of gambling harm who were known to the researchers shared the study advertisement flier on social media sites, and with their personal and professional networks, including peer support and advocacy groups. Snowball techniques were also used as those who participated in the study were asked to share the study flier with their social network. Using multiple recruitment strategies provided diversity among gamblers in terms of how recently they had gambled. In order to recruit a greater number of affected others, the researchers also sought permission from service providers, including mental health counselors and financial counselors, to distribute information about the study to their clients who may have been eligible to participate. There was a particular focus on ensuring that women were well represented in our recruitment strategies. While men generally have higher rates of ‘problem gambling,' the literature shows that women experience more financial stress from gambling (Koomson et al., 2022). However, researchers have highlighted that they are often excluded or underrepresented in gambling studies (McCarthy et al., 2019). It was also important to ensure that women's experiences were represented in the study as both gamblers and affected others (McCarthy et al., 2022b,c). All participants provided informed consent and were offered a $50 grocery voucher. Ethical approval was obtained from the Deakin University Human Ethics Research Committee [2021-003]. Semi-structured, audio-recorded interviews lasting ~60 min were conducted via Zoom or telephone by Authors One and Two. The interview guide used with gamblers focused on several themes: their history with gambling, views about the risks and harms associated with gambling, experiences of gambling related harm, and strategies to prevent gambling harm. The interviews with affected others focused on the same topics, however they were asked to reflect on their understanding of the gambling history of someone in their life, and their own experiences of gambling related harm. Throughout the data collection process, the interviews became more focused as further questions were asked in relation to key topics and ideas from discussions with previous participants (Hennink et al., 2020). Data collection ended when there was enough information from the interviews to answer the research questions (Malterud et al., 2016). ## 2.3. Data analysis Automated transcripts were generated by the Zoom software for online interviews, and audio recordings were transcribed by Author One for the telephone interviews. All transcripts were checked and edited for accuracy. The six phases of Braun and Clarke's [2021] reflexive thematic analysis were used to interpret and construct themes from the data. This process of interpretation occurred during and after the data collection process. Notes were taken following each interview about the key topics which were discussed, and data familiarization continued throughout the transcription process. The coding and theme development focused on the research questions. The data were coded based on the semantic (surface level) and latent (deeper more nuanced) meanings. These codes were grouped to identify patterns across the interviews and the research questions were used to construct preliminary themes. Rather than making comparisons between gamblers and affected others, the aim of the analysis was to present themes that reflected a central unifying concept that represented the different perspectives of the participants both within and across groups (Braun and Clarke, 2022). This allowed for a focus on diverse experiences as well as any similarities between different participants. This approach was important because we did not start with an assumption that all gamblers and all affected others would have similar experiences. Rather we aimed to construct a more nuanced interpretation of the lived experiences of participants. The researchers met regularly to discuss the interpretation of the data, construction of the themes, and the implications of the findings. ## 3. Results A total of twenty-one people participated in this study, including fifteen gamblers and six affected others. The mean age was 48.05 years (SD = 15.19). Approximately half of the participants were male ($$n = 11$$), and all of the affected others were women. Just over half of the participants lived in Victoria ($$n = 12$$) and the majority identified as Australian. Table 1 provides an overview of the demographics for each participant. The participants had a range of experiences with gambling and gambling harm. Some gamblers had not gambled for many years, while others had ceased gambling more recently or currently gambled at the time of the interview. Most affected others' family members currently gambled. Several participants including gamblers and affected others discussed how they or their family member had contemplated or attempted suicide as a direct result of their experience of gambling harm. This included two affected others who had a sibling die by gambling-related suicide. Table 1 \| Gamblers \| Gamblers.1 \| Gamblers.2 \| Gamblers.3 \| Gamblers.4 \| \| --- \| --- \| --- \| --- \| --- \| \| Age \| Gender \| State \| Ethnicity \| Main gambling products used \| \| 25 \| Male \| VIC \| White Jewish/Australian \| Casino table games, sports, horse, and dog race betting \| \| 31 \| Male \| QLD \| Australian \| Casino table games, poker machines, sports, and horserace betting \| \| 37 \| Male \| WA \| New Zealander \| Casino table games, sports betting \| \| 40 \| Male \| VIC \| Australian Chinese \| Horserace betting, casino table games \| \| 40 \| Male \| VIC \| Australian \| Sports and horserace betting \| \| 43 \| Male \| NSW \| Australian \| Sports and horserace betting \| \| 49 \| Male \| VIC \| Australian \| Sports betting \| \| 55 \| Male \| QLD \| Australian \| Horserace betting \| \| 56 \| Male \| VIC \| Portuguese \| Horserace betting, poker machines, lottery \| \| 57 \| Male \| VIC \| Australian \| Horserace betting \| \| 62 \| Male \| NSW \| Indigenous Australian \| Poker machines \| \| 64 \| Female \| VIC \| Australian \| Poker machines \| \| 69 \| Female \| VIC \| Australian \| Poker machines \| \| 71 \| Female \| VIC \| Australian \| Poker machines \| \| 73 \| Female \| VIC \| English/Anglo-Saxon \| Poker machines \| \| Affected others \| Affected others \| Affected others \| Affected others \| Affected others \| \| Age \| Gender \| State \| Ethnicity \| Person whose gambling they were harmed by \| \| 25 \| Female \| NSW \| Albanian \| Mother \| \| 29 \| Female \| ACT \| Australian \| Husband \| \| 30 \| Female \| WA \| Australian \| Brother \| \| 48 \| Female \| VIC \| Australian \| Ex-husband \| \| 49 \| Female \| WA \| Australian \| Son \| \| 56 \| Female \| VIC \| Australian \| Sister and brother \| Table 2 provides a summary of the four themes that were constructed from the data and demonstrates, where appropriate, any key similarities and differences between the perspectives and experiences of gamblers and affected others. Table 2 \| Theme \| Gamblers \| Affected others \| \| --- \| --- \| --- \| \| Theme one: Conceptualizing the role of responsibility in gambling harm \| • Many gamblers viewed gambling harm as a personal responsibility. • Harm was viewed as the result of their own gambling behavior. • Many did not think about the risks associated with gambling. • Some perceived gambling was a recreational activity that most people engaged in without experiencing harm. • Some suggested addiction impacted gamblers' ability to make rational decisions. • The gambling industry was also viewed as being responsible for gambling harm. \| • Some affected others viewed gambling harm as a personal responsibility. • Harm was viewed by some as the result of gamblers being irresponsible. • Some perceived gambling was a recreational activity that most people engaged in without experiencing harm. • Some suggested addiction impacted gamblers' ability to make rational decisions. • The gambling industry was also viewed as being responsible for gambling harm. \| \| Theme two: Applying responsible gambling to their own or someone else's gambling \| • Gamblers tried to take responsibility when they felt they could no longer control their gambling. • Tools to set gambling limits or self-exclude from gambling accounts and venues were easy to bypass. • Profession support services were hard to access and were not necessarily helpful. \| • Affected others felt they were responsible for helping their family member when their gambling became harmful. • Some tried help the gambler understand the impact of their gambling and take control of their gambling. • Some took financial responsibility to limit the impact gambling addiction had for the gambler, including bearing these outcomes themselves. \| \| Theme three: Impact and shame of not meeting the expectations of responsible gambling \| • Many blamed themselves for experiencing gambling harm and felt ashamed for not being able to control their gambling. • Some kept their gambling secret due to fear of judgment. \| • All affected others had been lied to by their family member about their gambling. • There was tension between affected others and their family member with some feeling frustrated or helpless. \| \| Theme four: Reframing and moving beyond responsible gambling messages \| • Gamblers criticized messages about the need to take personal responsibility because it overlooked their attempts to change their gambling behavior. • Personal responsibility messages were perceived as being ineffective at preventing harm and were not taken seriously or seen was being relevant. • These messages were seen as being hypocritical when coming from the industry that provides and promotes gambling products. • Gamblers suggested that messages should provide realistic portrayals of harms associated with gambling. • Some perceived that the government should implement practical action to reduce harm in addition to messaging. \| • Personal responsibility messages were perceived as being ineffective at preventing harm. • These messages were seen as being hypocritical when coming from the industry that provides and promotes gambling products. • Affected others suggested that messages should provide realistic portrayals of harms associated with gambling. • Some perceived that the government should implement practical action to reduce harm in addition to messaging. \| ## 3.1. Theme one: Conceptualizing the role of responsibility in gambling harm Participants (including both gamblers and affected others) generally conceptualized gambling and gambling harm as being the responsibility of the individual. This was because gambling harm was perceived as primarily the outcome of individual behavior. Some affected others perceived that their family members were responsible for the harm experienced because they gambled money they could not afford to lose, or they had not taken responsibility to change their gambling behaviors. Similarly, some gamblers conceptualized their own experiences of harm as the consequence of their own behavior because they did not budget or limit the money they gambled, and instead continued to gamble when they perceived they should have stopped. A few gamblers suggested that their harmful gambling was influenced by their personal belief that they could control the outcome of the bet or they could “beat the system”: Some participants viewed gambling as being an inherently risky activity and perceived that each individual needed to assess the risk associated with their own gambling: “…if you're not prepared to take a risk, don't do it”. However, many gamblers acknowledged that they did not initially think about the risks their own gambling posed to their own wellbeing, or how to manage these risks when they first began gambling. Many gamblers framed gambling as a dichotomy; either it was an activity that was recreational and low risk for most people, or it was harmful and addictive for others. This division between recreational and harmful gambling also contributed to the perception that “it's not dangerous for everybody,” and that most people could gamble recreationally because they were able to maintain control. Some gamblers described low-risk recreational gambling as being able to gamble occasionally, gamble for social or leisure reasons, and “walk away from it” when needed. For example, the following participant perceived that some people could enjoy gambling and stop when they need to: Similarly, some affected others also spoke about gambling as being an activity that was only harmful to some people. Some of these participants reflected on their own experiences with gambling or seeing others in their social network gamble without engaging in harmful behavior. For example, the following affected other did not perceive that her current partner was at risk of developing a gambling addiction because he engaged in it recreationally and with small amounts of money: Some participants (including both gamblers and affected others) perceived that individuals who experienced gambling addiction and the subsequent harm were different to those who could gamble recreationally. It was suggested that some people were more likely to engage in this type of gambling due to a range of individual factors that influenced their behavior and choices. For example, this included believing that they could win or control the outcome of the bet, having an “addictive personality,” or being “predisposed” to addiction due to genetics or the experiences of family members. The following participant compared himself to people he saw in the venues he attended who he perceived were able to gamble without experiencing harm and suggested that he was different from them because he will “never do that”: While gambling harm was framed as the consequence of individual behavior, there was also the perception among some gamblers and affected others that individuals were unable to make rational decisions about their gambling once addiction had occurred. Gamblers explained that it was as though “I'd been taken over,” and “your mind is being controlled.” This contributed to the perception among some that individuals could not be held responsible for their behavior which resulted from addiction. For example, the following participant rejected the idea of telling people experiencing addiction to take personal responsibility because it was not possible: In addition to the individual's role in gambling harm, some gamblers and affected others commented that the gambling industry was also responsible for gambling harm due to the provision and promotion of products that caused addiction and harm. This included criticisms regarding the extent to which gambling was marketed. Gambling marketing was perceived as creating “temptation,” particularly for gamblers who wanted to stop gambling. For example, a few gamblers and affected others suggested that the gambling industry used inducements sent via email and push notifications to mobile phones to encourage people to gamble. There was also acknowledgment among some that gambling products, particularly poker machines, were designed to maximize losses, including that the machines were programmed “…to slaughter, not bloody just skim a little bit [of money] off the top,” and prevented people from thinking about the risks associated with their gambling: ## 3.2. Theme two: Applying responsible gambling to their own or someone else's gambling Most gamblers described how they had attempted to take responsibility for their gambling, including trying to stop or reduce their gambling. This was typically done when they felt they could no longer control their gambling, or it was creating harm. Many gamblers discussed how they attempted to apply personal responsibility strategies but described unexpected challenges. First, while tools were available to support behavior change, gamblers explained ways in which these were ineffective. Tools to set limits or self-exclude were easy to work around and they could continue gambling. As these tools for online gambling products typically applied to individual companies, gamblers could simply open accounts with other companies. It was also suggested that the self-exclusion register at physical venues was rarely enforced by staff and, when it was, they could go to another venue. One participant recalled confronting venue staff when they did not enforce her self-exclusion and how she was told it was her responsibility not to attend the venue: Second, some gamblers had sought help from professional services such as mental health practitioners, financial counselors, general practitioners, and Gambler's Help services. However, these were not easy to access and, even when accessed, were not necessarily helpful. It was suggested that professionals did not always understand gambling harm or addiction: “I've spoke[n] to counselors who don't understand”. Others noted that they experienced delays in accessing professional support because services were underfunded, difficult to access, or did not offer the assistance they needed. One participant explained that his difficulties in accessing help had contributed to his ongoing gambling issues and led him to “be more irresponsible” with his gambling: There was a perception among affected others that they had a responsibility to help the gambler in their life when their gambling had become harmful. When gamblers were unable or unwilling to reduce their gambling, affected others felt as though they had to intervene and help the gambler take responsibility. While a few affected others had confronted their family member in an appeal to get them to change their behavior, others explained that their family member had approached them and asked for support to reduce their gambling. A few affected others reflected that their motivation to support their family member was the “love” they had for them, and the participants wanted to ease the harm their family member experienced. For example, one participant prioritized supporting her brother by loaning him “a lot of money” because she perceived that this prevented him from seeking alternative and riskier sources of money: A few affected others also suggested that there were external pressures to take responsibility for intervening in their family member's gambling. One participant explained how as a child she was told by the adults in her life that she needed to “convince [her] mother” that her gambling was harmful and needed to change, while another participant felt pressured to help her ex-husband while they were married because the broader community would judge her for his actions: One way some affected others perceived that they were taking responsibility was by trying to help the gambler take personal responsibility for their gambling. This included helping them to understand the impact their gambling had on themselves and those around them, and helping the gambler to self-exclude from gambling products or access professional support. A few affected others had taken responsibility for their family member's finances in order to reduce their access to money and prevent gambling or to ensure their broader financial responsibilities were met: Taking on financial responsibility meant some affected others also bore responsibility for the broader financial outcomes relating to the gambling addiction. Some affected others went to extreme efforts to limit the financial outcomes of the gambling addiction. For example, the mother of a gambler contacted a bank that loaned her son money that she believed he would not be able to pay back, while many affected others had contributed their own money in order to help the gambler meet their other financial responsibilities or repay debts. This often impacted their own financial situation when these funds were not repaid. One participant also highlighted how she continued to be negatively impacted by her ex-husband's gambling addiction because she had taken financial responsibility during their marriage and was now unable to take out loans or a mortgage: ## 3.3. Theme three: Impact and shame of not meeting the expectations of responsible gambling Many gamblers spoke about the impact of not being able to successfully implement responsibility strategies to control their gambling. These experiences were viewed by some gamblers as a personal fault and they used moralizing and judgmental language to describe themselves and others who experienced addiction—“absolute losers,” and “liars and cheaters”. A few gamblers commented that they could not explain or justify their gambling behavior because it did not reflect how they behaved in other areas of their lives or how they saw themselves. This contributed to feeling as though their experience of gambling harm meant there was something wrong with them that they could not explain: “you just can't understand why you did it because you're not that stupid generally.” There was shame and embarrassment associated with the inability to control their gambling and experiencing harm. The emotional impact of this was reflected by a few gamblers who stated that they had contemplated suicide, while others described instances where they became angry or cried in response to their gambling. While one participant described the “utter despair and frustration” she felt, the following participant explained how he “hated” how he lied and stole due to his gambling but that it had become a “compulsion”: Some gamblers explained how they hid their gambling from their social networks and at times socially isolated themselves because they found it difficult to be honest about their gambling. There was a fear of being open about their experiences of harm due to the perception that they would be judged for not being able to control their gambling. For a few gamblers, the fear of judgment was confirmed as they were told to “just stop” by people they had disclosed their gambling to, which reinforced to them to keep their experiences a secret. One participant explained how the shame associated with his gambling led him to become deceptive and contributed to further harm: Affected others suggested that they were negatively impacted when the gambler in their lives hid or lied about their gambling. All of the affected others stated they had been lied to, and most acknowledged that gamblers might not be honest about their gambling or be able to ask for help. However, many also acknowledged that this was most likely due to the associated shame. This created tension between the participants and their family member who gambled. Some felt frustrated when they were lied to or suggested that the gambler had not tried hard enough to change their gambling behaviors. Others indicated a helplessness when they could not support the gambler because they were not being honest. There was also a perception that they needed to be careful when talking to their family member because saying the wrong thing could contribute to their shame, trigger them to gamble more, or make them angry: ## 3.4. Theme four: Reframing and moving beyond responsible gambling messages Despite their own attempts to apply personal responsibility strategies, gamblers and affected others were largely critical of messages based on the responsible gambling and personal responsibility framing. Critical phrases were used to describe these messages including “a load of rubbish,” “bullshit,” and “pathetic” because they were perceived as being ineffective at preventing harm. A few gamblers commented that it was not possible to ‘gamble responsibly' either because gambling was “irresponsible,” or because they were not able make rational decisions while gambling. Some gamblers also suggested that messages such as ‘gamble responsibly' were meaningless and were not taken seriously by people who gamble. For example, the following participant suggested that ‘gamble responsibly' messages were treated as a joke among people that gamble: Participants often suggested that it was hypocritical for the gambling industry to tell people to take personal responsibility while providing and promoting addictive products. It was suggested that responsible gambling messages were a “token gesture” and were used by the gambling industry to deflect responsibility and appear as though they were responding to gambling harm. The following participant perceived that the gambling industry did not actually want people to change their gambling behavior: Many participants (including gamblers and affected others) suggested that there was a need to reframe messages away from responsible gambling toward something that would be more effective. The most common recommendation was the provision of honest and realistic information about the harms associated with gambling products via public messaging, and education in schools to prevent future harm. There was a perception that this would enable people experiencing harm to speak up and get help by destigmatizing harm and addiction, while also encouraging people to rethink their gambling and discourage young people from trying it in the first instance. Some also suggested that the messaging should be similar to that from other public health areas such as tobacco, alcohol and road accidents. They perceived that these “hard-hitting,” “off-putting,” and “brutal” campaigns were effective and recommended that a similar approach should be used in relation to gambling. For example, the following affected other suggested that people understood the harms relating to cigarettes because of public messaging, and that messaging about gambling should portray realistic harms that people can relate to their life rather than “superficial” messages: Some gamblers and affected others perceived that strategies beyond public messaging were needed in order to prevent harm and suggested practical actions that the government could implement. These included reducing the accessibility of gambling products, banning or reducing gambling marketing, reforming the self-exclusion register to make it more robust, and introducing mandatory deposit limits. Participants did not typically suggest that gambling should be prohibited and instead focused on creating safe gambling environments to protect those who gamble from the risks of harm. There was also a particular focus on the need to protect people who were vulnerable to or experience gambling addiction: ## 4. Discussion This study aimed to explore how people who have experienced gambling harm interpret and apply personal responsibility frames and ‘gamble responsibly' messages in their lives, and the alternative frames they think could be used in public messaging. Figure 1 depicts a model of the themes that were constructed from the data and descriptions of the key findings from each theme. The four themes relate to how gamblers and affected others conceptualized the role of responsibility in gambling harm, how they applied responsible gambling to their own or someone else's gambling, the impact of not meeting the expectation of responsible gambling, and their perceptions about reframing and moving beyond responsible gambling messages. Figure 1: Understanding how gamblers and affected others conceptualize and apply personal responsibility and the impact this has in their lives. The findings of this study raise three points of discussion relevant to the research questions. First, this study demonstrated that gamblers generally conceptualized gambling as an issue of personal responsibility despite some acknowledgment of the gambling industry's role in contributing to harm. This supports the findings of previous studies indicating that gamblers have internalized personal responsibility (Marko et al., 2022; Samuelsson and Cisneros Örnberg, 2022; Savard et al., 2022). This study also shows that the focus on personal responsibility for action still remains after experiencing harm and that even when the role of the gambling industry is acknowledged, blame is ultimately placed on the individual. This study also provides the new perspective that personal responsibility has been internalized by affected others with some perceiving that their family member was responsible for the harm that occurred. This shows that the dominant personal responsibility framing impacts not only gamblers but those around them and highlights the importance of including affected others in discussions about responsibility for harm. As public health researchers have clearly recognized that a broad range of socio-cultural, environmental, commercial and political determinants contribute to gambling harm (Goyder et al., 2020), the findings from this study appear to show that efforts to frame gambling as being about the individual, rather than the gambling industry or public policy, have been effective. As people construct meaning based on the information around them (Burr, 2015), framing can influence how people conceptualize issues by emphasizing specific information (Entman, 1993). This study provides further evidence for researchers' concerns about the impact of the dominance of personal responsibility framing and lack of counter-framing in and around the gambling market (Alexius, 2017; Marko et al., 2022). Second, this study provides evidence that supports calls to reframe messages and discourses relating to gambling away from personal responsibility and “responsible gambling” frames (Francis and Livingstone, 2021; Maani et al., 2022). Researchers have previously suggested that these frames create the perception that there is a responsible/controlled and irresponsible/uncontrolled way to gamble (Marko et al., 2022; van Schalkwyk et al., 2022). This study adds to the existing literature by demonstrating that people who perceive they engage in irresponsible or uncontrolled gambling try to correct this by acting upon the messages that have been given about personal responsibility. It further shows that affected others also try to act upon these messages by helping the gambler to change their behavior, or take responsibility to limit the financial impact of the gambling. This demonstrates that affected others have unique experiences that are distinct from those of gamblers and reinforces the need to include them in discussions about message framing which involve the lived experience community. This study also shows that gamblers' and affected others' efforts to apply responsibility appear to be largely disempowering because they create negative feelings, stigma and tension between gamblers and affected others. This is particularly evident when there is the perception that the gambler has not been successful in taking responsibility. While this has been seen in other areas of public health such as tobacco and food where people blame themselves for their poor health outcomes (Hamann et al., 2014; Pearl and Lebowitz, 2014), there remains a need for further research regarding the impact this has on affected others in the gambling area. This study also demonstrates that gamblers and affected others recommend reframing messages about portrayals of gambling harm. While messages about harms have been used in regard to tobacco, these framed the products as the cause of harm to highlight why individuals should not smoke, and also provided messages of encouragement to acknowledge the difficulties when trying to stop (Bayly et al., 2019). By comparison, the Australian Government recently announced new taglines to replace “gamble responsibly” messages; however the new taglines continue to focus on individual decision making such as asking gamblers to consider what “gambling [is] really costing you” (Visentin, 2022). The recommendations from participants in this study suggest these messages do not go far enough in reframing the message away from blaming gamblers and may contribute to the disempowerment of people who experience harm. Reframing gambling harm as the consequence of a problematic and harmful industry rather than individual behaviors may reduce the shame and stigma associated with experiencing gambling harm. As with tobacco (Durkin et al., 2022), independent research is urgently needed to identity which messages will be most effective as part of a comprehensive public health approach, and how these messages should be disseminated to the public. Previous tobacco and alcohol research has highlighted concerns about industry-funded campaigns (Henriksen et al., 2006; Jones et al., 2017); this study also indicates that messages that are perceived as coming from the gambling industry are not taken seriously by gamblers. Consideration is needed regarding the most effective sources of information and methods for communicating harm prevention messages to the public. Finally, this study showed that in addition to reframing messages about gambling, people who have experienced gambling harm clearly support a range of strategies which are consistent with the public health approach to harm prevention. This aligns with previous evidence that gamblers also want governments to take legislative steps to restrict the gambling industry (Marko et al., 2022; McCarthy et al., 2022a; Nyemcsok et al., 2022a), as they have done alongside reframing messaging in other public health areas such as tobacco (Chapman and Freeman, 2008). In the Australian state of Victoria where many of the participants for this study resided, the Public Health and Wellbeing Act recognizes that governments have a “a significant role in promoting and protecting the public health and wellbeing…[and] promoting conditions in which persons can be healthy” (Parliament of Victoria, 2008:s4). For government action to be comprehensive, it must use multiple evidence-based population-level strategies to target the different factors that contribute to gambling harm. There were two limitations associated with this study. First, the number of affected others included in the study were relatively low despite attempts to recruit a greater number. Second, we were unable to recruit affected others who were men which may unintentionally reinforce gendered stereotypes regarding women as affected others. Addressing these limitations in future studies to involve a more diverse range of affected others would provide a greater understanding of the similarities and differences in experiences between and within gamblers and affected others. This would provide a more nuanced understanding of how responsible gambling frames impact different groups' experiences of harm. Consideration is needed regarding how best to recruit affected others. However, the difficulties related to participant recruitment for this study may relate to the narrow focus of the broader research project which specifically sought to recruit people who had experienced housing-related harm as a result of gambling. ## 5. Conclusion This study demonstrates how those who have a lived experience of gambling harm internalize responsibility for gambling harm and attempt to apply responsibility to their own experiences. The framing of gambling as an issue of personal responsibility has dominated public messaging from the gambling industry, governments and some researchers. This study provides evidence for the negative impact these frames have on gamblers and affected others and underscores the need to reframe messages about harm. Public communication about gambling should move away from messages about individualized ‘responsible gambling' strategies to preventing harm, and toward evidence-based messages that will complement broader legislative and other changes to protect individuals and the community. ## Data availability statement The datasets presented in this article are not readily available because participants explicitly consented to only have their data shared with the immediate research team. ## Ethics statement The studies involving human participants were reviewed and approved by Deakin University Human Ethics Research Committee [2021-003]. The patients/participants provided their written, verbal, and informed consent to participate in this study. ## Author contributions SM and ST conceptualized and designed the study and conducted data collection. SM conducted the main qualitative analysis and produced the first draft of the manuscript. All authors contributed significantly to manuscript revision, read, and approved the final version. ## Conflict of interest SM has received an Australian Government Research Training Program stipend from Deakin University for her Ph.D., related to gambling. She has received funding for gambling research from Deakin University. ST has received funding for gambling research from the Australian Research Council Discovery Grant Scheme, the Victorian Responsible Gambling Foundation, and the New South Wales Office of Responsible Gambling, Deakin University, and Healthway. She has received travel expenses for gambling speaking engagements from the European Union, Beat the Odds Wales, the Office of Gaming and Racing ACT, and the Royal College of Psychiatrists Wales. She has received payment for peer review from the New South Wales Responsible Gambling Fund, and Gambling Research Australia. She is a member of the Gambling Harm Prevention Advisory Group for LotteryWest, and a board member of the International Confederation of Alcohol and Other Drugs Research Associations. She does not receive any financial compensation for these roles. She is currently Editor in Chief of Health Promotion International. HP has received funding for gambling research from the Australian Research Council Discovery Grant Scheme, the Victorian Responsible Gambling Foundation, the New South Wales Office of Responsible Gambling, VicHealth, and Deakin University. MD has received funding for gambling research from the Australian Research Council Discovery Grant Scheme, the Victorian Responsible Gambling Foundation Grants Scheme, and Healthway. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Application of the advance incision in robotic-assisted laparoscopic rectal anterior resection authors: - Yuhao Qiu - Ying Li - Zhenzhou Chen - Ninghui Chai - Xianping Liang - Dahong Zhang - Zhengqiang Wei journal: Frontiers in Surgery year: 2023 pmcid: PMC10028139 doi: 10.3389/fsurg.2023.1141672 license: CC BY 4.0 --- # Application of the advance incision in robotic-assisted laparoscopic rectal anterior resection ## Abstract ### Background The incidence of rectal cancer is increasing each year. Robotic surgery is being used more frequently in the surgical treatment of rectal cancer; however, several problems associated with robotic surgery persist, such as docking the robot repeatedly to perform auxiliary incisions and difficulty exposing the operative field of obese patients. Herein we introduce a new technology that effectively improves the operability and convenience of robotic rectal surgery. ### Objectives To simplify the surgical procedure, enhance operability, and improve healing of the surgical incision, we developed an advance incision (AI) technique for robotic-assisted laparoscopic rectal anterior resection, and compared its safety and feasibility with those of intraoperative incision. ### Methods Between January 2016 and October 2021, 102 patients with rectal cancer underwent robotic-assisted laparoscopic rectal anterior resection with an AI or intraoperative incision (iOI) incisions. We compared the perioperative, incisional, and oncologic outcomes between groups. ### Results No significant differences in the operating time, blood loss, time to first passage of flatus, time to first passage of stool, duration of hospitalization, and rate of overall postoperative complications were observed between groups. The mean time to perform auxiliary incisions was shorter in the AI group than in the iOI group (14.14 vs. 19.77 min; $p \leq 0.05$). The average incision length was shorter in the AI group than in the iOI group (6.12 vs. 7.29 cm; $p \leq 0.05$). Postoperative incision pain (visual analogue scale) was lower in the AI group than in the iOI group (2.5 vs. 2.9 $$p \leq 0.048$$). No significant differences in incision infection, incision hematoma, incision healing time, and long-term incision complications, including incision hernia and intestinal obstruction, were observed between groups. The recurrence (AI group vs. iOI group = $4.0\%$ vs. $5.77\%$) and metastasis rates (AI group vs. iOI group = $6.0\%$ vs. $5.77\%$) of cancer were similar between groups. ### Conclusion The advance incision is a safe and effective technique for robotic-assisted laparoscopic rectal anterior resection, which simplifies the surgical procedure, enhances operability, and improves healing of the surgical incision. ## Introduction Currently, the incidence of colorectal cancer and cancer-related mortality ranks third and second, respectively, worldwide [1]. For non-metastatic colorectal cancer, surgery is the preferred treatment. Surgical methods have gradually transitioned from traditional laparotomy to laparoscopic surgery and da Vinci robotic surgery. In 2006, Pigazzi et al. [ 2] described robotic total mesenteric excision (TME) for rectal cancer. Since then, da Vinci robots have been increasingly used for rectal cancer surgery. Because of its more flexible angle, wider field of view, and support of the primary surgeon's hand-eye coordination, the da Vinci robot provides unique advantages with respect to complete mesorectal excision (CME), lymph node dissection, and reduction of intra- and post-operative complications (3–5). Although the da Vinci robot has clear advantages in robotic-assisted laparoscopic rectal anterior resection, its relatively large size leads to inconvenience for assistants performing procedures such as making auxiliary incisions to remove specimens, placing the tubular stapler anvil, and completing the anastomosis. Therefore, some surgeons choose to dock the robot repeatedly to complete the laparoscopic anastomosis, thus substantially prolonging the operative time [6, 7] and increasing the wear and tear on the robot. In addition, only one assistant assists on the right side of the operating table during robot-assisted surgery. When emergencies occur, such as massive bleeding, rapid conversion to open surgery to stanch bleeding is difficult. Indeed, open conversion increases the risk of surgery because of robot obstruction and a shortage of assistants. In recent years, some surgeons have performed robotic transanal total mesorectal exclusion (R-TaTME or hybrid TaTME) to treat patients with low rectal tumors, obesity, or narrow pelvises [8, 9]. However, it still cannot solve the problem of repeated docking and has limitations in the treatment of high rectal cancer. To increase the appeal of robotic surgery and help promote the use of robotic surgery, we created and introduced the advance incision (AI) for robotic-assisted laparoscopic rectal anterior resection. Before the da Vinci robot is docked, the abdomen is entered in advance by selecting a suitable position, and the robotic surgery is completed with the help of an Alexis wound retractor. In this study, we describe the novel technique and evaluate its feasibility and safety. ## Law and ethics This was a retrospective study approved by the Medical Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (No.2022-K398) without the need for participants’ explicit consent. The Institutional Review Board of the First Affiliated Hospital of Chongqing Medical University approved the analysis of patients’ clinical and radiologic data. ## Patient selection This retrospective study was conducted in the Department of Gastrointestinal Surgery at the First Affiliated Hospital of Chongqing Medical University (Chongqing, China). This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement. A total of 152 patients underwent robotic-assisted laparoscopic rectal anterior resection for colorectal cancer from January 2016 to October 2021. According to the inclusion and exclusion criteria, 102 patients were included in the study. The patients were divided into AI and intraoperative incision (iOI) groups, with 50 and 52 patients, respectively. All patients underwent surgery performed by the same group of experienced surgeons (>10 years of experience in laparoscopic colorectal surgery and skilled in robotic-assisted colorectal surgery). The criteria used to select patients were as follows: [1] preoperative pathologic examination showing rectal adenocarcinoma; [2] preoperative magnetic resonance imaging (MRI) showing that circumferential resection margin and extramural vascular invasion are negative; and [3] the operation performed was a curative resection. The exclusion criteria were as follows: [1] presence of distant metastasis; [2] combined organ resection; [3] robotic-assisted laparoscopic rectal anterior resection with natural orifice specimen extraction surgery; and [4] history of neoadjuvant radiotherapy and chemotherapy. ## Operative procedures After general anesthesia, the patients were positioned in the modified lithotomy position. A robotic-assisted laparoscopic rectal anterior resection was performed with standard techniques, except for the incision-related steps [10]. TME and CME principles were followed for all patients [11, 12]. Placement of the ports in the iOI group was as follows: [1] a 12-mm trocar was placed 3–4 cm superior and to the right of the umbilicus (camera port C); [2] an 8-mm trocar was placed at McBurney's point (robot port R1); [3] an 8-mm trocar was placed at the midline of the left clavicle and horizontal to the camera port (robot port R2); [4] an 8-mm trocar was placed at the left anterior axillary line and horizontal to the camera port (robot port R3); [5] to mobilize the splenic flexure an 8-mm trocar was placed 3–4 cm below the xiphoid process and between the midline and right midclavicular line (robot port R4); and [6] a 12-mm trocar was placed at the vertical line passing through R1 (assistant port A; Figure 1). Figure 1: (A) The port placement in the intraoperative incision group. (B) The advance incision and port placement in the advance incision group. Before docking the robot, two surgeons made a longitudinal incision through the right rectus abdominis that was 5–7 cm in length and 3–4 cm from the umbilicus in the AI group. The upper edge of the incision was horizontal to the umbilicus. This incision is referred to as the AI (Figure 1B). After the AI was made, a small Alexis wound retractor was placed (Figure 2A). The Alexis wound retractor has an air-tight cover through which a 12-mm trocar was inserted and used as the assistant port. Placement of the camera and robot ports was the same as the iOI group ports (Figures 2B,C). The robot was docked adjacent to the patient's left thigh, and a pneumoperitoneum was established with the pneumoperitoneal pressure maintained at 12 mmHg (Figure 2D). Figure 2: (A) The advance incision with an alexis wound retractor. (B) Inserting the camera port under direct vision through the advance incision. (C) The photograph of the advance incision and the port placement in the advance incision group. (D) The photograph of docked robot in the advance incision group. The surgical assistant stood on the right side of the patient and assisted in the operation through the assistant port (Figure 3A). We used an intermediate approach and performed a colectomy through a medial-to-lateral approach in all patients. Starting from the sacral cape level, the mesentery was stripped upward along the abdominal aorta. Then, Toldt's space was expanded, the inferior mesenteric artery and vein were denuded, and the lymph nodes were excised. For middle or low rectal cancer, the inferior mesenteric artery was ligated distally to the left colic artery to ensure adequate perfusion. The left ureter, gonadal vessels, and autonomic nerves were safeguarded intraoperatively. The colon and rectum were mobilized with an electro-coagulation hook or ultrasonic knife according to the TME and CME principles. A linear stapler (45 or 60 mm) was used to divide the colon at >2 cm from the distal end of the tumor. Figure 3: (A) The assistant assists in robotic surgery through the advance incision. (B) Removal of the specimen through the advance incision. (C) Embedding the tubular stapler anvil and replacing the proximal colon into the abdomen through the advance incision. (D) The photograph of the advance incision, port incisions, and the surgical drain after suturing. When the the previous steps was completed, the pneumoperitoneum was suspended. And we choes not to dock the robot. The assistant port incision was extended according to the tumor size by one assistant, with a length of 3–4 cm, and the Alexis wound retractor was placed in the iOI group. There was no need to make another incision in the AI group. After release of the pneumoperitoneum, the airtight cover of the Alexis wound retractor was removed in the AI group. The specimen was removed, and the colon was resected approximately 10 cm from the tumor. No ischemia was observed in the proximal colon. Next, a purse string suture was used to embed the anvil of a 29-mm tubular stapler (Figures 3B,C). The proximal colon was replaced into the abdomen, and the airtight cover of the Alexis wound retractor was covered. Finally, a 29-mm tubular stapler was inserted into the anus and anastomosed under direct vision after the pneumoperitoneum was re-established. The pneumatic leak test was performed by injection of 50 ml of air into the colorectum from the anus to confirm the integrity of the stapled anastomosis. If the pneumatic leak test was positive, additional sutures were placed to strengthen the anastomosis. For patients with a positive pneumatic leak test or an anastomosis <7 cm from the anus, a drain was placed into the rectum through the anus to decrease the rectal pressure. And the drain prevents the faecal load from contacting anastomosis, thereby preventing leakage of faeces into the peritoneal cavity [13]. A surgical drain was placed in the pelvic cavity or left paracolic gutter close to the anastomotic site. The peritoneum was then closed with a continuous 2–0 absorbable surgical suture, and the anterior sheath of the rectus abdominis was closed by an interrupted 2–0 absorbable surgical suture. Before being closed, the skin incision was rinsed with 200 ml of dilute iodophor, and the skin was disinfected with iodophor. Finally, the incision was closed with simple interrupted non-absorbable silk sutures (Figure 3D). ## Statistical analysis SPSS statistical software was used for data analysis (SPSS version 26.0; SPSS, Inc., Chicago, IL, United States). Quantitative variables are presented with descriptive statistics, including the median and range. Nominal variables were compared with chi-square test or Fisher's exact test. A p value < 0.05 was considered statistically significant. ## Patient characteristics From January 2016 to October 2021, 152 patients underwent robotic-assisted laparoscopic rectal anterior resection at the First Affiliated Hospital of Chongqing Medical University. Twelve patients underwent combined organ resection, 17 patients underwent natural orifice specimen extraction surgery, 14 patients received neoadjuvant radiotherapy and chemotherapy, and 7 patients were excluded because of incomplete clinical data. Finally, 102 patients were included in the statistical analysis. Among the 102 patients, 50 received an advance incision in the experimental group (AI group), and 52 patients received an intraoperative incision in the control group (iOI group). The baseline characteristics of the two groups are listed in Table 1. No significant differences were found in sex, age, body mass index (BMI), tumor height above the anal verge, tumor stage, American Society of Anesthesiologists Classification (ASA Class) [14], and prior abdominal surgery between groups. Table 1 \| Characteristic \| AI group \| iOI group \| p value \| \| --- \| --- \| --- \| --- \| \| Number \| 50 \| 52 \| - \| \| Sex (male/female) \| 26/24 \| 27/25 \| 0.994 \| \| Age (mean ± SD, year) \| 63.92 ± 11.65 \| 64.29 ± 9.98 \| 0.878 \| \| BMI (mean ± SD, Kg/m2) \| 23.96 ± 3.86 \| 23.85 ± 2.99 \| 0.869 \| \| Tumor height above anal verge (mean ± SD, cm) \| 10.72 ± 6.125 \| 10.73 ± 6.277 \| 0.991 \| \| Tumor stage \| \| \| 0.612 \| \| AJCC I stage \| 6 \| 7 \| \| \| AJCC II stage \| 18 \| 23 \| \| \| AJCC III stage \| 26 \| 22 \| \| \| ASA class, no. (%) \| \| \| 0.803a \| \| I \| 2 (4.0%) \| 4 (7.7%) \| \| \| II \| 23 (46.0%) \| 21 (40.4%) \| \| \| III \| 22 (44.0%) \| 25 (48.1%) \| \| \| IV \| 3 (6.0%) \| 2 (3.8%) \| \| \| Prior abdominal surgery, no. (%) \| \| \| 0.723 \| \| Yes \| 9 (18.0%) \| 8 (15.4%) \| \| \| No \| 41 (82.0%) \| 44 (84.6%) \| \| ## Surgical procedure Four patients had positive pneumatic leak tests (AI group, $$n = 2$$; iOI group, $$n = 2$$). The anastomosis was reinforced with absorbable sutures, and a surgical drain was placed into the rectum through the anus to decrease the rectal pressure in these patients. All four patients recovered postoperatively, with no anastomosis-related complications. The incision of one patient in the AI group was appropriately prolonged intraoperatively because the tumor was larger than expected; the specimen was removed without difficulty, and no intestinal compression or rupture occurred. The patient did not experience any incision-related complications, such as an incision infection or hematoma. One patient had air leakage of pneumoperitoneum through the AI intraoperatively. The incision was narrowed by sutures, and a large piece of wet gauze was used to wrap the Alexis wound retractor to increase the tightness. The operation was successfully completed. ## Comparison of postoperative outcomes Comparisons of postoperative parameters between patients in the AI and iOI groups are shown in Table 2. No significant differences were observed in the operative time, blood loss, time to first passage of flatus, time to first passage of stool, duration of hospitalization, and rate of overall postoperative complications between groups. Table 2 \| Parameter \| AI group \| iOI group \| p value \| \| --- \| --- \| --- \| --- \| \| Operative time (mean ± SD, min) \| 203.28 ± 52.49 \| 206.94 ± 34.56 \| 0.677 \| \| Blood loss (mean ± SD, ml) \| 48.10 ± 31.86 \| 54.92 ± 31.21 \| 0.277 \| \| Time to first passage of flatus (mean ± SD, day) \| 2.96 ± 1.38 \| 3.02 ± 2.17 \| 0.870 \| \| Time to first passage of stool (mean ± SD, day) \| 4.40 ± 1.78 \| 4.37 ± 2.84 \| 0.942 \| \| Duration of hospitalization (mean ± SD, day) \| 8.84 ± 2.91 \| 9.23 ± 4.77 \| 0.620 \| \| Postoperative complications, no. \| 8 \| 7 \| 0.717 \| \| Anastomotic leakage \| 1 \| 1 \| 1.000a \| \| Blood in stool \| 1 \| 0 \| 0.490b \| \| Bowel obstruction \| 2 \| 3 \| 1.000a \| \| Abdominal infection \| 2 \| 1 \| 0.972a \| \| Chyle fistula \| 1 \| 2 \| 1.000a \| \| Urinary retention \| 1 \| 0 \| 0.490b \| Two patients (one patient in each group) experienced anastomotic leakage. The patient in the iOI group underwent a double-lumen ileostomy because of a severe abdominal infection. The patient in the AI group was cured by non-surgical treatment, including antibiotics, rehydration, and nutritional support. One patient in the AI group had hematochezia at the first day after the operation. The color of the patient's faeces was bright red, and the bleeding volume was approximately 50 ml. The possibility of anastomotic bleeding was considered high. After stanching bleeding with medicines such as Hemocoagulase Bothrops Atrox for Injection, Carbazochrome Sodium Sulfonate for Injection, the patient was cured without anastomotic complications. Except for patients with anastomotic leakage, there were three patients who had positive drainage fluid bacterial cultures (AI group, $$n = 2$$; iOI group, $$n = 1$$). The patients had abdominal pain and fever, and the inflammatory indices, including the leukocyte count, and the C-reactive protein and procalcitonin levels, were elevated, thus suggesting an abdominal infection. All three patients were cured after antibiotic treatment. Five patients had bowel obstruction postoperatively (AI group, $$n = 2$$; iOI group, $$n = 3$$). Despite the possibility of postoperative intestinal adhesion, the patients recovered after non-surgical treatment, including antibiotics, nutritional support, and gastrointestinal decompression. Three patients had chylous fistula postoperatively (AI group, $$n = 1$$; iOI group, $$n = 2$$) and positive chylous tests. The surgical drain was left in place until the drainage fluid was clear and the drainage fluid was <20 ml per day, and the chylous test was negative. Urinary retention occurred in one patient in the AI group postoperatively. Seven days postoperatively, the patient had difficulty urinating after removal of the urinary catheter. B-scan ultrasonography suggested that the residual urine volume was 150 ml. Despite the possibility of a postoperative neurogenic bladder, urinary retention was resolved after bladder training and self-catheterization for 3 months. ## Comparison of incisional short-term outcomes Comparisons of incisional short-term outcomes between patients in the AI and iOI groups are shown in Table 3. The mean time for making an auxiliary incision differed significantly between groups (AI group vs. iOI group = 14.14 vs.19.77 min $p \leq 0.05$). Moreover, the AI group had a shorter incision length than the iOI group (AI group vs. iOI group = 6.12 vs.7.29 cm $p \leq 0.05$). In addition, no significant differences were observed in the incidence of incision infection, hematoma, or incision healing time between groups. Six patients developed an incision infection postoperatively (AI group, $$n = 2$$; iOI group, $$n = 4$$), and the incision secretion bacterial cultures were positive. We removed the sutures of these patients’ incisions. And we filled the infected incisions with gauze to drain the secretion. With the exception of one patient with an abdominal infection, the other patients were not treated with antibiotics, and all incisions healed uneventfully. Four patients developed incision exudate and were diagnosed with fat liquefaction; their bacterial cultures were negative (AI group, $$n = 2$$; iOI group, $$n = 2$$). The patients showed improvement after removing sutures and draining with gauze. Two patients in the iOI group developed incision hematoma postoperatively. We found that the incisions oozed blood and there were subcutaneous hematomas. After removing the sutures, we cleaned the blood clots and sutured to stop bleeding. Then we sterilized and re-sutured the incisions. No incision infections occurred, and the incisions healed well. We routinely scored pain postoperatively [visual analogue scale (VAS) score]. The degree of postoperative incisional pain in the AI group was significantly lower than that in the iOI group (VAS scores: AI group = 2.5; iOI group = 2.88; $$p \leq 0.048$$). Table 3 \| Parameter \| AI group \| iOI group \| p value \| \| --- \| --- \| --- \| --- \| \| Time of performing auxiliary incision (mean ± SD, min) \| 14.14 ± 3.04 \| 19.77 ± 2.73 \| <0.05 \| \| Length of the incision (mean ± SD, cm) \| 6.12 ± 0.68 \| 7.29 ± 0.81 \| <0.05 \| \| Incisional infection, no. \| 2 \| 4 \| 0.710a \| \| Incisional hematoma, no. \| 0 \| 2 \| 0.496b \| \| Fat liquefaction, no. \| 2 \| 2 \| 1.000a \| \| Incisional pain in day 3 (VAS 1-10) \| 2.50 ± 0.974 \| 2.88 ± 0.963 \| 0.048 \| \| Healing time (mean ± SD, day) \| 7.18 ± 1.79 \| 7.69 ± 2.21 \| 0.202 \| ## Comparison of survival outcomes between groups All patients underwent a radical R0 resection, that was confirmed by pathologic evaluation. The mean duration of follow-up was 24.6 months [AI group: 21.2 months (range, 3.0–36.0 months); iOI group: 25.7 months (range, 3.0–39.0 months)]. The overall recurrence and metastasis rates were $4.9\%$ ($\frac{5}{102}$) and $5.9\%$ ($\frac{6}{102}$), respectively. The rates of recurrence [AI group vs. iOI group ($4.0\%$ vs. $5.8\%$)] and metastasis [AI group vs. iOI group ($6.0\%$ vs. $5.8\%$)] were similar between groups. Two patients in the AI group (T3N2 and T4N1) and three patients in the iOI group (T4N0, T3N1, and T4N2) developed local recurrence. Three patients in the AI group (T3N1, T4N1, and T4N2) developed hepatic metastasis. One patient in the iOI group (T3N0) had pulmonary metastasis, and two patients (T3N2 and T4N1) had hepatic metastasis postoperatively (Table 4). Tumor staging was performed according to the 8th edition of the American Joint Committee on Cancer Staging Manual [15]. Table 4 \| TNM stage \| Number \| Number.1 \| Local recurrence \| Local recurrence.1 \| Distant metastasis \| Distant metastasis.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| TNM stage \| AI group \| iOI group \| AI group \| iOI group \| AI group \| iOI group \| \| T1N0 \| 4 \| 4 \| 0 \| 0 \| 0 \| 0 \| \| T2N0 \| 2 \| 3 \| 0 \| 0 \| 0 \| 0 \| \| T3N0 \| 5 \| 9 \| 0 \| 0 \| 0 \| 1 \| \| T4N0 \| 13 \| 14 \| 0 \| 1 \| 0 \| 0 \| \| T1N1/2 \| 1 \| 0 \| 0 \| 0 \| 0 \| 0 \| \| T2N1/2 \| 3 \| 1 \| 0 \| 0 \| 0 \| 0 \| \| T3N1/2 \| 3 \| 5 \| 1 \| 1 \| 1 \| 1 \| \| T4N1/2 \| 19 \| 16 \| 1 \| 1 \| 2 \| 1 \| We also followed long-term incision complications, including incisional hernia and intestinal obstruction. No incisional hernias occurred in the AI group. One patient in the iOI group experienced an incisional hernia 15 months postoperatively. The defect size was approximately 4 cm × 5 cm, and the hernia was reducible. The patient received an abdominal belt without surgical treatment. One patient in the iOI group experienced repeated episodes of intestinal obstruction postoperatively at an interval of approximately 3 months. A CT scan revealed that the obstruction site was located in the pelvic cavity, and indicated an absence of recurrence or metastasis. Because of the possibility of adhesive intestinal obstruction, the patient underwent surgical treatment. No tumor recurrence was observed intraoperatively, however, an adhesive band in the pelvic cavity formed at the obstruction point. ## Discussion In 2006 Pigazzi et al. [ 2] reported robotic TME for rectal cancer. In recent years, with continual innovations in robotic surgery technology, robotic surgery has been more widely adopted in patients with rectal cancer; however, because of the relatively large size of robotic equipment, the surgical assistant has limited space to maneuver, thus hindering removal of specimens and placement of the tubular stapler anvil. To simplify the operative process and improve operative efficiency, we developed an advance incision for robotic-assisted laparoscopic rectal anterior resection. Compared with other ways to perform auxiliary incisions in robotic-assisted laparoscopic rectal anterior resection, this technology is safe and feasible, and it effectively improves the prognosis of postoperative wounds, according to our findings. Thus, the advance incision technique has important clinical application value. We observed no significant differences in the time to first passage of flatus, time to first passage of stool, duration of hospitalization, and rate of overall postoperative complications between groups. These findings might have been because the operations were performed by the same surgeon, and all surgical procedures followed TME standards. The only difference was the sequence of perform incisions, which had little effect on the procedure within the abdominal cavity. The postoperative recovery of patients was not usually affected. In our study, the overall operative time in the AI group was 206.94 min. Baik et al. [ 16] have reported an average operative time for da Vinci robot surgery of 217.1 min. Our operative time was shorter, possibly because they undocked the robot to place the tubular stapler anvil [16]. Because we have reduced the steps of docking the robot. Therefore, the use of the advance incision effectively shortened the time of robotic rectal surgery comparing with other researches. But the difference between two groups was not significant in total operation time in our study, because the number of times docking the robot was the same. As for the surgical process of the two groups of patients, only the moment of performing the incision was changed, and other operation steps were the same. And performing the advance incision is only a small part of the operation process, which determines that the operation time is more related to the intra-abdominal operation steps. At the same time different patients have different conditions whitch may affect the results of average operation time in each group. Although time of performing the auxiliary incisions was shorter in the AI group, if compared with the overall operation time, its limited difference in the time of performing the auxiliary incisions will be diluted. So there was no statistical difference in the overall time between the two groups, but there was statistical difference in time for performing the auxiliary incisions. The incidence of postoperative complications was $16.0\%$ and $13.5\%$ in the AI and iOI groups ($$p \leq 0.717$$), respectively. The absence of significant differences between groups suggested that the advance incision is safe and feasible. Kang et al. [ 17] have reported an incidence of postoperative complications in robotic-assisted laparoscopic rectal anterior resections of $19.0\%$. Alimoglu et al. [ 18] have reported a postoperative complication rate after robotic rectal surgery of $16.0\%$. The results of these studies are similar to our results, thus suggesting that the AI is safe and feasible, and does not lead to increased postoperative complications. Herein, the AI group had a significantly shorter time of performing the auxiliary incision, a significantly shorter incision length, and significantly lower postoperative incision pain, possibly because the auxiliary incision was made before docking the robot, thus enabling cooperation among surgeons, superior hemostasis, and better incision healing. The surgeon's intention to shorten the incision due to the fear of air leakage through the advance incision may cause the difference. But the auxiliary incision was performed by one assistant in iOI group. Due to the inconvenience of operating by one person, to better expose the tissue and better stop bleeding, the surgeon may extend the length of the incision involuntarily. Studies have shown that robotic surgery decreases the incision infection rate below that of laparoscopic rectal anterior resection [19, 20]. David et al. [ 21] have reported an incidence of incision infection after robotic rectal surgery of $8.9\%$. In our study, the overall incision infection rate was $6.0\%$ ($4.0\%$ in the AI group and $7.7\%$ in the iOI group). The lower infection rate in the AI group might have been because the AI was closer to the upper abdomen than other auxiliary incisions, and the incision was shorter. Therefore, the advance incision effectively decreases the incision infection rate after robotic surgery and has clinical application value. The overall postoperative rectal cancer recurrence rate was $4.9\%$, and the postoperative metastasis rate was $5.9\%$. Lee et al. [ 22] have reported a local recurrence rate after robotic rectal cancer surgery of $5.9\%$, a value similar to our results. Moreover, our study indicated similar recurrence and metastasis rates in the AI and iOI groups, thus suggesting that the advance incision is safe. Although there were differences in the opening time and position of the incision between the two groups, the other operative processes were the same. The two surgical methods complied with the principles of TME and CME for colorectal cancer. Furthermore, we did not observe an increase in implant metastasis in the advance incision, possibly because of the relative distance between the incision and the tumor and the use of an Alexis wound retractor. In addition, opening the incision in advance did not lead to an increase in the incidence of postoperative adhesive ileus, possibly because the occurrence of this condition is related primarily to the operative site and method, but not the time for performing the incision. In conclusion, the advance incision is safe and feasible with respect to long-term complications and prognosis. It is worth mentioning that we found the following advantages of the advance incision in clinical applications: [1] Before the trocar is inserted into the camera port, in contrast to the advance incision, other surgical methods use blind puncture with the Veress needle, thereby greatly increasing the risk of accidental injury [23]. For patients with a history of abdominal surgery, the difficulty and risk of inserting trocars caused by abdominal adhesions are substantially greater. The advance incision can separate the adhesions in the abdominal cavity under direct vision or through the single-port laparoscopic technique [24, 25] before trocar insertion. [ 2] In the event of an intraoperative emergency, such as uncontrollable massive hemorrhage, the use of an advance incision can notably save the time required for a laparotomy: the advance incision can be extended to achieve rapid laparotomy, and bleeding can even be stopped through the advance incision. For robotic surgery requiring substantial time to docking the robot, the advance incision greatly improves the safety of the operation. [ 3] The advance incision increases the flexibility of the assistant, allows the assistant to better cooperate with the primary surgeon, and improves the operative efficiency. [ 4] In obese patients, exposure of the surgical field can be difficult in traditional laparoscopic surgery (26–28). The presence of an advance incision enables intraoperative placement of large gauze to displace organs, such as the small intestine, and help expose the surgical field. Notably, the following aspects should be considered in the application of the advance incision. [ 1] The patient's condition should be evaluated in detail preoperatively, the tumor size should be preliminarily evaluated with CT and MRI, and the length of the advance incision should be adjusted appropriately according to the tumor size to avoid excessive extrusion during specimen removal. [ 2] The advance incision may increase the risk of pneumoperitoneum leakage. If the pneumoperitoneum pressure decreases, the assistant can use a suture to narrow the incision, and wrap the Alexis wound retractor with large wet gauze to strengthen the sealing performance. This study has several limitations. First, this was a retrospective study with a small sample size. Hence, larger prospective studies are required to confirm the results of this study. Second, the decision to perform an AI or iOI was made by the operating surgeon, thus potentially leading to selection bias; however, all the surgical procedures were completed according to the standard TME surgical procedures; the only difference was the time to perform auxiliary incisions. We believe that the influence of this selection bias was limited and had little influence on the experimental results. Thus, the advance incision is safe and feasible in robotic-assisted laparoscopic rectal anterior resection and has clinical value. ## Conclusion The advance incision is a safe and effective technique for robotic-assisted laparoscopic rectal anterior resection that simplifies the surgical procedure, enhances operability, and improves the healing of the surgical incision. The application of an advance incision, which may support the promotion of robotic surgery, has important clinical value. Future prospective randomized trials are warranted to validate the findings of our study. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by The Medical Ethics Committee of the First Affiliated Hospital of Chongqing Medical University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions YQ and YL made substantial contributions to conception and design. ZC, NC, XL and DZ contributed to the acquisition of data, analysis, and interpretation. YQ wrote the manuscript. ZW supervised the work and edited the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: 'The shared genetic architecture of suicidal behaviour and psychiatric disorders: A genomic structural equation modelling study' authors: - Tahira Kootbodien - Leslie London - Lorna J. Martin - Joel Defo - Raj Ramesar journal: Frontiers in Genetics year: 2023 pmcid: PMC10028147 doi: 10.3389/fgene.2023.1083969 license: CC BY 4.0 --- # The shared genetic architecture of suicidal behaviour and psychiatric disorders: A genomic structural equation modelling study ## Abstract Background: Suicidal behaviour (SB) refers to behaviours, ranging from non-fatal suicidal behaviour, such as suicidal ideation and attempt, to completed suicide. Despite recent advancements in genomic technology and statistical methods, it is unclear to what extent the spectrum of suicidal behaviour is explained by shared genetic aetiology. Methods: We identified nine genome-wide association statistics of suicidal behaviour (sample sizes, n, ranging from 62,648 to 125,844), ten psychiatric traits [n up to 386,533] and collectively, nine summary datasets of anthropometric, behavioural and socioeconomic-related traits [n ranging from 58,610 to 941,280]. We calculated the genetic correlation among these traits and modelled this using genomic structural equation modelling, identified shared biological processes and pathways between suicidal behaviour and psychiatric disorders and evaluated potential causal associations using Mendelian randomisation. Results: Among populations of European ancestry, we observed strong positive genetic correlations between suicide ideation, attempt and self-harm (rg range, 0.71–1.09) and moderate to strong genetic correlations between suicidal behaviour traits and a range of psychiatric disorders, most notably, major depression disorder (rg = 0.86, $$p \leq 1.62$$ × 10−36). Multivariate analysis revealed a common factor structure for suicidal behaviour traits, major depression, attention deficit hyperactivity disorder (ADHD) and alcohol use disorder. The derived common factor explained $38.7\%$ of the shared variance across the traits. We identified 2,951 genes and 98 sub-network hub genes associated with the common factor, including pathways associated with developmental biology, signal transduction and RNA degradation. We found suggestive evidence for the protective effects of higher household income level on suicide attempt [OR = 0.55 (0.44–0.70), $$p \leq 1.29$$ × 10−5] and while further investigation is needed, a nominal significant effect of smoking on suicide attempt [OR = 1.24 (1.04–1.44), $$p \leq 0.026$$]. Conclusion: Our findings provide evidence of shared aetiology between suicidal behaviour and psychiatric disorders and indicate potential common molecular mechanisms contributing to the overlapping pathophysiology. These findings provide a better understanding of the complex genetic architecture of suicidal behaviour and have implications for the prevention and treatment of suicidal behaviour. ## 1 Introduction Suicidal behaviour is a major public health concern. It is estimated that approximately 700,000 individuals die by suicide every year; with a global suicide rate of 9.0 per 100,000 population (WHO, 2021). According to the Global Burden of Diseases, Injury and Risk Factors Study (GBD 2019), suicidal behaviour was estimated to be responsible for nearly 34.1 million disability-adjusted life years (DALYs) globally in 2019, of which the majority occurred in those aged 10–49 years (Vos et al., 2020; IHME, 2022). Worldwide, suicide is the fourth leading cause of death in 15–29-year-olds (WHO, 2021). Suicidal behaviour is a broad and complex term used to describe suicidal thoughts and a range of self-injurious behaviour involving intent to die (suicide attempt, self-harm and death) (Posner et al., 2007). Attempted suicide is considered an important risk factor for subsequent suicide (Hawton et al., 2015) and is 25–30 times more common than completed suicide (Schmidtke et al., 1996). The risk of death after re-attempting suicide is higher in the first year after an attempted suicide, with $2.3\%$ of subsequent re-attempts resulting in death (Bostwick et al., 2016). While there has been substantial evidence that individuals with suicidal thoughts are at increased risk for later or subsequent suicidal ideation, attempts and death (Ribeiro et al., 2016), most individuals may never act on their thoughts (Nock et al., 2009). Previous research has explored the progression of suicidal thoughts to suicidal behaviour by applying various theories of suicide (Nock et al., 2013; May and Klonsky, 2016). Several studies have highlighted that the risk factors involved in the development of suicidal ideation are different from those who transition to suicide attempts (Nock et al., 2008; Klonsky et al., 2017). Given that suicidal behaviour is an outcome that results from many factors, and the spectrum of behaviour may reflect a continuum of suicide risk (Sveticic and De Leo, 2012), it is important to understand the pathways leading to completed suicide. Understanding the pathways from less to severe suicidal behaviour is relevant as it provides additional opportunities for suicide prevention at different stages of risk. Suicidal behaviour is partly genetic, with moderate heritability estimates ranging from $38\%$–$55\%$ in adoption, twin and family studies (reviewed by Brent and Mann, 2005; Voracek and Loibl, 2007; Brent and Melhem, 2008) and $17\%$ and $36\%$ for suicide attempt and ideation respectively, when controlling for psychiatric illness (Fu et al., 2002). It is well established that psychiatric comorbidities play an important role in the development of suicide, as approximately $90\%$ of individuals who die by suicide have been reported to have a diagnosed psychiatric disorder (Arsenault-Lapierre et al., 2004). Psychiatric disorders such as depression, bipolar mood disorders, schizophrenia, post-traumatic stress disorder, substance use and eating disorders have been associated with suicide (Nock et al., 2010). Suicide has also been linked to attention deficit hyperactivity disorder (ADHD) (Giupponi et al., 2018) and sleep disorders (Bernert et al., 2015). Other risk factors include smoking (Poorolajal and Darvishi, 2016), poverty (Iemmi et al., 2016) and educational disparities (Lorant et al., 2021). Moreover, suicidal behaviour is also included as part of the diagnostic criteria for major depression and bipolar disorders (Fehling and Selby, 2021), meaning that suicide or suicidal behaviour is considered to be a symptom of these disorders. Studies have shown that many psychiatric disorders share a common set of genetic factors (Caspi et al., 2014; Lee et al., 2019; Allegrini et al., 2020). The shared genetic liability captured onto a single dimension called the “p factor,” may explain why so many psychiatric disorders are comorbid (Plomin et al., 2016). The theoretical concept, the p factor, suggests that components of the underlying pathology of psychiatric disorders may be shared across several (if not all) psychiatric disorders. This framework was further supported by Allegrini and others who reported that the p factor remained stable across childhood and adolescence over a life course, suggesting that the shared genetic influences of psychiatric disorders in childhood is also linked to the development of adult psychiatric disorders (Allegrini et al., 2020). While still in its infancy, research findings from investigations on the p factor suggest that the comorbidity of several psychiatric disorders may be explained by a common or shared genetic pathway/s. While genome-wide association studies (GWAS) have continued to explain only a small proportion of the heritability of suicidal behaviour, the increase in the availability of data from studies with larger sample sizes over the last few years, has expanded the scope of available statistical methods to improve the understanding of suicide burden (Wang et al., 2011; Loos, 2020). One such method, the analyses of single nucleotide polymorphism (SNP)-based genetic correlations using genomic structural equation modelling (GenomicSEM), has identified patterns of shared genetic architecture across many psychiatric disorders (Grotzinger et al., 2019; Lee et al., 2019). In this study, we proposed a common factor model that represents an extension of the general psychopathology or genomic “p factor” that includes suicidal behaviour using Genomic SEM. We performed a gene/pathway-specific meta-analysis and functional enrichment to identify a set of genes at the subnetwork level significantly associated with the common factor. We applied Mendelian randomisation to identify potentially pleiotropic and causal relationships between modifiable risk factors and suicidal behaviour and further highlighted potential drugs interacting with the subnetwork genes that may be targeted for future drug development. ## 2.1 Description of GWAS summary data This study was conducted using 28 publicly available genome-wide association studies (GWAS) summary data generated by previous studies. We identified nine SB traits, ten psychiatric traits, and five behavioural and two anthropometric and socioeconomic-related variables, respectively (Table 1, web links for downloading data provided). Population ancestry was grouped as European if the study population was described as “Caucasian” or “White” by the author and as East Asian if the study population was described as “Japanese” or “Han Chinese”. Suicidal behaviour datasets were derived from GWAS samples of both sexes of European ancestry for suicidal ideation ($$n = 4$$), suicide attempt ($$n = 2$$), and self-harm ($$n = 2$$), and summary statistics of completed suicide in an East Asian population. TABLE 1 \| Phenotype/Reference \| Ancestry \| Consortium/Source \| Sample size \| Variable \| #Cases \| #Controls \| Web links/URLs \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Suicidal behaviour \| \| \| \| \| \| \| \| \| Suicidal ideation \| \| \| \| \| \| \| \| \| Recent thoughts of suicide or self-harm \| EUR \| United Kingdom Biobank/Neale lab \| 125844.0 \| Continuous \| \| \| https://www.nealelab.is/uk-biobank \| \| Thought life not worth living (TLNWL) \| EUR \| United Kingdom Biobank/Neale lab \| 117291.0 \| Continuous \| \| \| https://www.nealelab.is/uk-biobank \| \| Thoughts of death during worst depression \| EUR \| United Kingdom Biobank/Neale lab \| 62648.0 \| Binary \| 32630.0 \| 30018.0 \| https://www.nealelab.is/uk-biobank \| \| Ever contemplated self-harm (ECSH) \| EUR \| United Kingdom Biobank/Neale lab \| 117610.0 \| Continuous \| \| \| https://www.nealelab.is/uk-biobank \| \| Suicide attempt \| \| \| \| \| \| \| \| \| Attempted suicide (Erlangsen et al., 2018) (SA) \| EUR \| iPSYCH \| 50264.0 \| Binary \| 6024.0 \| 44240.0 \| https://ipsych.dk/en/research/downloads/ \| \| Ever attempted suicide \| EUR \| United Kingdom Biobank/Neale lab \| 4933.0 \| Binary \| 2.658 \| 2.275 \| https://www.nealelab.is/uk-biobank \| \| Self-harm \| \| \| \| \| \| \| \| \| Ever self-harmed (ESH) \| EUR \| United Kingdom Biobank/Neale lab \| 117733.0 \| Binary \| 5099.0 \| 112634.0 \| https://www.nealelab.is/uk-biobank \| \| Seeking mental health services \| EUR \| United Kingdom Biobank/Neale lab \| 117733.0 \| Binary \| 1693.0 \| 116040.0 \| https://www.nealelab.is/uk-biobank \| \| Completed suicide \| \| \| \| \| \| \| \| \| Suicide death (Otsuka et al., 2019) \| EAS \| \| 14795.0 \| Binary \| 746.0 \| 14049.0 \| https://humandbs.biosciencedbc.jp/hum0196-v1 \| \| Psychiatric traits \| \| \| \| \| \| \| \| \| Schizophrenia (Pardinas et al., 2018) \| EUR \| Clozuk + PGC2 \| 105318.0 \| Binary \| 40675.0 \| 64643.0 \| https://walters.psycm.cf.ac.uk/ \| \| Schizophrenia (Lam et al., 2019) \| EAS \| PGC \| 58140.0 \| Binary \| 22778.0 \| 35362.0 \| https://www.med.unc.edu/pgc/download-results/ \| \| Bipolar Disorder (Stahl et al., 2019) \| EUR \| PGC2 BD \| 51710.0 \| Binary \| 20352.0 \| 31358.0 \| https://www.med.unc.edu/pgc/download-results/ \| \| MDD (Giankopolou et al., 2021) \| EAS \| PGC \| 194548.0 \| Binary \| 15771.0 \| 178777.0 \| https://www.med.unc.edu/pgc/download-results/ \| \| MDD (Wray et al., 2018) \| EUR \| PGC \| 480359.0 \| Binary \| 135458.0 \| 344901.0 \| https://www.med.unc.edu/pgc/download-results/ \| \| Anorexia Nervosa (Duncan et al., 2017) \| EUR \| PGC \| 14477.0 \| Binary \| 3495.0 \| 10982.0 \| https://www.med.unc.edu/pgc/download-results/ \| \| PTSD (Duncan et al., 2018) \| EUR \| PGC \| 9954.0 \| Binary \| 2489.0 \| 7465.0 \| https://www.med.unc.edu/pgc/download-results/ \| \| ADHD (Demontis et al., 2018) \| EUR \| PGC \| 53293.0 \| Binary \| 19099.0 \| 34194.0 \| https://www.med.unc.edu/pgc/download-results/ \| \| Alcohol use disorder (Walters et al., 2018) \| EUR \| PGC \| 38686.0 \| Binary \| 10206.0 \| 28480.0 \| https://www.med.unc.edu/pgc/download-results/ \| \| Insomnia (Jansen et al., 2019) \| EUR \| United Kingdom Biobank \| 386533.0 \| Binary \| 109402.0 \| 277131.0 \| https://ctg.cncr.nl/software/summary_statistics \| \| Behavioural traits \| \| \| \| \| \| \| \| \| Drinks per week (Liu et al., 2019) \| EUR \| GSCAN \| 941280.0 \| Continuous \| \| \| https://conservancy.umn.edu/handle/11299/201564 \| \| Drinks per week (Matoba et al., 2020) \| EA \| GWAS catalogue \| 58610.0 \| Continuous \| \| \| https://www.ebi.ac.uk/gwas/downloads/summary-statistics/ \| \| Cigarettes per day (Liu et al., 2019) \| EUR \| GSCAN \| 337334.0 \| Continuous \| \| \| https://conservancy.umn.edu/handle/11299/201564 \| \| Cigarettes per day (Matoba et al., 2019) \| EA \| BBJ \| 72655.0 \| Continuous \| \| \| http://jenger.riken.jp/en/result \| \| Smoking (ever vs never) Kanai et al., 2021 \| EA \| BBJ \| 176166.0 \| Binary \| 88277.0 \| 87889.0 \| https://pheweb.jp/pheno/Smoking_Ever_Never \| \| Anthropometric traits \| \| \| \| \| \| \| \| \| Body mass index (Locke et al., 2015) \| EUR \| GIANT consortium \| 322154.0 \| Continuous \| \| \| https://www.nealelab.is/uk-biobank \| \| Body mass index (Sakau & Kanai et al., 2021) \| EA \| BBJ \| 163835.0 \| Continuous \| \| \| https://pheweb.jp/pheno/BMI \| \| Socioeconomic traits \| \| \| \| \| \| \| \| \| Household Income (Hill et al., 2016) \| EUR \| United Kingdom Biobank \| 112151.0 \| Continuous \| \| \| http://www.ccace.ed.ac.uk/node/335 \| \| Education in years (Okbay et al., 2016) \| EUR \| SSGAC \| 293723.0 \| Continuous \| \| \| https://thessgac.com/papers/ \| Briefly, the self-report measures of suicide ideation and self-harm were derived from GWAS studies of the United Kingdom Biobank (UKB) population (sample sizes ranged from 62,648 to 125,844) and accessed from the Neale lab (see weblinks/URLs, Table 1). Data within the UKB are structured in datasets and identified using field codes. Suicide ideation measures included recent (i.e., over the last 2 weeks) thoughts of suicide or self-harm (UKB field 20513); thoughts that life was not worth living (TLNWL, UKB field 20479); ever contemplated self-harm (ECSH, UKB field 20485), and having thoughts of death during worst of depression (UKB field 20437). Information on suicide ideation was obtained from three questions in the United Kingdom Biobank: (Recent thoughts of self-harm) “Over the last 2 weeks, how often have you been bothered by any of the following problems?; ( TLNWL) “Many people have thoughts that life is not worth living. Have you felt that way?” and (ECSH) “Have you contemplated harming yourself (for example, by cutting, biting, hitting yourself or taking an overdose)?”. The first two questions have three options: “no,” “yes, once” and “yes, more than once”. ECSH have four options: “not at all,” “several days,” “more than half the days” and “nearly every day”. Two datasets of self-reported self-harm include ever self-harmed (ESH, field 20480, $$n = 117$$,610) and attempted self-harm and needed hospital treatment (UKB field 20554, $$n = 117$$,733). Attempted suicide datasets were obtained from the United Kingdom Biobank study, a self-report measure indicating having ever attempted suicide (UKB field 20483; $$n = 4$$,933) and attempted suicide cases ($$n = 6$$,024) and controls ($$n = 44$$,240) from a GWAS study from the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) ($$n = 50$$,254). GWAS summary statistics were identified (Table 1) for psychiatric traits among European populations ($$n = 7$$; schizophrenia, bipolar disorder, major depression disorder, anorexia nervosa, PTSD, ADHD and insomnia) and East Asian populations ($$n = 2$$, schizophrenia and major depressive disorder). Behavioural traits included the average number of drinks per week and smoking habits among individuals of European and East Asian ancestry. Drinks per week (DPW), defined as the average number of drinks a participant reported drinking each week, aggregated across all types of alcohol, was examined in a combined approach with the GSCAN consortium and United Kingdom Biobank (UKB) (Liu et al., 2019) ($$n = 941$$,280), while cigarettes per day were defined as the average number of cigarettes smoked per day, either as a current or former smoker (Liu et al., 2019) ($$n = 337$$,334). Summary-level data was obtained for socioeconomic-related traits, i.e., household monthly income from United Kingdom Biobank (Hill et al., 2016) and education achievement, measured in school years (Okbay et al., 2016). The summary datasets included in this study are in the public domain and contain de-identified and anonymised data; thus, ethical approval from an institutional review board was not required for this study. ## Data formatting Datasets were formatted according to requirements for linkage disequilibrium score regression (LDSC) and genomic structural equation modelling (SEM) (Bulik-Sullivan et al., 2015; Grotzinger et al., 2019). We obtained publicly available pre-computed linkage disequilibrium (LD) scores and weights of the 1,000 Genomes European and East Asian reference (https://data.broadinstitute.org/alkesgroup/LDSCORE/). GWAS summary statistics were filtered for SNPs included in HapMap3 to reduce the likelihood of bias induced by poor imputation quality. SNPs were excluded if minor allele frequency (MAF) < $1\%$ and information (INFO) scores <0.9 or if they were located in the human major histocompatibility complex (MHC) region. Datasets without a marker name (rsID) were annotated using ANNOVAR software, with avsnp142, an abbreviated version of dbSNP 142 with left-normalization, on human genome build hg19 (Wang et al., 2010). ## SNP-based heritability SNP-based heritability estimates and pairwise genetic correlation were calculated for each dataset using LDSC software (https://github.com/bulik/ldsc) (Bulik-Sullivan et al., 2015). SNP-based heritability is the proportion (that ranges from 0 to 1) of variance of the phenotype that is attributable to all common SNPs used in a GWAS. Heritability estimates are presented in Table 2 and expressed on the observed scale. Lower heritability estimates with larger standard errors relative to the estimate indicated that there was not enough power to detect the SNP-based heritability estimate based on available datasets. Lower heritability estimates with larger standard errors relative to the estimate indicated larger uncertainty in the SNP-based heritability estimate. The genomic inflation factor (or lambda genomic control factor, λGC) compares the median of the resulting chi-squared statistics (χ2) divided by the expected median of the chi-squared distribution and was used to assess systematic bias or genomic inflation present in the GWAS summary data due to population stratification. A λGC estimate of around 1, indicates no systematic bias. An LDSC intercept near one indicates little or no confounding and larger than 1.3 indicates that the results might be affected by confounding bias (Bulik-Sullivan et al., 2015). SNP intercepts indicated no confounding bias in this study. The results were visualised using the corrplot package in R (R Core Team, 2017) and the correlation dot plot in SRplot (http://www.bioinformatics.com.cn/srplot). We retained four [Thought life is not worth living (TLNWL), Ever contemplated self-harm (ECSH), Attempted suicide (SA), Ever self-harmed (ESH)] of the nine suicidal behaviour GWAS summary data with genetic correlation estimates with heritability z-scores above 4, as scores below 4 do not produce reliable estimates (Bulik-Sullivan et al., 2015; Grotzinger et al., 2019). TABLE 2 \| Phenotype/Reference \| Ancestry \| # of SNPs \| SNP-based heritability (SE) \| z-score \| p \| Mean χ2 \| λ GC \| Intercept (SE)a \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Suicidal behaviour (SB) \| \| \| \| \| \| \| \| \| \| Suicidal ideation \| \| \| \| \| \| \| \| \| \| Recent thoughts of suicide or self-harm \| EUR \| 9561902.0 \| 0.0143 (0.0038) \| 3.76 \| 8.50 × 10−5 \| 1.0402 \| 1.0426 \| 1.0067 (0.0064) \| \| Thought life not worth living (TLNWL) \| EUR \| 1096648.0 \| 0.0735 (0.0054) \| 13.61 \| 1.74 × 10−42 \| 1.1788 \| 1.1578 \| 1.0087 (0.0072) \| \| Thoughts of death \| EUR \| 13559508.0 \| 0.0246 (0.0071) \| 3.46 \| 0.0003 \| 1.0314 \| 1.0309 \| 1.0012 (0.0065) \| \| Ever contemplated self-harm (ECSH) \| EUR \| 11386518.0 \| 0.0427 (0.0051) \| 8.38 \| 2.65 × 10−17 \| 1.119 \| 1.1093 \| 1.0206 (0.0068) \| \| Suicidal attempt \| \| \| \| \| \| \| \| \| \| Attempted suicide (SA) \| EUR \| 11601089.0 \| 0.0799 (0.0123) \| 6.49 \| 4.29 × 10−11 \| 1.1107 \| 1.0988 \| 1.0225 (0.0092) \| \| Ever attempted suicide \| EUR \| 10941854.0 \| 0.1461 (0.0892) \| 1.64 \| 0.0505 \| 1.0085 \| 1.0061 \| 0.9946 (0.0062) \| \| Completed suicide \| \| \| \| \| \| \| \| \| \| Completed suicide (Otsuka et al., 2019) \| EA \| 8381404.0 \| 0.0776 (0.0303) \| 2.56 \| 0.0052 \| 1.0756 \| 1.0741 \| 1.0514 (0.0075) \| \| Self-harm \| \| \| \| \| \| \| \| \| \| Ever self-harmed (ESH) \| EUR \| 12075154.0 \| 0.0217 (0.0044) \| 4.93 \| 4.11 × 10−7 \| 1.0613 \| 1.0536 \| 1.0107 (0.0066) \| \| SH needing hospital treatment \| EUR \| 10169094.0 \| 0.0129 (0.0038) \| 3.39 \| 0.0004 \| 1.0364 \| 1.041 \| 1.0065 (0.0062) \| \| Psychiatric disorders \| \| \| \| \| \| \| \| \| \| Schizophrenia (Pardinas et al., 2018) \| EUR \| 1153380.0 \| 0.4100 (0.0138) \| 29.71 \| 2.85 × 10−194 \| 1.9325 \| 1.6822 \| 1.0702 (0.0113) \| \| Schizophrenia (Lam et al., 2019) \| EA \| 10694924.0 \| 0.3784 (0.0222) \| 17.05 \| 1.75 × 10−65 \| 1.31 \| 1.2464 \| 1.0002 (0.0096) \| \| Bipolar Disorder (Stahl et al., 2019) \| EUR \| 1184385.0 \| 0.3872 (0.0190) \| 20.39 \| 1.03 × 10−92 \| 1.367 \| 1.3061 \| 1.0189 (0.0081) \| \| MDD (Wray et al., 2018) \| EUR \| 1081131.0 \| 0.0774 (0.0047) \| 15.74 \| 9.29 × 10−55 \| 1.2659 \| 1.2365 \| 0.9954 (0.0092) \| \| MDD (Giankopolou et al., 2021) \| EA \| 7440942.0 \| 0.0080 (0.0022) \| 3.64 \| 0.0001 \| 1.0419 \| 1.0345 \| 1.0093 (0.0065) \| \| Anorexia nervosa (Duncan et al., 2017) \| EUR \| 10120601.0 \| 0.2403 (0.0382) \| 6.29 \| 1.59 × 10−10 \| 1.0793 \| 1.0772 \| 1.0089 (0.0095) \| \| PTSD (Duncan et al., 2018) \| EUR \| 13206098.0 \| 0.0464 (0.0205) \| 2.26 \| 0.0112 \| 1.0127 \| 1.0165 \| 0.9939 (0.0059) \| \| ADHD (Demontis et al., 2018) \| EUR \| 8047421.0 \| 0.2268 (0.0145) \| 15.64 \| 1.94 × 10−55 \| 1.2966 \| 1.2531 \| 1.0336 (0.0102) \| \| Alcohol use disorder (Walters et al., 2018) \| EUR \| 9271144.0 \| 0.0952 (0.0199) \| 4.78 \| 8.76 × 10−7 \| 1.0601 \| 1.0588 \| 1.0182 (0.0063) \| \| Insomnia (Jansen et al., 2019) \| EUR \| 1117678.0 \| 0.0456 (0.0019) \| 24.0 \| 1.39 × 10−127 \| 1.3617 \| 1.3061 \| 1.0140 (0.0078) \| \| Behavioural traits \| \| \| \| \| \| \| \| \| \| Drinks per week (Liu et al., 2019) \| EUR \| 11916706.0 \| 0.0485 (0.0021) \| 23.09 \| 2.92 × 10−118 \| 1.4472 \| 1.3169 \| 0.9267 (0.0084) \| \| Drinks per week (Matoba et al., 2020) \| EA \| 5961480.0 \| 0.0731 (0.0420) \| 1.74 \| 0.0819 \| 1.0892 \| 1.0225 \| 1.0009 (0.0080) \| \| Cigarettes per day (Liu et al., 2019) \| EUR \| 12003613.0 \| 0.0724 (0.0068) \| 10.65 \| 8.72 × 10−27 \| 1.3301 \| 1.2201 \| 0.9595 (0.0095) \| \| Cigarettes per day (Matoba et al., 2019) \| EA \| 5925778.0 \| 0.0669 (0.0116) \| 5.76 \| 4.46 × 10−9 \| 1.1059 \| 1.0957 \| 1.0096 (0.0088) \| \| Smoking (ever vs never) Kanai et al., 2019 \| EA \| 13531752.0 \| 0.0290 (0.0042) \| 6.9 \| 2.60 × 10−12 \| 1.1092 \| 1.0975 \| 1.0024 (0.0081) \| \| Anthropometric traits \| \| \| \| \| \| \| \| \| \| Body mass index (Locke et al., 2015) \| EUR \| 2554637.0 \| 0.1297 (0.0056) \| 23.16 \| 5.76 × 10−119 \| 1.2603 \| 1.0772 \| 0.6729 (0.0079) \| \| Body mass index (Sakau & Kanai et al., 2021) \| EA \| 13236464.0 \| 0.1772 (0.0078) \| 22.72 \| 1.42 × 10−114 \| 1.6636 \| 1.4926 \| 1.0740 (0.0188) \| \| Socioeconomic-related traits \| \| \| \| \| \| \| \| \| \| Income (Hill et al., 2016) \| EUR \| 1217311.0 \| 0.0599 (0.0056) \| 10.7 \| 5.09 × 10−27 \| 1.1613 \| 1.1428 \| 1.0290 (0.0071) \| \| Education years (Okbay et al., 2016) \| EUR \| 8146840.0 \| 0.1108 (0.0037) \| 29.95 \| 2.19 × 10−197 \| 1.6445 \| 1.4745 \| 0.9377 (0.0092) \| ## Genetic correlation We calculated pairwise genetic correlation, i.e., the standardised proportion of the variance shared by the phenotypes that can be attributed to genetic factors, using LDSC, a method that is not biased by sample overlap. Correlations are reported as the coefficient ±standard error. To note, the LDSC estimator is unbounded and can produce genetic correlation estimates outside of −1 to 1 due to sampling variation. ( See https://groups.google.com/g/ldsc_users/c/3jtyM4mmTGs). Genetic correlations were corrected for multiple testing based on the total number of correlations by applying a Bonferroni corrected threshold of $p \leq 0.05$/52, corrected for four suicidal behaviour traits x 13 psychiatric, sociodemographic and behavioural traits, 9.615 × 10−4 for GWAS studies of European ancestry and seven East Asian studies of completed suicide and psychiatric disorders ($p \leq 0.05$/7 = 0.007). ## 2.3 Genomic structural equation modelling We performed exploratory and confirmatory factor analysis using the R-package Genomic Structural Equation Modelling (GenomicSEM) (Grotzinger et al., 2019). This method performs structural equation modelling using GWAS summary statistics, allowing us to explore the genetic factor structure of the suicidal behaviour traits and psychiatric traits. We used the Genomic SEM’s multivariable LD score regression method to estimate the genetic covariance matrix (S) and sampling covariance matrix (V) for all traits. All SNPs were standardized using the sumstats function in Genomic SEM (Grotzinger et al., 2019). We fit models using genetic covariance and sampling covariance matrices to examine the genome-wide factor structure of the data. We derived a single genomic factor or common factor containing genome-wide factor loadings representing each SNP contribution to the shared liability of suicidal behaviour and psychiatric disorders. Because “Ever contemplated self-harm” (ECSH) was highly correlated with “Thought life not worth living” (TLNWL), we retained the suicidal ideation trait with the highest SNP heritability z-score i.e., TLNWL (z-score = 13.61). Next, we performed an exploratory factor analysis of the S matrix with one, two and three factors using promax rotation in the R package factanal to guide the construction of a follow-up model. Standardised loadings of more than 0.4 were retained. We assessed model fit by comparing recommended test results and cut-offs; a good fit is indicated by a Comparative Fit Index (CFI) ≥ 0.95, Standardized Room Mean Square Residual (SRMR) ≤ 0.05 and lower AIC values indicate a better fit (Grotzinger et al., 2019). We extended genomicSEM to examine the relationship between the common factor and socioeconomic-related (education years and household income) and behavioural risk factors (smoking and average drinks per week). Because of the low SNP-based heritability z-scores (z-scores <4) observed among populations of East Asian ancestry, genomic SEM analyses were conducted on datasets of European ancestry populations only. ## 2.4 Gene and pathway-specific meta-analysis We performed gene/pathway-specific meta-analysis by combining the effect size of multiple SNPs within genes and genes within subnetwork/pathways using ancMETA, a Bayesian graph-based framework (Chimusa and Defo, 2022), for the derived common factor (TLNWL, ESH, SA, MDD, ADHD, and AUD). AncMETA uses a Bayesian posterior probability approach that extracts common SNPs, combines the results into known biological protein-protein network datasets, performs the meta-analysis at gene and sub-network level and identifies the most significant subnetwork hubs to understand the biological pathways (Chimusa and Defo, 2022). Common SNPs ($$n = 6$$,870,289) were extracted from all studies and mapped to genes located within or less than 20 kb distance up/downstream of the protein-coding gene using FUMA (Watanabe et al., 2017), and were included as potential candidate genes for ancMETA analysis. Input SNPs were mapped to 16,530 protein-coding genes at gene level, of which 2,951 genes were considered to have a fixed effect, meaning the effect of each gene is assumed to be shared equally across all six traits. The genome-wide significant threshold for the gene-based test was determined to be $$p \leq 0.05$$/16,530 = 3.02 × 10−6. At sub-network level, ancMETA identified 693 significant hub genes, of which 98 genes had a fixed effect. The genome-wide significant threshold was determined to be $$p \leq 0.05$$/693 = 7.22 × 10−5. We applied the most recent version of the human protein to protein interactions (PPI) network from the IntAct database (IntAct release 239) (Kerrien et al., 2012). We performed pathway enrichment analysis on the subnetwork genes based on gene ontology (GO) and KEGG and Reactome pathways and visualised the PPI network using the Cytoscape version 3.7.2, (Shannon et al., 2003), plug-in StringApp (Doncheva et al., 2019). GO included the enrichment of subnetwork hub genes in terms of molecular function, biological process and cellular component. A p-value of <0.05 statistical significance was set as an enrichment standard to determine the biological importance of hub genes. We identified drug-gene interactions through the Drug-Gene Interaction Database v4.0 (DGIdb 4.0) (Freshour et al., 2020), an open access database and a web interface (www.dgidb.org). DGIdb collects data on drug-gene interaction and druggable genes from 30 different sources and 22 databases (Freshour et al., 2020). We determined the second level classification (therapeutic subgroup) of each drug using the anatomical therapeutic chemical (ATC) classification from the World Health Organisation Collaborating Centre for Drug Statistics Methodology (https://www.whocc.no/atc_ddd_index/). We visualised the interaction between the genes significantly associated with the common factor and each therapeutic subgroup using the R package circlize v0.4.15 (Gu et al., 2014). ## 2.5 Mendelian randomisation We performed Mendelian randomisation to determine if the genetic correlations between the modifiable risk factors and suicidal behaviour arise from genes with pleiotropic effects and biological influences across the traits, or if the effects are causal. Mendelian randomisation uses genetic variants as a proxy for modifiable risk factors (an exposure) to estimate the causal effect on the outcome (Smith and Ebrahim, 2004). The principles of Mendelian randomisation can be applied to overcome bias by estimating the effect between the risk factor and outcome, in the absence of unmeasured confounders. However, the validity of Mendelian randomisation analysis is dependent on three assumptions: i) the instrument variable (genetic variant) should be associated with the exposure, ii) the instrument variable is independent of the outcome, conditional on the exposure and iii) the instrument variable is not associated with the unmeasured confounder (Burgess and Small, 2016). We used the twoSampleMR (Hemani et al., 2018), MRcML (Xue et al., 2021) and MR-APSS (Hu et al., 2022) packages in R to assess the potential causal effect of cigarettes smoked per day, alcoholic drinks per week, household income and educational achievement (school years) on suicidal behaviour risk where the cross-trait genetic correlation Bonferroni p-value > the corrected threshold of 9.615 × 10−4. For instrument variables (IV), we used the GWAS for the behavioural and socioeconomic-related traits listed in Table 1. We used the inverse-variance weighted (IVW) method with a multiplicative random effects model as the primary method and the weighted median, MR-Egger and RadialMR methods as sensitivity analyses and to detect pleiotropy. An MR-Egger intercept test of $p \leq 0.05$, indicates no evidence of directional pleiotropy. We used heterogeneity markers (Cochran Q-derived $p \leq 0.05$) from the IVW approach to represent potential horizontal pleiotropy. We applied RadialMR to detect potential outliers and removed the outliers to re-estimate the exposure-SB relationship. Genome-wide significant SNPs were selected at $p \leq 5$ × 10−8 significance and were clumped to ensure independence at linkage disequilibrium (LD) r 2 = 0.001 and distance of 10,000 kb. If an SNP from the instrument was unavailable in the outcome, an attempt to find proxies was made with a minimum LD r 2 = 0.8 and palindromic SNPs were aligned with minor allele frequency <0.3. Additional sensitivity analyses were performed using the constrained maximum likelihood and model averaging and Bayesian Information Criterion (cML-MA-BIC) method (Xue et al., 2021) and the Mendelian Randomisation Accounting for Pleiotropy and Sample Structure simultaneously (MR-APSS) approach (Hu et al., 2022). The cML-MA-BIC method accounts for correlated and uncorrelated horizontal pleiotropy and addresses potential violation of instrument variable assumptions identifying invalid instruments. If the goodness of fit p-value was >0.05, we applied the cML-MA-BIC method, otherwise the cML-MA-BIC-DP (data perturbation) method was applied. In addition to assessing horizontal pleitropy, the MR-APSS approach accounts for sample structure simultaneously and allows the inclusion of more genetic variants with moderate effects as instrument variables to improve statistical power without inflating type I errors (Hu et al., 2022). For MR-APSS, we applied its default instrument variable threshold of 5 × 10−5, while a threshold of 5 × 10−8 was applied for IVW, weighted median, MR Egger and cML-MA-BIC. The relationship between household income and TLNWL and ESH was not tested due to sample overlap as the three datasets were obtained from the UKBiobank cohort, and may introduce biased estimates. Reported estimates were converted to odds ratios where the outcome was binary, and interpreted using a conservative p-value threshold (0.05/number of factors with available summary statistics = 0.0083). ## 3.1 SNP-based heritability We found significant SNP-based heritability estimates of SB traits among European populations ranged from 0.0129 ± 0.0038 ($1.3\%$) for Self-harm needing hospital treatment to 0.1461 ± 0.0892 ($14.6\%$) for Ever attempted suicide (ESH), and 0.078 ± 0.0303 ($7.8\%$) for completed suicide for East Asian populations (Table 2). ## 3.2 Genetic correlation between suicidal behaviour (SB) and psychiatric, behavioural, anthropometric and socioeconomic-related traits We used cross-trait LD Score regression (LDSC) to estimate genetic correlations among suicidal behaviour (SB), psychiatric disorders and socioeconomic-related traits among populations of European ancestry. We observed strong positive and significant correlations within the SB traits [average genetic correlation (rg) = 0.92, range, 0.71–1.09], (Figure 1A). This means that genetic factors that increase the risk of suicidal ideation, also increase the risk of attempt and self-harm. The genetic correlations were strongest between suicide attempt (SA) and ECSH [rg = 1.09 ± standard error (SE) 0.14, $$p \leq 1.049$$ × 10−15] and between SA and ESH (rg = 0.99 ± 0.16, $$p \leq 1.027$$ × 10−9) and slightly lower between SA and TLNWL (rg = 0.71 ± 0.09, $$p \leq 2.382$$ × 10−26). As expected, suicidal ideation phenotypes (TLNWL and ECSH) were highly correlated (rg = 0.97 ± 0.03, $$p \leq 2.52$$ × 10−127). FIGURE 1: Pairwise LDSC-estimated genetic correlation between SB and psychiatric disorders, behavioural and SES traits (A) Heatmap showing the correlation between 17 traits among European populations, (B) a correlation dot plot showing the association between four suicidal behaviour traits (TLNWL, ECSH, SA and ESH)and psychiatric disorders, behavioural and socioeconomic (SES)-related traits among European populations. The size of the dot represents the strength of the correlation, and (C) a heatmap of seven traits including completed suicide among East Asian populations. The strength of the genetic correlation is presented as a heat scale on the x-axis with blue colour indicating positive and red colour representing negative correlations. Light colours represent lower correlation estimates, whereas darker colours indicate stronger correlations. Abbreviations: (SB traits) TLNWL = Thought life is not worth living, ECSH = Ever contemplated Self-harm, ESH = Ever self-harmed, SA = Suicide attempt; (Psychiatric, behavioural and socioeconomic-related traits) MDD = major depression disorder, SCZ = schizophrenia, BIP = bipolar disorder, AUD = alcohol use disorder, ADHD = attention deficit hyperactivity disorder, PTSD = post-traumatic stress disorder, ANRX = anorexia nervosa, INSM = insomnia, BMI = body mass index, DPW = drinks per week, CPD = cigarettes per day, INC = monthly income and EDU = education in years. After multiple testing correction ($$p \leq 0.05$$/52 = 0.000962), five psychiatric disorders, smoking and drinking habits, and education and monthly income were significantly genetically correlated with four SB traits among European populations (Supplementary Table S1; Figure 1B). The strongest correlation with the SB traits was MDD and ECSH (rg = 0.86 ± 0.07, $$p \leq 1.62$$ × 10−36). Moderate positive and significant genetic correlations were observed between schizophrenia and ECSH (rg = 0.30 ± 0.04, $$p \leq 1.39$$ × 10−12). Similarly, moderate positive genetic correlations were observed for bipolar disorder and ESH (rg = 0.34 ± 0.07, $$p \leq 1.11$$ × 10−5), ADHD and attempted suicide (SA) (rg = 0.59 ± 0.07, $$p \leq 1.41$$ × 10−19), and AUD and SA (rg = 0.54 ± 0.14, $$p \leq 0.0002$$). Among the behavioural traits, the strongest genetic correlations were observed for smoking habits and SA (rg = 0.35 ± 0.07, $$p \leq 8.27$$ × 10−7), and alcohol drinking habits and SA (rg = 0.17 ± 0.05, $$p \leq 0.0014$$). In contrast, education achievement (rg = −0.34 ± 0.05, $$p \leq 1.41$$ × 10−10) and monthly income (rg = −0.33 ± 0.09, $$p \leq 0.0004$$) were negatively associated with attempted suicide, meaning that educational achievement and household monthly income were protective against suicide attempt. Among East Asian populations (Figure 1C), completed suicide was moderately correlated with schizophrenia (rg = 0.35 ± 0.13, $$p \leq 0.0067$$). We observed no significant associations between completed suicide and MDD, drinking and smoking habits and BMI. ## 3.3 Genomic structural equation modelling First, we tested a model in which three SB traits (TLNWL, ESH and SA) and seven psychiatric traits (MDD, SCZ, BIP, AUD, ADHD, ANRX and INSM) loaded onto a single common latent factor (Table 3; Figure 2A). Model fit was fair for the common factor model in which the loadings were freely estimated (chi-square, X2[35] = 794.66, AIC = 834.66, CFI = 0.753, SRMR = 0.127). Standardised loadings indicated that MDD and SA loaded most strongly onto the common factor, while anorexia nervosa and insomnia loaded the weakest. We then assessed the fit of a correlated two-factor model where three suicidal behaviour traits were loaded onto the latent suicidal behaviour factor and seven psychiatric traits loaded onto a psychiatric latent factor (Figure 2B). We observed a strong correlation between the two latent factors (rg = 0.77 ± 0.04), however, the model fit remained suboptimal [X2[34] = 747.07, AIC = 789.07, CFI = 0.768, SRMR = 0.125]. Next, we conducted an exploratory factor analysis and examined different factor structures that would fit the data best (Supplementary Table S2). While both the one- (Figure 2A) and two-factor (Figure 2B) CFA solutions of the 10-items fit the data adequately, the second latent factor of the two-factor solution was underspecified and explained only $11.2\%$ of the variance. We then specified factor loadings to ≥0.4, decreasing the 10 items to six. The modified one factor model now had strong loadings for all six indicators (traits) and explained $61.3\%$ of the variance. To further improve model fit, we evaluated a revised common factor solution of six indicators that allowed for correlated indicator residuals between ADHD and AUD and between ESH and SA (Figure 2C). This model fit the data best across all model specifications [X2[7] = 51.31, AIC = 75.15, CFI = 0.954 and SRMR = 0.083], suggesting that this model may represent a common or shared genetic pathway/s to suicidal behaviour across MDD, ADHD and AUD. We extended genomic SEM to determine the genetic correlations between the revised common factor model [that represents SB (suicidal ideation, attempt and self-harm) and psychiatric disorders (MDD, ADHD and AUD)] and selected socioeconomic and behavioural traits. The revised common factor had a moderate positive correlation with smoking (rg = 0.47 ± 0.03) and inverse correlations with monthly income (rg = −0.52 ± 0.04) and education achievement (rg = −0.37 ± 0.02). Weak positive correlations were observed between the common factor and drinks per week (rg = 0.18 ± 0.02) and BMI (rg = 0.19 ± 0.02). In other words, the genetic factors that increase smoking and drinking habits, i.e., the number of cigarettes per day and drinks per week also increase SB/psychiatric disorders. In contrast, the genetic factors that influence an increase in education years and monthly income also decrease SB/psychiatric disorders; meaning higher education and household monthly income, a proxy for socioeconomic status is protective for non-fatal SB, MDD, ADHD and AUD. ## 3.4 Genes and pathways associated with the derived common factor ancMETA was used to perform gene and pathway-specific meta-analysis and estimate the aggregated genetic effects and the level of significance of the derived common factor (TLNWL, ESH, MDD, ADHD and AUD) on 16,530 genes. This technique identified 2,951 genes that were associated ($p \leq 3.02$ × 10−6) with the common factor at gene-level (Table 4; Supplementary Table S3) and 98 genes at sub-network level ($p \leq 7.22$ × 10−5, Supplementary Table S4; Figure 3). At gene-level, the most significant gene ($$p \leq 2.43$$ × 10−43) associated with the common factor was GDNF Family Receptor Alpha 3 (GFRA3), located on chromosome 5 and is involved in RAF/MAP kinase cascade pathway and nervous system development (Gaudet et al., 2011). Genes with significant but small effects across the six traits include the developmental pluripotency associated factor 4 (DPPA4) located on chromosome 3, ankyrin repeat domain 46 (ANKRD46) located on chromosome 8, KH domain containing 3 Like (KHDC3L) located on chromosome 6 and neuronal olfactomedin related ER localized protein 2 (OLFM2), located on chromosome 19. The most significant ($$p \leq 3.15$$ × 10−27) gene at sub-network level was transducer of ERBB2, 1 (TOB1), located on chromosome 17. In addition, the top sub-network genes were RAN binding protein 9 (RANBP9), located on chromosome 6, involved in developmental biology, signalling pathways and nervous system development; Serine and Arginine Rich Splicing Factor 3 (SRSF3), located on chromosome 6, Heat shock protein Family B (small) member 3 (HSPB3) located on chromosome 5, and Serine/Threonine Kinase 24 (STK24) on chromosome 13. We identified 25 Reactome pathways and four KEGG pathways linking the six traits (FDR<0.05, Figure 4). KEGG pathways were related primarily with genetic information processing (RNA degradation), while Reactome pathways were related to developmental biology, particularly, nervous system development, signal transduction and gene expression (transcription). Together with two Reactome pathways (SMAD4 MH2 Domain Mutants in Cancer and SMAD$\frac{2}{3}$ MH2 Domain Mutants in Cancer), two KEGG pathways were related to pathways in cancer. Sub-network (hub) genes were mostly involved in developmental biology (Reactome pathway, FDR = 0.018), signal transduction (Reactome pathway, FDR = 0.047) and RNA degradation (KEGG pathway, FDR = 0.0028). We observed that SMAD3 and SMAD4 (SMAD family member 3 and 4) appeared in most enrichment pathways (Figure 3). In Gene Ontology (GO), we identified 373 categories jointly associated with the common factor: 322 biological processes, 32 cellular components and 19 molecular functions. GO enrichment analysis showed that the sub-network genes were mainly located in the cytosol (FDR = 2.6 × 10−14) and nuclear lumen (FDR = 1.69 × 10−9). Moreover, sub-network genes were enriched in molecular functions relating to protein binding (FDR = 1.37 × 10−10) and enzyme binding (FDR = 5.23 × 10−10). Likewise, cellular component organisation or biogenesis (FDR = 2.63 × 10−6) was identified as the most significant biological process. Most of the subnetwork genes were highly expressed in the central nervous system (FDR = 1.27 × 10−7), nervous system (FDR = 1.27 × 10−7), and the brain (FDR = 1.79 × 10−7, Figure 4D). FIGURE 4: Gene ontology (GO) functional analysis histogram (A), bar plot of Reactome (B) and KEGG pathway enrichment analysis (C) and enriched tissues (D). Drug-gene interactions: We explored potential drug-target genes among the 98 sub-network genes significantly associated ($$p \leq 7.22$$ × 10−5) with the common factor, for known drug interactions in the Drug Gene Interaction Database v4.0 (DGIdb 4.0) (Freshour et al., 2020). A total of 246 interactions were identified for 26 genes and 190 drugs (Supplementary Table S5). Anatomical therapeutic chemical (ATC) classifications were available for 185 drugs that were assigned to 47 therapeutic subgroups (Figure 5). The greatest number of drug-gene interactions were observed between antineoplastic agents (L01 drug classification) and SMAD4 (SMAD Family Member 4, $$n = 11$$), NOTCH1 (Notch Receptor 1, $$n = 9$$) and APEX1 (Apurinic/Apyrimidinic Endodeoxyribonuclease 1, $$n = 7$$). Additional interactions were observed between APEX1 and N04, anti-Parkinson drugs and between AGT (Angiotensinogen) and C09, drugs acting on the renin-angiotensin system. FIGURE 5: Chord diagram of sub-network genes associated with the common factor and the drug-gene interactions with the second level Anatomical Therapeutic Chemical (ATC) classification (therapeutic subgroup) of drugs. The width of each line represents the number of drugs known to interact with each gene. Therapeutic subgroup ATC Drug classifications: A02 = Drugs for acid-related disorders, A07 = antidiarrheals, intestinal anti-inflammatory/anti-infective agents, A10 = drugs used in diabetes, B01 = antithrombotic agents, B02 = antihemorrhagics, B05 = blood substitutes and perfusion solutions, C01 = cardiac therapy, C02 = antihypertensives, C03 = diuretics, C05 = vasoprotectives, C07 = beta blocker agents, C08 = calcium channel blockers, C09 = agents acting on renin-angiotensin system, C10 = lipid modifying agents, D05 = antipsoriatics, D08 = antiseptics and disinfectants, D09 = medicated dressing, D10 = anti-acne preparations, D11 = other dermatological preparations, G01 = gynecological anti-infective and antiseptics, G03 = sex-hormones and modulators of the genital system, G04 = urological, H01 = pituitary and hypothalamic hormones and analogues, H03 = thyroid therapy, J01 = antibacterial for systemic use, J02 = antimycotics for systemic use, J05 = antivirals for systemic use, L01 = antineoplastic agent, L02 = endocrine therapy, L03 = immunostimulants, L04 = immunosuppressants, M01 = anti-inflammatory and antirheumatic products, M03 = muscle relaxants, M09 = other drugs for disorders of the Musculo-skeletal system, N01 = anesthetics, N02 = analgesics, N03 = antiepileptics, N04 = anti-Parkinson drugs, N05 = psycholeptics, N06 = psychoanaleptics, N07 = other nervous system drugs, P01 = antiprotozoals, P02 = anthelmintics, R05 = cough and cold preparations, S01 = opthalmologicals, V03 = all other therapeutic products, V04 = therapeutics radiopharmaceuticals. ## 3.5 Causal effect of modifiable risk factors on suicidal behaviour We used the genetic variants associated with suicidal behaviour and genetic variants associated with smoking, alcohol drinking, education achievement and household income to determine the unique effects of each modifiable risk factor on suicidal behaviour. Mendelian randomisation analyses showed a nominal association at the $p \leq 0.05$ threshold of the potential effect of smoking on the risk of a suicide attempt (ORIVW 1.24, $95\%$ CI 1.03–1.49, $$p \leq 0.026$$), and suggested no causative relationship between smoking and suicidal ideation (TLNWL, ßIVW 0.017, SE 0.015, $$p \leq 0.263$$) or self-harm (ESH ORIVW 1.00, $95\%$ CI 0.99–1.01, $$p \leq 0.437$$) (Table 5; Supplementary Figure S1). The intercept from the MR Egger method for suicide attempt showed minimal indication of directional pleiotropy ($$p \leq 0.053$$), and there was evidence of substantial heterogeneity (Cochran’s Q statistic $$p \leq 3.53$$ × 10−3). High levels of heterogeneity in the estimated effects from each SNP are an indication of potential pleiotropic effects of some of the SNPs associated with smoking and suicide attempt. We conducted a sensitivity analysis, using a radial regression framework, and identified a variant (rs34406232) on the EGLN2 gene, as an outlier potentially introducing bias to IVW and MR Egger estimates (Supplementary Figure S1). After removing the outlier, the estimate of cigarette smoked per day on suicide attempt remained significant (random effects model: β IVW 0.27, SE = 0.08, $$p \leq 7.05$$ × 10−3) and the Cochran’s Q statistic for heterogeneity was 32.61 ($$p \leq 0.001$$), indicating that removing the SNP made no substantive difference to the results. In addition, the cML-MA-BIC-DP results (OR = 1.30, $95\%$ CI $1.14\%$–$1.47\%$, $$p \leq 4.16$$ × 10−5) and the MR-APSS method (OR = 2.596, $95\%$ CI 1.428–4.717, $$p \leq 1.75$$ × 10−3) were consistent with IVW method, with significant associations observed at the p-value threshold of 0.0083 (Supplementary Figure S2), suggesting a potential causative relationship between cigarettes smoked per day and suicide attempt. TABLE 5 \| Exposure \| Outcome \| Method \| N SNPs \| OR [95% CI]/β (SE) \| p \| Directional pleiotropy \| Directional pleiotropy.1 \| Heterogeneity \| Heterogeneity.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Exposure \| Outcome \| Method \| N SNPs \| OR [95% CI]/β (SE) \| p \| Intercept \| p \| Q \| p \| \| Cigarettes smoked per day \| TLNWL a \| MR Egger \| 22 \| −0.043 (0.020) \| 0.053 \| 0.005 \| 0.002 \| 20.03 \| 0.456 \| \| Cigarettes smoked per day \| TLNWL a \| IVW \| 22 \| 0.017 (0.015) \| 0.263 \| \| \| 32.07 \| 0.058 \| \| Cigarettes smoked per day \| TLNWL a \| Weighted median \| 22 \| 0.001 (0.016) \| 0.957 \| \| \| \| \| \| Cigarettes smoked per day \| TLNWL a \| cML-MA-BIC \| 22 \| 0.075 (0.026) \| 0.004 \| \| \| \| \| \| Cigarettes smoked per day \| TLNWL a \| MR-APSS \| 165 \| 0.289 (0.089) \| 1.23 × 10−3 \| \| \| \| \| \| Cigarettes smoked per day \| ESH \| MR Egger \| 22 \| 0.989 [0.978–1.002] \| 0.108 \| 0.001 \| 0.019 \| 24.03 \| 0.241 \| \| Cigarettes smoked per day \| ESH \| IVW \| 22 \| 1.002 [0.994–1.010] \| 0.519 \| \| \| 31.85 \| 0.060 \| \| Cigarettes smoked per day \| ESH \| Weighted median \| 22 \| 0.997 [0.987–1.005] \| 0.437 \| \| \| \| \| \| Cigarettes smoked per day \| ESH \| cML-MA-BIC \| 22 \| 1.002 [0.939–1.069] \| 0.574 \| \| \| \| \| \| Cigarettes smoked per day \| ESH \| MR-APSS \| 165 \| 1.352 [1.127–1.622] \| 1.15 × 10−3 \| \| \| \| \| \| Cigarettes smoked per day \| SA \| MR Egger \| 14 \| 0.711 [0.418–1.209] \| 0.232 \| 0.025 \| 0.053 \| 32.26 \| 1.26 × 10−3 \| \| Cigarettes smoked per day \| SA \| IVW \| 14 \| 1.237 [1.026–1.492] \| 0.026 \| \| \| 44.69 \| 3.53 × 10−5 \| \| Cigarettes smoked per day \| SA \| Weighted median \| 14 \| 1.224 [1.038–1.443] \| 0.016 \| \| \| \| \| \| Cigarettes smoked per day \| SA \| cML-MA-BIC-DP \| 14 \| 1.297 [1.145–1.468] \| 4.16 × 10−5 \| \| \| \| \| \| Cigarettes smoked per day \| SA \| MR-APSS \| 165 \| 2.596 [1.428–4.717] \| 1.75 × 10−3 \| \| \| \| \| \| Alcoholic drinks per week \| SA \| MR Egger \| 21 \| 1.075 [0.731–1.579] \| 0.718 \| 0.004 \| 0.349 \| 49.57 \| 0.0001 \| \| Alcoholic drinks per week \| SA \| IVW \| 21 \| 1.233 [0.948–1.602] \| 0.117 \| \| \| 51.97 \| 0.0001 \| \| Alcoholic drinks per week \| SA \| Weighted median \| 21 \| 1.097 [0.834–1.362] \| 0.400 \| \| \| \| \| \| Alcoholic drinks per week \| SA \| cML-MA-BIC-DP \| 21 \| 1.191 [1.005–1.414] \| 0.044 \| \| \| \| \| \| Alcoholic drinks per week \| SA \| MR-APSS \| 231 \| 1.359 [0.942–1.961] \| 0.010 \| \| \| \| \| \| Education (school years) \| SA \| MR Egger \| 53 \| 1.051 [0.307–3.591] \| 0.936 \| −0.003 \| 0.738 \| 176.32 \| 1.03 × 10−31 \| \| Education (school years) \| SA \| IVW \| 53 \| 0.854 [0.685–1.065] \| 0.162 \| \| \| 176.77 \| 1.69 × 10−15 \| \| Education (school years) \| SA \| Weighted median \| 53 \| 0.884 [0.714–1.094] \| 0.259 \| \| \| \| \| \| Education (school years) \| SA \| cML-MA-BIC \| 53 \| 0.882 [0.769–1.012] \| 0.073 \| \| \| \| \| \| Education (school years) \| SA \| MR-APSS \| 392 \| 0.526 [0.381–0.727] \| 9.61 × 10−5 \| \| \| \| \| \| Household income \| SA \| MR Egger \| 37 \| 1.294 [0.463–3.615] \| 0.625 \| −0.016 \| 0.106 \| 104.24 \| 8.35 × 10−9 \| \| Household income \| SA \| IVW \| 37 \| 0.554 [0.437–0.704] \| 1.29 × 10−5 \| \| \| 112.43 \| 8.58 × 10−10 \| \| Household income \| SA \| Weighted median \| 37 \| 0.606 [0.479–0.766] \| 2.85 × 10−5 \| \| \| \| \| \| Household income \| SA \| cML-MA-BIC-DP \| 37 \| 0.603 [0.525–0.691] \| 3.85 × 10−11 \| \| \| \| \| \| Household income \| SA \| MR-APSS \| 296 \| 0.202 [0.051–0.920] \| 0.038 \| \| \| \| \| We also observed a potential beneficial effect of household income level on suicide attempt, with genetically predicted higher household income (odds ratio per one standard deviation increase in household income) potentially leading to a $45\%$ decrease in the probability of attempting suicide (ORIVW 0.55, $95\%$ CI 0.44–0.70, $$p \leq 1.29$$ × 10−5) (Table 5; Supplementary Figure S3). Similarly, the MR Egger intercept ($$p \leq 0.106$$) suggests directional pleiotropy was not biasing the estimate, while the Cochran’s Q statistic ($$p \leq 8.35$$ × 10−3) showed high levels of heterogeneity, indicating that some SNPs are pleiotropic but the average pleiotropic effect is close to zero and therefore balanced. We conducted a sensitivity analysis removing two variants (rs11665242 on the DCC gene and rs589914 on the RP11-734C14.2 gene) and the effects remained constant (random effects model β IVW = −0.502, SE = 0.111, $$p \leq 7.398$$ × 10−5). Additional sensitivity analyses showed that the cML-MA-BIC-DP method (OR 0.60, $95\%$ CI 0.53–0.69, $$p \leq 3.85$$ × 10−11) yielded a similar result to IVW and weighted median methods (Table 5), while the association from MR-APSS method was nominally significant (OR 0.20, $95\%$ CI 0.05–0.92, $$p \leq 0.038$$) (Table 5; Supplementary Figure S2). These findings show a potential inverse relationship between higher household income and suicide attempt. Our findings did not suggest a causal relationship association between suicide attempt and alcohol drinks per week or educational achievement (school years). There was no indication of directional pleiotropy (MR Egger intercept $$p \leq 0.349$$), however, the Cochran’s Q statistic ($$p \leq 0.0001$$) showed heterogeneity between individual SNP estimates at the global level ($$p \leq 0.0001$$), suggesting that some SNPs are pleiotropic but the average pleiotropic effect is close to zero (Supplementary Figure S3). We identified SNP rs4309187 in the DRD2 gene as a potential outlier and re-estimated the model after removing the outlier and the p-value for Cochran’s Q statistic remained significant ($$p \leq 0.006$$). ## 4 Discussion In the present study, we analysed summary-level data from large-scale GWAS to examine the genetic correlation between suicidal behaviour and psychiatric disorders using genomic structural equation modelling. We observed strong genetic correlations between suicidal behaviour traits and moderate to strong genetic correlations between suicidal behaviour and psychiatric disorders, particularly major depression disorder. Exploratory factor analysis of individuals of European ancestry revealed a single factor that represents a common or shared genetic pathway/s to suicidal behaviour across major depression, alcohol use disorder and ADHD. We identified 98 sub-network hub genes associated with the common factor and observed pathways enriched in developmental biology, signal transduction, gene transcription and RNA degradation. Most of the subnetwork hub genes were highly expressed in the central nervous system. We identified several drug-gene interactions, involving genes in the common or shared genetic pathways that may be worth investigating as potential targets for the prevention and treatment of MDD, alcohol use disorder, ADHD and suicidal behaviour (common factor). The observed strong genetic correlations within the non-fatal suicidal behaviour traits suggest that suicidal ideation, self-harm and attempted suicide have a shared genetic component and provides support for the possibility that suicidal behaviour may exist on a spectrum of behaviours from thinking of suicide to acting on these thoughts (Caspi et al., 2014). However, as a separate construct, non-fatal suicidal behaviour was not genetically distinct, but rather our findings suggest an interconnected network of suicidal behaviour and major depression, ADHD and alcohol use disorder that supports established epidemiological (Turecki and Brent, 2016; Fazel and Runeson, 2020) and genomic associations (Mirkovic et al., 2016; DiBlasi et al., 2021). We identified significant positive correlations between non-fatal suicidal behaviour and psychiatric disorders, with the strongest correlation observed for major depressive disorders in individuals of European ancestry. Genetic factors play an important role in the aetiology of psychiatric disorders, with heritability estimates from twin and family studies ranging from $32\%$ to $79\%$ for major depression (Sullivan et al., 2000; Smoller et al., 2019), $77\%$–$88\%$ for ADHD (Faraone and Larsson, 2019), $81\%$ for schizophrenia, and $57\%$ for substance use disorders (Sullivan et al., 2012). There is well-supported evidence that psychiatric disorders are polygenic, that many common variants with small effects contribute to an increased risk (Sullivan et al., 2018) and GWAS studies have shown significant genetic overlap between psychiatric traits (Lee et al., 2019; Lee et al., 2019). Depression is a well-known risk factor for suicidal behaviour (Malone et al., 1995) and several recent large GWAS have shown an overlap between suicide attempt and depression using genetic correlation analyses or polygenic risk scoring (PRS) (Levey et al., 2019; Mullins et al., 2019; Ruderfer et al., 2019; Strawbridge et al., 2019). Further, Mullins et al. [ 2019] reported PRS for major depression was associated with an increased risk of attempted suicide for individuals with major depression and schizophrenia (Mullins et al., 2019). ADHD, a neurodevelopmental disorder, has been associated with depression, schizophrenia and substance use disorder in later life (Tistarelli et al., 2020), as well as an increased risk of attempted and completed suicide (Ljung et al., 2014), suggesting common underlying risk variants contribute to these disorders. A recent meta-analysis showed alcohol use disorder increases the risk of suicidal ideation, attempt and suicide completion (Darvishi et al., 2015). Further, findings from recent GWAS studies showed that PRS of completed suicide was associated with greater alcohol use and schizophrenia (Docherty et al., 2020), while attempted suicide was genetically correlated with alcohol dependence (Mullins et al., 2022). These findings suggest that there is a component of common genetic variation that is shared between suicidal behaviour and MDD, ADHD, schizophrenia and alcohol use disorder. It is possible that cross-trait assortive mating, which is explained by individuals choosing partners with specific characteristics that have no genetic relationship, may have substantially inflated the genetic correlation estimates and biased the Mendelian randomisation results (Border et al., 2022). Assortive mating across psychiatric disorders can increase the correlation between the traits of the parents, which in turn increases the correlation between the psychiatric traits of their offspring (Nordsletten et al., 2016), and may explain the genetic comorbidity across psychiatric disorders. We identified 98 potential sub-network (hub) genes and key pathways associated with the common factor. Among the hub genes, TOB1, RANBP9, SRSF3, HSPB3 and STK24 were among the most significant. Findings from enrichment analysis suggest that the hub genes were mainly involved in developmental biology, signal transduction, gene transcription and RNA degradation pathways. SMAD3 and SMAD4 genes, observed in most enrichment pathways are members of the SMAD family, and code for intracellular signal transducer proteins involved in transforming growth factor-beta (TGF-ß) signalling. The TGF-beta/SMAD signalling pathway plays an important role in neurogenesis in the hippocampus and has been implicated in the development of mood disorders and the manifestation of depression and anxiety disorders (Hiew et al., 2021). Interestingly, variants in SMAD3 have also been linked to smoking behaviour (Justice et al., 2017). Among the top hub genes associated with the common factor, TOB1, RANBP9, HSPB3, and SRSF3 were also linked to neurodegenerative disorders, such as Alzheimer’s disease, Parkinson’s, and amyotrophic lateral sclerosis, through various pathways. The RNA degradation pathway, linked to TOB1 as indicated by the KEGG enrichment analysis, is a critical step in the control of various biological pathways. In neurons, the non-sense-mediated RNA decay (NMD) pathway serves as a regulatory mechanism to control mRNA, and mutations in the NMD genes have been associated with neurodevelopmental disorders, such as schizophrenia and neurodegenerative disorders, such as amyotrophic lateral sclerosis (Jaffrey and Wilkinson, 2018). TOB1, which codes for an antiproliferative protein that targets mRNA deadenylation and decay (Hosoda et al., 2011), has previously been associated with neurodegenerative disorders (Weskamp and Barmada, 2018), such as multiple sclerosis (Gironi et al., 2016) and a TOB1 deletion has been associated with hippocampus-mediated acute stress response in animal models (Youssef et al., 2022). The primary role of the signal transduction pathway is to regulate overall growth and behaviour. RANBP9 has been implicated in the nervous system development pathway and the regulation of a number of signalling pathways, including the signal transduction pathway. RANBP9 interacts with proteins involved in Alzheimer’s disease and has been associated with schizophrenia (Das et al., 2017). HSPB3 [heat shock protein family B (small) member 3], is involved in the inhibition of the apoptosis pathway and regulates cell death by inhibiting actin polymerization. HSPB3 has previously been linked to alcohol dependence (Kapoor et al., 2014) and neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease (Vendredy et al., 2020). SRSF3 (serine and arginine-rich splicing factor 3) plays a key role in the metabolism of RNA/gene transcription (Watanuki et al., 2008). Abnormal expression of SRSF3 can lead to aberrant gene splicing and the development of neurodegenerative disorders (Xiong et al., 2022). STK24 (sterine/threonine kinase 24), promotes apoptosis in response to stress stimuli and caspase activation and can act as a regulator of axon regeneration in optic and radial nerves and is involved in programmed cell death (Mardakheh et al., 2016). STK24 has been implicated in unipolar depression (Howard et al., 2019; Levey et al., 2019) and schizophrenia (Lam et al., 2019). It is worth noting that processes related to neurodegeneration may be due to the older age of study participants in UKBiobank from whom suicidal behaviour traits were obtained. Taken together, psychiatric and neurodegenerative disorders represent a heterogeneous group of neurological conditions and future studies investigating the shared molecular characteristics between suicidal behaviour, MDD, ADHD and alcohol use disorder should be explored in a younger target population to better understand the pathophysiological mechanisms that underlie psychiatric and neurodegenerative disorders. We found positive genetic correlations between suicidal behaviour and modifiable risk behaviours such as smoking and average alcohol drinking per week, that are consistent with the observed increase in these behaviours among individuals with suicidal behaviour (Poorolajal and Darvishi, 2016; Polimanti et al., 2021) and are indicative of a shared genetic basis for these traits. The prevalence of tobacco smoking is known to be higher among individuals with mental health conditions compared to the general population (Prochaska et al., 2017). Further, tobacco smoking is considered an independent risk factor for suicidal behaviour; a meta-analysis showed that smokers are at higher risk of suicidal ideation (OR = 2.05, $95\%$ CI 1.53–2.28), suicide attempt (OR = 2.84, $95\%$ CI 1.49–4.19) and completed suicide (RR = 1.83, $95\%$ CI 1.64–2.02) (Poorolajal and Darvishi, 2016). A causal association was found between earlier smoking initiation, lifetime smoking, depression and schizophrenia (Wootton et al., 2020). We used genetic variants associated with smoking and found nominally significant MR results, pointing to thepotential harmful effect of smoking intensity (increased cigarettes smoked per day) on suicide attempt, although findings were not consistent across all sensitivity analyses. Nevertheless, our results align with literature on the relationship between smoking and suicidal behaviour (Poorolajal and Darvishi, 2016) and merit further investigation for including smoking cessation and prevention in suicide prevention programs. The negative genetic correlations between suicidal behaviour and socioeconomic-related variables, i.e., education achievement and monthly income support previously reported associations between indicators of poverty and suicidal behaviour (Iemmi et al., 2016; Lorant et al., 2021). In addition, we found suggestive evidence for the protective effect of genetically predicted higher household income level on the risk of suicide attempt. Earlier work by Dohrenwend et al. have suggested that the high rate of mental disorders in disadvantaged populations can be explained by the social selection theory, that individuals with mental illness have a predisposition to declining socioeconomic status due to possible genetic factors, hospitalisations related to mental illness, and/or loss of work (Dohrenwend et al., 1992). Our study findings suggest that individuals with major depression, ADHD or alcohol use disorder are at increased risk of suicidal behaviour. Understanding the shared biological mechanisms and pathways that may account for the similarities between suicidal behaviour and psychiatric disorders at the epidemiological, neuropathological, and molecular levels could provide potential avenues to treatment and prevention strategies. We found a number of interactions between the hub genes and the ATC therapeutic sub-groups. These exploratory findings, to be interpreted with caution, suggest that pharmaceutical treatments that are currently available may target the genetic component of the common factor. The most notable drug-gene interactions were observed between drugs grouped in the L01 drug classification, which comprises antineoplastic and immune-modulating agents, and SMAD4 and NOTCH1 genes. Additional drug-gene interactions were observed for APEX1 (Apurinic/Apyrimidinic Endodeoxyribonuclease 1) and N04 drug classification, which comprises of anti-Parkinson drugs, and includes anticholinergic and dopaminergic agents. Our study has limitations. First, we planned to examine the full spectrum of fatal and non-fatal suicidal behaviour in the exploratory factor analysis. However, only one of the nine publicly available genome-wide summary datasets consisted of individuals who completed suicide and the population was of East Asian ancestry. Owing to the confounding effects of ancestral variation in LD score regression, our factor analysis included only non-fatal suicidal behaviour data of individuals of European ancestry. Therefore, the findings from the genetic factor analyses relate only to non-fatal suicidal behaviour and do not include completed suicide. Second, the modest SNP-based heritability (z-scores <4) of completed suicides and psychiatric traits of East Asian populations meant that we could not explore the factor structure of these traits independently for individuals of East Asian ancestry. As most suicides in the world occur in low- and middle-income countries (WHO, 2021), the current analysis should be extended to include diverse populations, e.g., African and ad-mixed populations as sufficient data becomes available. This is crucial to understanding the link between suicidal behaviour and psychiatric traits to advance precision medicine efforts in countries and populations with mixed genetic ancestry patterns, where it is needed most. Third, four of the ten suicidal behaviour traits had low SNP-based heritability estimates and were therefore underpowered and not included in the genetic factor analyses. This reduced the number of datasets available for analysis; however, we were able to include at least one dataset that represented each of the SB phenotypes: suicidal ideation, suicide attempt, self-harm or completed suicide. Fourth, while Mendelian randomisation is less likely to be affected by confounding compared to observational studies, this method is limited by the number of instrumental variables available. In our study, the instrumental variables were adequate for the exposures but we were unable to test reverse causality due to the low number of instruments or lack of suitable variants for suicidal behaviour. Fifth, suicidal behaviour was defined either by self-reported items or cases identified by ICD-10 coding of hospital inpatient and death registries. Therefore, some misclassifications are expected in individuals who may have underreported their symptoms, which may underestimate suicidal behaviour. Sixth, there are known sex differences in the genetic influences of psychiatric disorders (Merikangas and Almasy, 2020) and sex-specific effects have also been identified in individuals with suicidal behaviour (Kia-Keating et al., 2007; Powers et al., 2020). We could not analyse our data stratified by sex, as sex-specific summary datasets for all datasets were not available. However, as larger, well-powered summary statistics become available, this could be addressed in the future. Lastly, this study is limited by the suicidal behaviour data that was publicly available. Because fatal suicidal behaviour is less common than non-fatal suicidal behaviour, there is less data available for completed suicides as GWAS studies of rare outcomes require more time and resources to obtain large sample sizes. ## 5 Conclusion In conclusion, our study results support previous findings of genetic overlap between suicidal behaviour and psychiatric disorders. This highlights the importance of further investigation into the overlapping influences of these phenotypes with larger sample sizes and diverse ancestry. Understanding the biology reflected by the shared genes and related pathways could provide new directions in revealing shared etiologies that could help prioritise targets for suicidal behaviour for early intervention. ## Data availability statement The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. ## Ethics statement Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study. ## Author contributions Concept and design: TK and RR. Acquisition, analysis or interpretation of data: TK, RR, LL, JD, and LM. Statistical analysis: TK and JD. Drafting of the article: TK. Critical revision of the article for important intellectual content: TK, RR, LL, JD, and LM. Supervision: RR, LL, and LM. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1083969/full#supplementary-material ## References 1. Allegrini A. G., Cheesman R., Rimfeld K., Selzam S., Pingault J. B., Eley T. C.. The p factor: Genetic analyses support a general dimension of psychopathology in childhood and adolescence. J. 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--- title: Novel prognostic nomograms in cervical cancer based on analysis of 1075 patients authors: - Qunxian Rao - Xue Han - Yuan Wei - Hui Zhou - Yajie Gong - Meimei Guan - Xiaoyan Feng - Huaiwu Lu - Qingsong Chen journal: Cancer Medicine year: 2022 pmcid: PMC10028162 doi: 10.1002/cam4.5335 license: CC BY 4.0 --- # Novel prognostic nomograms in cervical cancer based on analysis of 1075 patients ## Abstract Two nomograms were subsequently constructed for DFS and OS prognostication, respectively, and showed high performance in terms of discrimination, calibration, and clinical applicability. ### Objective To explore the factors affecting the prognosis of cervical cancer (CC), and to construct and evaluate predictive nomograms to guide individualized clinical treatment. ### Methods The clinicopathological and follow‐up data of CC patients from June 2013 to December 2019 in Sun Yat‐sen Memorial Hospital of Sun Yat‐sen University were retrospectively analyzed. Log‐rank test was used for univariate survival analysis, and Cox multivariate regression was used to identify independent prognostic factors, based on which nomogram models were established and evaluated in multiple aspects. ### Results Patients were randomly assigned into the training ($$n = 746$$) and validation sets ($$n = 329$$). Survival analysis of the training set identified cervical myometrial invasion, parametrial involvement, and malignant tumor history as prognosticators of postoperative DFS and pathological type, cervical myometrial invasion, and history of STD for OS. C‐index was 0.799 and 0.839 for the nomograms for DFS and OS, respectively. Calibration curves and Brier scores also indicated high performance. Importantly, decision curve analysis suggested great clinical applicability of these nomograms. ### Conclusions In this study, we analyzed a cohort of 1075 CC patients and identified DFS‐ or OS‐associated clinicohistologic characteristics. Two nomograms were subsequently constructed for DFS and OS prognostication, respectively, and showed high performance in terms of discrimination, calibration, and clinical applicability. These models may facilitate individualized treatment and patient selection for clinical trials. Future investigations with larger cohorts and prospective designs are warranted for validating these prognostic models. ## INTRODUCTION Cervical cancer (CC) is one of the most common gynecological malignancies. In 2020, there were approximately 604,000 CC cases and 342,000 related deaths worldwide. 1 In a study involving 184 countries, CC was the most common cancer diagnosis in 45 countries. 2 The incidence of CC is rising year by year, 3 and a Latin American study showed a tendency of increasing proportion of younger patients. 4 Nearly all cases of CC can be attributed to infection with human papillomavirus (HPV). However, a Chinese study suggested a growing patient population despite HPV vaccination programs. 5 Moreover, although most patients can be cured by local treatment, approximately $20\%$ patients develop recurrence and/or metastasis. 6 Therefore, CC is likely to impose a heavier burden on public health and cancer care. A handful of independent prognostic factors have been proposed to date, including tumor size, vascular invasion, and pelvic lymph node (PLN) metastasis, although consensus has not been established for all. 7, 8, 9, 10, 11, 12 A study on 110 patients with early stage CC who received surgical intervention showed that PLN metastasis affected prognosis. 8 In particular, Zheng et al. 13 proposed a nomogram model for stage IA1‐IIA patients, which identified PLN metastasis as a strong poor prognosticator. In this retrospective study, we identified clinicopathological factors associated with overall and DFS of 1075 Chinese CC patients, based on which a nomogram model was subsequently constructed and validated. In addition, we also measure the clinical applicability of the model using decision curve analysis. ## Patients We included 1081 women with stage IA‐IVB CC who received treatment at the Sun Yat‐sen Memorial Hospital of Sun Yat‐sen University in China from June 2013 to December 2019. After excluding pregnant patients ($$n = 5$$) or those who also had ovarian cancer ($$n = 1$$), data for 1075 cases were used for subsequent analyses (Figure 1). FIGURE 1: Study selection process. Information regarding clinicopathological features and follow‐up were retrieved from the electronic medical records. Interrogated clinicopathological features included age at diagnosis, FIGO stage, menopause, ovarian metastasis (resection), parity, history of STD, history of malignant tumor, diabetes, liver disease, cardiovascular and cerebrovascular diseases, histological type, tumor differentiation, PLN metastasis status, para‐aortic lymph node metastasis status, tumor size, vascular invasion, cervical junction invasion, cervical myometrial invasion, parametrial invasion, vaginal fornix involvement. Follow‐up data were collected regarding occurrence of and time to disease recurrence, metastasis, and death. DFS was defined as time interval from surgical excision to disease recurrence or death, and OS was defined as the interval from diagnosis to death or last follow‐up. The study was conducted in accordance with the ethical standards laid down in the Declaration of Helsinki. All participants have signed informed consent, and institutional Review Board approval was obtained from the Medical Ethics Board at the Sun Yat‐sen Memorial Hospital. ## Statistical analysis The cohort was randomly divided into training and validation sets at 7:3 ratio. The training set was subjected to Kaplan–Meier and log‐rank analyses to examine their association with DFS or OS, and factors with p‐values of <0.1 were selected for multivariate Cox regression analysis. The results of Cox regression were then used to establish prognostic nomograms. Nomograms were evaluated for discrimination ability with the consistency index (C‐index) and area under curve (AUC) of the corresponding receiver operating characteristic (ROC). 14 Calibration curves were plotted to verify the accuracy and reliability of the nomogram. The Brier score was also used to evaluate the overall performance of the model. 15 Decision curve analysis (DCA) was also performed to show the clinical usefulness of the nomogram. All statistical analyses were performed with SPSS (25.0), GraphPad Prism (8.4.3) and the R programming language (4.0.5). The R statistical packages “rms,” “survival,” “foreign,” and “survivalROC” were used to calculate the C‐index and plot the calibration curves, the ROC curves. All statistical tests were two‐sided with the significance level set at 0.05. ## Demographic and clinical characteristics The 1075 patients included in this study were randomly assigned into training ($$n = 746$$) or validation set ($$n = 329$$). Age at CC diagnosis age was 22–79 (median 49) years in the intact cohort, 22–79 (median 49 years) in the training set, and 22–79 (median 48 years) in the validation set. The two sets also had similar durations of follow‐up, with a median of 34.38 (range 0.20–86.80) months for the training set and 31.03 (0.00–77.40) months for the validation set. Other important clinicopathologic features were also comparable between the two sets, including histologic subtype, metastasis to the ovaries, tumor size, rate of vascular, cervical junction, or myometrial invasion, vaginal fornix involvement, history of malignancies or sexually transmitted disease (STD), and other medical conditions (Table 1). In particular, the PLN metastases ratio were $23.7\%$ and $18.2\%$, and the para‐aortic lymph node metastasis ratio were $0.8\%$ and $0.9\%$ in training and validation sets, respectively. TABLE 1 \| Feature \| Total (n = 1075) \| Training set (n = 746) \| Validation set (n = 329) \| \| --- \| --- \| --- \| --- \| \| Age \| Age \| Age \| Age \| \| <30 \| 43 (4.0%) \| 30 (4.0%) \| 13 (4.0%) \| \| 30–60 \| 887 (82.5%) \| 611 (81.9%) \| 276 (83.9%) \| \| ≥60 \| 145 (13.4%) \| 105 (14.1%) \| 40 (12.1%) \| \| FIGO stage \| FIGO stage \| FIGO stage \| FIGO stage \| \| I \| 816 (75.9%) \| 558 (74.8%) \| 258 (78.4%) \| \| II \| 222 (20.7%) \| 162 (21.7%) \| 60 (18.2%) \| \| III‐IV \| 37 (3.4%) \| 26 (3.5%) \| 11 (3.4%) \| \| Pathological type \| Pathological type \| Pathological type \| Pathological type \| \| SCC \| 823 (76.6%) \| 582 (78.0%) \| 241 (73.3%) \| \| AC \| 166 (15.4%) \| 105 (14.1) \| 61 (18.5%) \| \| Other \| 86 (8.0%) \| 59 (7.9%) \| 27 (8.2%) \| \| Vascular invasion \| Vascular invasion \| Vascular invasion \| Vascular invasion \| \| No \| 619 (57.6%) \| 408 (54.7%) \| 211 (64.1%) \| \| Yes \| 456 (42.4%) \| 338 (45.3%) \| 118 (35.9%) \| \| Cervical junction invasion \| Cervical junction invasion \| Cervical junction invasion \| Cervical junction invasion \| \| No \| 747 (69.5%) \| 522 (70.0%) \| 225 (68.4%) \| \| Yes \| 328 (30.5%) \| 224 (30.0%) \| 104 (31.6%) \| \| Myometrial invasion \| Myometrial invasion \| Myometrial invasion \| Myometrial invasion \| \| No \| 230 (21.4%) \| 159 (21.3%) \| 71 (21.6%) \| \| <2/3 \| 355 (33.0%) \| 239 (32.0%) \| 116 (35.3%) \| \| 2/3‐all \| 302 (28.1%) \| 213 (28.6%) \| 89 (27.0%) \| \| All \| 188 (17.5%) \| 135 (18.1%) \| 53 (16.1%) \| \| Parametrial involvement \| Parametrial involvement \| Parametrial involvement \| Parametrial involvement \| \| No \| 1058 (98.4%) \| 734 (98.4%) \| 324 (98.5%) \| \| Yes \| 17(1.6%) \| 12 (1.6%) \| 5 (1.5%) \| \| Vaginal fornix involvement \| Vaginal fornix involvement \| Vaginal fornix involvement \| Vaginal fornix involvement \| \| No \| 852 (79.3%) \| 583 (78.2%) \| 269 (81.8%) \| \| Yes \| 223 (20.7%) \| 163 (21.8%) \| 60 (18.2%) \| \| Ovarian metastasis \| Ovarian metastasis \| Ovarian metastasis \| Ovarian metastasis \| \| No \| 1064 (99.0%) \| 738 (98.9%) \| 326 (99.1%) \| \| Yes \| 11 (1.0%) \| 8 (1.1%) \| 3 (0.9%) \| \| History of malignant tumor \| History of malignant tumor \| History of malignant tumor \| History of malignant tumor \| \| No \| 1059 (98.5%) \| 735 (98.5%) \| 324 (98.5%) \| \| Yes \| 16 (1.5%) \| 11 (1.5%) \| 5 (1.5%) \| \| Tumor diameter \| Tumor diameter \| Tumor diameter \| Tumor diameter \| \| <4 cm \| 856 (79.6%) \| 593 (79.4%) \| 263 (79.9%) \| \| 4‐8 cm \| 211 (19.6%) \| 146 (19.6%) \| 65 (19.8%) \| \| ≥8 cm \| 8 (0.7%) \| 7 (1.0%) \| 1 (0.3%) \| \| History of STD \| History of STD \| History of STD \| History of STD \| \| No \| 1047 (97.4%) \| 724 (97.1%) \| 323 (98.2%) \| \| Yes \| 28 (2.6%) \| 22 (2.9%) \| 6 (1.8%) \| \| Diabetes \| Diabetes \| Diabetes \| Diabetes \| \| No \| 980 (91.2%) \| 676 (90.6%) \| 304 (92.4%) \| \| Yes \| 95 (8.8%) \| 70 (9.4%) \| 25 (7.6%) \| \| With cardiovascular and cerebrovascular diseases \| With cardiovascular and cerebrovascular diseases \| With cardiovascular and cerebrovascular diseases \| With cardiovascular and cerebrovascular diseases \| \| No \| 906 (84.3%) \| 625 (83.8%) \| 281 (85.4%) \| \| Yes \| 169 (15.7%) \| 121 (16.2%) \| 48 (14.6%) \| \| Pelvic lymph node metastasis \| Pelvic lymph node metastasis \| Pelvic lymph node metastasis \| Pelvic lymph node metastasis \| \| No \| 838 (78.0%) \| 569 (76.3%) \| 269 (81.8%) \| \| Yes \| 237 (22.0%) \| 177 (23.7%) \| 60 (18.2%) \| \| Para‐aortic lymph node metastasis \| Para‐aortic lymph node metastasis \| Para‐aortic lymph node metastasis \| Para‐aortic lymph node metastasis \| \| No \| 1066 (99.2%) \| 740 (99.2%) \| 326 (99.1%) \| \| Yes \| 9 (0.8%) \| 6 (0.8%) \| 3 (0.9%) \| ## Prognostic factors predicting postoperative survival of CC patients We started identifying prognostic factors for postoperative survival by interrogating association between each factor and DFS at p‐values with. Univariate log‐rank analysis revealed a lack of prognostic significant for factors such as age and presence of para‐aortic lymph node metastasis (Table 2). In contrast, presence of PLN metastasis was strongly associated with DFS. There was a trend toward association between postoperative survival and history of STD or concurrent cardiovascular and cerebrovascular diseases, while a few other factors showed significant association with DFS, including tumor diameter, cervical junction invasion, myometrial invasion, parametrial involvement, vaginal fornix involvement, and history of malignant tumor. Multivariate Cox regression analysis was then performed with factors that $p \leq 0.1.$ The results indicated that cervical myometrial invasion, parametrial involvement, and history of malignant tumor were independent prognostic factors for DFS in CC (Table 3). The same approach was applied to identify OS‐associated clinicohistologic features, which included FIGO stage, pathological type, vascular invasion, cervical junction invasion, myometrial invasion, parametrial involvement, vaginal fornix involvement, PLN metastasis, and history of STD per univariate analysis. Multivariate Cox regression analysis showed that cervical myometrial invasion, pathological type, and history of STD were independent prognostic factors for postoperative OS (Table 3). ## Construction and evaluation of nomograms for DFS or OS prediction Cervical myometrial invasion, parametrial involvement, and history of malignant tumor were used to construct a nomogram model for predicting DFS and 3‐ and 5‐year DFS rates (Figure 2), and pathological type, cervical myometrial invasion, and history of STD were used to construct a nomogram for predicting postoperative OS and 3‐ and 5‐year OS rates (Figure 3). FIGURE 2: A nomogram for predicting DFS. HMT, history of malignant tumor; MI, myometrial invasion; PI, parametrial involvement FIGURE 3: A nomogram for predicting postoperative OS. MI, myometrial invasion; PT, pathological type; STD, sexually transmitted diseases We then comprehensively evaluated the nomograms for discrimination, calibration and clinical utility with C‐index, receiver operating characteristic (ROC), Brier score, calibration plot, and DCA analysis (Table 4, Figure 4, Figure 5, Figure 6, Figure 7). The nomogram for DFS showed a C‐index of 0.775 ($95\%$ confidence interval: 0.707–0.842) for the training set and 0.799 (0.711–0.888) for validation set. AUCs for 1‐ to 5‐year predictions ranged 0.713–0.756 in the training set and 0.732–0.770 in the validation set (Figure 4). Brier scores for 1‐ to 5‐year predictions ranged $3.67\%$–$12.05\%$ in the training set and $3.95\%$–$12.52\%$ in the validation set. The nomogram for postoperative OS generally showed slightly better performance in these aspects. The C‐index of 0.831 (0.768–0.894) was for the training set and 0.839 (0.756–0.921) for validation set. AUCs for 1‐ to 5‐year predictions ranged 0.736–0.860 in the training set and 0.761–0.828 in the validation set (Figure 4). Moreover, Brier scores for 1‐ to 5‐year predictions ranged $2.00\%$–$9.61\%$ in the training set and $1.20\%$–$8.17\%$ in the validation set. Calibration plots for 3‐year and 5‐year DFS/OS rates were plotted, comparing nomogram‐predicted with actual outcomes and indicated high quality of the nomogram (Figure 5, Figure 6). Furthermore, DCA analysis showed the value of the two models. The net benefit of prognostic mnomograms was larger than that in the other two scenarios (all screening or nonscreening) in a wide range of threshold probabilities as displayed in Figure 7. ## Performance of the nomogram in stratifying risk of patients To further analyze the feasibility and validity of the prognostic nomogram, we divided the cervical cancer patients into different subgroups after sorting by total risk score. Survival analyses demonstrated significant distinctions between subgroups in the training set ($p \leq 0.05$). Same method was performed in the validation set and survival differences were observed among subgroups ($p \leq 0.05$). As presented in Figure 8, each subgroup established a distinct prognosis, and the corresponding Kaplan–Meier survival curves were delineated respectively. FIGURE 8: Kaplan–Meier survival curve. Kaplan–Meier survival curves for (A) the DFS nomogram in the training set; (B) the DFS nomogram in the validation set; (C) the OS nomogram in the training set; (D) the OS nomogram in the validation set. ## DISCUSSION CC currently ranks the fourth most common cancer according to global statistics. 16 Identifying clinicopathologic features with prognostic significance and establishing prognosis models may facilitate individualized treatment and patient selection for clinical trials. This study identified cervical myometrial invasion, parametrial involvement and history of malignant tumor as independent prognosticators for DFS and pathological subtype, cervical myometrial invasion and history of STD for OS in CC. Furthermore, a nomogram model was established based on these prognostic factors for predict 3‐ and 5‐ years survival rates and showed good performance in terms of discrimination, calibration, and clinical applicability. A handful of prognostic factors have been proposed for CC, although consensus remains to be established for some, including pathological factors. 17, 18, 19 CC can be classified into squamous cell carcinoma (SCC), adenocarcinoma (AC), adenosquamous carcinoma, clear cell carcinoma, cervical villous tubular papillary adenocarcinoma, and others. The first three subtypes are the most common ones, with SCC accounting for about $85\%$ of CC cases. 20, 21, 22 AC is more prone to ovarian metastasis than SCC and is generally considered an important prognostic factor. 23, 24 A study of postoperative disease‐specific survival (DSS) for stage IA2‐IIB cervical cancer also found that AC was associated with poor prognosis. 25 Our results further suggested AC as independent prognosticator for OS. In addition to pathological subtype, there is a large body of evidence supporting the prognostic significance of vascular invasion and PLN metastasis in CC. 7, 8, 9, 10, 11 An analysis of 79 CC patients identified vascular invasion, lymph node metastasis and clinical stage as predictors of OS. 12 In particular, the negative prognostic impact of PLN metastasis was supported by other investigations 9, 10 and was recommended by the National Comprehensive Cancer Network (NCCN) guidelines as a high‐risk factor after surgery. 26 In our study, vascular invasion and PLN metastasis was identified to have an adverse impact on cervical cancer, but it was not an independent prognostic factor for DFS and OS. Additionally, a meta‐analysis of 25 studies with a total of 6500 patients suggested prognostic relevance for tumor size in addition to lymph node metastasis and vascular invasion. 27, 28, 29 Tumor size is also an intermediate risk factor per NCCN guidelines. There is evidence that tumors with diameters >4 cm were associated with greater risk of disease recurrence. 30 However, we did not observe a similar significant association for either DFS or OS ($p \leq 0.05$ for both). This discrepancy may be attributable to the limited number of patients with tumors with diameters >4 cm ($$n = 153$$ and 66 for the training and validation sets, respectively), thereby reducing statistical power. NCCN guidelines for CC also recommend parametrial involvement as a high risk factor after surgery, which was supported by a study of 110 early CC patients, which showed associated parametrial involvement with poor prognosis after surgical intervention. 8 Reported incidence rates of parametrial involvement in early CC vary considerably from $0.6\%$ to $32.5\%$. 31, 32, 33, 34 In this study, parametrial involvement occurred in $2.3\%$ stage I and II patients and was identified as an independent poor prognosticator for DFS, thereby supporting previous findings. Prognostic significance of cervical myometrial invasion remains under debate. 12 There is evidence linking depth of myometrial invasion and 5‐year survival rate. Our study identified myometrial invasion is an independent prognosticator for both OS and DFS. In contrast, FIGO stage was not significantly associated with DFS or OS despite being an established prognostic factor. 35, 36 This distinction may at least be partly due to the absence of discrimination between stage IA and IB in the electronic medical records. Notably, we also identified a novel potential poor prognosticator of DFS, i.e. malignant tumor history. This may be related to the decline in physical function caused by the patient's previous suffering from other cancer. 14 In this study, 16 patients with a history of other malignancies mainly had breast cancer, followed by thyroid cancer. A nomogram incorporating malignant tumor history showed consistent power of discrimination (C‐index = 0.775 and 0.799 for training and validation sets, respectively). To our knowledge, our is the first report linking history of malignant tumor with DFS. In the future, further large‐scale, prospective studies are warranted to validate this finding. Interestingly, a history of STD was negatively associated with OS in this study. Existing research mainly focuses on the history of STD and CC incidence instead of prognosis. Patients with a history of STD in this study mostly had syphilis, followed by condyloma acuminatum. Syphilis infection has been found necessary for the CC pathogenesis following HPV infection and proposed as a predictor of invasive CC. 37, 38 Condyloma acuminatum is also an HPV‐related condition. Low‐risk HPV infections typically cause condyloma acuminatum, 39 although high‐risk HPV infections have been found in $31\%$ of condyloma acuminata. 40 This novel finding in this study, which suggests prognostic relevance for syphilis or condyloma acuminata, warrants further clinical validation and may serve as a basis for insights into the underlying biology. Nomograms are an established tool for estimating cancer prognosis in quantitative terms and has been adopted in CC. 41 In a pioneering study, Polterauer and colleagues studied 528 CC cases and constructed a nomogram for OS prediction based on age, FIGO stage, PLN metastasis, percentage of invaded lymph nodes, parametrial invasion, and tumor size citation (Table 5). The C‐index is 0.723, and a calibration chart is created based on internal cross‐validation of bootstrap resampling, which has good consistency and accuracy. 41 In addition, Zheng et al. 13 constructed a model for OS in early CC using body mass index, blood albumin level, platelets, white blood cell count, tumor differentiation, and PLN status were independent prognostic factors. The model achieved good discrimination (C‐index = 0.74). In study of 1563 cases, Zhou et al. 42 predicted 5‐year OS based on pathological subtype, lymph node metastasis, lymphatic vascular space invasion, interstitial invasion, parametrial invasion, and tumor diameter and achieved a C‐index of 0.71. Importantly, a nomogram by Feng et al. Used age, race, histology, extension range, tumor size, radiotherapy and surgery prognostic factor for predicting the OS of stage IIIC1 CC achieved good discrimination (C‐index = 0.687) and was externally verified with SEER data sets. The authors also evaluated the model's clinical adaptability with evaluated by DCA. 43 TABLE 5 \| Study \| Endpoint \| C‐index (95% CI) \| Brier score \| Calibration curve \| DCA \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| This study \| DFS, OS \| 0.775 (95%CI:0.707–0.842); 0.831 (95%CI:0.768–0.894) \| Yes \| Yes \| Yes \| \| Polterauer, Grimm et al. \| OS \| 0.723 \| No \| Yes \| No \| \| Liu Q et al. \| OS, CSS \| 0.831 (95%CI:0.815–0.847); 0.855 (95%CI:0.839–0.871) \| No \| Yes \| Yes \| \| Zheng RR et al. \| OS \| 0.74 (95% CI:0.68–0.80) \| No \| Yes \| No \| \| Zhou H et al. \| OS \| 0.71 (95% CI:0.65–0.77) \| No \| Yes \| No \| \| Feng et al. \| OS, CSS \| 0.687;0.692 \| No \| Yes \| Yes \| Our study established a nomogram for predicting DFS based on cervical myometrial invasion, parametrial invasion and malignant tumor history and one for predicting OS based on pathological type, cervical myometrial invasion and history of STD. As far as we know, we are the first study to link the history of malignant tumor and STD with poor prognosis in CC patients and establish nomograms. In addition, both models were evaluated with C‐index, ROC curve, Brier score, and DCA analysis, all of which indicated high performance and clinical applicability. C‐index for the two models were 0.775 (0.707–0.842) for DFS and 0.831 (0.768–0.894) for OS in the training set and 0.799 (0.711–0.888) for DFS and 0.839 (0.756–0.921) for OS in the validation set. Calibration curves of 3‐year and 5‐year were also created based on internal validation of bootstrap resampling and showed remarkable accuracy. Decision curve analysis (DCA) can calculate the net benefit of a predictive model to measure its clinical utility. DCA was performed to evaluate the clinical applicability of the constructed nomograms when quantifying the net improvement benefits under different threshold probabilities. 44 After validation, DCA confirmed that our nomograms have better clinical benefits and utility in predicting the survival of patients with CC. Despite having achieved prognostic accuracy, our study is not devoid of limitations. In addition to the retrospective, single‐center nature, the nomogram models, which did not undergo validation with an external cohort. Secondly, the calibration curve suggested low performance in predicting 5‐year survival rates for OS and DFS chart is poor, which may be owing to the higher proportion of censored events at this time point. ## CONCLUSION In this study, we analyzed a cohort of 1075 CC patients and identified DFS‐ or OS‐associated clinicohistologic characteristics. Two nomograms were subsequently constructed for DFS and OS prognostication, respectively, and showed high performance in terms of discrimination, calibration, and clinical applicability. Further research is warranted for validating these nomograms with larger cohorts and prospective studies. ## AUTHOR CONTRIBUTIONS Qunxian Rao: Conceptualization (equal); formal analysis (equal); methodology (equal); writing – original draft (equal). Xue HAN: Conceptualization (equal); project administration (equal); writing – original draft (equal); writing – review and editing (equal). Yuan Wei: Conceptualization (equal); data curation (equal); writing – review and editing (equal). Hui Zhou: Conceptualization (equal); data curation (equal); writing – review and editing (equal). Yajie Gong: Conceptualization (equal); data curation (equal); writing – original draft (equal). Mei‐mei Guan: Conceptualization (equal); data curation (equal); writing – original draft (equal). Xiaoyan Feng: Conceptualization (equal); data curation (equal); writing – original draft (equal). Huaiwu Lu: Conceptualization (equal); project administration (equal); resources (equal); supervision (equal). Qingsong Chen: Conceptualization (equal); formal analysis (equal); methodology (equal); validation (equal); writing – review and editing (equal). ## FUNDING INFORMATION This research was supported by Medical Science and Technology Foundation of Guangdong Province (grant No.2019GCZX012 [QSC]). ## CONFLICT OF INTEREST There is no financial support or financial agreement, bonds, or any other conflict of interest involving the authors and companies, which may have an interest in the publication of this paper. ## REVIEW BOARD/COMMITTEE APPROVAL Institutional Review Board approval was obtained from the Medical Ethics Board at the Sun Yat‐sen Memorial Hospital (No. SYSEC‐KY‐KS‐2021‐336). ## INFORMED CONSENT All participants have signed informed consent. ## DATA AVAILABILITY STATEMENT The data that supports the findings of this study are available in the supplementary material of this article. ## References 1. Sung H, Ferlay J, Siegel RL. 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--- title: A pyroptosis‐related lncRNA signature in bladder cancer authors: - Peng Wang - Zhiqiang Wang - Liping Zhu - Yilan Sun - Leandro Castellano - Justin Stebbing - Zhentao Yu - Ling Peng journal: Cancer Medicine year: 2022 pmcid: PMC10028168 doi: 10.1002/cam4.5344 license: CC BY 4.0 --- # A pyroptosis‐related lncRNA signature in bladder cancer ## Abstract The clinical and RNA‐sequencing data of bladder cancer patients were downloaded from The Cancer Genome Atlas (TCGA). Pyroptosis‐related genes between tumor tissues and normal were analyzed. The signature from 25 pyroptosis‐related IncRNAs was screened. A pyroptosis‐related lncRNA signature was constructed based on Cox regression analyses. The prognosis of bladder cancer patients in the high‐risk group was significantly inferior compared with those in the low‐risk group. Receiver operating characteristic curve (ROC) analysis examined the signature on overall survival. Analysis of the immune landscape and PD‐L1 expression showed that PD‐L1 is upregulated in the high‐risk group. The immunocyte subtypes of the two groups were different. Pyroptosis‐related lncRNAs have a potential role in cancer immunology and may serve as prognostic or therapeutic targets. ### Purpose Pyroptosis, a type of programmed cell death, is implicated in the tumorigenesis, development and migration of cancer, which can be regulated by long non‐coding RNAs (lncRNAs). Our research aimed to investigate the prognostic role of pyroptosis‐related lncRNAs and the relationship to the tumor immune microenvironment through bioinformatics analysis. ### Methods The clinical and RNA‐sequencing data of bladder cancer patients were downloaded from The Cancer Genome Atlas (TCGA). And 412 bladder cancer subjects with clinical information were divided into training and testing cohort. And 52 reported pyroptosis‐related genes were used to screen pyroptosis‐related lncRNAs. A pyroptosis‐related lncRNA signature was constructed based on Cox regression analyses. ### Results A 9‐pyroptosis‐related‐lncRNA signature was identified to separate patients with bladder cancer into two groups. The prognosis of bladder cancer patients in the high‐risk group was significantly inferior compared with those in the low‐risk group. Risk scores were validated to develop an independent prognostic indicator based on multivariate Cox regression analysis. Receiver operating characteristic curve (ROC) analysis examined the signature on overall survival. The area under time‐dependent ROC curve (AUC) at 1‐, 3, and 5‐years measured 0.747, 0.783, and 0.768, respectively. Analysis of the immune landscape and PD‐L1 expression showed that PD‐L1 is upregulated in the high‐risk group. The immunocyte subtypes of the two groups were different. ### Conclusion A novel pyroptosis‐related lncRNA signature was identified with prognostic value for bladder cancer patients. Pyroptosis‐related lncRNAs have a potential role in cancer immunology and may serve as prognostic or therapeutic targets. ## INTRODUCTION Bladder cancer is the ninth common malignancy in the world and the most commonly diagnosed cancer of the urinary system. 1, 2 About one quarter of bladder cancer patients have muscle‐invasive or metastatic disease 3, 4 and approximately half of the patients with muscle‐invasive disease will relapse or metastasize after surgery. 5, 6 Chemotherapy and immune checkpoint inhibitors (ICIs) have provided survival benefits for patients with metastatic bladder cancer, but clinical outcomes have varied among patients receiving standard therapy. 7 In order to improve bladder cancer survival, many are investigating biomarkers to inform prognosis and treatment response. Chemotherapeutic drugs inhibit cell proliferation and induce programmed cell death (PCD), thus exerting anti‐tumor effects. However, cancer cells can become resistant to PCD during chemotherapy, promoting recurrence. Previously, apoptosis was regarded as the main type of PCD, however, cancer cells can escape cell death through various mechanisms. 8 Pyroptosis, an emerging type of PCD, can be induced by cancer chemotherapy. 9, 10, 11 Targeting other forms of PCD provides a potential strategy to overcome chemotherapy resistance. Pyroptosis release danger‐associated signaling molecules and cytokines, which in turn activate the immune system. 12 Pyroptosis has a proinflammatory effect, which is related to the regulation of the tumor immune microenvironment. Expression of gasdermin D (GSDMD), executor of pyroptosis, is required for effector CD8+ T cell responses. 13 The role of pyroptosis in the anti‐tumor function of natural killer (NK) cells has also been shown in a recent study. 14 Long non‐coding RNAs (long ncRNAs, lncRNAs) are a type RNA, with lengths exceeding 200 bps that do not code for proteins. 15 LncRNAs are comprised of heterogeneous group of transcripts that regulate gene expression. They are involved in various diseases via multiple mechanisms, such as transcriptional, post‐transcriptional, and epigenetic modifications. Recent evidence has revealed that lncRNAs are regulators of cell pyroptosis. 16, 17, 18, 19 The regulation of lncRNAs in pyroptosis is also involved in various cancer types. For example, knockdown of SNHG7 (small nucleolar RNA host gene 7), a lncRNA gene, increased the expression levels of NLRP3 (NOD‐, LRR‐ and pyrin domain‐containing protein 3) and interleukin‐1β, resulting in pyroptosis. 20 Knockdown of MEG3 could reverse the inhibition of cisplatin on tumor growth and metastasis thorough NLRP3/caspase‐1/GSDMD pathway‐mediated pyroptosis. 21 Based on the importance of lncRNAs, further research is warranted. 22 The current study aimed to explore pyroptosis‐related lncRNAs in bladder cancer, which could provide evidence on the signaling pathways implicated in pyroptosis in bladder cancer and link to patient prognosis. In addition, we also analyze the relationship of pyroptosis and the tumor immune microenvironment which could provide information for use of immunotherapy in bladder cancer. ## Data source Data were retrieved from TCGA database (http://cancergenome.nih.gov/) on August 02, 2021: RNA‐seq transcriptome and clinicopathological data from 433 and 412 bladder cancer (BLCA) patients, respectively; RNA‐seq transcriptome from 19 healthy controls. The different numbers of RNA‐seq transcriptome of clinical patients were matched. FPKM (fragment per kilobase of exon model per million) data were downloaded for differential analysis. The 412 BLCA patients were separated into a training and a testing cohort in a 1:1 ratio using the “caret” R package. Then, tumor mutation burden (TMB) per megabase was calculated. The flow chart of our study was illustrated in Figure 1. FIGURE 1: Flow diagram of our study. ## Differentially expressed pyroptosis‐related genes The information of 52 pyroptosis‐related genes (PRGs) were obtained from previous literature 23, 24, 25, 26, 27, 28, 29, 30, 31, 32 (Table S1). To observe differences in pyroptosis‐related genes and their co‐expressed lncRNAs between bladder cancer and control groups, heatmaps and boxplots were generated by using the “limma” package, and univariate Cox regression was performed to screen the signature in pyroptosis‐related lncRNAs. The Search Tool for the Reval of Interacting Genes (STRING) was used to construct a protein–protein interaction (PPI) network for 52 PRGs. ## Detection of regulators of pyroptosis and co‐expression lncRNAs Expression of the 52 pyroptosis regulators was analyzed. Coexpression analysis was then performed, and the filter conditions are “pvalueFilter = 0.001” and “correlation coefficient = 0.4”. The “igraph” R package was used to obtain the expression data co‐expression network for lncRNAs. The differences in pyroptosis‐related regulators and their co‐expressed lncRNAs between bladder cancer and control groups were shown. The signature of 25 selected pyroptosis‐related lncRNAs was screened using univariate Cox regression. ## Consensus clustering The patients were assigned into two categories using “ConsensusClusterPlus” package with 1000 iterations and the resample rate of $80\%$. The algorithm was 1000 permutations for random sampling. Overall survival of the two clusters was compared. The functions and downstream access of the two clusters were explored using gene set enrichment analysis (GSEA). ## Construction of the prognostic signature Least absolute shrinkage and selection operator (LASSO) regression analysis was used to establish a pyroptosis‐related lncRNAs‐associated prognostic model (PLPM). The risk score was calculated as below: The risk score=∑$i = 1$nCoefi×Expri where Expr i represents the expression level of gene i, and coef i indicates the regression coefficient of gene i in the signature. The risk score of each sample was calculated. Patients were separated into high‐ and low‐risk groups based on the median risk score. ## Evaluation of prognostic value of the signature The difference of overall survival between high‐ and low‐risk groups in the training and testing cohorts was investigated. ROC curves were implemented, and the AUC was calculated. Cox regression models were used to prove whether the risk score is an independent factor for prognosis. ## Genomic alteration and co‐expression level of PD‐L1 PD‐L1 mutations and deletions copy number alterations (CNAs) in bladder cancer patients were analyzed from the cBioPortal tool (http://cbioportal.org). The OncoPrint displayed genetic alterations of PD‐L1 in bladder cancer samples. The association between PD‐L1 expression and pyroptosis‐related lncRNAs was depicted using “corrplot” package. ## Evaluation of immune infiltration The immune‐scores in the bladder cancer patients were calculated via the ESTIMATE algorithm. The fraction scores in each tumor sample for 22 immune cell subtypes were identified by CIBERSORT (cell type identification by estimating relative subtypes of RNA transcripts). The algorithm was 1000 permutations, and samples with $P \leq 0.05$ were incorporated. The immune infiltration levels were compared. ## Statistical analysis The statistical analyses were performed by R software (4.0.3). The prognostic model was constructed in LASSO Cox regression analysis. A difference of $p \leq 0.05$ indicated statistical significance. In some cases, “$p \leq 0.05$” gave too many results, and in these settings, “$p \leq 0.01$” was used as the filter factor. ## DEGs between tumor and normal and tissues The expressions of 52 pyroptosis‐related genes were compared in TCGA database from 414 tumor and 19 normal tissues. And 29 DEGs were identified. Among them, six genes (ELANE, IL6, NLRP1, NLRP3, CHMP7, and CHMP3) were downregulated while 23 other genes (GPX4, CHMP2A, CHMP4A, CHMP4B, CHMP4C, BAX, IL1A, TP53, TP63, NLRP2, NLRP7, PLCG1, CASP3, CASP5, CASP6, CASP8, GSDMB, GSDMD, PYCARD, BAK1, AIM2, CYCS, and HMGB1) were upregulated in the tumor group. RNA levels of the 29 genes are shown in Figure 2A. To further investigate the interactions of the pyroptosis‐related genes, a PPI analysis was conducted and the results are shown in Figure 2B. The minimum required interaction score was 0.40, and we determined that AIM2, IRF1, PYCARD, IL1B, IL18, NLRP3, HMGB1, CASP8, TNF, IL6, CASP4, NLRC4, CASP5, CASP1, TP53, CYCS, and CASP3 were hub genes. Among them, except for IRF1, IL1B, IL18, CASP1, TNF, CASP4, NLRC4, and CASP1, other genes were all DEGs between normal and tumor tissues. The correlation network is presented in Figure 2C. These findings demonstrated that pyroptosis regulators play a vital role in bladder cancer. The lncRNAs co‐expressed with the 52 pyroptosis regulators were determined by analyzing data of RNA‐seq transcriptome using a co‐expression network (Figure 2D; Table S1). FIGURE 2: Expression and interaction of PRGs. (A) Heatmap of the PRGs between tumor tissues (T, red) and normal (N, blue). Green indicates low expression level, and red represents high expression level. p < 0.05; p < 0.01; p < 0.001. (B) The interactions of the PRGs were shown in PPI network (interaction score = 0.40). (C) Correlation network of PRGs. Red line indicates positive correlation, and blue line represents negative correlation. Clinical data from TCGA for survival time and survival status were merged with expression of pyroptosis‐related lncRNAs. And 25 lncRNAs related to prognosis were selected using “survival” package with the screening condition “$p \leq 0.01$” (Table 1). Univariate Cox regression analyses were performed to investigate the relationship between the 25 lncRNAs and overall survival (Figure 3A). Among them, 16 lncRNAs were protective and correlated with a better prognosis. The expression differences of the 25 pyroptosis‐related lncRNAs between bladder cancer and healthy controls were investigated. Results were shown in Figure 3B,C. The expression of the lncRNAs differed significantly between bladder cancer patients and healthy controls. Most of the lncRNAs are highly expressed in tumor group, except for SH3RF3‐AS1, RAP2C‐AS1, RBMS3‐AS3, and LINC02762. ## Consensus clustering of pyroptosis‐related lncRNAs Consensus clustering was performed, with $k = 2$–9 in a cumulative distribution function (CDF) (Figure 4A,B), where k represents the cluster count. $k = 2$ was the optimal clustering parameter (Figure 4C). Survival time and the expression level of the selected lncRNAs were combined. Finally, 406 patients were separated into two clusters, namely, cluster 1 ($$n = 136$$) and cluster 2 ($$n = 270$$), depending on expression of the pyroptosis‐related lncRNAs. Of note, early bladder cancer was associated with a cluster 1 regulatory pattern, and advanced stage was mainly associated with the cluster 2 regulatory pattern ($p \leq 0.001$, Figure 4D). Accordingly, the cluster 1 regulatory pattern has a better survival advantage (Figure 4E). FIGURE 4: Correlation between the pyroptosis‐related IncRNAs and clinicopathological feature. (A) Model of consensus clustering. (B) Relative change in area under the CDF curve. (C) TCGA‐BLCA cohort was separated into two clusters. (D) The correlation of the two clusters with clinicopathologic features. p < 0.01; *p < 0.001. (E) Overall survival of patients in the two clusters. Active signaling pathways were revealed by GSEA that differed between the two clusters. The filter condition was set as false discovery rate (FDR) q‐value <0.05, and the following pathways were found to be active in cluster 2 (Figure 5A): “focal adhesion,” “chemokine signaling pathway”, “leukocyte transendothelial migration”, “cytokine‐cytokine receptor interaction,” “natural killer cell mediated cytotoxicity,” “T cell receptor signaling pathway”, “Toll‐like receptor signaling pathway,” and “JAK/STAT signaling pathway”. These results proved that the clusters 2 is related to immune responses. As for the KEGG pathways barplot and bubble graph (Figure 5B,C), the immune‐associated pathways were highly active in cluster 2. There are no active pathways in cluster 1 using the same filter conditions. FIGURE 5: Functional analysis between the two cluster groups. (A) Functions and pathways of the two clusters were analyzed by GSEA. (B) KEGG pathways shown as barplot graph. (C) KEGG enrichment shown as bubble graph. ## PD‐L1 expression and pyroptosis‐related lncRNAs The differences in PD‐L1 expression between tumors and healthy controls and between clusters 1 and 2 were estimated (Figure 6A,B). No significant difference was observed in PD‐L1 expression between normal adjacent tissues and tumor samples. Compared to clusters 1, PD‐L1 expression was upregulated in clusters 2 ($p \leq 0.001$). PD‐L1 expression had a significantly positive association with the expression levels of LINC01711, LINC02762, and ETV7‐AS, whereas a significantly negative correlation was observed with the expression levels of ZKSCAN2‐DT, LINC02604, EHMT2‐AS1, SPAG5‐AS1, STAG3L5P‐PVRIG2P‐PILRB, PTOV1‐AS2, LINC00115, ZNF32‐AS2, ZNF32‐AS1, LINC01004, ZNF213‐AS1, SEC24B‐AS1, ARHGAP27P1‐BPTFP1‐KPNA2P3, NBR2, and SNHG20 (Figure 3C). In addition, the 25 lncRNAs were positively correlated with each other (Figure 6C). FIGURE 6: Correlation of PD‐L1 with pyroptosis‐related IncRNAs. (A) The expression level of PD‐L1 in tumor samples and healthy controls in TCGA cohort. (B) PD‐L1 upregulation in cluster 2 subtypes in bladder cancer, *p < 0.001. (C) Correlation of PD‐L1 and the pyroptosis‐related IncRNAs. (D) PD‐L1 alterations in BLCA cohort. (E) PD‐L1 alterations in BLCA cohort. (F) Overall survival between patients with and without PD‐L1 alterations. The types and frequency of PD‐L1 mutations in bladder cancer were determined via cBioPortal. As shown in Oncoprint, PD‐L1 is altered in $4\%$ of bladder cancer patients, including missense mutations, deep deletions, and amplifications (Figure 6D). Most of PD‐L1 alterations are missense mutations. The locations of PD‐L1 mutations in bladder cancer patients were shown in Figure 6E. No statistically significant differences were observed in patients with and without PD‐L1 alterations (Figure 6F). ## Consensus clustering for pyroptosis‐related lncRNAs with immune cell Immunescores (Figure 7A) and Stromalscores (Figure 7B) in each sample were calculated, and the two scores were combined to obtain Estimatescore (Figure 7C). There was a significant difference in the Immunescores, Stromalscores, and Estimatescores of the two clusters. The Stromalscores and Estimatescores were positively correlated with the bladder cancer stage (Figure 7D–F), indicating that the purity of tumor cells decreased with cancer progression. FIGURE 7: Diverse clinical features and immune cell infiltration in two clusters. The Immunescore (A), Stromalscore (B), and Estimatescore (C) in cluster 1/2 subtypes in TCGA cohort. The Immunescore (D), Stromalscore (E), and Estimatescore (F) in stage I–II/III–IV patients. (G) 22 immune cell types in the TCGA cohort. The fractions of 22 immune cell subtypes were analyzed between clusters 1 and 2 (Figure 7G). $p \leq 0.05$ was used as the screening condition. Cluster 1 had higher infiltration of naïve B cells, plasma cells, and regulatory T cells (Tregs), whereas cluster 2 was associated with memory‐activated T cells CD4, resting NK cells, and M2 macrophages. ## Construction and validation of prognostic signatures The usefulness of pyroptosis‐related lncRNAs for predicting bladder cancer patient prognosis was evaluated. And 406 patients separated into the training cohort (204 patients) and testing cohort (202 patients). A LASSO regression analysis was performed based on the expression levels of the 25 pyroptosis‐related lncRNAs in the TCGA training cohort. From this, nine important pyroptosis‐related lncRNAs were identified, which are EHMT2‐AS1, RBMS3‐AS3, STAG3L5P‐PVRIG2P‐PILRB, PTOV1‐AS2, SLC12A5‐AS1, RAP2C‐AS1, LINC01004, ETV7‐AS1, and HMGA2‐AS1 (Table 2). Patients were classified into high‐ and low‐risk groups based on the median risk scores estimated using the coefficients from the LASSO algorithm. The relationships between expression signatures of nine pyroptosis‐related lncRNAs, risk score, overall survival, and survival status were shown (Figure 8A,B). The results indicate that among the nine lncRNAs, five selected lncRNAs are highly expressed in the low‐risk group (EHMT2‐AS1, STAG3L5P‐PVRIG2P‐PILRB, PTOV1‐AS2, LINC01004 and ETV7‐AS1). Overall survival between the two groups were further analyzed (Figure 8C and D). Overall survival was significantly longer in the low‐risk group, irrespective of training or testing group ($p \leq 0.001$). ROC curve was generated, and AUC values were 0.708 and 0.662 in training and testing groups, respectively (Figure 8E,F). ## Correlation of risk score with clinicopathological factors, clusters, and immune‐scores Clinicopathological factors, cluster analysis and the immune‐scores were compared in high‐ and low‐risk groups. Expression differences of the 9 selected pyroptosis‐related lncRNAs were visualized (Figure 9A). Absolute expression of the 5 pyroptosis‐related lncRNAs was lower in the high‐group, namely EHMT2‐AS1, STAG3L5P‐PVRIG2P‐PILRB, PTOV1‐AS2, RAP2C‐AS1, and ETV7‐AS1. Higher risk scores were observed in high grade (Figure 9B), stage III–IV (Figure 9C) and cluster 2 (Figure 9D). PD‐L1 expression and risk score were further evaluated and a significant correlation was found between the high‐ and low‐risk groups (Figure 9E). The prognostic role of pyroptosis‐related lncRNAs in BLCA patients receiving chemotherapy was also analyzed. Patients with high‐risk score had worse prognosis when receiving chemotherapy (Figure 9F). There was no significant difference in prognosis between patients without receiving chemotherapy (Figure 9G). When compared with patients receiving chemotherapy, those who did not receive chemotherapy had worse prognosis (Figure 9H). FIGURE 9: Prognostic risk scores correlated with clinicopathological features and immunoscore. (A) Clinicopathologic features of high‐ and low‐risk groups were shown in heatmap. p < 0.05; p < 0.01; p < 0.001. Distribution of risk scores stratified by grade (B), stage (C), and cluster 1/2 (D). (E) PD‐L1 expression level in training cohort. (F) Kaplan–Meier curves for the overall survival of high‐ and low‐ risk patients receiving (F) and not receiving chemotherapy (G). (H) Kaplan–Meier curves for the overall survival of patients receiving and not receiving chemotherapy. The differences in overall survival of among gender, age, stage, grade, TNM staging were also determined. Except in stage I‐II and the low‐grade group, all the rest subgroups had a higher overall survival in low‐risk groups (Figure S1). Univariate and multivariate Cox analyses for overall survival were performed (Figure 10A–D). Multivariate analysis indicated that stage, age, and risk score were independent factors for worse prognosis. FIGURE 10: Cox regression analysis in the training and testing group. Univariate Cox regression in the training (A) and testing cohort (B) Multiple Cox regression in the training (C) and testing cohort (D). ## Correlation of pyroptosis‐related lncRNAs with immunocytes Risk scores with the immune cell infiltration of 22 subtypes were correlated (Figure 11A). Risk score was positively correlated with M0 macrophages ($$p \leq 5.7$$e−06) (Figure 11B) and M2 macrophages ($$p \leq 0.0012$$) (Figure 11C), and negatively correlated with regulatory T cells (Tregs) ($$p \leq 0.00028$$) (Figure 11D), memory activated CD4+ T cells ($$p \leq 0.024$$) (Figure 11E), follicular helper T cells ($$p \leq 7.4$$e−06) (Figure 11F), and CD8+ T cells ($$p \leq 1.3$$e−05) (Figure 11G). The finding suggested that the pyroptosis‐related lncRNA risk signature is related in the immune microenvironment of BLCA. FIGURE 11: Correlation of the risk score and infiltration of immune cells. (A) The relationship between risk score and infiltration levels of 22 immune cell types. M0 macrophages (B); M2 macrophages (C); regulatory T cells (Tregs) (D); memory‐activated CD4 T cells (E); follicular helper T cells (F); and CD8 T cells (G). ## Correlation of pyroptosis‐related lncRNAs‐associated prognostic model with TMB There were no significant differences in TMB between patients with high and low PLPM in the training or testing group (Figure 12A,B). However, high TMB was associated with better overall survival (Log‐rank test, $p \leq 0.001$, $$p \leq 0.040$$, respectively, Figure 12C,D). We investigated whether the combination of PLPM and TMB could be a better biomarker for prognosis. PLPM and TMB were integrated to stratify all the samples into: TMBhigh/PLPMlow, TMBlow/PLPMlow, TMBhigh/PLPMhigh, and TMBlow/PLPMhigh groups. As shown in Figure 12E,F, there were significant differences among all groups (Log‐rank test, $p \leq 0.001$, $$p \leq 0.003$$, respectively), and patients in the TMBhigh/PLPMlow group exhibited the highest overall survival. FIGURE 12: Relationship of the PLPM‐based risk signature with TMB. Comparison of TMB between PLPM‐high and PLPM‐low groups in training (A) and testing cohort (B) Kaplan–Meier survival analysis based on the TMB in training (C) and testing cohort (D) Kaplan–Meier survival analysis for four groups in training (E) and testing cohort (F). ## Establishment and validation of a nomogram Based on the clinical factors and risk score, a nomogram was constructed (Figure 13A). The prediction value of the nomogram was determined by calibration curve and the actual survival outcomes were shown from the 45‐degree line. The AUCs for the 1‐, 3‐, and 5‐year were 0.747, 0.783, and 0.768, respectively (Figure 13B). The nomogram had similar performance to that of an ideal model (Figure 13C). These findings suggested that the nomogram combining the signature and clinical factors had optimal prognostic accuracy. FIGURE 13: Construction and validation of the nomogram. (A) Nomogram constructed with clinical characterization and pyroptosis‐related lncRNAs risk signature. (B) ROC curve and AUC for 1‐, 3‐, and 5‐ year overall survival. (C) The calibration plot of the nomogram. ## DISCUSSION Pyroptosis is a distinct type of programmed cell death, which is characterized by DNA fragmentation, chromatin condensation, and leakage of cell content. 33 Pyroptosis can be chemically induced and may affect all stages of carcinogenesis. As an inflammatory form of cell death, pyroptosis would active the immune system. 34 Pyroptosis is a promising new target in cancer treatment, while many issues remain unsolved such as the interconnection between pyroptosis and host immunogenicity. LncRNAs serve essential roles during tumorigenesis. With advanced sequencing methods, a growing number of lncRNAs have been identified in various cancer types. 35 LncRNAs appear to regulate biological behaviors through epigenetic, transcription and post‐transcriptional processing. Increasing evidences suggest that lncRNAs are vital in mediating pyroptosis. Up to now, there are no studies on the prognostic role of pyroptosis‐associated lncRNAs in bladder cancer. We first investigated pyroptosis‐related lncRNAs signature of bladder cancer based on the TCGA dataset. Our analyses uncovered 29 pyroptosis‐related DEGs. GSEA and KEGG analyses indicated the genes are involved in extracellular matrix (ECM) receptor interaction, regulation of actin cytoskeleton, focal adhesion, cytokine receptor interaction, Toll‐like receptor signaling pathway, natural killer cell mediated cytotoxicity, JAK/STAT signaling pathway, and T cell receptor signaling pathways. Overall, nine differently expressed pyroptosis‐related lncRNAs were determined to be independent factors for the prognosis of bladder cancer. Among the identified lncRNAs, four of them were associated with tumor progression, namely, RBMS3‐AS3, SLC12A5‐AS1, RAP2C‐AS1, and HMGA2‐AS1. However, the role of the lncRNAs have conflicting results in other studies. For example, RBMS3‐AS3 was reported to act as a microRNA‐4534 sponge to upregulate VASH1 and inhibit prostate cancer. 36 SLC12A5‐AS1 was one of the top 25 upregulated lncRNAs in head and neck squamous cell carcinoma. 37 Low expression of RAP2C‐AS1 was associated with lymphatic invasion in clear cell carcinoma. 38 HMGA2‐AS1 was reported to be involved in pancreatic cancer progression. 39 On the other hand, five protective lncRNAs are identified based on the results of our study. EHMT2‐AS1 is lower in the high‐risk versus low‐risk group in bladder cancer, and is one of the m6A‐related lncRNAs for prognosis. 40 STAG3L5P‐PVRIG2P‐PILRB and other lncRNAs comprise a prognostic signature for survival of patients of bladder cancer. 41 PTOV1‐AS2 is one of the five m6A‐related lncRNAs of risk score signature of pancreatic cancer. 42 However, the prognostic value in cancer and pyroptosis are lacking for 2 lncRNAs (LINC01004 and ETV7‐AS1). These results suggested that the role of pyroptosis in tumor cell growth are promoting or inhibiting in different cancer types. The therapeutic directions of lncRNAs for the treatment of bladder cancer deserve further study. Pyroptotic cells release cellular components, which induce lymphocyte infiltration and inflammatory responses. Tumor‐infiltrating lymphocytes induce pyroptosis of tumor cells, which is a positive‐feedback loop of anti‐tumor immunity. 14 Pyroptosis in target cells could sensitize ICI‐resistant cancers to checkpoint blockade. 43 In this study, bladder patients were stratified into two categories of high‐ and low‐risk based on this prognostic model. The roles of immune infiltrating cells in tumor microenvironment and in the prognosis of bladder cancer were then explored. The results show that CD8+ T cells and activated dendritic cells of the high‐risk group were significantly reduced compared with those in low‐risk group, whereas the immune cells promoting tumor proliferation such as M2 macrophages were increased. The results suggested that pyroptosis is correlated with a proportion of immune cells in bladder cancer. Our results indicated PD‐L1 expression is upregulated in cluster 2 compared with cluster 1 subtype. To date, most research on PD‐L1 has focused on its immune checkpoint function. A non‐immune checkpoint function of PD‐L1 was reported, which is involved in the pyroptosis pathway. PD‐L1‐mediated expression of gasdermin C (GSDMC) could switch cancer apoptosis to pyroptosis. 44 In human pulmonary arterial smooth muscle cells, PD‐L1 is required for hypoxia‐induced pyroptosis, indicating that PD‐L1‐mediated pyroptosis also exists in other types of cells. 45 A previous study investigated the correlation of genes involved in pyroptosis and TMB in pan‐cancer. 12 In bladder cancer patients, the correlation is not significant. We analyzed the TMB status for high‐ and low‐risk bladder cancer patients. Although the TMB status are not significantly different in the two groups, TMB alone is a prognostic marker in bladder cancer patients. Furthermore, when TMB and risk score were combined together, they can jointly stratify bladder cancer patients into groups with different prognosis. In our study, patients receiving chemotherapy with high‐risk scores had a worse prognosis, while there was no survival difference of high‐ and low‐ risk score in patients not receiving chemotherapy. Recently findings have also revealed gasdermin E (GSDME) enhances cisplatin sensitivity by mediating pyroptosis to trigger immunocyte infiltration in NSLCC. 46 Further studies are warranted to discover the effect of chemotherapy on pyroptosis inducers in bladder cancer. However, data from TCGA are not sufficient to analysis the pyroptosis‐related lncRNAs signature based on immune checkpoint inhibitors. Our study has limitations. Firstly, clinical and sequencing data are derived from the TCGA database, therefore, the prognostic model require further validation with real‐world data. Secondly, the exploration of pyroptosis‐related lncRNAs signature and tumor immune microenvironment is preliminary. Thirdly, well‐known prognostic factors such as treatment with immune checkpoint inhibitors and other tumor markers were not incorporated into the nomogram as information on these parameters were incomplete. Our study explored pyroptosis‐related lncRNAs as a prognosis signature of bladder cancer, which can inform future treatment options for the diseases. Findings of our research shed light on potential biomarker and therapeutic targets in pyroptosis signaling pathways. ## AUTHOR CONTRIBUTIONS Zhiqiang Wang, Zhentao Yu and Ling Peng designed and supervised the study. Peng Wang, Zhiqiang Wang, Liping Zhu and Ling Peng analyzed the data and wrote the original draft. Ling Peng, Zhentao Yu, Yilan Sun, Leandro Castellano and Justin Stebbing edited the draft. All the authors have read and approved the final manuscript. ## FUNDING INFORMATION This study was supported by a grant from Medical Science Research Foundation of Health Bureau of Zhejiang Province (Grant number: 2022KY545) and a grant from the Administration of Traditional Chinese Medicine of Zhejiang Province (Grant number: 2022ZA021). ## CONFLICT OF INTEREST JS' conflicts can be found at https://www.nature.com/onc/editors. None are relevant here. Other authors none declared. ## ETHICS APPROVAL None applicable. ## CONSENT FOR PUBLICATION None applicable. ## DATA AVAILABILITY STATEMENT Publicly available datasets were analyzed in this study. ## References 1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin (2018) 68 394-424. PMID: 30207593 2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin (2019) 69 7-34. 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--- title: Cross-attention multi-branch CNN using DCE-MRI to classify breast cancer molecular subtypes authors: - Liang Sun - Haowen Tian - Hongwei Ge - Juan Tian - Yuxin Lin - Chang Liang - Tang Liu - Yiping Zhao journal: Frontiers in Oncology year: 2023 pmcid: PMC10028183 doi: 10.3389/fonc.2023.1107850 license: CC BY 4.0 --- # Cross-attention multi-branch CNN using DCE-MRI to classify breast cancer molecular subtypes ## Abstract ### Purpose The aim of this study is to improve the accuracy of classifying luminal or non-luminal subtypes of breast cancer by using computer algorithms based on DCE-MRI, and to validate the diagnostic efficacy of the model by considering the patient’s age of menarche and nodule size. ### Methods DCE-MRI images of patients with non-specific invasive breast cancer admitted to the Second Affiliated Hospital of Dalian Medical University were collected. There were 160 cases in total, with 84 cases of luminal type (luminal A and luminal B and 76 cases of non-luminal type (HER 2 overexpressing and triple negative). Patients were grouped according to thresholds of nodule sizes of 20 mm and age at menarche of 14 years. A cross-attention multi-branch net CAMBNET) was proposed based on the dataset to predict the molecular subtypes of breast cancer. Diagnostic performance was assessed by accuracy, sensitivity, specificity, F1 and area under the ROC curve (AUC). And the model is visualized with Grad-CAM. ### Results Several classical deep learning models were included for diagnostic performance comparison. Using 5-fold cross-validation on the test dataset, all the results of CAMBNET are significantly higher than the compared deep learning models. The average prediction recall, accuracy, precision, and AUC for luminal and non-luminal types of the dataset were $89.11\%$, $88.44\%$, $88.52\%$, and $96.10\%$, respectively. For patients with tumor size <20 mm, the CAMBNET had AUC of $83.45\%$ and ACC of $90.29\%$ for detecting triple-negative breast cancer. When classifying luminal from non-luminal subtypes for patients with age at menarche years, our CAMBNET model achieved an ACC of $92.37\%$, precision of $92.42\%$, recall of $93.33\%$, F1of $92.33\%$, and AUC of $99.95\%$. ### Conclusions The CAMBNET can be applied in molecular subtype classification of breasts. For patients with menarche at 14 years old, our model can yield more accurate results when classifying luminal and non-luminal subtypes. For patients with tumor sizes ≤20 mm, our model can yield more accurate result in detecting triple-negative breast cancer to improve patient prognosis and survival. ## Introduction Breast cancer is one of the most prevalent cancers in women and one of the main causes of cancer-related death in women under the age of 45. There are nearly 410000 patients who die of breast cancer annually all over the world [1, 2]. Breast cancer is highly heterogeneous. The different molecular subtypes of breast cancer are significantly different in treatment, radiochemotherapy sensitivity, and prognosis [3, 4]. Luminal type A breast cancer subtype well responds to endocrine therapy, has a low risk of recurrence and metastasis, and has a good prognosis. Luminal type B well responds to endocrine therapy, but is more proliferative than luminal type A and may easily recur in the early stages [5]. HER2-positive and triple-negative subtypes have a high malignancy grade and poor prognosis [6]. Meanwhile, HER2-positive well responds to targeted molecular therapy. Therefore, it is important to distinguish between luminal and non-luminal breast cancer for accurate treatment. Molecular typing of breast cancer mainly depends on immunohistochemical examination of biopsy specimens. Histopathological examination is not only invasive, time-consuming, and expensive, but also easily leads to infection, hematoma, and other complications. Because of the great heterogeneity of the tumor, the biopsy tissue cannot fully represent the biological behavior of the tumor. MRI has the advantages of being non-invasive, resolving soft tissue well, and non-radiation, and it has unique advantages for breast examination. The studies show that MRI imaging features are helpful in identifying molecular subtypes of breast cancer. luminal A and luminal B masses are irregularly shaped and have burr-like edges [7]. Triple-negative breast cancer usually shows a well-defined round mass with annular enhancement [8]. Therefore, the prediction of molecular subtypes of breast cancer based on MRI features can effectively reduce the number of biopsies, alleviate the pain of patients, reduce the burden on patients, and provide a reference for individualized treatment. However, predicting the molecular subtype of a tumor based on the MRI features of breast cancer is difficult because of two issues: [1] low contrast between the lesion area and normal tissue; [2] In clinical practice, the shapes of different subtypes of tumors are very similar, and the interpretation results of professional physicians are greatly influenced by the subjective factors of the physicians. So it is difficult to distinguish the molecular subtypes of breast cancer from the naked eye. Many traditional machine learning algorithms have been applied to relevant breast cancer analysis tasks (9–13). However, these traditional machine learning methods rely on manual feature extraction with strong a priori, poor model generalization, and low robustness, which are difficult to find discriminating features manually and solve the classification of breast cancer subtypes. In recent years, with the development of deep learning technology, deep learning algorithms have been widely used in medical image processing, such as tumor detection (14–16) and segmentation (17–19), benign and malignant differentiation (20–23), etc. A lot of work (24–27) has been devoted to the problems related to breast cancer subtype classification, They try to extract discriminative features of breast cancer MRI by using deep learning models. Their experimental results illustrate the feasibility of predicting the molecular subtypes of tumors based on the MRI features of breast cancer. At the same time, because these models are only direct applications or simple modifications of existing models, they do not make targeted measures to address the above-mentioned problems in breast cancer subtype classification. Therefore, these models cannot distinguish well between the different molecular subtypes of breast cancer. So to solve the above issues and improve the performance of breast cancer subtype classification based on breast MRI images, we propose a new deep network model CAMBNET to extract high-level feature information and focus on lesion objects. The model includes a multi-branch module, a cross-attention mechanism, and a deep feature extraction module. Specifically, using only a single branch for feature extraction may not be effective and the multi-branch module is used to extract richer features, In response to the problem of low contrast between lesions and normal areas in the data set and the very similar shape of lesions in different diseases, the attention mechanism has been widely used in similar problems. Therefore, we designed the cross-attention module to help the network pay attention to salient objects, and as the depth of the model increases, the model can extract deeper features and further improve the feature extraction capability of the model. So the deep feature extraction module is used to further extract the deep features. Due to many limitations such as the rarity of the disease and the lack of appropriately labeled medical expertise, resulting in a relatively small dataset for breast cancer subtype classification, we chose a smaller depth of model layers and constructed our deep learning model. Studies have shown that early menarche age increases the risk of luminal-type breast cancer, which may be related to endogenous estrogen exposure [28]. Tumor size is one of the indices to evaluate the staging of breast cancer and has an important significance for the selection of surgical methods. And small tumors offer limited imaging options, which can easily lead to misdiagnosis. The earlier the age of menarche, the higher the rate of axillary lymph node metastasis and the worse the prognosis of breast cancer patients [29]. Therefore, the initial aim of this study was to develop a new deep network model for predicting luminal and non-luminal subtypes of breast cancer using DCE-MRI images. We also investigated the diagnostic efficacy of different age groups for menarche (≤ 14 years and >14 years) and tumor size groups (≤ 20 mm and >20 mm). ## Data collection This is a retrospective study and is approved by the Second Affiliated Hospital of Dalian Medical University Ethics Committee. Non-specific invasive breast cancer patients admitted to our hospital from May 2017 to December 2019 were selected. The inclusion criteria consisted of: [1] patients with non-specific invasive breast cancer confirmed by biopsy or surgical pathology had complete immunohistochemical results and molecular subtypes were identified. [ 2] DCE-MRI was performed within a week before the operation. [ 3] complete clinical data, including age and menstrual status. The exclusion criteria consisted of [1] percutaneous biopsy or neoadjuvant chemotherapy or radiotherapy before MRI examination or [2] tumor was inconclusive because of artifacts or no visible region of interest (ROI) or [3] image quality was poor or [4] molecular typing of immunohistochemical data of pathologic diagnosis was incomplete. Ultimately, 160 patients with breast cancer were enrolled in the study, including 84 with luminal subtypes (luminal A and luminal B) versus 76 with non-luminal subtypes (HER2-positive and triple-negative). ## MRI technique Images were obtained with a 3.0-T magnetic resonance imaging scanner (Discovery 750W, GE). A special coil was used to scan the breast. Patients were in the prone position with the head tilted forward and the double breasts naturally suspended in the coil. T1WI, T2WI, DWI and 3D volume images of the breast (3D VIBRANT) were performed. The 3D VIBRANT scan parameters are as follows: TR 7.6 ms, TE 3.8 ms, layer thickness 1.2 mm, FOV 320 mm × 320 mm, flip angle 15°, matrix 288 × 288. The contrast agent was injected into the antecubital vein through a high-pressure injector (GE Company, USA). The flow rate of the contrast agent was 2 mL/s and the dose was 0.2 mmol/kg. After injection, 7 consecutive non interval scans were performed, each scan lasting 1 minute and 7 seconds. ## Immunohistochemical examination The receptors ER, PR, HER2, and Ki-67 were detected by immunohistochemistry. [ 1] ER/PR positivity was defined as ≥$1\%$ positive staining of tumor nuclei [2] HER-2 positivity was defined as Her-2 (3+), (-, and (1+) were defined as HER -2 negativity. Fluorescence in situ hybridization (FISH) was used to detect HER -2 fluorescence in situ hybridization [3] Ki-67 showed high expression (≥$14\%$) and low expression (<$14\%$). Breast cancer is classified into four subtypes according to receptor status, defined as follows: [1] Luminal A: ER and/or PR positive, HER -2 negative and KI-67 low expression [2] Luminal B: ER and/or PR positive, Ki-67-high expression, Her-2(positive or negative) [3] Her-2 positive: ER, PR negative, HER -2 positive [4] triple-negative: ER, PR, Her-2 negative. ## Image processing Region of interest (ROI) outlining the tumor region in MRI T1WI, T2W1, and DCE (selected third-stage enhanced images after contrast injection) by 2 senior diagnostic breast MRI physicians should include all tumor regions, including cystic and necrotic regions. As the physician outlines the specific contour of the tumor, we derive the minimum matrix covering the tumor by extracting the most marginal points in the four directions of the contour. These four points were added 10 pixels in their respective directions to crop their contour areas, and the cropped images were uniformly adjusted to 64 × 64 pixels by bilinear interpolation. And the image is normalized by transforms. Normalize. The specific process is shown in Figure 1. Figure 1: Data set processing process. (A) is the original image, (B) is the specific outline of the tumor sketched by the physician, (C) is the four edge points in the specific outline, (D) is the minimum matrix covering the tumor, (E) shows that 10 pixels are added to each direction of the four points to crop the outline area. and (F) is the cropped image uniformly resized to 64×64 pixels by a bilinear interpolation method. ## Data set partitioning Breast images from $20\%$ of the cases in the dataset were used as the test dataset, and $80\%$ were kept as the training dataset while ensuring that no patient images appeared in both the training and test sets. The number of each sub-dataset in the dataset is shown in Table 1. Table 1 \| Molecular subtypes \| cases \| cases.1 \| cases.2 \| cases.3 \| \| --- \| --- \| --- \| --- \| --- \| \| Molecular subtypes \| Size \| Size \| Age \| Age \| \| Molecular subtypes \| ≤ 20 \| >20 \| ≤ 14 \| >14 \| \| Luminal A \| 20 \| 18 \| 22 \| 16 \| \| Luminal B \| 27 \| 20 \| 30 \| 17 \| \| HER2+ \| 19 \| 28 \| 21 \| 26 \| \| Triple-negative \| 7 \| 21 \| 10 \| 18 \| \| Total \| 73 \| 87 \| 83 \| 77 \| ## Deep learning model In this paper, a multi-branch crossover network is proposed to extract high-level features. Two of the branches fuse the extracted features after passing through the cross-attention module, and then the fused features are fused with the shallow features extracted by SFEpath to improve the classification performance of MRI images for two different subtypes. The proposed framework is shown in Figure 2. From Figure 2, we can see that our proposed network architecture consists of three main parts: the three-branch framework, the cross-attention module, and the deep feature extraction module. The specific model parameters are shown in Table 2. We will explain these modules in detail in the following sections. Figure 2: Structure of the model (SFEpath, Shallow feature extraction path; LTTpath, Locating the tumor path; DFEM, Depth Feature extraction module.). TABLE_PLACEHOLDER:Table 2 ## Three-branch structure Due to the limited amount of data in the dataset, an overly complex model is too easy to over-fit. So three light branching paths are designed. From Figure 3A, SFB refers to the Squeeze-and-Excitation module [30] and SFEpath is added to the branching framework to extract depth features as the input of depth concern, and part of the original input feature information is directly transferred to the output features by using the residual connection. The residual connection can simplify the difficulty of feature learning, protect the integrity of feature information to a certain extent, and alleviate the problem of model degradation in deep networks. It enables the model to better learn the shallow information such as the texture and shape of the breast image and makes the features extracted by the model richer. Figure 3: Three-branch structure and Cross-attention Module. (Conv, Convolutional layer; Dwconv, Depthwise convolutional layer; SEB, Squeeze-and-Excitation Block). Since the contrast between tumor and background is low, a network capable of extracting multiple depth features from different branches is needed. To obtain more depth features, a multi-branch network was designed using its two branches (called LTTpath1 and LTTpath2), inspired by the Inception model. That is, the main structure of this network uses asymmetric rotating c 1×n and n×c 1 filters to reduce the parameter size and computational cost, instead of the traditional n×n. The two main effects are [1] the downscaling of the data and the introduction of more nonlinearities and [2] the improvement of the generalization. ## Cross-attention Module For the model to learn specific differences and relationships between different subtypes of breast cancer, the interference of irrelevant regions is suppressed. We propose a cross-attention mechanism that focuses on the salient features of each breast cancer subtype. Our proposed cross-attention module consists of a spatial attention module and a channel attention module, as shown in Figure 3B, where the spatial attention module and the channel attention module are single-path modules instead of the dual-path module of CBAM because in breast cancer subtype classification experiments we found that the combination of single-path with cross-path patterns is better than the combination of dual-path patterns or dual-path with cross-path patterns. To suppress the interference of irrelevant regions, we further utilize the spatial attention module and the channel attention module. For channel attention, it suppresses the less informative channels by learning channel attention weights in the channels as feature selectors that indicate the importance of each feature channel, channel attention focuses on “what” is meaningful for a given input image. Unlike the channel attention module, the spatial attention module is concerned with “where” the information part is, and as a complement to the channel attention module, spatial attention obtains the importance of each spatial location by learning spatial attention weights. They enable the network to identify key features by their spatial location and thus improve the feature representation of different subtypes. ## Depth feature extraction module The features learned from the three branches are fused according to their different characteristics. LTTpath1 and LTTpath2 complement each other’s information through additive operations. Since the features extracted from SFEpath are shallow information such as the texture and shape of the breast image, the features extracted from SFEpath are used as complementary information to the fused features of LTTpath1 and LTTpath2. The fusion is superimposed by a concatenation operation to reduce the loss of information. As shown in Figure 3A, the fusion of features from multiple branches by path4 reduces the size of the feature map by half and doubles the number of feature maps, maintaining the complexity of the network layer. The deep features are further extracted by increasing the number of channels and setting stride to 2 to remove the residual connected blocks, and finally, the extracted deep features are used for the classification of breast cancer subtypes. ## Parameter setting We implemented the proposed framework and conducted experiments using the Pytorch library. The parallel computation uses a GPU-equipped graphics processing unit (NVIDIA GeForce GTX 2060) to accelerate the processing of training and testing. The batch sizes for training and testing were set to 8 and 1, respectively. the maximum epoch time was set to 200, and the initialized learning rate was 0.002, multiplied by 0.95 every 10 epochs. we chose RMSprop as the optimizer for the training phase. Data overfitting is prevented by limiting the square size of kernel weights and using L2 regularization. The whole framework is trained in an end-to-end manner, and the model is trained with a backpropagation algorithm that saves the model parameters that perform best on the validation set. The whole training process takes 1 hour. The cross-entropy function is chosen as the classification loss function. ## Statistical analysis TP(True Positive):The number of samples judged to be correct among those judged to be positive. FP(False Positive):The number of misjudgments in samples judged positive. TN(True Negative)The number of correct samples among those judged negative. FN(False Negative):The number of judgment errors in samples judged negative. For each subtype of disease, we report five metrics, namely: Acc(Accuracy), Pre(Precision), Rec(Recall), F1(F1 score), and AUC(Area under the ROC curve). ## Comparison of CAMBNET and classical CNN The classification results of different methods according to the evaluation metrics are shown in Table 3. we also performed migration learning experiments on our proposed CAMBNET model. We selected 3200 images from the Breakhis database as the training set and 1010 images as the test set to initially train the model. During the training process, the training model parameters with the best classification results were saved, and then the model parameters were reloaded and further trained on the training set of the target dataset we collected. At the same time, this paper performs migration learning while performing real-time data enhancement on each breast MRI image in training. The main implementation method is to perform random rotation and flip along the diagonal of the image. According to Table 3, our model achieves the best results in all classification metrics, with an Accuracy of $88.44\%$, Precision of $88.52\%$, Recall of $89.11\%$, and F1 of $88.40\%$. Meanwhile, after transfer learning and data augmentation, the model can be further improved with $89.46\%$ for Accuracy,$89.83\%$ for Precision, $90.35\%$ for Recall, and $89.44\%$ for F1. As shown in Figure 4, the CAMBNET model has the best performance with an AUC value of $96\%$. Figure 4: Receiver operating characteristic (ROC) curves of correlation networks in test dataset. TABLE_PLACEHOLDER:Table 3 ## Comparison of CAMBNET with other methods Previous work has been done to classify breast cancer subtypes using existing machine learning methods or building deep learning models. Tianwen Xie et al [31] used KNN, SVM, and other machine learning methods to classify breast cancer subtypes and achieved good experimental results. Richard Ha et al [27] and Rong Sun et al [32] built models for breast cancer subtypes for classification. We compared our method with the specific methods used in the three papers mentioned above, and the experimental results are shown in Table 4. As can be seen from Table 4, the accuracy of the traditional machine learning methods is significantly lower than the classification accuracy of the deep learning models, while our model exceeds the previously mentioned model methods in all metrics, which fully illustrates the accuracy of our model. Table 4 \| Model \| Acc(%) \| Pre (%) \| Rec (%) \| F1(%) \| \| --- \| --- \| --- \| --- \| --- \| \| SVM \| 66.67 \| 69.63 \| 67.57 \| 66.76 \| \| KNN \| 74.15 \| 74.4 \| 74.15 \| 74.05 \| \| Richard Ha et al. (27) \| 82.31 \| 84.22 \| 83.82 \| 82.31 \| \| Rong Sun et al. (32) \| 84.35 \| 85.85 \| 85.72 \| 84.35 \| \| CAMBNET \| 88.44 \| 88.52 \| 89.11 \| 88.4 \| ## Multi-source data testing We collected and collated 40 images acquired by a 1.5T magnetic resonance scanner (HDXT, GE, USA) and replaced 40 images of the source data set with these 40 1.5T images, thus collating a multi-source data set. The CAMB model has also experimented with multi-source data. The specific experimental results are shown in Table 5, from which it can be seen that the indicators of the experiments have decreased. However, the CAMB model still achieves $82.29\%$ accuracy on the multi-source data set, and the experimental results show that the CAMB model has strong robustness. We analyze that this is due to the fact that the model uses cross-attention mechanisms, multi-branch paths, dropout, feature fusion, and other measures to ensure the robustness of the model. Table 5 \| Dataset \| Acc(%) \| Pre (%) \| Rec (%) \| F1(%) \| \| --- \| --- \| --- \| --- \| --- \| \| multi_sources \| 82.29 \| 82.8 \| 82.29 \| 82.22 \| \| single_source \| 88.44 \| 88.52 \| 89.11 \| 88.4 \| ## Effect of age at menarche on molecular subtype classification Table 6 shows the effect of the patient’s age at menarche (≤14 and >14 years) on the classification effect of the CAMBNET model [33]. The experimental results showed that the younger the age at menarche, the better the model classification effect, and the more significant the classification effect in distinguishing between luminal and non-luminal types. At the age of menarche >14 years, the CAMBNET model classified luminal and non-luminal types with an Accuracy of $82.58\%$, Precision of $83.06\%$, Recall of $82.85\%$, F1 of $82.57\%$, and AUC of $87.45\%$. The accuracy of the CAMBNET model in classifying luminal and non-luminal types was $92.37\%$ for Accuracy, $92.42\%$ for Precision, $93.33\%$ for Recall, $92.33\%$ for F1, and $99.95\%$ for AUC for age at menarche ≤14 years. The accuracy was $69.23\%$ in cases with age at menarche >14 years and $88.44\%$ in cases with age at menarche ≤14 years. Table 6 \| Dataset \| Acc(%) \| Pre (%) \| Rec (%) \| F1(%) \| AUC(%) \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Mage_large \| 82.58 \| 83.06 \| 82.85 \| 82.57 \| 87.45 \| \| Mage_small \| 92.37 \| 92.42 \| 93.33 \| 92.33 \| 99.95 \| \| Mage_large_LuminalA \| 69.23 \| 63.93 \| 62.46 \| 62.92 \| 56.01 \| \| Mage_small_LuminalA \| 88.44 \| 83.45 \| 65.11 \| 69.35 \| 85.68 \| ## Impact of tumor size on molecular subtype classification As shown in Table 7, we conducted experiments on the effectiveness of the CAMBNET model in different tumor size groups (≤2cm and >2cm). In the classification of luminal and non-luminal types, the CAMBNET model had better performance in differentiating luminal and non-luminal types in the >2 cm group with an AUC of $95.87\%$ and ACC of $88.07\%$. $89.35\%$ for Precision,$85.81\%$ for Recall, and $86.97\%$ for F1. However, in the classification experiment between triple-negative and non-triple-negative types, the CAMBNET model had better discriminatory performance in the <2 cm group with an AUC of $83.45\%$ and ACC of $90.29\%$. $94.79\%$ for Precision, $70.59\%$ for Recall, and $76.42\%$ for F1. Table 7 \| Dataset \| Acc(%) \| Pre (%) \| Rec (%) \| F1(%) \| AUC(%) \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| size_large \| 88.07 \| 89.35 \| 85.81 \| 86.97 \| 95.87 \| \| size_small \| 82.73 \| 85.24 \| 82.97 \| 82.48 \| 84.62 \| \| size_large_TN \| 86.62 \| 78.88 \| 62.86 \| 66.24 \| 48.45 \| \| size_small_TN \| 90.29 \| 94.79 \| 70.59 \| 76.42 \| 83.45 \| ## Visual analysis of CAMBNET Although the CAMBNET model has achieved high accuracy in breast cancer subtype classification, the lack of visual analysis severely limits its application in realistic tasks. Therefore, we experimentally demonstrate the reliability and feasibility of this method by performing a visual analysis of the CAMBNET model. First, we obtained the visual images of the feature shown in Figure 5, and the higher brightness in the feature map indicates higher attention and a higher contribution to the classification performance. Darker pixel regions such as blue indicate a smaller proportion of training and a smaller contribution to the classification performance. As shown in Figure 5, the focused regions of FEATURE MAP are consistent with the locations of key lesions that physicians focus on, which demonstrates that the method can well localize image features with clinical diagnostic value and proves the effectiveness of the CAMBNET model in breast cancer subtype classification. Figure 5: The visual images of feature. (A) the feature Source images. (B) the first convolutional layer images. (C) SFEpath images. (D) LTTpath1 images. (E) LTTpath2 images. (F) the three branches fused images. (G) DEFM images. To further demonstrate the effectiveness of the designed multi-branch attention network, we visualized the learned features with the CAMBNET model and the ResNet34 [34], DenseNet121 [35], Vgg16 [36] networks which have better performance in classification in the classical model by Grad-CAM. Grad-CAM is a gradient-weighted class combining gradient information with the feature mapping activation mapping method. Given an input sample, Grad-CAM first calculates the gradient of the target class for each feature map in the last convolutional layer and performs a global average pooling of the gradients. The importance weight of each feature map is obtained by global average pooling. Then, the weighted activation of the feature maps is calculated based on the importance weights to obtain a gradient-weighted class activation map. The gradient-weighted class activation map can be used to locate the important regions with class discriminative properties in the input samples. The results are shown in Figure 6, and we can see that the focus region of our designed multi-branch attention network is mainly on the tumor itself compared with other classical networks. Meanwhile, the focus of other models is often not on the tumor itself but on other non-focus regions. This indicates that compared with other classical models, the CAMBNET model can better learn the features of important regions and focus on the discriminative features between different subtypes, and finally achieve accurate classification of subtypes. Figure 6: Visualization of CAMB CNN. (A) Source images. (B) CAMBNet images. (C) ResNet34 images. (D) DenseNet121 images. (E) Vgg16 images. ## Discussion It has been reported that the histological features based on DCE-MRI images of the breast are helpful to differentiate the molecular types of breast cancer. Fan et al. [ 28] found that the imaging omics model based on DCE-MRI was good at identifying the molecular subtypes of breast cancer. Agner et al. [ 37] retrospectively analyzed the DCE-MRI images of 76 patients with breast cancer and analyzed the differences between triple-negative breast cancer (TNBC) and other molecular subtypes. Sun et al. [ 37] retrospectively analyzed the DCE-MRI images of 266 breast cancer patients and used a convolutional neural network (CNN) to distinguish breast cancer subtypes (luminal and non-luminal). The average prediction specificity, accuracy, precision, and area under the ROC curve were 0.958, 0.852, 0.961, and 0.867, respectively. Another study [26] also used a convolutional neural network (CNN) algorithm to predict the molecular subtype of breast cancer based on the MRI features of breast cancer and achieved good diagnostic efficiency. The above study demonstrates the feasibility of using deep learning to classify different molecular subtypes of breast cancer. To further improve the performance of breast cancer subtype classification based on breast MRI images, we propose a new deep network model to extract high level feature information and focus on lesion objects. Experiments conducted on the MRI dataset of 160 clinical breast tumor patients obtained from the Second Hospital of Dalian Medical University showed that the recall, accuracy, precision, and area under the ROC curve of our method were $89.11\%$, $88.44\%$, $88.52\%$, and $96.10\%$ for luminal and non-luminal types. The above experimental results verify the effectiveness of the model, and we used transfer learning and data augmentation for the CAMBNET model to further improve the model’s ability to classify breast cancer subtypes. Among them, Accuracy of $89.46\%$, Precision of $89.83\%$, Recall of $90.35\%$, and F1 of $89.44\%$. Histopathological analysis of breast cancer has achieved high accuracy in recent years. Chuang Zhu et al. [ 38] proposed a method for histopathological image classification of breast cancer by combining multiple compact convolutional neural networks (CNN). Mustafa I. Jaber et al. [ 39] developed a deep learning method for subtype classification of tumors using only breast biopsy tissue sections. Related work achieved high accuracy rates and we compared these models with our model and showed that the results achieved by both are comparable. However, MRI has the advantage of being noninvasive and fast, whereas histopathological images are invasive and also have slower feedback of results, so our work still has a relative advantage. And the interpretability of the machine learning results was achieved through the visual analysis of the CAMBNET model. It shows that the method is reliable and feasible. TNM staging of breast cancer is of great importance for guiding treatment, evaluating the curative effect, and assessing prognosis [40]. Whether the maximum diameter of the tumor is more than 2 cm is the most important index for distinguishing T1 from T2. TNBC has the highest invasiveness and the worst prognosis. Some studies [41] have found that the diameter of the primary tumor of TNBC positively correlates with the axillary lymph node metastasis rate. When the tumor diameter exceeds 2 cm, the ipsilateral axillary lymph node metastasis rate increases by $50\%$ [42, 43]. In this study, our model performed best in distinguishing TNBC from NTNBC in the group with tumor diameter ≤ 2 cm. The accuracy is $90.29\%$ and the AUC value is $83.45\%$, which is helpful for the early diagnosis and treatment of TNBC, improving the prognosis and survival rate of patients. Early age of menarche is one of the risk factors for breast cancer [44]. The younger the age of menarche, the earlier a woman is exposed to estrogen. Studies have shown that the earlier the age of menarche, the worse the degree of differentiation and prognosis of breast cancer patients, and the higher the rate of axillary lymph node metastasis [29]. Studies have reported that the earlier the age of menarche, the higher the incidence of non-luminal breast cancer and the higher the malignancy, and the worse the prognosis of non-luminal breast cancer compared to luminal breast cancer. For the classification of luminal and non-luminal breast cancer, our results show that the ACC value for menarche age ≤ 14 years is $92.37\%$, precision is $92.42\%$, recall is $93.33\%$, F1 is $92.33\%$, and AUC is $99.95\%$. At the same time, we investigated the classification efficiency of our model in luminal type A and non-luminal type A. The results showed that the ACC diagnostic efficiency for menarche age ≤ 14 years was $88.44\%$, precision $83.45\%$, recall $65.11\%$, F1 $69.35\%$, and AUC $85.68\%$. Our model is more valuable in classifying luminal and non-luminal types of breast cancer patients with menarche age ≤ 14 years. In this study, there are also some limitations, firstly, only the classification of intraluminal subtypes/non-luminal subtypes was performed in this paper, because the dataset is not sufficient relative to the task of four subtypes. Moreover, annotating such images is laborious and time-consuming, and subsequent work can be performed for weakly supervised or unsupervised learning. Meanwhile, the authors have some textual supplementary information at hand, which can be considered for subsequent experiments to be applied by distillation learning and other methods. ## Conclusion In summary, the experimental results show that our novel deep learning algorithm based on multi-branch feature fusion and attention mechanism has high accuracy in predicting molecular subtypes of breast cancer, Our model might be more valuable in classifying luminal from non-luminal subtypes for patients with age at menarche ≤14 years. For patients with tumor sizes ≤20 mm, our model could be helpful in more accurately detecting triple-negative breast cancer to improve patient prognosis and survival. So our novel deep learning algorithm has greater potential for future clinical applications. In the near future, we will collect more data to build a larger and more comprehensive breast cancer subtype database to better study the problem of breast cancer subtype classification, aiming to comprehensively assist physicians in the clinical diagnosis and treatment of breast cancer subtypes. ## Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. ## Ethics statement The studies involving human participants were reviewed and approved by Ethics Committee of the Second Hospital of Dalian Medical University Second Hospital of Dalian Medical University. The patients/participants provided their written informed consent to participate in this study. ## Author contributions LS, HG: Design of this study, writing review and editing. HT: Data processing, design of algorithm, experimental validation, writing of original manuscript. TL, YZ: Design of this study, writing of original manuscript and data collation. JT, YL, CL: Writing of original manuscript, data collation. All authors contributed to this article and approved the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Pharmacological potential of Withania somnifera (L.) Dunal and Tinospora cordifolia (Willd.) Miers on the experimental models of COVID-19, T cell differentiation, and neutrophil functions authors: - Zaigham Abbas Rizvi - Prabhakar Babele - Upasna Madan - Srikanth Sadhu - Manas Ranjan Tripathy - Sandeep Goswami - Shailendra Mani - Madhu Dikshit - Amit Awasthi journal: Frontiers in Immunology year: 2023 pmcid: PMC10028191 doi: 10.3389/fimmu.2023.1138215 license: CC BY 4.0 --- # Pharmacological potential of Withania somnifera (L.) Dunal and Tinospora cordifolia (Willd.) Miers on the experimental models of COVID-19, T cell differentiation, and neutrophil functions ## Abstract Cytokine release syndrome (CRS) due to severe acute respiratory coronavirus-2 (SARS-CoV-2) infection leads to life-threatening pneumonia which has been associated with coronavirus disease (COVID-19) pathologies. Centuries-old Asian traditional medicines such as Withania somnifera (L.) Dunal (WS) and Tinospora cordifolia (Willd.) Miers (TC) possess potent immunomodulatory effects and were used by the AYUSH ministry, in India during the COVID-19 pandemic. In the present study, we investigated WS and TC’s anti-viral and immunomodulatory efficacy at the human equivalent doses using suitable in vitro and in vivo models. While both WS and TC showed immuno-modulatory potential, WS showed robust protection against loss in body weight, viral load, and pulmonary pathology in the hamster model of SARS-CoV2. In vitro pretreatment of mice and human neutrophils with WS and TC had no adverse effect on PMA, calcium ionophore, and TRLM-induced ROS generation, phagocytosis, bactericidal activity, and NETs formation. Interestingly, WS significantly suppressed the pro-inflammatory cytokines-induced Th1, Th2, and Th17 differentiation. We also used hACE2 transgenic mice to further investigate the efficacy of WS against acute SARS-CoV2 infection. Prophylactic treatment of WS in the hACE2 mice model showed significant protection against body weight loss, inflammation, and the lung viral load. The results obtained indicate that WS promoted the immunosuppressive environment in the hamster and hACE2 transgenic mice models and limited the worsening of the disease by reducing inflammation, suggesting that WS might be useful against other acute viral infections. The present study thus provides pre-clinical efficacy data to demonstrate a robust protective effect of WS against COVID-19 through its broader immunomodulatory activity ## Introduction The first reported case of SARS-CoV-2 was in 2019 and has since then become a predominant cause of global morbidity and mortality [1]. COVID-19 was declared a pandemic by the World Health Organization (WHO) in March 2020 demanding the development of therapeutic interventions and vaccine candidates to mitigate COVID-19-related pathology and mortality (2–5). One of the hallmarks of severe COVID-19 is cytokine release syndrome (CRS) which is responsible for elevated pro-inflammatory cytokines in the pulmonary region leading to respiratory distress (6–9). Moreover, attenuated functionality in other major organs or multiple organ failure has also been manifested in a significant number of COVID-19 cases (10–12). Active vaccination strategy helped in alleviating the COVID-19 severity and related death, however, the continuous evolution of the ancestral virus using acquiring mutations led to immune evasion and poorer protection against variants of concern (VoC) and emerging variants of SARS-CoV-2 [13, 14]. In addition, COVID-19 vaccines may not be sufficient to confer protection in immunocompromised individuals or the individuals with co-morbid conditions. Therapeutic antiviral drugs such as Remdesivir (RDV) and immunosuppression by Dexamethasone (DXM) were the most acceptable therapeutic options against SARS-CoV-2 infection. While DXM was effective in reducing the overall morbidity and mortality arising due to COVID-19 and showed success in clinical trials, a randomized clinical trial of RDV did not show any significant protection in COVID-19 deaths and was marginally successful in giving relief from clinical symptoms [15, 16]. In addition, a major issue with synthetic drugs is also their off-target reactivity limiting their usage in clinical cases. DXM, for example, is a broad immuno-suppressant drug and is known to suppress the overall immune response which may lead to a rise in opportunistic pathogenic infections and other life-threatening complications [17]. The alternate strategy of COVID-19 management includes prophylaxis or preventative strategies with immunomodulators to improve immunity against SARS-CoV-2 infection. In this regard, plant-derived immunomodulators have gained considerable interest owing to their prolonged human use and better safety. The emerging line of evidence has shown that the use of herbal extracts from traditional medicine systems might help in mitigating the COVID-19 pathology (18–20). In the current study, we investigated the efficacy of WS (Ashwagandha), TC (Guduchi), and Piper longum L. (Thippali), in the in vitro cell-based systems and animal models. WS, a shrub traditionally used in India and other Asian countries, is known to possess immunomodulatory properties. Previous in-vitro and in-vivo studies have shown that WS could play a role in the regulation of inflammation by suppressing pro-inflammatory cytokines (21–24). More recently, in-silico docking studies showed the potent inhibitory potential of Withanone, an active ingredient of WS, against SARS-CoV-2 virulence proteins such as spike protein [22, 25]. Other reports have shown Withaferin A, a bioactive steroidal lactone derived from WS, to be anti-inflammatory by reducing the levels of inflammatory cytokines such as IL-6 and TNFα which is desirable in COVID-19 patients to alleviate pulmonary pathology [26, 27]. More recently, we have also characterized the anti-viral component of WS which was found to exhibit potent anti-viral property in-vitro [28]. Similarly, TC and PL have also been shown to modulate inflammatory responses in various disease conditions. TC, particularly, was found to exert anti-viral activity against SARS-CoV-2 in in-silico and in-vitro studies [25]. However, there is still a lack of evidence on the protective efficacy and immunomodulatory potential of these herbal extracts in the in-vivo models of COVID-19. To address these questions, we used hamster (chronic model) and hACE2.Tg mice (acute model) to evaluate the protective efficacy of WS, TC, and PL and the immunological correlates of protection in COVID-19. Our data from the hamster challenge study showed that prophylactic dosing of WS, but not TC or TC in combination with PL (TC+PL), was able to significantly reduce body weight loss. In line with this, WS dosing showed significantly decreased lung viral load, pulmonary pathology, and suppression of inflammatory cytokine mRNA expression. In contrast, the TC group showed robust anti-inflammatory potential but no viral load and pathology alleviation. Next, we used cellular T-cell assay to show potent inhibition of Th1, Th2, and Th17 differentiation in presence of WS, while TC was found to inhibit Th1, Th2, and Th17 differentiation only at higher doses. To understand the effector immune population, we used hACE2.Tg model and show that WS administration results in boosting the immunosuppressive environment in SARS-CoV-2 infected mice which leads to amelioration of pulmonary pathology and significant protection against COVID-19 morbidity and mortality. Together, we provide data from moderate and acute SARS-CoV-2 animal challenge studies suggesting robust protective efficacy by prophylactic WS and determining the immune correlates of protection. Our in-vitro and in-vivo results support the translational value of WS against COVID-19 and provide the basis for further clinical evaluations. ## Materials and methods WS, TC, and PL extract was provided by National Medicinal Plant Board and was used as per pharmacopeial standards in the current study for both in-vitro and in-vivo evaluations. ## Animal ethics and biosafety statement 6-8 weeks of K18-humanized ACE2 transgenic mice (hACE2. Tg mice) were initially procured from Jackson’s laboratory and then bred and maintained at the small animal facility (SAF), THST. Golden Syrian hamsters (6-9 weeks) were procured from the Central drug research institute (CDRI) and were used for experimentation post-quarantine. The animals were randomly divided into 5 groups based on their body weight viz uninfected (UI), Infected (I), Infected treated with remdesivir (I+RDV), Infected treated with WS (I+WS), Infected treated with TC (I+TC) and infected treated with TC+PL (I+TC+PL). The hamster prophylactic WS, TC or TC+PL group started receiving twice-daily oral doses of 130 mg/kg ($0.5\%$ CMC preparation) 5 days before the challenge and continued till the endpoint. The hACE2 transgenic mice group started receiving prophylactic WS, TC or TC+PL group started receiving twice-daily oral doses of 78 mg/kg ($0.5\%$ CMC preparation) 5 days before the challenge and continued till the endpoint. The hamster remdesivir control group received 15mpk (subcutaneous: sc) on 1 day before and 1 day after the challenge while hACE2 transgenic mice received 25mpk (intraperitoneal: ip) injections of remdesivir started on the same day of infection and continued till the end point. The animals were shifted to ABSL3 1 day prior to the challenge. Live intranasal infection of SARS-CoV-2 SARS-Related Coronavirus 2, Isolate USA-WA$\frac{1}{2020}$) 105PFU/100μl (for hamster) and 105PFU/50μl (for mice) or with DMEM mock control was established with the help of catheter under mild anesthetized by using ketamine (150mg/kg) and xylazine (10mg/kg) intraperitoneal injection inside ABSL3 facility (29–34). Experimental protocols related to handling and experimentation was approved by RCGM, institutional biosafety, and IAEC (IAEC/THSTI/105) animal ethics committee. ## Preparation and characterization of WS, TC, PL extract 1g of dry powder of WS (roots), TC (stem), and PL (seeds) were dissolved in 100 ml of water at 37°C overnight in a shaker incubator to obtain the herbal extracts. The following day, the suspended extract was centrifuged at high speed 10000 x g for 30 min. The supernatant thus obtained was filtrated by using a 0.45 filter. The filtrate obtained was assumed to be $100\%$ aqueous extract which was further diluted in water to achieve a dosing concentration. The filtrate was further used for evaluating the composition and was previously published [28]. ## Virus culture and titration Dulbecco’s Modified Eagle Medium (DMEM) complete media containing 4.5 g/L D-glucose, 100,000 U/L Penicillin-Streptomycin, 100 mg/L sodium pyruvate, 25mM HEPES and $2\%$ FBS was used to propagate and titrate SARS-Related Coronavirus 2, Isolate USA-WA$\frac{1}{2020}$ virus in Vero E6 cell line. The plaque-purified stocks of virus were prepared and used inside at ABSL3 facility at IDRF, THSTI in accordance with the IBSC and RCGM protocols. ## Gross clinical parameters of SARS-CoV2 infection For mice experiment the endpoint of the study was day 6 post-challenge, while for hamster study the endpoint was 4 days post-challenge. The animal body weight was recorded for everyday post challenge. At the end point all the animals were sacrificed and a necropsy was performed to investigate lungs and spleen. Gross morphological changes were recorded and imaging was performed for excised lungs and spleen. For histological analysis, left lower lobe of the lung was excised and fixed in $10\%$ formalin [31, 33, 35]. Lungs were homogenized in Trizol for RNA isolation while spleen was either homogenized (hamster) or used for flow cytometry (hACE2 transgenic mice) [30]. The homogenized samples were immediately stored at -80 °C till further use. Serum samples isolated from blood w immediately stored at -80 °C till further use. ## Viral load Isolated lung was homogenized in 2ml Trizol reagent (Invitrogen) and RNA was isolated by Trizol-Choloform method. Yield of RNA was quantitated by nano-drop and 1 µg of RNA was use to reverse-transcribed to cDNA using the iScript cDNA synthesis kit (BIORAD; #1708891) (Roche). 1:5 diluted cDNAs was used for qPCR by using KAPA SYBR® FAST qPCR Master Mix (5X) Universal Kit (KK4600) on Fast 7500 Dx real-time PCR system (Applied Biosystems) and the results were analyzed with SDS2.1 software [30, 33]. Briefly, 200 ng of RNA was used as a template for reverse transcription-polymerase chain reaction (RT-PCR). The CDC-approved commercial kit was used for of SARS-CoV-2 N gene: 5′-GACCCCAAAATCAGCGAAAT-3′ (Forward), 5′-TCTGGTTACTGCCAGTTGAATCTG-3′ (Reverse). Hypoxanthine-guanine phosphoribosyl transferase (HGPRT) gene was used as an endogenous control for normalization through quantitative RT-PCR. The relative expression of each gene was expressed as fold change and was calculated by subtracting the cycling threshold (Ct) value of hypoxanthine-guanine phosphoribosyl transferase (HGPRT-endogenous control gene) from the Ct value of the target gene (ΔCT). Fold change was then calculated according to the formula POWER(2,-ΔCT)10,000 [36, 37]. ## qPCR from splenocytes RNA isolated from spleen samples were converted into cDNA as described above. Thereafter, the relative expression of each gene was expressed as fold change and was calculated by subtracting the cycling threshold (Ct) value of hypoxanthine-guanine phosphoribosyl transferase (HGPRT-endogenous control gene) from the Ct value of target gene (ΔCT). Fold change was then calculated according to the formula POWER (2, -ΔCT)10,000 (36–38). The list of the primers is provided in Table 1 as follows. Table 1 \| Gene \| Forward \| Reverse \| \| --- \| --- \| --- \| \| HGPRT \| GATAGATCCACTCCCATAACTG \| TACCTTCAACAATCAAGACATTC \| \| tryptase β2 \| TCGCCACTGTATCCCCTGAA \| CTAGGCACCCTTGACTTTGC \| \| chymase \| ATGAACCACCCTCGGACACT \| AGAAGGGGGCTTTGCATTCC \| \| muc1 \| CGGAAGAACTATGGGCAGCT \| GCCACTACTGGGTTGGTGTAAG \| \| Sftp-D \| TGAGCATGACAGACGTGGAC \| GGCTTAGAACTCGCAGACGA \| \| Eotaxin \| ATGTGCTCTCAGGTCATCGC \| TCCTCAGTTGTCCCCATCCT \| \| PAI-1 \| CCGTGGAACCAGAACGAGAT \| ACCAGAATGAGGCGTGTCAG \| \| IFNγ \| TGTTGCTCTGCCTCACTCAGG \| AAGACGAGGTCCCCTCCATTC \| \| TNFα \| AGAATCCGGGCAGGTCTACT \| TATCCCGGCAGCTTGTGTTT \| \| IL13 \| AAATGGCGGGTTCTGTGC \| AATATCCTCTGGGTCTTGTAGATGG \| \| IL17A \| ATGTCCAAACACTGAGGCCAA \| GCGAAGTGGATCTGTTGAGGT \| \| IL10 \| GGTTGCCAAACCTTATCAGAA ATG \| TTCACCTGTTCCACAGCCTTG \| \| IL6 \| GGACAATGACTATGTGTTGTTAGAA \| AGGCAAATTTCCCAATTGTATCCAG \| ## Histology Formalin-fixed samples of lungs were embedded in paraffin blocks, sectioned and stained with hematoxylin and eosin dye as previously described [35, 36]. Strained lung samples were then analysed and imaged at 40X. Histological assessment for pathological features was done by professional histologist in a blinded manner and scoring was carried out on a scale of 0-5 (where 0 indicated the absence of histological feature while 5 indicated the highest score). Disease index score was calculated by the addition of all the individual histological scores. ## In vitro differentiation of T cells The single cell suspension was prepared from spleen and lymph nodes of 6–8 weeks old C57BL/6 mice. The cells were activated using soluble anti-CD3 (2ug/ml) and differentiated into Th1 conditions by adding recombinant mouse IL-12 (15ng/ml) cytokine or Th2 conditions by adding recombinant mouse IL-4 (15ng/ml) cytokine or Th17 conditions by adding TGF-beta (2ng/ml) plus IL-6 cytokine (25ng/ml) [37, 39]. WS or TC was added in concentrations ranging from 10ug/ml to 1000ug/ml at the start of culture. Cells were harvested after 72 hours of culture. Intracellular cytokine staining was performed to check the expression of IFN-gamma, IL-4 and IL-17 cytokine for Th1, Th2 and Th17 cells respectively. ## Intracellular cytokine staining Surface markers were stained for 15–20 min in room temperature in PBS with $1\%$ FBS, then were fixed in Cytofix and permeabilized with Perm/Wash Buffer using Fixation Permeabilization solution kit and stained anti-IL-17A; anti-IFN-gamma, anti-IL-4 diluted in Perm/Wash buffer. All antibodies were used in 1:500 dilution. The cells were then taken for flow cytometry using BD FACS-CantoII and data was analyzed with FlowJo software [32, 36, 37]. ## Isolation of murine BMDNs and human peripheral neutrophils Murine BMDNs were isolated according to the method described by Rizvi et al., 2022 from long bones of C57BL/6 wild-type male mice (12–16 weeks, 20–25 g) [31]. After flushing the long bones with HBSS + $0.1\%$ BSA, BMDNs were collected between the $81\%$ and $62\%$ layers of the Percoll (Sigma GE17-0891-02) density gradient. Cell viability and purity were checked by Trypan blue and anti-Ly6G (Thermo 14-5931-82) and anti-CD11b (Thermo 14-0112-82) antibodies, respectively. Similarly, human PMNs were also isolated from the peripheral blood of healthy individuals, after sedimenting the RBCs with $6\%$ dextran at 37°C. Isolated neutrophils were assessed by CD15 (Thermo 14-0159-82) labeling for their purity. All the studies on mice were approved by the institutional animal (THSTI/105) and human (THS$\frac{1.8.1}{100}$) ethical committees, DBT-THSTI, Faridabad. ## Cell viability assay Different concentrations of the extracts, ranging from 100-1000 µg/ml were used to determine, if any, cytotoxicity on neutrophils up to 240 min [40]. 1.0x106 per ml cells were incubated with propidium iodide (PI, 50 μg/ml, Sigma P4170) for 15 min and analyzed on BD FACS Canto cell analyzer (BD Biosciences, USA). ## Intracellular ROS and mtROS analysis Both cytosolic and mitochondrial ROS were measured with DCFH-DA (10 μM, Sigma D6883) and MitoSOX (10 μM, Thermo M36008), respectively as previously reported [40]. Extract pre-incubated cells (1.0 x 106 cells/ml) were treated with different interventions such as TRLM (10 μM, MedChem HY-109104), PMA (10-100 nM, Sigma P1585), A23187 (1-5 μM, Sigma C5149), ionomycin (1-4 μM, Sigma I9657), NAC (10 μM, Sigma A9165), and MitoTEMPO (10 μM, Sigma SML0737). DMSO ($0.1\%$) was used as a control. A minimum of 10,000 events were acquired for each sample using BD FACS Canto II. ## NETosis assay 5.0 x 104 cells were incubated with both the extract for 60 min at 37°C, followed by treatment with TRLM (10 uM), PMA (10 uM), A23187 (1 uM), ionomycin (1 uM), or vehicle (DMSO $0.1\%$). SYTOX Green (100 nM, Thermo S7020) was used to monitor the fluorescence at different time periods up to 240 min in a plate reader at 37°C (Synergy 2; BioTek) as described earlier (Rizvi et al., 2022). Additionally, immunofluorescence images were also developed using anti-MPO (Santa Cruz Sc390109) and anti-H4Cit3 (Sigma 07-596) antibodies by confocal microscope (Olympus FV3000) at 100X resolution. ## Phagocytosis and bactericidal assay Phagocytosis was accessed by adding PE-labelled latex beads (Sigma L2778) to extract pre-treated PMNs (1.0 x 104) at a 1:50 ratio using FACS [41]. The bactericidal activity of neutrophils was accessed by first incubating the cells with extracts and then treating them with kanamycin-resistant E. coli for 30 min at 37°C. Internalized bacteria were plated on LB agar after the lysis of PMNs. The killing activity is expressed as a percent of CFU in the presence/absence of PMNs. ## Statistical analysis All the experiments have been carried out independently in triplicate. Results are being expressed as mean ± SEM. Multiple group comparisons have been performed using one-way ANOVA followed by the Bonferroni test using GraphPad Prism 8. The differences have been considered as statistically significant when the p-value was < 0.05. ## Prophylactic use of WS, but not TC, limits SARS-CoV-2-induced pulmonary pathology in hamsters To determine and evaluate the therapeutic potential of WS and TC (two commonly used traditional herbs) against COVID-19, we used a previously established hamster model for SARS-CoV-2 infection which has been shown to mimic moderate COVID-19 pathology [30, 31, 33, 42, 43]. The dosing regimen involved prophylactic intra-gastric administration of WS, TC, or TC+PL for 5 days before intranasal SARS-CoV-2 challenge in hamsters which was continued till the end point of the study (4 days post-infection, dpi). RDV was used as a prototypic anti-viral as a positive control to compare the in vivo results. Though, RDV in clinical trials results in marginal protection against morbidity, in animal studies RDV has been shown to provide robust protection against COVID-19. The schematic summary of the dosing regimen and study design is shown in Figure 1A. Our hamster challenge data indicated that prophylactic dosing of WS was able to significantly reduce the body mass loss following SARS-CoV-2 infection as compared to the (I+WS). This protection against mass loss was found to be similar to that of the RDV-treated group (Figure 1B). However, both TC and TC+PL treated groups showed 4-$8\%$ body mass loss at 4 dpi when compared to the uninfected group (UI) (Figure 1B). Since protection in body mass loss is correlated with the decreased lung viral load and pathology, we next examined the gross morphological manifestation in excised lungs post necropsy and the corresponding lung viral load. Our data show significantly reduced pathological features and relative N gene expression in the lungs in the WS treatment group, as compared to the infected group, which was similar to the reduction seen in the RDV group. The TC and TC+PL group showed no significant reduction in the pathology and viral load in the lungs (Figures 1C, D). Figure 1: Prophylactic efficacy of selected herbal extract on the SARS-CoV-2 infected hamsters. (A) Schematic representation of dosing regimen for prophylactic treatment of WS, TC or TC in combination with PL, positive control remdesivir (RDV), infection control (I) or uninfected hamster group. All the animals except the uninfected control was intranasally challenged with 105 pfu SARS-CoV-2 on day 0 and sacrificed on 4-day post infection (dpi). (B) Body mass of the animals were monitored post challenge and was plotted as %age change as compared to its day 0 body mass. (C) Representative images of harvested lungs post necropsy. (D) Relative viral load by N gene expression by qPCR shown as bar graph mean ± SEM. Histological analysis of left lung lower lobe was carried out post necropsy. The samples were fixed in 10% neutral formalin solution, paraffin embedded, sectioned and hematoxylin (H) & eosin (E) stained. Stained sections were then imaged at 10X and assessed by trained pathologist for histological features. (E) Representative images of HE stained lungs showing pneumonitis (blue), bronchitis (red), epithelial injury (green) and inflammation (yellow). (F) Blinded pathological score for pneumonitis, bronchitis, lung injury, epithelial injury and inflammation as assessed by trained pathologist. (G) mRNA expression of key genes involved in cellular injury of lungs. For each experiment N=5. One way-Anova using non-parametric Kruskal-Wallis test for multiple comparison. P < 0.05, P < 0.01, P < 0.001, **P < 0.0001. In a significant percentage of clinical cases, COVID-19 is characterized by inflammation in the lungs leading to pneumonitis and cellular injury [44]. We, therefore, set out to understand the degree of protection in pulmonary pathology by WS, TC, and TC+PL groups. Blinded-random histopathological assessment of the lung sections by a trained pathologist showed robust overall protection in the hamsters receiving WS in terms of overall mitigation in pneumonitis, bronchitis, epithelial injury, lung injury, and inflammation score which was Together, we provide pre-clinical data from mild and severe infection models suggesting robust protection by WS against COVID-19 through its broader immunomodulatory activity. Our study supports the evaluation of WS alone or as a formulation for therapeutic intervention against acute viral infections. Similar to the degree of protection in the RDV-treated group. TC and TC+PL groups, however, failed to alleviate the overall pathological score of the lungs (Figures 1E, F). Next, to understand the mechanism we evaluated the expression of genes involved in lung injury. Chymase and tryptase are effector enzymes secreted by mast cells that have been implicated in COVID-19 pulmonary pathology [45, 46]. On the other hand, secretion of mucin-1 (muc1) and surfactant protein D (sftp-D) are important defense mechanisms in the lungs against pathogenic infection, while elevated plasminogen activator inhibitor-I (PAI-1) is a risk factor for thrombosis and has been shown to be correlated with COVID-19 severity [30]. Eotaxin is a lung injury-associated gene whose lung expression is upregulated in the case of severe COVID-19 [30, 47]. Our data showed that both WS and TC were able to significantly decrease the mRNA expression of chymase, tryptase, sftpD, and muc1, though the fold change inhibition observed in the WS group was dramatically more prominent than that observed in the TC group. Notably, WS, but not TC, group showed a decrease in exotoxin and PA1 as well (Figure 1G). However, the combinatorial effect of TC with PL showed no alleviation in the gene expression markers for lung injury. In conclusion, we show that hamsters receiving prophylactic dosing of WS but not TC or TC in combination with PL alleviates the pulmonary pathology induced by COVID-19. ## Prophylactic WS promotes the anti-inflammatory response to COVID-19 During the active phase of SARS-CoV-2 infection, immune cells are recruited in the lungs leading to an aggressive inflammatory response that is correlated to morbidity and mortality [44]. Previous studies have shown that the inflammatory profile of the lungs corroborates well with the inflammatory profile of the spleen in COVID-19 animal models [30, 31]. Furthermore, splenomegaly has been shown to be one of the crucial COVID-19 severity indicators in the hamster model [30]. In line with the previously published reports, we found a significant increase in the spleen length and mass in the infected hamsters while the hamsters receiving WS and RDV showed a significant reduction in the body spleen length and mass increase as compared to the infected control (Figures 2A, B). Severe COVID-19 patients have elevated pro-inflammatory cytokines and diminished anti-inflammatory cytokines levels. Therapeutic drugs such as DXM which were successful in decreasing the pro-inflammatory cytokines were found to be effective in clinical trials. Therefore, we tested the immune-modulatory potential of WS, TC, and TC+PL in SARS-CoV-2-infected hamsters. Our mRNA expression data from the spleen shows that both WS and TC showed potent anti-inflammatory potential in lowering the expression of IL-6, IL4, IL13, and TNF-α. Notably, WS showed inhibitory potential for IL-17 cytokine which is pathogenic for pulmonary injury, and significantly boosted the expression of IL-10 cytokine and foxp3 transcription factor which are crucial for the induction of regulatory T cells (Tregs) (Figures 2C, D). There were no significant changes observed in the expression of IFN-γ cytokine and t-bet transcription factor which is responsible for the induction of Th1 response. The TC+PL group also showed non-significant modulations compared to the I group. Together, we found both WS and TC to show immunomodulatory potential in COVID-19 hamsters. WS was more potent in the induction of anti-inflammatory response. Figure 2: Immunomodulatory effects of WS or TC on the infected hamsters. Immunomodulatory activity of prophylactic treatment of WS or TC on infected vs uninfected hamsters were studied. (A) representative spleen images harvested post necropsy. (B) changes in spleen mass to body mass ratio for different groups (C) modulation in the mRNA expression of pro-inflammatory cytokines and (D) transcription factors. For each experiment N=5. One way-Anova using non-parametric Kruskal-Wallis test for multiple comparisons. P < 0.05, P < 0.01, P < 0.001, **P < 0.0001. ## Effect on TRLM-PMA/ionophore-stimulated ROS and mtROS production in human PMNs and murine BMDNs Neutrophils engage the pathogens by TLRs via recognizing PAMPs: among the discovered TLRs, endosomal TLR $\frac{7}{8}$ binds viral single-stranded RNA as in SARS-CoV-2. Various TLR$\frac{7}{8}$ agonists induce neutrophil activation [48] however, little is known about a putative link between TLR$\frac{7}{8}$ signaling and neutrophil responses. In the present study, we found a significant increase in different neutrophil functions against priming of TLR$\frac{7}{8}$ by TRLM prior induction with PMA and/or ionophores. WS and TC have been extensively characterized elsewhere as an alternative or complementary remedy for oxidative and inflammatory diseases owing to the presence of a range of alkaloids, polyphenols, terpenes, flavonoids, coumarins and other phytochemicals. Therefore, to determine the effect of WS and TC on ROS and mtROS production, DCF-DA and mitoTEMPO were added respectively, to the cells followed by priming with TRLM and induction with PMA and ionophores. A marked decrease in ROS production in TC-treated PMNs, from $43\%$ (50 μg/ml) to $52\%$ (100 μg/ml, $p \leq 0.05$) was observed when stimulated with TRLM-PMA whereas TRLM-ionomycin led to a decrease of $35\%$ at 50 μg/ml and $49\%$ at 100 μg/ml, $p \leq 0.05$ (Figures 3A, B). WS also had similar effects in limiting the formation of superoxide anions elicited by TRLM-PMA ($19\%$ at 50 μg/ml and $30\%$ at 100 μg/ml, $p \leq 0.05$) or TRLM-ionomycin (maximum decrease of $21\%$ at 100 μg/ml, $p \leq 0.05$) (Figures 3C, D). A similar trend was also seen in murine datasets (Figures S1A–D). Further, the ability of TC, 100 μg/ml to inhibit mtROS production in PMNs revealed a $40\%$ and $23\%$ reduction with TRLM-PMA and TRLM-ionomycin respectively (Figures 3E–F). A somewhat lower percent reduction was seen in WS- exposed cells with TRLM-PMA ($19\%$), and TRLM-ionomycin ($12\%$) at 100 μg/ml of WS (Figures 3G, H). Similar to the TC and WS effect on PMNs, murine cells also showed more potent inhibition on TRLM-PMA mediated cytosolic and mitochondrial radical production (Figures S1E–H). Notably, WS and TC both were comparatively more efficient in reducing PMA-mediated ROS and mtROS production in neutrophils from humans or mice. Figure 3: Effect of WS and TC on TRLM primed-PMA/ionomycin induced NETs formation in human PMNs. After pre-incubation with different concentrations TC and WS, PMNs were treated with TRLM (10 µg/ml) for 30 min and stimulated with sub-maximal concentration of PMA (12.5 nM) and ionomycin (2 µM) for 30 min. SYTOX Green (100 nM) was used to monitor extracellular DNA release using a plate reader (A, B: TC; C, D: WS). Total MFI in each experimental condition is expressed as Mean ± SEM of min 3 experiments. NETosis in human PMNs was also monitored using immunofluorescence imaging with DAPI (blue), anti-MPO antibody (green), and anti-H4Cit3 antibody (red, E–H). Representative fields are shown at 100X with a scale bar of 10 µm. Bar diagram represents quantification of percent NETs forming cells as calculated from five transects from three independent experiments. Statistical analysis consisted of one-way ANOVA followed by Bonferroni’s test (p < 0.05, *p < 0.01, vs respective control groups; C, control; V, VAS2870; D, Diltiazem; WS100, WS 100 μg/ml; WS300, WS 300 μg/ml; TC100, TC 100 μg/ml; TC300, TC 300 μg/ml. ## Effect on NETosis in human PMNs and murine BMDNs Since sera and postmortem lung biopsies from COVID-19 patients have a high concentration of NET components especially in the inflammatory interstitial lesions and airways [49], elucidating their detailed mechanism could be highly useful. Although NETs formation has been considered a defensive microbicidal phenomenon to exterminate the invading foreign pathogens [50] but a loss of its control and their persistent presence in inflammation results in host tissue damage. A steep rise in NETosis of more than $50\%$ was observed in PMNs primed with TRLM before exposing PMA or ionophores. Supplementing the cells with TC but not WS could inhibit double-stranded DNA release, a hallmark for NETs formation. TC exhibited an inhibitory effect on NETs in a concentration-dependent manner. A low concentration of TC has no significant effect on the inhibition of NETs in neutrophils, however, higher concentrations exerted an inhibitory effect on the release of dsDNA. PMA-induced NETosis in human PMNs was reduced from $13\%$ to $25\%$ after treatment with 100 and 300 µg/ml, $p \leq 0.05$ of TC respectively (Figure 4A). In contrast, with TRLM-ionomycin stimulation TC did not exert a noticeable reduction in DNA release; a maximum of $15\%$ inhibition was seen at 300 μg/ml (Figure 4B). Treatment of cells with WS did not reveal a significant down-regulation of NETosis (Figures 4C, D). Further, similar effects were replicated in the mouse model also using murine BMDNs as shown in Figures S1I–L. Our immunofluorescence images further corroborated the fluorimetry data. Figures 4E, F showed that the pre-treatment of PMNs with 300 μg/ml TC prevented the diffused and web-like state of TRLM-PMA treated cells as evidenced by the reduction of percent NETs forming cells, MPO, and H4Cit3 expression. Incubation of TC with the TRLM-ionomycin group did not result in much elimination of characteristic DNA fibers extrusion, except with shrinkage of nuclear diameter. Results obtained thus indicate that TC might contribute to the regulation of neutrophil NETs formation via modulating the PMA-mediated signaling pathways involved in NETosis. Figure 4: Effect of WS and TC on TRLM primed-PMA/ionomycin induced cytosolic ROS and mtROS production in human PMNs. PMNs pre-incubated at different concentrations of TC and WS were treated with TRLM (10 µg/ml) for 30 min and stimulated with sub-maximal concentration of PMA (12.5 nM) and ionomycin (2 µM) for 30 min. DCF-DA (10 µM) and MitoSOX (10 µM) were used for cytosolic ROS and mtROS detection, respectively using flow cytometry. All the data are represented as Mean ± SEM, n = min 3 per group, and statistical analysis consisted of one-way ANOVA followed by Bonferroni’s test (A–F) (p < 0.01, vs respective control groups;. C, control; N, N-acetyl cysteine; MT, MitoTEMPO; WS50, WS 50 μg/ml; WS100, WS 100 μg/ml; TC50, TC 50 μg/ml; TC100, TC 100 μg/ml. ## Effect of WS on phagocytosis by human PMNs In addition to degranulation and NETs, phagocytosis is another critical anti-microbial function of neutrophils. The measurable effect of WS and TC on neutrophils led us to study the role of these extracts on the phagocytic potential of these immune first responders. TC and WS showed a modest reduction of $17\%$ and $13\%$ ($p \leq 0.05$), respectively only at the high concentration (300 µg/ml, Figures S2A, B), while 1-100 µg/ml of both the herbal extracts did not elicit any noticeable change. Also, pre-treatment of human peripheral neutrophils with TC and WS did not impart any significant effect on the killing activities of phagocytes; approximately $74\%$ and $41\%$ reduction in E. coli growth was observed when bacteria were incubated with TC and WS-treated PMNs, respectively (Figures S2C, D). ## Effect of WS, and TC on Th1, Th2, and Th17 polarization T helper cell subset responses determine the clinical outcome of SARS-CoV-2 infection [51, 52]. An appropriate Th1 cell response is required to clear the virus when the infection is initially established. However, prolonged Th1 cell activation precedes cytokine storm and priming of Th2 responses, leading to a poor prognosis [51, 53]. Patients with severe Covid infection show high levels of IL-17 and GM-CSF [44, 54]. Th17 cells lead to the recruitment of neutrophils and increase vascular permeability and leakage, causing lung damage [55]. Thus, preventing the hyperactivation of pro-inflammatory T cells (Th1, Th2, and Th17) will help reduce the disease’s severity. To study the immunomodulatory role of WS, we studied the effect on in vitro differentiation of different Th cell subsets like Th1, Th2, and Th17 cells (Figure 1). WS showed effective inhibition in the differentiation of Th1, Th2, and Th17 cells with an increase in doses (Figures 5A–I). IC50 values were calculated to compare its efficacy in inhibiting different Th cell subsets. The IC50 value of WS for Th1, Th2, and Th17 cells inhibition were 490.9ug/ml, 185.8 ug/ml, and 488.7ug/ml respectively (Figures 5C, F, I). These observations show that WS is a more potent inhibitor of Th2 cells, followed by Th17 and Th1 cells. We also studied the effect of TC on in vitro differentiation of helper T cell subsets Th1, Th2, and Th17 cells (Supplementary Figures S3A–I). It showed a marginal inhibition of Th1, Th2, and Th17 cells with an increase in doses. The IC50 value of TC for Th1, Th2, and Th17 cells inhibition was 1294 ug/ml, 1330 ug/ml, and 1679 ug/ml (Supplementary Figures S3A–I). These observations show that TC is not as good an inhibitor of pro-inflammatory T-cell differentiation as compared to WS. DXM was used as a positive control since it is a well-known immunosuppressive drug (Supplementary Figures S4A–I). IC50 values of DXM were 517.6nM, 1364pM, and 3162pM for Th1, Th2, and Th17 cells respectively (Supplementary Figures S4A–I). Together, through our in-vitro assay, we show that WS exhibits immune-suppressive potential and could inhibit the differentiation of Th1, Th2, and Th17 cells similar to the DXM-mediated inhibition. Figure 5: Dose kinetics of WS response on in vitro differentiation of Th1, Th2 and Th17 cells from naïve CD4+ T cells. Sorted naïve CD4+ T cells from mouse spleen and lymph nodes were activated using soluble anti-CD3 antibody and differentiated into helper T (Th)2 (A, B), Th17 cells (D, E) and Th1 subtypes (G, H) by using different cytokines viz recombinant mouse IL-4; TGF-β + IL-6 and IL-12 cytokines respectively. WS was added in concentrations ranging from 10ug/ml to 1000ug/ml initially at the time of cell seeding. After 72 h of incubation IL-4, Il-17 and IFN-gamma production was measured respectively for Th2, Th17 and Th1 cells by intracellular cytokine staining. IC50 values were calculated using Graph pad prism software (C, F, I). **P < 0.0001 by one-way ANOVA. ## WS mitigates COVID-19 pathology and improves overall survival in hACE2.Tg mice Screening of anti-viral or immunomodulatory drugs against COVID-19 has so far relied on two in vivo models viz hamster and hACE2.Tg mice model both of which mimic clinical symptoms of COVID-19 yet significantly differ in the disease pathology [30, 31, 43, 56]. While hamsters have been shown to develop mild to moderate COVID-19 pathology following intranasal infection and mimic the majority of the clinical cases, hACE2.Tg mice, on the other hand, are a lethal model for SARS-CoV-2 infection and result in severe respiratory distress leading to $100\%$ mortality by 6-8 days post-infection (dpi). In order to understand the protective efficacy of WS during acute infection, which is known to occur in a less but a significant number of clinical cases, we used hACE2.Tg mice model (Figure 6A). TC and TC+PL were not evaluated in hACE2.Tg mice, since they did not show significant protection in the hamster model previously. Our data from hACE2.Tg mice challenge study showed about 8-$10\%$ recovery in the body mass in the WS-treated group as compared to the I control (Figure 6B). The overall survival of the hACE2.Tg mice improved by 2 days in WS treated group which was marginal yet significant as compared to the I control (Figure 6C). We next examined the gross morphological changes, viral load and pathological features in the excised lungs post necropsy on 6 dpi. Our data show that the WS group showed significant alleviation in gross morphological changes and lung viral load in the WS group as compared to the I control (Figures 6D, E). The H & E histopathological assessment results showed robust protection in the overall pathological scores in the WS group as compared to the I control (Figures 6F, G). RDV group showed log10 2-fold decreased lung viral load, however, the pathological disease index score of RDV and WS was found to be similar. Taken together, we found significant mitigation in COVID-19 pathology and lung viral load in the WS group which is reminiscent of robust protective efficacy. Figure 6: Assessment of protective efficacy of WS in acute SARS-CoV-2 infection model of hACE2 transgenic mice. To evaluate the effect of prophylactic treatment of WS on severe SARS-CoV-2 infection, we used hACE2 mice model for acute infection and compared it with RDV control. (A) Schematic representation showing treatment regimen for WS and RDV. Mice were intranasally infected with SARS-CoV-2 and (B) %age changes in body mass and (C) mortality was monitored and plotted. (D) Representative excised lung images 6 days post infection (E) Lung viral load presented as Log10 N copy number (F) Lower lung lobe was used for HE staining (G) and assessed for pathological features by blinded scoring by trained pathologist. For each experiment N=5. One way-Anova using non-parametric Kruskal-Wallis test for multiple comparisons. P < 0.05, P < 0.01, P < 0.001, **P < 0.0001. ## WS treatment results in the boosting of MDSCs in hACE2.Tg mice COVID-19 is characterized by lymphopenia resulting in dysregulation of immune profile and function [57]. To test the immunomodulatory potential of WS in mice infected with SARS-CoV-2, we carried out flow cytometry-based immunophenotyping of the major immune population in WS treated vs non-treated group. In line with the previously published reports, our data from lymph-node cells shows that intranasal SARS-CoV-2 infection causes severe lymphopenia in hACE2.Tg mice were characterized by a significant decrease in lymphocytes (CD45+), total T cells (CD3+) cells, T helper cells (CD4+), and cytotoxic T (CD8+) cells (Figures 7A–C). This skewed immune profile was rescued in the RDV group. WS group also showed recovery in depleted CD45+ cells but failed to show any significant recovery in T cell frequency. In addition to T cell depletion, COVID-19 is also characterized by a high frequency of inflammatory monocytes and myeloid-derived suppressor cells (MDSCs) in clinical cases [58]. In line with this, we found a high frequency of monocytes in the lymph nodes in I control which was significantly reduced in WS or RDV-treated group. Moreover, the high levels of MDSCs were significantly suppressed in both WS and RDV groups. However, we did not find any observable difference in the percentage frequency of NK, NKT, macrophages, or neutrophils (Figures 7D–E). Taken together, we found that WS-treated mice show a rescuing effect in the dysregulated immune profile following SARS-CoV-2 infection. Figure 7: Changes in the major immune populations of infected hACE2 mice with or without treatment. Flow cytometry-based quantitation was done to evaluate changes in the major immune population in the lymph nodes of sacrificed animals at 6 dpi. The % age frequency was plotted as bar graph along with the representative contour plot (A) CD45+ population (B) CD3+ T lymphocytes, NK cells and NKT cells (C) CD4+ T helper cells and CD8+ T cytotoxic cells (D) Macrophages (E) Monocytes, neutrophils and MDSCs population. For each experiment N=5. One way-Anova using non-parametric Kruskal-Wallis test for multiple comparisons. P < 0.05, *P < 0.01. ## WS effectively inhibits inflammatory cytokine in hACE2.Tg mice In order to understand the immunomodulatory potential of WS in the in-vivo settings, we performed intracellular cytokine staining (ICS) of phorbol myristate acetate (PMA), Ionomycin-activated lymph-node cells isolated from challenged hACE2.Tg mice. Based on the results obtained from ICS, we did not find any difference in Th1 (CD4+IFNγ+) and Th2 (CD4+IL-4+) response in the WS group, however, there was 2-3-folds inhibition of Th17 cells (CD4+IL17A+) and TNFα secreting CD4+ T cells in WS group as compared to the I control (Figures 8A–D). We also studied the effector cytokine response in CD8+ T cells and NK cells compartment. Effector cytotoxic response is correlated with improved survival and morbidity in COVID-19 cases, while NK cell activity is crucial for early viral clearance and immunity. Our data shows WS did not significantly modulate the cytokine profile of CD8+ and NK cells, when compared to the I control profile (Supplementary Figures S5A–G). However, we did see significant inhibition in the CD8+IFNγ+ T cell response in presence of WS in-vivo. Taken together, CD4+ T cell-specific inhibition was mediated by WS in-vivo, while cytokine response in the cytotoxic T cell compartment was relatively unaltered. Figure 8: Changes in the effector cytokines of CD4+ T cells of infected hACE2 mice with or without treatment. Flow cytometry-based quantitation was done to evaluate changes in the major immune population in the lymph nodes of sacrificed animals at 6 dpi. The % age frequency was plotted as bar graph along with the representative contour plot (A) CD4+IFNγ+ cells (B) CD4+IL4+ cells (C) CD4+IL17A+ cells (D) CD4+TNFα+ cells for each experiment N=5. One way-Anova using non-parametric Kruskal-Wallis test for multiple comparison. P < 0.05, *P < 0.01. ## WS mitigates COVID-19 pathology in the animal model by anti-inflammatory response In summary, we show by using two animal models viz hamster and hACE2.Tg mice that animals receiving prophylactic treatment of WS were broadly protected against COVID-19 both in terms of pulmonary pathology and lung viral load. This protection was further shown to be associated with immunosuppressive activity by WS, especially in the CD4+ T cells effector response both in-vitro and in-vivo (Figure 9). Figure 9: Summary figure highlighting the study design and novel findings from the study. ## Discussion Since its first reported case, COVID-19 has led to an unprecedented number of clinical cases and mortality warranting urgent prophylactic and therapeutic interventions. The vaccination strategy was found to be largely useful against the SARS-CoV-2 ancestral strain, however, the subsequent rise in mutant strains led to immune evasion and decreased efficacy of vaccines [13, 14]. So far only a few therapeutic drugs have been approved by FDA for COVID-19, while new drugs based on chemical entities require relatively more time for development and are also associated with off-target or safety concerns [5]. Interventions based on time-tested herbal extracts have offered an exciting alternate strategy due to their prolonged human use, acceptance, and safety as well as efficacy against infectious diseases [19, 59]. In the current study, we used well-characterized extracts of WS and TC two commonly used Asian traditional medicines to investigate their protective effect against COVID-19 by using hamster and hACE2.Tg mice model. WS contains a large number of phytoconstituents including steroids, alkaloids, saponins, glycosides, volatile oils, sitoindosides, and others with various pharmacological activities [60]. Various chemical constituents such as diterpenoid lactones, glycosides, steroids, sesquiterpenoid, phenolics, aliphatic compounds, essential oils, fatty acids, and polysaccharides are present in TC with known biological activities [61] Various groups have shown that WS constituents could act as a potent inhibitor of SARS-CoV-2. In silico studies have shown that Withanone interacts and blocks the activity of both spike glycoprotein and 3CLpro protease which are crucial for virus entry and replication [62]. Furthermore, the detailed interactive sites of Withanone and its non-covalent interactions were also elucidated suggesting that Withanone/WS could exhibit protective efficacy in-vivo [26]. Withaferin A, which is another Withanolide derived from WS, has also been shown to possess anti-viral and immunomodulatory activity as per the in-silico studies [26]. A detailed characterization of the active ingredients of WS along with other herbal extracts was reported recently by our group, in which the in-vitro anti-viral activity of WS extract components was shown for the first time [28]. On the other hand, TC, another important herbal extract, has also been previously used and shown to have anti-viral potential through docking studies [25]. Though there existed in-silico evidence supporting the rationale that WS and TC may be helpful drug candidates for anti-viral activity against SARS-CoV-2, these observations lacked experimental animal model efficacy studies. Moreover, a recently published report by Kataria S et al, 2022 found that Ayurvedic formulation containing TC and PL in addition to the first line of treatment for COVID-19 was beneficial in reducing the duration of hospitalization and time of recovery for COVID-19 patients warranting studies aimed at evaluating the combinatorial effect of TC and PL in the in-vivo model. However, the immunological correlates of protection elicited by WS or TC, if any, against COVID-19 have also been investigated. In the hamster COVID-19 model, WS showed robust rescuing in the loss of body weight and pulmonary pathologies which were comparable to the RDV group validating the protective efficacy of WS. Notably, the WS group showed a 7-8 folds decrease in the lung viral load of infected hamsters while no protection was seen in other groups viz TC and TC+PL. It is, therefore, reasonable to argue that WS could inhibit the SARS-CoV-2 entry by blocking its interactive sites as shown previously through in-silico studies. However, in-vitro validation of the anti-viral potential of WS warrants further examination. Moreover, the decrease in lung viral load also led to the overall recovery of the pulmonary pathology in the WS group. The immunomodulatory potential of Withanolides has been well documented and has been shown to promote an anti-inflammatory environment [22]. In line with this, we found that WS-treated hamsters exhibited a significant reduction in the mRNA expression of pro-inflammatory cytokines and boosted the expression of anti-inflammatory cytokines and transcription factors in the hamsters infected with SARS-CoV-2. Since the inflammatory response has been implicated in pulmonary pathology, increased risk of hospitalization and mortality, it could be argued that the anti-inflammatory potential of WS could be the basis of protection together with its anti-viral activity. This was an important finding which showed in parts that WS in COVID-19 led to immunomodulatory effects beneficial for recovery. The other major arm of immunity that plays a key role in the COVID-19 protective response is the adaptive immune response mediated by T & B cells. Due to a lack of antibody resources for the hamsters to study cellular immunological response, we used hACE2.Tg mice to study the immunological response following infection and the effect of WS [32, 56]. Our hACE2.Tg mice data gave two crucial insights, it showed that the WS group may not be fully protected in severe COVID-19 cases as infected hACE2.Tg mice though marginally protected ultimately died following SARS-CoV-2 infection. It is likely that the WS-mediated SARS-CoV-2 inhibition through spike interaction and other entry protease inhibition might not be sufficient to prevent the viral entry and multiplication into the hACE2-expressing lung epithelial cells and therefore might allow virus entry and replication leading to disease pathology. However, notably, the overall pulmonary pathology as examined by histopathological assessment did show significantly less lung injury. Two, immunophenotyping data from hACE2.Tg mice showed recovery from the lymphopenia and dysregulated immune profile in the WS group. Interestingly, we found that the effector cytokine response specific to Th cells was specifically inhibited in presence of WS both in-vitro and in-vivo. Though, WS exhibited inhibition of the differentiation of Th1, Th2, and Th17 cells in-vitro, in the infected hACE2.Tg mice WS treated only showed significant inhibition in Th17 and TNFα secreting CD4+ T cells. Notably, heightened TNFα levels have been correlated with a higher risk of mortality in COVID-19 cases and were associated with cellular injury. Since WS inhibitory potential was specific for CD4+ T cells and not CD8+ T cells or NK cells it is a possibility that WS may be interfering with Th cell activation signaling or downstream signaling involved in the effector response. In addition, WS was also found to suppress the frequency of MDSCs and monocytes which have been shown to promote COVID-19 severity. It is a possibility that the immunomodulatory effect of WS could be acting through both innate and adaptive arm of immunity. Extensive and meticulously designed studies are needed to investigate further the molecular basis for this specificity. In the infected mice, we did not find any modulation in the neutrophil number. In infected human subjects, however, the neutrophil number is enhanced and NETosis has been linked with thrombosis in COVID-19 pathologies. We, therefore, performed in vitro studies on mice BMDNs and human neutrophils. To further look into the possible mechanism of protection, we carried out a detailed investigation of immunological changes. In one part of the study, TRLM primed and PMA and calcium ionophores-induced neutrophil functions were studied in the presence of WS and TC. The ROS generation pathways in the neutrophils have been classically mediated either by NOX2-dependent via the activation of protein kinase C [63, 64] or NOX2-independent by calcium-activated small conductance potassium channel (SK3) and/or non-selective mitochondrial permeability transition pore (mPTP) in inducing mtROS production via intracellular Ca2+ flux [65, 66] Additionally, many reports had also postulated the crosstalk of NOX2 activation and mtROS production and thus represents a feed-forward vicious cycle for ROS generation in oxidative stress [67]. By using mitochondrial-targeted inhibitors of NOX2, Vorobjeva et al. [ 2020] showed the involvement of both NOX2-derived ROS and mtROS in NETosis in human PMNs [66]. We also observed a large amount of ROS and mtROS production induced by PMA and/or ionophores. WS and TC have been known to contain anti-oxidants with potent free-radical scavenging abilities. We found a significant reduction in the oxidative stress in TC and WS pretreated cells as demonstrated by their ability to suppress both ROS and mtROS. However, the anti-oxidant properties of both WS and TC were comparatively more pronounced in the case of PMA than ionophores. This indicates a putative role of WS and TC primarily as the NOX2-targeted effector, however, the inhibitory effect of these herbal extracts on the mtROS – NOX2 feed-forward cycle cannot be omitted. NETosis is a distinct process of cell death unlike necrosis, apoptosis, or necroptosis [50]; the molecular processes involved in NETosis are now better understood [66, 68]. NETs are characterized by initial morphological changes and histone modifications followed by mechanical changes leading to chromatin decondensation and an irreversible rupture of nuclear and cell envelope (Neubert et al., 2018). Moreover, Awasthi et al. [ 2016] had reported the involvement of TLRs in NETosis; they have found that blocking TLR-2 and -6 with specific antibodies could significantly inhibit oxLDL induced NETosis [69]. In agreement with this, we have also found a notable reduction in NETs formation in TC pre-treated cells in response to TRLM primed and PMA/calcium ionophores activation. We found that TC predominantly inhibited neutrophil NOX-2 activity as evidenced by immunolabeling of MPO and citrullinated histones. NOX-2 is the most abundant protein and an important source of superoxide radicals in neutrophils that mediate PMA-induced NETosis. Since TC preferentially inhibited ROS, mtROS, and NETs production largely stimulated by PMA, we postulate that TC antagonizes an upstream component at the early stage of NOX2-mediated NETosis. Neutrophils are the first phagocytic cells to reach the site of infection or injury [70]. WS and TC did not reveal any significant adversity on phagocytosis by neutrophils when these were pre-treated with up to 300 µg/ml concentrations of the extracts; however, a low of $20\%$ was observed with TC. Moreover, the intracellular bactericidal capacity of neutrophils also did not show any notable change with the extracts. Together, these results suggested that modulation of neutrophil functionality could be one of the contributing factors for WS-mediated protection against COVID-19. ## Future prospects Our results provide the first direct evidence that prophylactic WS administration is affecting in mitigating the pathology of COVID-19 which is mediated by the anti-inflammatory potential of WS through suppression of effector T helper cell response. Though there is pool of literature available based on computational analysis as well as in-vitro activity suggesting the active pharmaceutical ingredients of WS which may be essential for these immunomodulatory potentials, but lacks data from animal studies. Future experiments should be designed to investigate and decipher the therapeutically potential of WS ingredient and to optimize its dosage which could be taken for randomized clinical trials. Moreover, the pharmacological potential of WS in combination with COVID-19 anti-viral drug or vaccine candidates could be exploited to better understand the synergistic effect of the treatment. In addition, future proof of concept studies could be designed for other infectious diseases which are known to cause cytokine release syndrome such as Influenza, to test if it helps mitigate the disease. ## Conclusion Though ancient knowledge of medicinal potential of herbs existed in Ayurvedic science since long we did not have much scientific evidence about its protective/preventative efficacy from animal studies and more so on COVID19. In this study, by combining hamster and hACE2 transgenic mice model we provide direct evidence that prophylactic treatment of WS mitigates COVID19 through its anti-inflammatory properties. Our findings are important in the context of a continuously evolving virus that leads to immune evasion by previous vaccination and warrants a more robust therapeutic approach against emerging variants of SARS-CoV-2. We also defined the immunological correlates of protection based on in-vitro and in-vivo studies and believe that the potent anti-inflammatory potential of WS could be further exploited against other infectious diseases and inflammatory disorders (one such clinical trial, CTRI/$\frac{2021}{06}$/034496, for this is already conducted in India for WS). Finally, our study supports the use of WS to prevent COVID-19 pathologies and may also be evaluated for its efficacy against other viral infections. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding authors. ## Ethics statement The animal study was reviewed and approved by Institutional animal ethics committee (IAEC) THSTI. ## Author contributions Conceived, designed, and supervised the study: MD, AA. Performed the experiments: ZR, PB, SS, UM. ABSL3 experiment: ZR, MT. FACS: PB and UM. qPCR: ZR. Viral load: ZR. Histology: ZR. Confocal microscopy: PB. Analyzed the data: ZR, PB, and UM. Contributed reagents/materials/analysis tools: MD, AA. Wrote the manuscript: ZR, PB, AA, MD. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1138215/full#supplementary-material ## References 1. 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--- title: Effect of dietary phenylalanine on growth performance and intestinal health of triploid rainbow trout (Oncorhynchus mykiss) in low fishmeal diets authors: - Shuze Zhang - Chang’an Wang - Siyuan Liu - Yaling Wang - Shaoxia Lu - Shicheng Han - Haibo Jiang - Hongbai Liu - Yuhong Yang journal: Frontiers in Nutrition year: 2023 pmcid: PMC10028192 doi: 10.3389/fnut.2023.1008822 license: CC BY 4.0 --- # Effect of dietary phenylalanine on growth performance and intestinal health of triploid rainbow trout (Oncorhynchus mykiss) in low fishmeal diets ## Abstract This study aimed to investigate the effects of phenylalanine on the growth, digestive capacity, antioxidant capability, and intestinal health of triploid rainbow trout (Oncorhynchus mykiss) fed a low fish meal diet ($15\%$). Five isonitrogenous and isoenergetic diets with different dietary phenylalanine levels (1.82, 2.03, 2.29, 2.64, and $3.01\%$) were fed to triplicate groups of 20 fish (initial mean body weight of 36.76 ± 3.13 g). The weight gain rate and specific growth rate were significantly lower ($p \leq 0.05$) in the $3.01\%$ group. The trypsin activity in the $2.03\%$ group was significantly higher than that in the control group ($p \leq 0.05$). Amylase activity peaked in the $2.64\%$ treatment group. Serum superoxide dismutase, catalase, and lysozyme had the highest values in the $2.03\%$ treatment group. Liver superoxide dismutase and catalase reached their maximum values in the $2.03\%$ treatment group, and lysozyme had the highest value in the $2.29\%$ treatment group. Malondialdehyde levels in both the liver and serum were at their lowest in the $2.29\%$ treatment group. Interleukin factors IL-1β and IL-6 both reached a minimum in the $2.03\%$ group and were significantly lower than in the control group, while IL-10 reached a maximum in the $2.03\%$ group ($p \leq 0.05$). The tight junction protein-related genes occludin, claudin-1, and ZO-1 all attained their highest levels in the $2.03\%$ treatment group and were significantly higher compared to the control group ($p \leq 0.05$). The intestinal villi length and muscle layer thickness were also improved in the $2.03\%$ group ($p \leq 0.05$). In conclusion, dietary phenylalanine effectively improved the growth, digestion, absorption capacity, antioxidant capacity, and intestinal health of O. mykiss. Using a quadratic curve model analysis based on WGR, the dietary phenylalanine requirement of triploid O. mykiss fed a low fish meal diet ($15\%$) was $2.13\%$. ## Introduction Fish meal is the main protein source for fish feed [1]. Due to limited fish meal resources and rising prices, the use of fish meal in fish feed has always been reduced by replacing animal protein sources with plant protein sources in the past decades [2]. However, there are some problems, such as nutrient deficiency and indigestion after eating, which hinder the development of fish meal replacement. Fish farming depends on whether the nutritional balance of the feed received by the fish is up to standard [3]. Lack of nutrients, especially essential amino acids, can have a serious impact on the growth and health of fish [4]. In plant-based feed formulations, critical amino acids including methionine, lysine, and threonine are frequently restricted by amino acids that are frequently added as feed supplements. Plant protein sources may be low in other essential amino acids compared with fish meal [5]. Furthermore, fish have lower availability and utilization of plant protein, thereby affecting the availability of essential amino acids in fish. Current practice in the formulation of fish diets is to add methionine, lysine, and threonine to prevent deficiency of essential amino acids [6]. Levels of amino acids in the standard were determined and optimized for purified, semi-purified, or fishmeal-based diets and may be insufficient for fish fed a plant-based diet. Phenylalanine is an aromatic amino acid, which is one of the essential amino acids for fish [7]. It is converted to tyrosine in the liver and kidneys, which in turn is a precursor to epinephrine and norepinephrine, thyroid hormone, triiodothyronine, and thyroxine [8]. They participate in the functional role of brain chemistry by crossing the blood–brain barrier [9]. Tyrosine is also known as a semi-essential amino acid due to the need for additional phenylalanine supplementation to meet the growth and metabolic requirements of fish production when tyrosine levels are insufficient. Aromatic amino acids have an irreplaceable role throughout growth, metabolism, and protein synthesis. It has been determined that a deficiency of phenylalanine in fish will result in decreased feed utilization, reduced antioxidant performance, and impaired growth performance [10, 11]. The improvement of phenylalanine in the growth performance of fish may be related to its ability to improve the feed utilization of fish. In previous studies, it was found that the feed utilization rate of aquatic animals such as Indian major carp (Cirrhinus mrigala) [12] and catfish Heteropneustes fossilis [13] increased with an increase in phenylalanine levels. Molecules like mTOR are able to integrate and regulate the relationship between various nutrients and growth signals in order to regulate the balance between the body’s growth and proliferation rate and the intake of external nutrients. The expression of IGF-1 and mTOR in the hepatopancreas was significantly activated by the addition of phenylalanine to the diet of Portunus trituberculatus [14]. After the upstream mTOR pathway is activated, the downstream S6K1 and 4EBP-1 genes will also show higher expression levels. As an important digestive gland, the pancreas secretes a variety of enzymes that can digest protein, lipids, and so on. After the protease passes through the pancreatic duct, it forms trypsin under the action of enterokinase and further activates other proteases. Lipase hydrolyzes glycerides and phospholipids by cutting off lipid bonds [15]. Phenylalanine can promote the secretion of protease and bicarbonate in the dog pancreas [16]. However, studies in chickens showed that phenylalanine could not promote the secretion of amylase [17]. There are few reports on the effects of phenylalanine on the growth and development of fish digestive organs. Only the digestive performance of Jian carp (Cyprinus carpio var. Jian) was improved after the phenylalanine supplement [18]. Therefore, there may be a positive significance in studying the effect of phenylalanine on the digestive ability of trout. To resist the damage caused by oxidation, fish also contain antioxidant enzymes, including superoxide dismutase (SOD) and catalase (CAT) [19]. Phenylalanine is a precursor of tyrosine, which in turn is a precursor of dopamine and thyroxine. In cultured astrocytes, dopamine increased extracellular SOD protein expression and cell surface SOD activity [20]. Thyroxine increased GPx activity and GSH levels in erythrocytes [21]. These findings clarify the effects of the antioxidant properties given by phenylalanine to fish. In juvenile carp, it was found that a lack or excess of phenylalanine down-regulated CAT activity, while excess phenylalanine down-regulated SOD gene expression; CAT and SOD gene expression could be up-regulated only when added in appropriate amounts [22]. In grass carp (Ctenopharyngodon idella), it was shown that 9.57 g/kg of dietary phenylalanine could reduce malondialdehyde (MDA) content in the gills [22]. The intestinal immune barrier in fish is mainly controlled by intestine-associated lymphoid tissues such as monocytes, lymphocytes, macrophages, and granulocytes [23]. Nutrients, on the other hand, can modulate the immune system of the intestine by affecting the structural integrity of the intestine [24]. In fish, phenylalanine is secreted to produce melanin [25]. It was found that melanin can reduce the production of cytokines such as interleukin 1-beta (IL-1β) and interleukin-6 (IL-6) in the body. However, whether dietary phenylalanine has a similar effect on trout has not been reported yet, so whether there is a correlation between phenylalanine and these cytokines deserves further study. According to the food and agriculture organization of the United Nations (FAO), the annual production of farmed salmon and trout exceeds 3 million tons, making it the third-largest aquaculture species in the world as of 2020 [26]. Recently, trout farming in China has developed rapidly and has become one of the main coldwater fish farming species in China, with annual production already reaching 30,000 tons [27]. Triploid Oncorhynchus mykiss has a faster growth rate, a lower feed coefficient, and a higher meat content than diploid, and it is now the main cultured species of coldwater fish in China [28]. The main objective of this study was to investigate the effects of dietary phenylalanine levels on growth performance, intestinal digestive and immune enzyme activity, intestinal gene expression of inflammation and tight junction protein, and the antioxidant capacity of digestive organs of triploid O. mykiss fed a low fish meal diet. This will be essential as triploid O. mykiss feeds move toward precision formulation. ## Feed formulation and preparation According to the nutritional needs of O. mykiss, fish meal and soybean meal were employed as the protein sources, soybean oil and fish oil were used as the sources of lipids, and dextrin was included as the carbohydrate sources. The basic feed with a crude protein level of $41.01\%$ and a crude lipid level of $11.76\%$ was prepared as the control group [phenylalanine level of $1.82\%$ (G1)]. To achieve $2.03\%$ (G2), $2.29\%$ (G3), $2.64\%$ (G4), and $3.01\%$ (G5) phenylalanine levels in the feed, 0.30, 0.60, 0.90, and $1.20\%$ L-phenylalanine (Sigma, $99\%$) were added, respectively. The tyrosine content was $1.15\%$ (G1), $0.93\%$ (G2), $1.03\%$ (G3), $0.97\%$ (G4), and $1.14\%$ (G5), respectively. Prepare the ingredients according to the formula, and then put them into the mixer and mix well. Ingredients were finely ground before mixing (<250 μm) and then blended with minerals and vitamins. After adding the lipid source, all ingredients were thoroughly mixed for 25 min. Distilled water was then added to achieve the right pellet consistency. The mixture was further homogenized, and a pelletizer (GYJ-250B, Dashiqiao Bao Feed Machinery Factory) was used to form 1-mm pellets. Pellets were dried until the moisture content decreased to about $10\%$ in a ventilated oven at 60°C, and were then stored at −20°C for further use. The formula and nutritional level of the experimental feed are shown in Table 1, and the composition and amount of amino acids in the feed are shown in Table 2. ## Feeding trial Triploid O. mykiss was purchased from Egremorin Industries (Benxi, China) and acclimated for 15 days. Control diets were fed throughout the acclimate period. Before the feeding experiment, a total of 300 fish with an initial average weight of (36.76 ± 3.13 g) were allocated to 15 tanks, with 20 healthy and uniform fish per replicate, and three replicates per treatment group. The experiment was carried out in an indoor aquarium with a controlled water circulation system. A feeding trial was conducted for 8 weeks, during which the fish were fed the test diets twice daily, at 9:00 a.m. and 4:00 p.m., until satiation. Feeding condition: the water source was aerated tap water. Water temperature was maintained at 14 ± 0.5°C. The water dissolved oxygen concentration is >6.0 mg/L, NO2–-N < 0.02 mg/L, pH 6.8–7.1, and NH4+-N < 0.2 mg/L, respectively. Water quality parameters were measured using a YSI-556 multi-parameter water quality meter (YSI Inc., Yellow Springs, OH, USA). One-third of the water is changed every afternoon to ensure water clarity and sufficient dissolved oxygen. ## Sample collection At the end of the experiment, fish were starved for 24 h to allow emptying of the digestive tract contents prior to sampling. All fish were weighed to calculate weight gain rate and other growth indicators [ME204E, Mettler-Toledo Technologies (China) Co.]. Nine fish were randomly selected from each treatment group and anesthetized with tricaine methanesulfonate MS-222 (75 mg/L). Blood samples were obtained from the tail vein, then centrifuged at 4,000 × g for 10 min at 4°C, and the supernatant was extracted as serum. The serum was stored at −20°C for subsequent serum biochemical assays. The mid-intestines of three fish were stored at −40°C for biochemical analyses. The intestines of the other three fish were removed and immediately frozen in liquid nitrogen and stored at −80°C at the end of sampling for subsequent gene expression assays. The other three fish intestines were stored in Bouin’s solution for future histomorphological observation. ## Nutritional content The experimental diets and fish were analyzed using an AOAC-based protocol [29]. Moisture content was determined by drying the samples in an oven at 105°C until a constant weight was obtained. Crude protein (N × 6.25) was analyzed by measuring nitrogen using the Kjeldahl method (2300, FOSS, Sweden). Ash content was analyzed by carbonization at 300°C for 30 min, followed by incineration at 550°C for 4 h. Crude lipid was measured by the Soxhlet method (Extraction System-811, BUCHI, Switzerland). ## Amino acid determination Before the start of amino acid determination of fish and feed, 40–50 mg (accurate to 0.1 mg) of the sample was weighed with an electronic analytical balance, and 10 ml of hydrochloric acid with a concentration of 6 mol/L was added. The ampoule was then heated by an alcoholic blowtorch and sealed immediately, and then placed in a constant temperature oven for 22 h of hydrolysis, setting the temperature at 110°C. After cooling, 10 ml of 6 mol/L sodium hydroxide solution was added to the alkali neutralization. Then the solution was poured into a 100 ml volumetric flask, fixed with 0.02 mol/L hydrochloric acid, and mixed well with the sample hydrolysis solution. The sample was filtered through a 0.2 μm filter membrane into the sample bottle before the machine, and then was determined by an automatic amino acid analyzer (L-8900, Hitachi, Japan). ## Biochemical analysis Biochemical analysis assays were performed using commercially available kits according to the manufacturer’s protocol (Nanjing Jiancheng Institute of Biological Engineering, Nanjing, China). CAT (A007-2-1) activity was determined by measuring the decrease in H2O2 concentration at 240 nm. The reaction mixture contained 50 mm of potassium phosphate buffer (pH 7.0) and 10.6 mM of freshly prepared H2O2. SOD) (A001-3-2) activity was measured spectrophotometrically using xanthine/xanthine oxidase as a source of superoxide radicals. The reaction mixture consisted of 50 mM potassium phosphate buffer (pH 7.8), 0.1 mM EDTA, 0.1 mM xanthine, 0.013 mM cytochrome c, and 0.024 IU/ml xanthine oxidase. An activity unit was defined as the amount of enzyme required to produce $50\%$ inhibition of the rate of reduction of ferrocyanic measured at 550 nm. The amount of lysozyme (LZM; A050-1-1) was measured by a turbidimetric assay. By destroying the β-1,4-glycosidic bond between n-acetyl acetylmuramic acid and n-acetyl glucosaccharide in the cell wall, the cell wall insoluble monosaccharide is decomposed into soluble glycopeptides, resulting in the rupture of the cell wall and the escape of the contents to make the bacteria dissolve. Lipid peroxidation was analyzed in MDA (A003-1-2) equivalents using a thiobarbituric acid reaction. The reaction was carried out at a colorimetric wavelength of 532 nm. Homogenized intestinal samples were centrifuged at 6,000 × g for 20 min at 4°C in 10 volumes (w/v) of ice-cold saline. Subsequently, the supernatant was used for biochemical analysis using a lipase assay kit (LPS; A054-2-1) [30] and an amylase assay kit (AMS; C016-1-1) [31]. Trypsin (A080-2-2) [32] content was determined by the UV colorimetric method; amylase (AMS) activity was determined by the starch iodine colorimetric method; lipase (LPS) content was determined by the colorimetric method, and protein content was determined by the Thomas Brilliant Blue method [33, 34]. All kits were purchased from Nanjing Jiancheng Reagent Company and used according to the instructions. ## Histological examination The mid-intestines of three fish in each replicate were randomly fixed in Bouin’s solution for 48 h, then washed several times with water to remove the fixative, and embedded by conventional paraffin immersion. A microtome (KD 1508) was used to cut sections to a thickness of 6 μm. Sections were successively destained with ethanol, stained with hematoxylin and eosin, and finally sealed with neutral resin. After observation with a microscope (Leica MD 4000B), there were more than 10 intestinal slices in each group. ## Real-time quantitative PCR Total RNA was isolated from intestinal tissues using RNAiso Plus (TaKaRa, China). The quality of the RNA was determined by analyzing the integrity of the RNA by agarose gel electrophoresis and confirming the absorbance ratio at A260/A280 nm between 1.8 and 2.0. The proposed RNA was reverse transcribed to cDNA using the PrimeScript™ RT reagent kit (TaKaRa, Dalian, China) and stored at −80°C in the refrigerator until use. Quantitative PCR (qPCR) was performed on a LightCycler® 480 thermal cycler (Roche, Germany) in a total volume of 10 ml using a Light Cycler® 480 SYBR Green I Master (Roche, Germany), following the manufacturer’s protocol. All amplification reactions were compared using three replicates. All primer sequences in this experiment were referenced to the primer sequence of the O. mykiss gene published by Lee et al. [ 30] and Evenhuis et al. [ 35], as detailed in Table 3. β-Actin was used as an internal reference gene for the normalization of cDNA loading [36]. The cycling conditions were 95°C for 30 s followed by 35 cycles of 95°C for 5 s, 59°C for 10 s, and 72°C for 30 s. Expression results were analyzed by the 2–ΔΔ CT method. TABLE 3 \| Genes \| Primer sequences forward (5′-3′) \| Primer sequences reverse (5′-3′) \| Amplicon (bp) \| Accession number \| Primer efficiency (%) \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| β-Actin \| F: GCCGGCCGCGACCTCACAGACTAC \| R: CGGCCGTGGTGGTGAAGCTGTAAC \| 73 \| AC00648 \| 99.65 \| \| IL-1β \| F: CTCTACCTGTCCTGCTCCAAA \| R: ATGTCCGTGCTGATGAACC \| 194 \| AB010701.1 \| 92.0 \| \| IL-6 \| F: CAATCAACCCTACTCCCCTCT \| R: CCTCCACTACCTCAGCAACC \| 91 \| FR715329 \| 96.0 \| \| IL-2 \| F: AGAATGTCAGCCAGCCTTGT \| R: TCTCAGACTCATCCCCTCAGT \| 69 \| NM_001124657.1 \| 95.0 \| \| IL-10 \| F: CGACTTTAAATCTCCCATCGAC \| R: GCATTGGACGATCTCTTTCTTC \| 70 \| AB118099.1 \| 96.0 \| \| TNF-α \| F: CCACACACTGGGCTCTTCTT \| R: GTCCGAATAGCGGGAAATAA \| 128 \| AJ278085.1 \| 96.0 \| \| TGF-β \| F: TCCGCTTCAAAATATCAGGG \| R: TGATGGCATTTTCATGGCTA \| 71 \| AJ007836.1 \| 95.0 \| \| NF-κB \| F: CAGGACCGCAACATACTGGA \| R: GCTGCTTCCTCTGTTGTTCCA \| 92 \| XM_031794907.1 \| 95.0 \| \| Claudin-1 \| F: TAGCATCCACGATCA \| R: GAGCCTTCACTGGAGC \| 124 \| BK00876 \| 96.0 \| \| ZO-1 \| F: CTGCTGGACGAAGGGA \| R: GGCCTTTATCCTGCAT \| 191 \| HQ6560 \| 95.0 \| \| Occludin \| F: ATGGCTCAATCTACAGG \| R: GAGATACTGGTTGACCAACC \| 102 \| FR904483.1 \| 95.0 \| \| TOR \| F: CCAAAGAGATGCAGAAGCCACA \| R: CTCTCTCATACGCTCTCCCT \| 178 \| XM_020506200.2 \| 98.0 \| \| IGF-1 \| F: ACTGTGCCCCTGCAAGTCT \| R: CTGTGCTGTCCTACGCTCTG \| 159 \| M81904 \| 93.0 \| \| GH \| F: CAAAGTGGGCATCAA \| R: GTTCCTCCTGACGT \| 139 \| NM_0011246 \| 96.0 \| \| GHR \| F: TCCCCTTCACCAGGA \| R: TCATTCTGCAGTGGC \| 148 \| AB10083 \| 97.0 \| \| S6K1 \| F: CCTCCTCATGACACCCTGCT \| R: TCTTCTGGTCCGTTGGCAAA \| 129 \| XM_029674978.1 \| 94.0 \| \| 4EBP-1 \| F: GGGGAACTCTGTTCAGCACA \| R: AATGTTGGGGAGAGAGCACG \| 117 \| NM_004095 \| 94.0 \| ## Calculation formula of growth performance W0 is the initial body mass of the fish (g); Wt is the terminal body mass (g); Lt is the terminal body length of the fish (cm); Wf is the feed intake (g); T is the test day (d) [37]. Statistical software SPSS 20.0 for Windows (SPSS Inc., Chicago, IL, USA) was used to conduct the one-way analysis of variance and Duncan’s multiple comparisons of the data. All data were expressed as mean ± standard error (SE), with $p \leq 0.05$ used as the significant difference standard [36]. The quadratic regression analysis of significant difference indices was carried out by Graphpad Prism 8.0 to determine the optimal demand range of phenylalanine for triploid O. mykiss under the condition of low fish meal [38]. The bar charts in the article were also plotted using Graphpad Prism 8.0. ## Growth performance and somatic indices As dietary phenylalanine levels increased, the WGR and SGR of triploid O. mykiss increased and then decreased, reaching a maximum in the $2.03\%$ group and being significantly higher than the $3.01\%$ group ($p \leq 0.05$) and the VSI ratio in the $2.03\%$ group being significantly higher in all groups ($p \leq 0.05$) (Table 4). Primary, secondary, and tertiary linear regression equations were analyzed for WGR and SGR of triploid O. mykiss to determine the optimal addition of phenylalanine under low fishmeal feed conditions (Table 5). By comparing the R2 values, the quadratic equation provided good fits. From the regression analysis, it was shown that the WGR of triploid O. mykiss had a significant quadratic response to the increase in phenylalanine levels in the diet. The optimal phenylalanine requirement for triploid O. mykiss based on WGR was estimated to be $2.13\%$ (Figures 1, 2). ## Effects of dietary phenylalanine levels on nutritional composition in triploid O. mykiss fed low fish meal diets Whole-body crude protein levels peaked in the $2.03\%$ group, which was significantly higher than the control group ($p \leq 0.05$). The highest whole fish lipid content was obtained when fed $2.03\%$ phenylalanine and was significantly different from the other groups ($p \leq 0.05$) (Table 6). Meanwhile, dietary phenylalanine levels did not significantly affect the moisture and crude ash composition of triploid O. mykiss whole fish. TABLE 6 \| Indices \| Groups \| Groups.1 \| Groups.2 \| Groups.3 \| Groups.4 \| Groups.5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| G1 (1.82%) \| G2 (2.03%) \| G3 (2.29%) \| G4 (2.64%) \| G5 (3.01%) \| SE \| \| Moisture \| 70.01 ± 1.37 \| 68.72 ± 1.78 \| 68.78 ± 0.76 \| 69.62 ± 0.65 \| 69.30 ± 1.76 \| 0.03 \| \| Crude protein \| 14.21 ± 1.50a \| 15.28 ± 2.99b \| 14.68 ± 2.97ab \| 14.30 ± 1.56a \| 13.17 ± 1.38a \| 0.21 \| \| Crude lipid \| 10.46 ± 1.15a \| 10.93 ± 1.14b \| 10.71 ± 2.63a \| 10.67 ± 0.79a \| 10.52 ± 1.88a \| 0.16 \| \| Ash \| 2.38 ± 0.02 \| 2.50 ± 0.09 \| 2.48 ± 0.13 \| 2.49 ± 0.07 \| 2.35 ± 0.04 \| 0.04 \| ## Effects of dietary phenylalanine levels on amino acid composition in triploid O. mykiss fed low fish meal diets Under low fish meal feed conditions, dietary phenylalanine levels significantly affected the amino acid profile ($p \leq 0.05$), except for valine ($p \leq 0.05$). Dietary phenylalanine had no significant effect ($p \leq 0.05$) on the levels of the first limiting amino acid, methionine, and the second limiting amino acid, lysine (Table 7). The tyrosine content at the end of the experiment was $1.78\%$ (G1), $1.77\%$ (G2), $1.71\%$ (G3), $1.79\%$ (G4), and $1.76\%$ (G5), respectively. There was no significant difference between the treatment groups ($p \leq 0.05$). TABLE 7 \| Indices \| Groups \| Groups.1 \| Groups.2 \| Groups.3 \| Groups.4 \| Groups.5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| G1 (1.82%) \| G2 (2.03%) \| G3 (2.29%) \| G4 (2.64%) \| G5 (3.01%) \| SE \| \| Essential amino acid \| Essential amino acid \| Essential amino acid \| Essential amino acid \| Essential amino acid \| Essential amino acid \| Essential amino acid \| \| Thr \| 2.68 ± 0.32 \| 2.64 ± 0.11 \| 2.28 ± 0.18 \| 2.70 ± 0.08 \| 2.68 ± 0.30 \| 0.32 \| \| Val \| 2.79 ± 0.16ab \| 2.76 ± 0.20ab \| 2.39 ± 0.23a \| 2.93 ± 0.19b \| 2.59 ± 0.27ab \| 0.03 \| \| Met \| 2.11 ± 0.01 \| 2.10 ± 0.01 \| 2.10 ± 0.01 \| 2.11 ± 0.01 \| 2.10 ± 0.01 \| 0.04 \| \| Ile \| 2.47 ± 0.10 \| 2.47 ± 0.16 \| 2.29 ± 0.21 \| 2.63 ± 0.14 \| 2.39 ± 0.23 \| 0.17 \| \| Leu \| 6.46 ± 0.25 \| 6.36 ± 0.39 \| 5.90 ± 0.71 \| 6.56 ± 0.33 \| 6.08 ± 0.68 \| 0.36 \| \| Phe \| 2.41 ± 0.02a \| 2.57 ± 0.19a \| 2.79 ± 0.09b \| 2.84 ± 0.12b \| 3.03 ± 0.09c \| 0.02 \| \| Lys \| 8.05 ± 0.34 \| 8.13 ± 0.23 \| 7.84 ± 0.72 \| 8.26 ± 0.26 \| 8.36 ± 0.01 \| 0.13 \| \| His \| 1.38 ± 0.20 \| 1.19 ± 0.16 \| 1.21 ± 0.20 \| 1.28 ± 0.15 \| 1.11 ± 0.13 \| 0.32 \| \| Arg \| 4.25 ± 0.35 \| 3.96 ± 0.27 \| 3.79 ± 0.58 \| 4.17 ± 0.29 \| 3.83 ± 0.40 \| 0.07 \| \| Non-essential amino acid \| Non-essential amino acid \| Non-essential amino acid \| Non-essential amino acid \| Non-essential amino acid \| Non-essential amino acid \| Non-essential amino acid \| \| Asp \| 9.03 ± 0.54 \| 9.34 ± 0.23 \| 9.29 ± 0.01 \| 9.35 ± 0.22 \| 9.33 ± 0.37 \| 0.27 \| \| Ser \| 2.88 ± 0.29b \| 2.64 ± 0.41b \| 2.05 ± 0.30a \| 2.65 ± 0.19b \| 2.32 ± 0.26ab \| 0.25 \| \| Glu \| 10.83 ± 0.14b \| 10.72 ± 0.08ab \| 10.59 ± 0.13a \| 10.77 ± 0.09ab \| 10.63 ± 0.08ab \| 0.43 \| \| Gly \| 4.20 ± 0.65b \| 3.58 ± 0.28ab \| 3.24 ± 0.41a \| 3.76 ± 0.38ab \| 3.29 ± 0.37a \| 0.06 \| \| Ala \| 4.24 ± 0.33 \| 4.08 ± 0.01 \| 4.15 ± 0.12 \| 4.37 ± 0.25 \| 4.10 ± 0.01 \| 0.24 \| \| Cys \| 1.16 ± 0.03b \| 0.88 ± 0.08a \| 0.91 ± 0.09a \| 1.10 ± 0.12b \| 0.88 ± 0.08a \| 0.04 \| \| Tyr \| 1.78 ± 0.04 \| 1.77 ± 0.13 \| 1.71 ± 0.23 \| 1.79 ± 0.08 \| 1.76 ± 0.12 \| 0.13 \| \| Pro \| 2.66 ± 0.21b \| 2.19 ± 0.29ab \| 2.02 ± 0.38a \| 2.26 ± 0.25ab \| 1.90 ± 0.25a \| 0.37 \| \| Total \| 69.57 ± 2.57 \| 67.17 ± 2.68 \| 64.19 ± 3.79 \| 69.20 ± 3.10 \| 65.32 ± 3.75 \| 0.21 \| ## Effects of dietary phenylalanine levels on the antioxidant capacity in triploid O. mykiss fed low fish meal diets The effects of dietary phenylalanine on antioxidant parameters in the serum and liver are displayed in Table 8. The serum SOD reached a maximum in the $2.03\%$ group and was significantly higher than the control group ($p \leq 0.05$). There was no significant difference in liver SOD among different treatment groups ($p \leq 0.05$). Liver CAT peaked in the $2.03\%$ treatment group and was significantly higher than in the other treatment groups ($p \leq 0.05$). There was no significant difference in serum MDA among treatment groups ($p \leq 0.05$), while liver MDA showed a trend of increasing and then stabilizing, reaching the maximum in the $2.29\%$ group ($p \leq 0.05$). Serum and liver LZM reached the highest values at 2.03 and $2.29\%$ of phenylalanine content, respectively, and were significantly different compared to the control group ($p \leq 0.05$). TABLE 8 \| Indices \| Groups \| Groups.1 \| Groups.2 \| Groups.3 \| Groups.4 \| Groups.5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| G1 (1.82%) \| G2 (2.03%) \| G3 (2.29%) \| G4 (2.64%) \| G5 (3.01%) \| SE \| \| SOD (U/mL) \| SOD (U/mL) \| SOD (U/mL) \| SOD (U/mL) \| SOD (U/mL) \| SOD (U/mL) \| SOD (U/mL) \| \| Serum \| 39.65 ± 3.62b \| 46.48 ± 6.24c \| 34.76 ± 2.89b \| 36.83 ± 3.16b \| 27.84 ± 1.93a \| 1.25 \| \| Liver \| 10.22 ± 1.58 \| 12.85 ± 2.59 \| 10.94 ± 2.66 \| 11.10 ± 2.92 \| 12.76 ± 1.09 \| 0.07 \| \| CAT (U/mL) \| CAT (U/mL) \| CAT (U/mL) \| CAT (U/mL) \| CAT (U/mL) \| CAT (U/mL) \| CAT (U/mL) \| \| Serum \| 21.30 ± 11.60a \| 37.51 ± 18.22b \| 24.74 ± 5.53a \| 32.30 ± 7.70b \| 33.78 ± 12.13b \| 1.56 \| \| Liver \| 17.33 ± 1.23a \| 21.98 ± 3.34b \| 17.59 ± 2.98a \| 17.85 ± 4.46a \| 21.35 ± 2.33b \| 1.32 \| \| MDA (nmol/mL) \| MDA (nmol/mL) \| MDA (nmol/mL) \| MDA (nmol/mL) \| MDA (nmol/mL) \| MDA (nmol/mL) \| MDA (nmol/mL) \| \| Serum \| 2.00 ± 0.15 \| 1.76 ± 0.23 \| 1.66 ± 0.44 \| 2.00 ± 0.51 \| 2.12 ± 0.40 \| 0.11 \| \| Liver \| 5.15 ± 0.88b \| 3.85 ± 0.64a \| 3.56 ± 0.33a \| 3.81 ± 0.14a \| 3.94 ± 0.22a \| 0.32 \| \| LZM (U/mL) \| LZM (U/mL) \| LZM (U/mL) \| LZM (U/mL) \| LZM (U/mL) \| LZM (U/mL) \| LZM (U/mL) \| \| Serum \| 75.84 ± 7.32a \| 94.67 ± 11.00b \| 93.27 ± 7.21b \| 73.12 ± 8.01a \| 64.78 ± 8.14a \| 0.44 \| \| Liver \| 20.05 ± 8.40a \| 40.36 ± 2.14b \| 54.67 ± 8.84c \| 44.19 ± 8.37bc \| 22.95 ± 6.48a \| 1.89 \| ## Effects of dietary phenylalanine levels on the intestinal digestive enzyme in triploid O. mykiss fed low fish meal diets The effects of different dietary phenylalanine levels on the intestinal digestive enzyme activity of triploid O. mykiss are shown in Table 9. Trypsin activity was significantly higher in the $2.03\%$ group than in the control group ($p \leq 0.05$). The AMS activity in the 2.03 and $2.29\%$ groups was significantly higher than that in the control and other treatment groups ($p \leq 0.05$), but showed a gradual decrease with the increase in phenylalanine level. Dietary phenylalanine levels had no significant effect on the LPS activity of triploid O. mykiss ($p \leq 0.05$). TABLE 9 \| Indices \| Groups \| Groups.1 \| Groups.2 \| Groups.3 \| Groups.4 \| Groups.5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| G1 (1.82%) \| G2 (2.03%) \| G3 (2.29%) \| G4 (2.64%) \| G5 (3.01%) \| SE \| \| Trypsin (U/mgprot) \| 1,716.59 ± 685.53a \| 4,602.81 ± 717.13c \| 3,006.85 ± 687.26b \| 2,874.80 ± 650.82b \| 2,900.62 ± 778.87b \| 78.76 \| \| Lipase (U/gprot) \| 43.39 ± 13.27 \| 43.43 ± 11.51 \| 46.07 ± 6.79 \| 48.67 ± 8.55 \| 44.55 ± 6.32 \| 2.76 \| \| Amylase (U/gprot) \| 72.38 ± 6.24a \| 106.82 ± 12.06b \| 176.07 ± 21.17c \| 85.03 ± 3.93ab \| 92.56 ± 12.41ab \| 8.97 \| ## Effects of dietary phenylalanine levels on the intestinal tissue morphology of triploid O. mykiss Dietary phenylalanine levels had significant effects on the structural morphology of the intestine of O. mykiss. In Figure 3A ($1.82\%$ group), the intestinal villi were neatly arranged, and the surface striate margin was smooth. In Figure 3B ($2.03\%$ group), the length of the villi was longer and there were more cup-shaped cells and epithelial cells. In Figure 3C ($2.29\%$ group), the length of the villi reached its longest length and was significantly higher than the other treatment groups. However, the thickness of the muscle layer was thinner than that of the second group. In Figure 3D ($2.64\%$ group), the nucleus shift phenomenon began to appear, and the apical part of the villi started to shed. In Figure 3E ($3.01\%$ group), the intestinal muscular thickness was significantly lower, and the nuclei of the epithelial cells shifted significantly. FIGURE 3: Intestine histology. Representative histological sections of the intestine from O. mykiss fed the different experimental diets. Scale bar, 230 μm. Panel (A) was G1 phenylalanine level in intestine tissue 100×; Panel (B) was G2 phenylalanine level in intestine tissue 100×; Panel (C) was G3 phenylalanine level in intestine tissue 100×; Panel (D) was G4 phenylalanine level in intestine tissue 100×; and panel (E) was G5 phenylalanine level in intestine tissue 100×. The length of the villi and the thickness of the muscular layer are shown in Table 10. Villi length reached a maximum in the $2.29\%$ treatment group and was significantly higher than that in the other treatment groups ($p \leq 0.05$). The thickness of the muscular layer was significantly higher in the $2.03\%$ treatment group than that in the control group ($p \leq 0.05$). TABLE 10 \| Indices \| Groups \| Groups.1 \| Groups.2 \| Groups.3 \| Groups.4 \| Groups.5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| G1 (1.82%) \| G2 (2.03%) \| G3 (2.29%) \| G4 (2.64%) \| G5 (3.01%) \| SE \| \| Villus length \| 442.44 ± 3.30a \| 511.88 ± 18.31b \| 684.44 ± 4.26c \| 464.97 ± 36.59ab \| 465.11 ± 15.93ab \| 8.86 \| \| Muscular layer thickness \| 88.15 ± 5.38b \| 101.22 ± 7.56b \| 61.53 ± 4.17a \| 67.88 ± 0.94a \| 67.55 ± 8.38a \| 2.64 \| ## Expression of IGF-1, GH, GHR, TOR, S6K1, and 4EBP-1 in the intestine of O. mykiss Dietary phenylalanine levels significantly affected the expression of intestinal growth-related genes in triploid O. mykiss ($p \leq 0.05$) (Figure 4). The expression levels of mTOR, downstream S6K1, and 4EBP-1 genes in the $2.03\%$ treatment group reached their highest values, and there were significant differences with the $3.01\%$ treatment group ($p \leq 0.05$). Similarly, GHR and GH gene expression levels were all highest in the $2.03\%$ treatment group and significantly higher than the control group ($p \leq 0.05$). FIGURE 4: Growth gene expression of the intestine. Lowercase letters (a, b, or c) indicate a significant effect of liver growth on gene expression (p < 0.05). GH, growth hormone; IGF-1, insulin growth factor 1; GHR, growth hormone receptor; 4E-BP, eukaryotic initiation factor 4E-binding proteins; S6K1, S6 kinase 1. ## Expression of cytokines IL-1β, IL-2, IL-6, IL-10, TGF-β, TNF-α, and NF-κB in the intestine of O. mykiss Dietary phenylalanine levels significantly affected the expression of interleukin (IL-1β, IL-2, IL-6, and IL-10) genes, TGF-β, and TNF-α in the intestine of triploid O. mykiss ($p \leq 0.05$) (Figure 5). IL-1β gene expression reached a minimum at a phenylalanine level of $2.03\%$, which was significantly lower than in the control group ($p \leq 0.05$). The expression of pro-inflammatory factors IL-2 and IL-6 was lowest in the 2.29 and $2.03\%$ treatment groups, respectively, which was significantly different from the $3.01\%$ treatment group ($p \leq 0.05$). FIGURE 5: Immunity gene expression of the intestine. Lowercase letters (a, b, or c) indicate a significant effect of immune-related gene expression in the intestine (p < 0.05). IL-1β, interleukin-1β; IL-2, interleukin-2; IL-6, interleukin-6; IL-10, interleukin-10; TNF-α, tumor necrosis factor-α; TGF-β, transforming growth factor-β; NF-κB, nuclear transcription factors. In triploid O. mykiss fed low fish meal diets, dietary phenylalanine levels had a significant effect on the expression of intestinal tumor necrosis factor (TNF-α) and nuclear factor-κB (NF-κB) genes ($p \leq 0.05$). TNF-β gene expression was lower in the $2.29\%$ treatment group than in the control group ($p \leq 0.05$). TGF-β gene expression was highest in the $2.29\%$ treatment group. TGF-β gene expression reached a maximum in the $2.29\%$ treatment group. The nuclear transcription factor NF-κB also differed significantly among the groups. Compared to the other treatment groups, $2.03\%$ of the treatment groups had significantly lower NF-κB mRNA expression ($p \leq 0.05$), while there was no significant difference among the G3–G5 groups. ## Effects of dietary phenylalanine levels on intestinal tight junction protein-related genes in triploid O. mykiss fed low fish meal diets The expression of the intestinal tight junction protein gene was gradually increased as dietary phenylalanine levels ranged from 1.82 to $2.29\%$. The occludin gene in triploid O. mykiss showed a trend of increasing and then decreasing compared with the control group ($p \leq 0.05$) (Figure 6). The claudin-1 gene reached a maximum in the $2.03\%$ groups and was significantly higher than that in the other groups ($p \leq 0.05$). The ZO-1 gene expression peaked in the $2.03\%$ group, which was significantly different from the control group ($p \leq 0.05$). FIGURE 6: Tight junction protein gene expression of the intestine. Lowercase letters (a, b, or c) indicate significant effects of intestinal tight junction proteins relative to gene expression (p < 0.05). ## Effects of phenylalanine levels on the growth performance of triploid O. mykiss Phenylalanine is an EAA for protein synthesis and growth stimulation in fish. Dietary phenylalanine can enhance fish feeding and increase the WGR and SGR of fish. This study showed that in low fish meal diets (phenylalanine level of $1.82\%$), the WGR and SGR of triploid O. mykiss showed a trend of increasing and then decreasing with increasing phenylalanine levels. Similar results were observed in Indian major carp and silver perch [12, 39, 40]. In pomfrets (Pampus punctatissimus), it was found that the lack or excess of phenylalanine in the diet would lead to reduced growth performance and feed conversion rate [41]. Phenylalanine deficiency and excess will disrupt the amino acid balance of the feed. The balance of amino acids in the feed will be disrupted, affecting the absorption and utilization of amino acids in the feed by fish, reducing the utilization of feed and protein synthesis, and thus inhibiting growth [42]. It has been shown that the reduced growth performance of fish due to excess phenylalanine may be due to the energy consumption of excess phenylalanine in the body acting on deamidation, resulting in the oxidation of large amounts of phenyl pyruvic acid deposited in the body, producing toxic and even pathogenic effects [43]. In Nile tilapia, it was shown that excess phenylalanine did not affect its growth performance [44]. Other factors influenced by the cultural environment, such as water temperature, fish size, and amino acid composition, may also explain these disparities [45]. It has also been suggested that the inhibitory effect of phenylalanine on fish growth may be because the body uses part of its energy to excrete nitrogen, which is because excess amino acids are easily degraded by the body and excreted in the form of nitrogen [46]. However, the inhibitory effect of excess phenylalanine on fish growth is not conclusive, and more studies will be needed. ## Effects on the expression of genes related to growth in triploid O. mykiss Intestinal health affects protein synthesis in the organism, which is regulated by TOR signaling molecules [47]. When certain specific changes occur in the internal environment of the organism, the downstream effector protein S6K1 is regulated by TOR genes, thus participating in the regulation of cell growth, differentiation, and proliferation processes, while the downstream 4EBP-1 gene is also regulated by mTOR genes, regulating the growth process of the organism [48]. When the S6K1 protein is activated in the cell, it phosphorylates several sites, including ribosomal protein S6, to promote the formation of the translation initiation complex [49]. Silva found that IGF-1 is sensitive to changes in nutrients, especially amino acids [50]. The relatively complex interaction between different hormones affects the growth regulation of hormones, among which GH, GHR, and IGF-1 are considered to be the most important growth-regulating genes. IGF-1 viability affects the secretion of growth hormones. The presence of growth hormone in the organism promotes the synthesis and release of this hormone, and the action of growth hormone on IGF-1 is mediated by the growth hormone receptor GHR, so GH-GHR binding is necessary to stimulate IGF-I synthesis and release [51]. This study showed that the growth rate of triploid O. mykiss was slower when phenylalanine was deficient in the fish, but when phenylalanine was excessive, the WGR of O. mykiss had a more pronounced slowdown than when it was deficient. The expression of GH genes was highest at $2.29\%$ phenylalanine level in the low fish meal diet, and the IGF-1 gene peaked at $2.03\%$ and was significantly different from those of other groups. This has the same trend as the results obtained in Nile tilapia [52]. The dietary amino acid imbalance was reported to reduce the expression level of the hepatic IGF-I gene in junco (Rachycentron canadum) [53] and Japanese seabass (Lateolabrax japonicus) [24]. This is consistent with the findings of hybrid grouper larvae (Epinephelus fuscointestinestatus♀ × Epinephelus lanceolatus♂) [54], in which the treatment group with added complex protein had significantly higher daily feed intake and significantly higher rapamycin (TOR) liver target gene expression levels [55]. In other amino acid studies, higher relative mRNA expression levels of rapamycin (TOR) and eukaryotic translation initiation factor 4E-binding protein (4E-BP) were observed in 17.5 and 15.0 g/kg Leu diets [56], In the study of valine on rainbow trout growth gene expression, TOR mRNA and elF4E binding protein (4E-BP) expression were observed to be higher at 18.0 g/kg Val [57], The most significant effect of leucine on TOR and 4E-BP mRNA gene expression levels in rainbow trout was 13.5 g/kg [58], the same trend as the results of this experiment. In conclusion, dietary phenylalanine had an improved effect on the expression of growth-related genes in triploid O. mykiss with low fish meal diets. However, for our study, not explaining our results at the protein level is a shortcoming, and we will do more studies in the future to explain this mechanism and explain it in the discussion. ## Effects of dietary phenylalanine levels on the antioxidant capacity of triploid O. mykiss Phenylalanine is a specific amino acid containing a phenyl ring structure that binds to hydroxyl radicals and eliminates hydroxyl radicals as well as reactive oxygen species (ROS) from the muscle. Oxidative stress occurs when the production of excess ROS overwhelms the antioxidant defense system, leading to cytopathology [59]. The main enzymes that have the role of oxidant scavengers are SOD, catalase, and glutathione peroxidase. Non-enzymatic antioxidants include glutathione and other thiol compounds [60]. In this experiment, dietary phenylalanine levels reduced the MDA content and increased the SOD content in the liver, thereby inhibiting oxidative damage caused by lipids and proteins. A previous study showed that phenylalanine could inhibit lipid peroxidation and protein oxidation by reducing ROS production in fish gills and that the phenylalanine deficiency group had significantly reduced resistance to superoxide anions and hydroxyl radicals. It was suggested that this could be related to the fact that the phenyl ring of phenylalanine can combine with hydroxyl radicals to form three hydroxylation products that can have a positive effect on scavenging free radicals [61]. It has also been reported that the effect of phenylalanine on SOD activity may be related to its ability to promote the release of dopamine, which enhances extracellular SOD protein expression and cell surface SOD activity in rat astrocytes [62]. However, whether dietary phenylalanine can stimulate the release of dopamine in fish has not been studied. Similar results were obtained in the present experiments in Pagrus major [63] but the serum CAT levels did not differ significantly in this experiment in triploid O. mykiss, which may be due to the different sensitivity of different fish to the stimulation of CAT in the intestine. ## Effects of dietary phenylalanine levels on the digestion of triploid O. mykiss The ability of fish to digest and absorb nutrients is closely related to the activity of intestinal digestive enzymes. Phenylalanine improves the digestive capacity of fish by promoting the growth of the pancreas and intestine, which in turn improves the secretion of digestive enzymes and thus the digestive level of fish [64]. In this experiment, the addition of phenylalanine to low fish meal diets significantly increased the intestinal trypsin and amylase activities of triploid O. mykiss, both of which reached a maximum in the $2.03\%$ treatment group but did not have a significant effect on lipase activity. In Nile tilapia, there were significant differences in lipase activity but no significant differences in amylase activity in the intestine, which may be due to differences in the location of the digestive enzyme assay [65]. But how phenylalanine affects the secretion of intestinal digestive enzymes has not been studied. In the gibel carp study, phenylalanine significantly increased hepatopancreas weight, intestinal length, intestinal weight, intestinal fold height, hepatopancreas, and intestinal trypsin, chymotrypsin, amylase, and lipase activities in gibel carp, and had significant effects on digestion-related indices in gibel carp [66]. In contrast to the present experiment, grass carp [22], as an herbivorous fish, has a different ability to digest lipids than that of triploid O. mykiss. ## Effects of phenylalanine levels on immunity-related indices in triploid O. mykiss Fish may convert phenylalanine into tyrosine, which can then be turned into melanin and catecholamines, which are significant immunomodulators with immunomodulatory activities [67]. To date, IL-1β findings have been published in many fish species, including grass carp (C. idella) [68], O. mykiss [69], European sea bass (Dicentrarchus labrax) [70], Atlantic salmon (Salmo salar) [71], Nile tilapia [72] and channel catfish (Ictalurus punctatus) [73]. IL-6 was discovered in Japanese flounder (Paralichthys olivaceus) [74], O. mykiss [75], gilthead seabream (Sparus aurata) [76], bluntnose seabream (Megalobrama amblycephala) [77] and roughy (Larimichthys crocea) [78]. IL-8 has been cloned and identified in many fish species. These include Atlantic cod (Gadus morhua) [79], O. mykiss [80], Japanese flounder [81], and zebrafish (Brachydanio rerio) [82]. This study showed that the dietary phenylalanine to low fish meal diets had a positive effect on regulating the expression of genes related to intestinal immunity in triploid O. mykiss. The pro-inflammatory factors IL-2 and IL-6 reached minimal values in the 2.03 and $2.29\%$ treatment groups and were significantly higher than in the control group. IL-1β reached minimal values in the $2.03\%$ treatment group and was significantly lower than in the other treatment groups. The expression of IL-10, an anti-inflammatory factor, was highest in the 2.03 and $2.29\%$ treatment groups and was significantly higher than in the other treatment groups. There are few reports on the effect of phenylalanine on intestinal inflammatory factors in fish. However, in humans, melanin can inhibit the production of cytokines such as IL-1β and IL-6 by human blood mononuclear cells because phenylalanine is a prerequisite for tyrosine, which can produce melanin, so we hypothesize that the gene expression of cytokines such as IL-1β, IL-6, and IL-2 in triploid O. mykiss is positively influenced by phenylalanine [83]. Phenylalanine has been reported to reduce the number of peripheral blood lymphocytes in mice (Mus musculus). The production of peripheral blood lymphocytes in mice is stimulated by tetrahydrobiopterin, and phenylalanine promotes the production of tetrahydrobiopterin [84]. Therefore, we speculate that phenylalanine also affects the expression of cytokines in triploid O. mykiss by affecting the number of its peripheral blood lymphocytes. However, the relevant studies on fish are few and need further validation. ## Effects of dietary phenylalanine levels on expression of tight junction protein-related genes in triploid O. mykiss Fish intestinal health relies on a physical barrier composed of tightly linked proteins and epithelial cells. This study showed that either deficiency or excess of phenylalanine downregulated the expression of intestinal occludin, claudin-1, and ZO-1 in triploid O. mykiss. It has been shown that the function of the intestinal barrier is related to the inhibitory effect of phenylalanine on inflammatory factors. For instance, in human cells, IL-8 regulates the expression of occludin in vascular cells [85]. Tumor necrosis factor-α is also involved in tight junction protein expression regulation, which follows the same pattern as our experimental results. Lysine [86], arginine [87], methionine [88], and isoleucine [89] have all been studied for their effects on the expression of intestinal tight junction protein-related genes in fish, but less research has been done on phenylalanine. In grass carp, dietary phenylalanine could effectively improve the expression of intestinal tight junction proteins, with the highest expression of claudin-1, ZO-1, and occludin mRNA levels at $1.15\%$ feeding [90]. The expression of claudin-1, ZO-1, and occludin reached the highest values at a $2.03\%$ phenylalanine level, which may be related to the different requirements of phenylalanine in the fish itself. As a result, adding appropriate phenylalanine to feed improves the regulation of tight junction protein expression in the organism and plays an important role in maintaining intestinal health. ## Conclusion Dietary phenylalanine levels (2.03–$2.64\%$) significantly increased the expression of intestinal growth-related genes and had a regulatory effect on the expression of immune-related genes in triploid O. mykiss fed a low fish meal diet ($15\%$). Meanwhile, growth performance and body composition-related indicators have also been significantly improved. Using SGR and WGR as evaluation indices, the optimal requirement of phenylalanine for triploid O. mykiss was $2.13\%$ by quadratic regression analysis. Based on the current research, the optimal phenylalanine addition level can be further explored to replace a fish meal with plant protein to provide a theoretical basis for the optimization of an artificial compound feed for triploid O. mykiss. ## Data availability statement The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors. ## Ethics statement The animal study was reviewed and approved by the Committee for the Welfare and Ethics of the Laboratory Animals of Heilongjiang River Fisheries Research Institute, CAFS. Written informed consent was obtained from the owners for the participation of their animals in this study. ## Author contributions SZ completed the experiments and wrote the manuscript. CW, HL, and YY provided the experimental design and financial support. YW, SLiu, and HJ had key roles in the data processing and mapping processes. SH and SLu contributed to the test shop equipment and water quality control. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Senthil N, Probir D, Mohammad A, Mahmoud T, Shoyeb K, Chandan M. Potential of microalgae as a sustainable feed ingredient for aquaculture.. (2021) 341 1-20. DOI: 10.1016/j.jbiotec.2021.09.003 2. Hardy RW. 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--- title: 'Development of a food preservative from sea buckthorn together with chitosan: Application in and characterization of fresh-cut lettuce storage' authors: - Kexin Feng - Xiaolin Feng - Weijian Tan - Qinhua Zheng - Wenting Zhong - Caiyu Liao - Yuntong Liu - Shangjian Li - Wenzhong Hu journal: Frontiers in Microbiology year: 2023 pmcid: PMC10028195 doi: 10.3389/fmicb.2023.1080365 license: CC BY 4.0 --- # Development of a food preservative from sea buckthorn together with chitosan: Application in and characterization of fresh-cut lettuce storage ## Abstract The purpose was to create a novel composite food preservative for fresh-cut lettuce using flavonoids and chitosan from sea buckthorn leaves (SBL). Sea buckthorn leaves were extracted with ethanol as the extraction solvent and ultrasonic-assisted extraction to obtain flavonoid from sea buckthorn leaf crude (FSL), and then the FSL was secondarily purified with AB-8 resin and polyamide resin to obtain flavonoid from sea buckthorn leaf purified (FSL-1). Different concentrations of FSL-1 and chitosan were made into a composite preservative (FCCP) by magnetic stirring and other methods, containing $1\%$ chitosan preservative (CP) alone, 0.5–2 mg/ml of FSL-1 and $1\%$ chitosan composite preservative (FCCP-1, FCCP-2, FCCP-3, and FCCP-4), and the FSL-1 concentrations were analyzed the effect of FSL-1 concentration on the physicochemical properties of the composite preservatives, including their film-forming ability, antioxidant capacity and ability to prevent bacterial growth, was analyzed. To further investigate the effect of the combined preservatives on fresh-cut lettuce, different FCCPs were applied to the surface was stored at 4°C for 7 days. Then the changes in weight loss, hardness, browning index, total chlorophyll content, SOD and MDA were analyzed. It was used to assess the physicochemical indicators of fresh-cut lettuce throughout storage. According to the results of Fourier transform infrared spectroscopy, FSL-1 and chitosan interacted to form hydrogen bonds, and the contact angle and viscosity of FCCP increased on both horizontal glass and polystyrene plates, indicating the good film-forming properties of the composite preservation solution. With the diameter of the antibacterial zone of Staphylococcus aureus, Escherichia coli, Salmonella typhimurium, and Listeria monocytogenes being (21.39 ± 0.22), (17.43 ± 0.24), (15.30 ± 0.12), and (14.43 ± 0.24) mm, respectively. It was proved that the antibacterial activity of FCCP became stronger with the increase of FSL-1 concentration and had the best antibacterial effect on S. aureus. The complex preservative showed the best scavenging effect on ferric reducing antioxidant capacity, DPPH radicals ($96.64\%$) and 2,2’-Azinobis- (3-ethylbenzthiazoline-6-sulphonate) (ABTS) radicals ($99.42\%$) when FSL-1 was added at 2 mg/ml. When fresh-cut lettuce was coated with FCCP for the same storage time, various indicators of lettuce such as weight loss, hardness, browning index, SOD activity and MDA content were better than the control group showing good potential in fresh-cut vegetables and fruits preservation. FCCP holds great promise for food safety quality and shelf-life extension as a new natural food preservative. The waste utilization of sea buckthorn leaves can greatly improve his utilization and economic benefits. ## Introduction A deciduous shrub or tree belonging to the genus Hippophaerhamnoides in the family Elaeagnaceae is known as Hippophaerhamnoides Linn (sea buckthorn; Ciesarová et al., 2020). Due to its quick growth and strong root structure, which can withstand wind and sand and lessen soil erosion, sea buckthorn offers excellent ecological advantages. The SBL’s significant therapeutic potential is due to its abundance of active components, which include proteins, polysaccharides, organic acids, alkaloids, flavonoids, amino acids, carotenoids, chlorophyll, and trace minerals (Michel et al., 2012; Li et al., 2021b). However, a lot of academics and businesspeople focus more on the study and application of its fruits and seeds while ignoring the study and advancement of sea buckthorn leaves (SBL), leading to a significant loss of sea buckthorn by-product resources. In addition, SBL has the highest concentration of flavonoids of all sea buckthorn parts (Cho et al., 2014), and flavonoids of sea buckthorn leaf (FSL) are primarily known for their pharmacological effects, which include antioxidant, antibacterial, aging-delaying, cardiovascular disease-preventing, enhancing immune function, regulating blood sugar and blood lipids, and inhibiting tumor growth. According to the research, the extract of FSL in the Gansu area mainly contains rutin, kaempferol, quercetin, isorhamnetin, and prunetin, and the contents of prunetin and isorhamnetin were significantly higher than those of seeds and fruits, and prunetin has various pharmacological activities such as anti-inflammatory and analgesic, anti-tumor, hypoglycemic and hepatoprotective, while isorhamnetin has pharmacological effects of hypoglycemic and lipid-lowering activities (Hui et al., 2017). These properties of FSL are important for enhance the antibacterial activity and antioxidant activity of active food packaging films are highly desirable, but their application in the latter has not been explored. As the safety of synthetic antioxidants has been questioned, the search for safe and efficient natural antioxidants has become an important research direction for food additives (Pundir et al., 2021). By including antioxidants, antimicrobial agents, or other active ingredients, compound preservation agents are a novel way to preserve food (Chaudhary et al., 2020). Adding antioxidants, antimicrobial agents, or other active substances to a compound preservation agent can extend the shelf life of food. Deacetylated chitin, often known as chitosan (CS), is a naturally occurring polysaccharide that is obtained from crab and shrimp shells. It is excellent as a substance for a food preservation agent since it forms nice films, is biodegradable, antimicrobial, and safe (Talón et al., 2017). A compound preservative’s effect is preferable to a single natural preservative’s poor performance. Fresh-cut fruits and vegetables are frequently preserved with chitosan compound. Everyone enjoys LactucasativaL (lettuce), which is also known as green leaf lettuce and is nutritious, fresh, and crisp. Fresh-cut lettuce, however, experiences mechanical damage as well as a number of unfavorable physiological and biochemical changes, including surface browning and wilting, microbial infestation, and nutrient loss, which reduce sensory quality, commercial value, and shelf life. Studies have shown that film coating treatment can limit the loss of chlorophyll and ascorbic acid, delay the appearance of browning, and inhibit the increase in weight loss rate and decrease in hardness of fresh-cut fruit and vegetable lettuce. In this paper, we first analyzed the optimal extraction conditions and purification method of FSL, and prepared an antibacterial and antioxidant FSL-1 and chitosan compound preservative (FCCP) combined with chitosan. The physical properties of FCCP, including infrared absorption spectra, viscosity, and contact angle, were then evaluated. The potential of FCCP for fresh-cut lettuce preservation was further evaluated, including measurement of weight loss, browning index, hardness, chlorophyll content, superoxide dismutase activity (SOD) and malondialdehyde content (MDA), to provide a research basis and direction for the development and utilization of FSL-1 in the pharmaceutical, food and nutraceutical industries, so that SBL can be turned into treasure to a greater extent and the added value of sea buckthorn resources can be improved. ## Materials Sea buckthorn leaves (Gansu Huipeng Herb Company, China), Staphylococcus aureus (S. aureus; GDMCC1.12442), Escherichia coli (E.coli; GDMCC 1.173), Salmonella typhimurium (S. typhimurium; GDMCC 1.1442), Listeria monocytogenes (L. monocytogenes; GDMCC1.2408; Guangdong Microbial Strain Conservation Center, China); chitosan ($90\%$ deacetylation degree; Shanghai Yuanye Biotechnology Co., Ltd., China); lettuce (Chengdu Emerald Mountain Treasure Agriculture Co., Ltd., China); SOD kit, MDA kit and protein kit (BCA method; Nanjing Jiancheng Institute of Biological Engineering Co., Ltd., China); Vitamin C (VC; Shanghai Yuanye Biotechnology Co., Ltd., China); LB broth agar medium (Shanghai Yuanye Biotechnology Co., Ltd., China). Sea buckthorn leaves was authenticated by Zhang Runrong, senior engineer of traditional Chinese medicine, College of Pharmacy and Food Science, Zhuhai College of Science and Technology, Zhuhai, Guangdong Province, China. ## Extraction and purification of FSL-1 The optimal extraction conditions of sea buckthorn leaf flavonoids were obtained by single-factor and response surface experiments. The appropriate amount of defatted SBL powder was weighed and sieved to 0.18 mm, and FSL was extracted by Ultrasonic Extractor (XH-300A, XiangHu Technologies, China) with $47\%$ ethanol in the ratio of 1:19. The extraction time was set to 34 min, and the extraction temperature was 65°C. After freeze-drying at −40°C using vacuum freeze–dryer (FD-250101, FTFDS, China), store it in −20°C refrigerator, the FSL was centrifuged at room temperature for 10 min (4,000 rpm/min), transferred the supernatant, concentrated and lyophilized to obtain FSL crude extracts (Culina et al., 2021). In the purification, the crude FSL were separated and purified by using macroporous resin AB-8, and then the FSL was purified for the second time by using polyamide, and the eluate was lyophilized to obtain the purified flavonoids from SBL (FSL-1), which was stored in the refrigerator at −20°C (Wan et al., 2014). The extraction, isolation, and purification procedure of FSL-1 is shown in Figure 1. Figure 1: The extraction and purification procedure of FSL-1. ## Content identification of FSL The standard control was selected as rutin, and the sample solution was prepared into concentration gradients of 0.02, 0.04, 0.06, 0.08, 0.10, 0.12 mg/ml, and the absorbance values were determined according to the Al(NO3)3–NaNO2 colorimetric method (Yuan-yuan et al., 2011), which was selected at 500 nm. The extracted sample solution was diluted into each suitable concentration gradient, and the determination was carried out with reference to the method of standard curve, and then the content of flavonoids (mg/mL) in SBL flavonoids extract was determined quickly according to the conversion of standard curve. The content of total flavonoids in SBL was calculated according to the following formula: C: flavonoid concentration of SBL (mg/mL); V: sample volume; D: dilution multiple; m: mass of SBL (g). ## Preparation of FCCP Different concentrations of FSL-1 were dissolved in $1.0\%$ chitosan solution and agitated with constant magnetic force (1800 r/min) for 1 h at room temperature 25°C with ultrasonic shaking for 15 min to obtain flavonoid of sea buckthorn leaf and chitosan complex preservative (FCCP) and set aside. The FCCP was divided into five groups for experiments include $1.0\%$ chitosan preservative (CP), 0.5 mg/mLFSL-1 + $1.0\%$ chitosan preservative (FCCP-1), 1.0 mg/mLFSL-1 + $1.0\%$ chitosan preservative (FCCP-2), 1.5 mg/mLFSL-1 + $1.0\%$ chitosan preservative (FCCP-3), and 2.0 mg/mLFSL-1 + $1.0\%$ chitosan preservative (FCCP-4) was mixed well in proportion to obtain FCCP. ## Determination of Fourier transform infrared spectroscopy The fully dried KBr and flavonoid samples FSL-1 and FCCP were ground in a dry environment at a mass ratio of 100:1 ~ 200:1, mixed thoroughly with an onyx mortar, pressed and then scanned with a FTIR spectrometer (Spectrum 3, Perkin Elmer, United States) in the range of 400 cm−1 ~ 4,000 cm−1 (Hu et al., 2016). ## Determination of coating properties of FCCP solution Chitosan film solution (40 μl) was dropped onto a clean and flat glass plate and a polystyrene plastic plate (2.6 × 7.6 cm2), and the contact angle size of the film solution on the surface of two different contact substrates at room temperature 25°C was measured by Contact angle measuring instrument (SL200L2, Shanghai Soren Information Technology Co., Ltd., China). Chitosan film solution (10 ml) was added into the Digital Viscometer (SNB-2, Shanghai Jingtian Electronic Instruments Co., Ltd., China) and the viscosity of FCCP was measured at room temperature of 25°C. ## Determination of antibacterial performance test The diameter of the antibacterial zone using the punching method was used to assess the antibacterial activity of FCCP against S. aureus, E. coli, S. typhimurium, and L. monocytogenes, four food-borne pathogens. The bacterial strains were first incubated in nutrient broth at 37°C for 24 h. Then, 0.2 ml of 106 CFU/ml bacterial culture was added and dispersed uniformly on LB agar plates. A circular membrane (1 cm in diameter) was placed on the plate and the bacteria were incubated at 37°C for 12 h. The diameter of the antibacterial zone was measured by vernier calipers. ## Determination of DPPH free radical scavenging activity The DPPH free radical scavenging activities of the samples were tested according to a reported (Genskowsky et al., 2015) procedure with some modifications. Briefly, the DPPH test solution was first diluted with methanol to 0.6 mM to obtain an absorbance of 0.7–0.9 at 517 nm. The stock solutions of FSL-1 (100 μl) and FCCP (100 μl) were continuously diluted in half with distilled water on a 96-well plate separately, and reacted with 100 μl of DPPH radical solution for 30 min in dark. The absorbance of the reaction solution and the initial DPPH radical solution diluted with distilled water were measured with a UV–vis reader at 517 nm. VC was used as the positive control. When FCCP mass concentration is 0, it is chitosan alone. The antioxidant capacity of the FSL-1 and the FCCP coating solutions was calculated by the following equation: where A0 is the absorbance of the DPPH radical solution (100 μl) at 517 nm, A1 is the absorbance of the reaction solution at 517 nm. ## Determination of ABTS free radical scavenging activity The ABTS scavenging activities of the samples were tested according to a reported procedure with some modifications (Re et al., 1999). The ABTS radical cation was generated by reacting 10 ml of 7 mM ABTS stock solution with 10 ml of 2.45 mM potassium persulfate solution in the dark at room temperature overnight. The resulting mixture was diluted to obtain a stock solution with an absorbance of 0.7 ± 0.005 units at 734 nm. VC was used as the positive control. The same procedure for the ABTS was used to determine the antioxidant performance of FSL-1 and FCCP to ABTS free radicals using the same as the formula in 2.8. ## Determination of ferric-reducing antioxidant power The method of ferric-reducing antioxidant power was determined by referring to the method with some modifications (Tang et al., 2014). 1 ml of flavonoids samples were added into 2.5 ml PBS (pH = 6.6) and 2.5 ml $1\%$ potassium ferricyanide solution. Put it in a water bath at 50°C for 20 min, add 2.5 ml of $10\%$ trichloroacetic acid and shake well. Centrifugation at 3,000 r/min for 10 min. Take 2.5 ml supernatant, add 0.5 ml $0.1\%$ ferric chloride solution and 2.5 ml distilled water, and shake well. The absorbance of the reaction solution measured at 700 nm with Microplate Reader (Epoch, Bio Tek Instruments Inc, United States) was denoted as Ai and that of the control group was denoted as A0 with distilled water instead of the sample. VC was used as the positive control. The higher the absorbance value indicates the stronger the total reducing ability. ## Application for storing fresh-cut lettuce Lettuce was washed with 100 mg/l sodium hypochlorite solution, dried in a sterile operation table at room temperature of 25°C, then cut into 1 cm3 and immersed in different preservation solutions (CP, FCCP-1, FCCP-2, FCCP-3, and FCCP-4) for 10 min, and stored in a 4°C refrigerator for 7 days after film formation at natural temperature, and data such as weight and BI were measured every 24 h. Distilled water was used instead of the preservation solution as a control. ## Determination of weight loss rate The following formula was used to calculate the weight loss of fresh-cut lettuce: where W1 and Wi are the weight of fresh-cut lettuce at 0 day and nth days, respectively. ## Determination of hardness The hardness of fresh-cut lettuce was measured using a mass spectrometer (TA.TOUCH, Shanghai Baosheng Company, China) with a probe diameter of 5 mm and the following parameters: trigger force 0.4 N; detection rate 1.5 mm/s; post-measurement rate 3 mm/s; compression distance 2.5 mm (Rinaldi et al., 2019). ## Determination of browning index BI was used to quantify the browning degree of fresh-cut lettuce during storage. The color parameters (L, a, and b) of the samples were measured using a colorimeter (CR-10 PLUS, Konica Minolta, Tokyo, Japan). The BI of fresh-cut lettuce was calculated as follows: Where ΔL∗, Δa∗, and Δb∗are the change of fresh-cut lettuce at 0 day to 7 days. L∗ represents the black and white value, a∗ represents the red and green value, b∗ represents the yellow and blue value. ## Determination of total chlorophyll content After 7 days of storage at 4°C, 1 g of each lettuce was weighed and placed in a pre-cooled mortar using $80\%$ acetone grinding method, 5 ml of extract ($80\%$ acetone) and a small amount of quartz sand were added and ground to a homogeneous form, filtered through 7 cm filter paper, washed with extract and fixed to 25 ml, and the absorbance values were measured immediately at 645 and 663 nm (Costache et al., 2012). The chlorophyll content of lettuce was calculated as shown below: A663, absorbance at 663 nm; A645, absorbance at 645 nm; V, total volume of chlorophyll extracts (mL); m, weight of the sample (g). ## Determination of SOD activity and MDA concentration First-day fresh-cut lettuce as a control and fresh-cut lettuce stored at 4°C for 7 days were made into homogenates and measured and calculated according to the appropriate kit instructions (Li et al., 2021a). ## Statistical analysis Data are shown as mean ± SEM, and statistical analysis was performed using Origin 2021, GraphPad Prism 8, and EXCEL 2019. To compare the differences between multiple groups, a one-way analysis of variance (ANOVA) was performed in this software. $p \leq 0.05$ indicates statistical significance. ## Analysis of extraction and purification contents The FSL content was 66.89 mg/ml by ultrasonic extraction, and then the FSL purity was increased from 43.00 to $62.25\%$ by the separation and purification of FSL with the choice of macroporous resin AB-8, and then the second purification of FSL with the choice of polyamide resin, under which the sample was a purple-black powder and the purity of FSL was increased to $80.75\%$. After the above steps, FSL-1 was eventually available. ## Analysis of FT-IR spectroscopy The parent nuclei of flavonoids contain hydroxyl, phenolic hydroxyl, methoxy, alkoxy, benzene and other groups with infrared characteristic absorption peaks at 3,100–3,460 cm−1, 1,600–1,640 cm−1, and 1,380 cm−1, 1,260 cm−1, 1,090 cm−1. As can be seen from Figure 2 (FSL-1), the broad and strong absorption peaks at 3,385 cm−1 wave number indicate the presence of a large number of phenolic hydroxyl groups; the characteristic absorption peaks at 2,917 cm−1, 2,853 cm−1, 1,380 cm−1 are –CH3, –CH2, indicating the presence of hydrogen on saturated carbon; the characteristic peaks at 1,630, 1,610, 1,540, and 1,450 cm−1 are benzene ring peaks. The absorption peaks at 1670 cm−1 are $C = 0$ stretching vibration peaks, in which the group may be conjugated with the hydroxyl group causing its absorption peak to move to lower wave number, and at 1,260 and 1,090 cm−1 can be attributed to the antisymmetric and symmetric stretching vibration peaks of the ether bond, Analysis results indicate that the extract is a flavonoid (Agatonovic-Kustrin et al., 2021). Figure 2:* The results of Fourier transform infrared (FT-IR) spectroscopy of FSL-1, CP, FCCP-1, FCCP-2, FCCP-3, and FCCP-4. The IR spectrograms of different membranes were measured to confirm the intermolecular interactions between membrane components. As shown in Figure 2, the peak of CP at 3,254 cm−1 corresponds to the O–H and N–H stretching vibrations of CS and the O–H stretching vibration of the FSL-1 fraction in FCCP, the peak at 2,869 cm−1 corresponds to the C–H stretching vibration of CS and FSL-1, the peak at 1,630 cm−1 corresponds to the $C = 0$ stretching vibration of amide I in CS The peak at 1,549 cm−1 corresponds to the N–H stretching vibration of CS, the peak at 1,406 cm−1 corresponds to the C–H stretching vibration of CS and FSL-1, and the peak at 1,024–1,150 cm−1 corresponds to the pyranose ring stretching vibration of CS (Bi et al., 2020). The IR spectrograms of CP, FCCP-1, FCCP-2, FCCP-3, and FCCP-4 changed slightly after the addition of flavonoids. FCCP-1, FCCP-2, FCCP-3, and FCCP-4 showed wider and stronger O–H and N–H stretching vibrational bands at 3,223–3,270 cm−1 than CP, which was attributed to the large amount of hydroxyl groups in flavonoids. Compared with CP, the films containing flavonoids were red-shifted or blue-shifted at 3,223–3,270 cm−1 (O–H and N–H stretching bands) and 1,535–1,549 cm−1 (N–H stretching bands; Kaya et al., 2018). The band shifts were mainly caused by intermolecular hydrogen bonds formed between the hydroxyl groups of flavonoids and other membrane components (CS). Similar band shifts were also observed in other flavonoid-rich membranes due to the formation of intermolecular interactions. However, in the inclusion compound, the characteristic peaks with wave numbers of 1692.28 cm−1 (C=C stretching vibration) and 1509.23 cm−1 (C=O stretching vibration) disappeared, which led to the speculation that the C ring of sea buckthorn flavonoids might be encapsulated within the CS (Li et al., 2016; Artiga-Artigas et al., 2018). ## Analysis of coating properties of FCCP solution The film-forming effect of chitosan is closely related to the flow and deformation of the film solution, so it is important to study the rheological behavior of the film solution for the quality control of the compound preservative. The viscosities of different FCCP as shown in Figure 3A, as the concentration of FSL-1 increases, the viscosity of FCCP decreases, then increases and then decreases, and the viscosity of FCCP-2 reaches the maximum and FCCP-4 reaches the minimum, with a difference of $28\%$. Fruit preservatives should have a certain water retention capacity. This is because fruits will lose water and shrink during storage. Therefore, we measure the hydrophilicity of the coating by contact angle. For more effective preservative purposes, the chitosan solution should have excellent wettability on both hydrophilic and hydrophobic surfaces. As a basic indicator of wettability, the contact angles of FCCP on horizontal glass and polystyrene plates are shown in Figures 3B,C, respectively. The contact angles on both glass plates are less than 40°, indicating that FCCP easily diffuses on hydrophilic surfaces, while the contact angles on polystyrene substrates range from 70° to 80°. The contact angles on both substrates increase with increasing FSL-1, which means that FCCP wets worse on the glass plate versus the polystyrene plate. The contact angle reflects the water resistance of the film surface, the larger the contact angle, the stronger the hydrophobicity, when the contact angle > 65°, it means that the film has a hydrophilic surface with certain water resistance, on the contrary, it means that the film surface is extremely hydrophilic. The contact angle of CP was 68.4°, but after adding FSL-1, the contact angle increased up to 11°, probably due to the interaction between FSL-1 and CS, which made the hydrophobicity stronger. As the concentration of FSL-1 increased, the surface roughness of the composite membrane increased due to the increase of surface-active ingredients, which caused the contact angle of the composite membrane to increase gradually and the hydrophilicity to decrease gradually. Overall, the contact angle of all groups of films showed hydrophilic (θ < 90°), which is suitable for low moisture food packaging (Figure 3). Figure 3: (A) Viscosity of FSL-1, CP,FCCP-1,FCCP-2,FCCP-3 and FCCP-4. (B) Contact angles of FSL-1,CP,FCCP-1,FCCP-2,FCCP-3 and FCCP-4 in glass plates. (C) Contact angles of FSL-1, CP, FCCP-1,FCCP-2,FCCP-3 and FCCP-4 on polystyrene plates. ## Analysis of antibacterial zone According to Table 1, the size of the diameter of the antibacterial zone against foodborne pathogenic bacteria. It can be seen that FCCP has inhibitory effect on all four foodborne pathogenic bacteria, but there are obvious individual differences in the tolerance of the four pathogenic bacteria to FCCP. Under the same conditions, CP also had a certain antibacterial effect, but the antibacterial zone boundary was not obvious enough compared with other FCCP added with different concentrations of FSL-1. The antibacterial effect of FCCP on the four bacteria was better than that of CP, and the antibacterial effect of FCCP-2 was stronger as the concentration of FSL-1 increased. A strong antibacterial effect was obtained when the antibacterial cycle diameter was 21.39 ± 0.22 mm for S. aureus, 17.43 ± 0.24 mm for E. coli, 15.30 ± 0.12 mm for S. typhimurium and 14.43 ± 0.24 mm for the L. monocytogenes. While the strongest FCCP-2, which showed the strongest activity for Sauers and showed the weakest activity for Limnocyonines. It demonstrates that the compound’s antibacterial properties are optimal and that it can better preserve freshly cut fruits and vegetables. Table 1 \| Grouping \| Antibacterial zone (mm) \| Antibacterial zone (mm).1 \| Antibacterial zone (mm).2 \| Antibacterial zone (mm).3 \| \| --- \| --- \| --- \| --- \| --- \| \| Grouping \| Staphylococcus aureus \| Escherichia coli \| Salmonella typhimurium \| Listeria monocytogenes \| \| Control \| NE \| NE \| NE \| NE \| \| CP \| 20.26 ± 0.23c \| 15.25 ± 0.45c \| 13.83 ± 0.21c \| 12.04 ± 0.50b \| \| FCCP-1 \| 20.53 ± 0.21bc \| 16.38 ± 0.31b \| 14.18 ± 0.26b \| 14.09 ± 0.16a \| \| FCCP-2 \| 20.74 ± 0.11bc \| 16.47 ± 0.32b \| 14.30 ± 0.28b \| 14.18 ± 0.27a \| \| FCCP-3 \| 20.78 ± 0.45b \| 16.62 ± 0.38ab \| 14.30 ± 0.44b \| 14.30 ± 0.28a \| \| FCCP-4 \| 21.39 ± 0.22a \| 17.43 ± 0.24a \| 15.30 ± 0.12a \| 14.43 ± 0.24a \| ## Analysis of DPPH free radical scavenging activity Domestic and international studies have shown that FSL-1 has optimum antioxidant effects. As demonstrated in Table 2 and Figure 4. 0.5 mg/ml scavenging rate tended to be stable; while FSL-1 scavenging rate was stable at about $87\%$ at mass concentrations greater than the DPPH radical scavenging ability of FCCP at a concentration of 2.0 mg/ml was higher than that of the positive control VC at the same concentration, with a maximum scavenging rate of about $97\%$. The scavenging rate of chitosan preservative alone was only $18.74\%$, indicating that FSL-1 can greatly improve the antioxidant performance of the composite preservative with the increase of content. ## Analysis of ABTS free radical scavenging activity The total antioxidant capacity was determined by the ABTS method, Table 3 and Figure 5 shows that the mass concentrations of FSL-1, FCCP and positive control VC were positively correlated with the total antioxidant capacity at mass concentrations of 0–2.0 mg/ml, and the higher the mass concentration, the stronger the total antioxidant capacity. The ABTS radical scavenging rate of chitosan preservative alone was only $25\%$, though the total antioxidant capacity of both FSL-1 and FCCP was lower than that of the positive control VC under the same mass concentration conditions. The scavenging rate of ABTS radicals was stabilized at $99.42\%$ after FCCP reached a mass concentration of 2.0 mg/ml. The decrease in the total antioxidant capacity of FCCP might be due to the high concentration of FSL-1, which led to a decrease in the total antioxidant capacity of the compound film solution due to the longer resting time during the process of flow forming. Longer, part of FSL-1 precipitated out in the form of flocculent, and the content of TP active substances in the compound film decreased, thus affecting the capturing ability of the polyphenolic compounds in the compound film for DPPH radicals. ## Analysis of ferric-reducing antioxidant power The results are shown in Table 4 and Figure 6, the ferric-reducing antioxidant power showed a positive correlation with the content of FSL-1, the higher the content of FSL-1, the higher the total reducing capacity and the difference was significant ($p \leq 0.05$). The total reducing capacity of FCCP was basically equal to that of the positive control VC, and the total reducing capacity of FSL-1 was lower than that of FCCP when and only when the mass concentration was 2.0 mg/ml, furthermore the absorbance of chitosan preservative alone was only 0.124, indicating its poor total reducing capacity, and the addition of FSL-1 improved the total antioxidant capacity of the composite preservative. And the enhancement of the total reducing capacity slowed down with the increase of mass concentration. In summary, FCCP has optimum antioxidant capacity and can be used as a natural food preservative. ## Analysis of weight loss rate Moisture loss and nutrient depletion can lead to weight loss of fresh-cut lettuce, resulting in weight loss and loss of freshness. As shown in Figure 7A, the weight loss rate of fresh-cut lettuce during storage was on the rise, rising rapidly in the first 2 days, and the weight loss rate of the control group was nearly $25\%$ after 7 days of storage. The weight loss rate rose slowly after FCCP treatment, which was significantly lower than that of the control group, indicating that FCCP coating treatment could effectively inhibit the weight loss of fresh-cut lettuce, of which the weight loss rate was the lowest after FCCP-2 treatment, which was $42.35\%$ less than that of the control group. Figure 7: The effect of FCCP on weight loss (A), hardness (B) and changes in the appearance (C) of fresh-cut lettuce stored for 7 days. ## Analysis of hardness Moisture loss and destruction of cell structure can lead to a decrease in hardness and cause soft tissue distraction, so hardness is an important indicator of the sensory quality of fresh-cut lettuce. The effect of FCCP on hardness is shown in Figure 7B, the hardness of fresh-cut lettuce showed a decreasing trend during storage, and compared with the initial value, the hardness of the control group decreased by $37.38\%$ after 7 days of storage. The hardness of the FCCP coated group was always higher than that of the control group, which indicated that FCCP could delay the decrease of the hardness of fresh-cut lettuce, in which the hardness of the FCCP-2 treated group was higher than that of the other treated groups, and the hardness changed the least before and after storage, which had the optimum effect on the maintenance of the hardness of fresh-cut lettuce. The hardness of fresh-cut lettuce was maintained optimum. Figure 7C shows the effect of different FCCP on the quality of appearance of fresh-cut lettuce during storage. Before storage, all fresh-cut lettuce was green, and after coating, fresh-cut lettuce was yellowish in color, but all were rich in water and full of flesh. Fresh-cut lettuce without film became yellow and wrinkled after 7 days of storage, and shrunk severely due to water loss. It is worth noting that the color of fresh-cut romaine lettuce packaged with FCCP coating was only slightly darkened and there was almost no noticeable change in shape. The color change of fresh-cut lettuce during storage is consistent with the results of BI below. Compared with other FCCPs, FCCP-2 has a moderate contact angle and viscosity, so it is less hydrophilic and has better film-forming properties, which can reduce respiration and metabolism, thus reducing shrinkage and browning caused by rapid cell dehydration and cell wall hydrolysis in appearance caused by respiration and metabolism, which is beneficial for preserving cut vegetables and fruits. In addition, CG-WE films have strong antioxidant activity, which is also an important reason for inhibiting browning of fresh-cut lettuce. Thus, the above results suggest that FCCP can improve the storage quality of fresh-cut apples, while FCCP-2 showed the best performance. ## Analysis of BI Lettuce is subject to tissue browning during fresh-cut processing and storage, which reduces its commercial value (Kim et al., 2014). The lettuce was green after the cutting treatment, but as the storage period was extended, the surface browned, the color darkened and the sensory quality decreased. As shown in Figure 8, the L* and b* values after FCCP treatment were higher than the control group, and the a* value and browning index were lower than the control group, indicating that FCCP treatment could inhibit the decrease of L* and b* values and the increase of a* value and browning index. FCCP helped maintain the appearance and color of fresh-cut lettuce and inhibit the aggravation of browning, among which, the browning index of FCCP-2 treatment group remained low throughout the storage period, and the browning after 7 days. The browning index of the FCCP-2 treatment group remained low throughout the storage period, and the browning index was $53.15\%$ lower than that of the control group after 7 days, which was better than the other groups in inhibiting browning of fresh-cut lettuce. Figure 8: The effect of FCCP on browning index. (A) represents the black and white value, (B) represents the red and green value, (C) represents the yellow and blue value, (D) The effect of FCCP on browning index. ## Analysis of total chlorophyll content Chlorophyll degradation occurs in green vegetables during storage, so changes in chlorophyll content are often used as an important indicator of changes in the quality of green vegetables quality (Peng et al., 2015). As shown in Figure 9A, the chlorophyll content of each group was similar on day 0, but the chlorophyll content of fresh-cut lettuce treated with FCCP was consistently higher than that of the control group after 7 days of storage, indicating that FCCP can inhibit chlorophyll loss from fresh-cut lettuce, with the minimal change chlorophyll content of 6.78 mg/100 g after FCCP-2 treatment, and after 7 days of storage at 4°C, the fresh-cut lettuce of the control and CP groups The chlorophyll content decreased to 4.67 and 5.52 mg/100 g. The differences in chlorophyll content between treatment and control groups and between treatment groups were significant ($p \leq 0.05$) and consistent with the results of the above chromaticity study. Figure 9: The effect of FCCP on total chlorophyll content (A) SOD activity (B) and MDA concentration (C). ## Analysis of SOD activity and MDA concentration As shown in Figure 9B, the content of superoxide dismutase (SOD) determines the rate of oxygen radical production by fruit and vegetable cells during storage. Higher SOD activity indicates that fresh-cut lettuces can maintain its reactive oxygen metabolism balance. At the beginning of storage, mechanical damage resulted in higher SOD enzyme activity, but as fresh-cut lettuce was stored at 4°C for 7 days, SOD activity was significantly higher in the FCCP-2 treated group than in the control group ($p \leq 0.05$). The SOD activities of the control, CP and FCCP-2 groups were 127.8 U/mgprot, respectively. 137.4 U/mgprot and 269.9 U/mgprot, which indicated that FCCP has an important role in maintaining the activity of reactive oxygen scavenging enzymes in fresh-cut lettuce, preventing the accumulation of reactive oxygen species and the occurrence of membrane lipid peroxidation, delaying senescence and inhibiting fruit softening. Malondialdehyde content is a key index to evaluate the degree of aging and membrane peroxidation of fruits and vegetables. Figure 9C shows that the MDA content was low at day 0. The control group reached 1.06 mgprot/mL after 7 days of storage at 4°C, and its MDA content was 51. $48\%$ higher than that of the FCCP-2 treatment group, indicating that the malondialdehyde content of the FCCP treatment group and the control group after 7 days of storage of fresh-cut lettuce. The significant difference ($p \leq 0.05$) in the content of malondialdehyde between the FCCP-treated and control groups after 7 days of storage indicates that the FCCP-coated treatment was better able to limit the membrane peroxidation process, thus maintaining the integrity of lettuce cells. Overall, it indicates that FCCP inhibits browning of fresh-cut lettuce by inducing SOD activity, reducing MDA accumulation, and mitigating lipid peroxidation. ## Discussion Organic solvent extraction, enzyme-assisted extraction, ultrasonic extraction, microwave extraction, and recently developed extraction and separation techniques including supercritical fluid extraction, subcritical water extraction, and ionic liquid extraction can all be used to extract FSL (Liu et al., 2022). Since it was found in this investigation to have a higher extraction rate and be more suitable for large-scale production, the ultrasonic-assisted extraction method was chosen for extraction. The purification of FSL-1 was found to work optimum with polyamide resin (Zhang et al., 2010) and AB-8 macroporous resin (Cui et al., 2018). In order to increase the race of FSL-1 extraction and purification, this research combined the practical use of these two purification techniques for the first time, and the purification rate was increased by $18.53\%$. Chitosan preservation products can frequently be used to store fresh-cut fruits and vegetables. Infrared spectroscopy has shown that the presence of many hydroxyl groups in phenolics can interact with the hydroxyl groups in chitosan to create hydrogen groups, improving FCCP capacity to form films. One of the most important elements in the success of the surface coating of fresh-cut fruits and vegetables is the coating solution’s viscosity. The addition of FSL-1 affected the chitosan matrix, which in turn increased the preservation solution’s viscosity, resulting in a more uniform formation of the surface coating by FCCP (Zhang et al., 2010). By measuring the contact angle of FCCP on glass and polystyrene plastic plates, it can be seen that the nature of chitosan film is significantly influenced by FSL-1 and concentration (Wang et al., 2011; Jagodzinska et al., 2021). Fresh-cut fruits and vegetables have both a high viscosity adhesion and a certain wettability to diffuse on their surface. With increasing FSL-1 and concentration, the film-forming solution’s wettability on the glass plate dramatically decreased while just marginally declining on the polystyrene substrate. As a result, FCCP has higher film-forming and water resistance qualities. The application of FSL-1 in naturally occurring compound food preservatives has not been studied yet, despite flavonoid extracts being often used in natural food preservatives (Jiang et al., 2022). Fresh-cut fruits and vegetables are very susceptible to microbial infestation such as foodborne pathogens during preservation leading to rapid decay and shortened shelf life, so natural food preservatives with excellent antibacterial ability make very necessary, and according to available reports, FSL-1 has the strongest antibacterial activity against S. aureus (Criste et al., 2020), consistent with the results of this experimental study. The antimicrobial activity of FCCP was mainly derived from CS. The antimicrobial mechanism of CS is based on electrostatic interactions between protonated amino groups and negatively charged residues on the surface of microorganisms, leading to leakage of intracellular components that exert antimicrobial activity (Hosseinnejad and Jafari, 2016). The addition of flavonoids significantly enhanced the antimicrobial activity of the preservative. Researchers have proposed several mechanisms to explain the antimicrobial activity of flavonoids: [1] flavonoids can form compound with cell wall components and inhibit microbial adhesion; [2] flavonoids can disrupt the integrity of microbial cell membranes (Farhadi et al., 2019); [3] flavonoids can interfere with the synthesis of nucleic acids in microorganisms (Gorniak et al., 2019). Although FCCP showed strong antibacterial activity against S. aureus and E. coli, while it was weak against S. typhimurium and L. monocytogenes. It is worth noting that the addition of flavonoids enhanced the antimicrobial activity of the preservatives to different degrees. Fruits are easily oxidized when exposed to air, and lead to the reduce of skin resistance, which causes the susceptibility to disease by microorganisms, and results in loss of nutrients and a decline in quality. In addition, the infection of external pathogens can cause the oxidative stress reaction inside the fruit to establish the defense system, which also accelerates the senescence of the fruit (Wang et al., 2019). The packaging materials with optimal antioxidant properties can block the invasion of free radicals and act as a barrier on the fruit surface. Therefore, it is necessary to test the antioxidant capacity of the coating materials, and the addition of natural substances such as flavonoids can improve the antioxidant capacity, and the results of this study showed that the results are generally consistent with previous studies (Shaheen et al., 2016), and FSL-1 and FCCP materials have outstanding scavenging effects on DPPH, ABTS, and total antioxidant capacity, but the chitosan preservative alone antioxidant capacity was weak, and the results showed that FSL-1 and CS made into a composite preservative could greatly improve the antioxidant capacity of CP. In other words, the antibacterial ability of FSL-1 and the antioxidant ability of CP can be perfectly combined to obtain a coating material with both antibacterial and antioxidant abilities. Fresh-cut lettuce’s appearance was shown to be substantially affected by browning and dehydration after receiving treatment in the blank group. However, those treated with FCCP coating kept their appearance intact, and then the quality evaluation of fresh-cut lettuce was derived from some indicators. Firstly, the weight loss of the control and experimental groups stored at 4°C for 7 days was compared, and it was found that the weight loss rate of the experimental group was significantly lower than that of the control group, which indicated that the FCCP coating treatment could effectively reduce the weight loss of fresh-cut lettuce, probably due to the fact that FCCP affected the permeability of lettuce to CO2, O2 and water vapor, reducing the evaporation of water through the pores of the lettuce epidermis, thus allowing the internal pressure to saturate and thus reducing the weight loss rate (Nor and Ding, 2020). Hardness is also an important index to judge fruit maturity and quality evaluation, for the increased activities of the enzyme related to the cell degradation during storage, which is susceptible to bring the loss of firmness, even the microbial invasion. The L* and b* values of fresh-cut lettuce gradually decreased during storage, while the a* values gradually increased, but chlorophyll degradation occurs in green vegetables during storage, so the chlorophyll content was consistent with the results of the study about BI. It was found that chlorophyll content, SOD activity and MDA content were higher after FCCP treatment than in the control group, suggesting that FCCP can protect chlorophyll loss, maintain SOD enzyme activity and protect cells from damage. The structure of the study is consistent with other reports (Li et al., 2021a) It is basically consistent that FCCP helps to maintain the quality of fresh-cut lettuce and enhance the nutritional value of fresh-cut lettuce. Importantly, in the experimental group of FCCPs, FCCP-2 showed the best freshness preservation performance in fresh-cut lettuce with the lowest weight loss of only $15.23\%$, the smallest variation in hardness and BI, the highest SOD enzyme activity and the lowest MDA content, indicating that the optimal use of FSL-1 in fresh-cut lettuce applications should be 1 mg/ml, which is in accordance with the permissible addition of natural preservatives to food. In conclusion, FCCP can be used as a natural food preservative to extend the shelf life of fresh-cut fruits and vegetables. It provides research basis and direction for the development and utilization of SBL in the pharmaceutical, food and health product industries, so that SBL can be turned into treasure to a greater extent and the added value of sea buckthorn resources can be improved. However, there are still shortcomings in this study, the inhibition of fungi (Aspergillus niger and Penicillium) by FSL was not evident in the preliminary experiments and the ability of this preservative against fungi was not analyzed. In addition, monthly variations may affect the concentration of antioxidant active compounds in sea buckthorn, and the side effects on humans regarding the dose used or the high dose have not been studied. Subsequent experiments will investigate more specifically the composition and content of the experimental sea buckthorn leaf flavonoids, using HPLC or LC–MS instruments for the quantification and characterization of the purified extracts. In recent years, with the in-depth research and application development of sea buckthorn leaves, related products have been released. Therefore, there is a wide prospect of developing new food, health food and pharmaceuticals from sea buckthorn leaves. ## Conclusion The combination of chitosan and FSL-1 creates a new type of complex preservative. The antibacterial and antioxidant properties of FCCP were greatly enhanced by the addition of FSL-1. Fourier transform infrared spectroscopy data showed that FSL interacted with chitosan to produce intermolecular hydrogen bonds, which allowed FSL-1 to bind to the CS matrix. The results showed that the viscosity and contact angle of the composite preservative increased, with the highest viscosity of 6.88 mPas for FCCP-2 and the maximum contact angle of 31.93° in the glass plate with high hydrophobicity, indicating its superior film-forming properties. The antibacterial properties of the composite film were improved by the addition of FSL-1, among which FCCP-2 had the best antibacterial effect on S. aureus (20.74 ± 0.11 mm), but the antibacterial effect on L. monocytogenes (14.18 ± 0.27 mm) was poor. The film was coated with FCCP as an active packaging material to preserve fresh-cut lettuce when stored at 4°C for 7 days. Compared with fresh-cut lettuce without film coating, FCCP-2 coated lettuce was superior to the other test groups in terms of weight loss, hardness, browning index, SOD activity and MDA content. Since FSL-1 is made from agricultural waste and has the qualities of resource conservation and sustainability, FCCP has great potential as a food preservation material. Our results showed that FCCPs, especially FCCP-2, added with 1.0 mg/ml FSL-1, showed the best freshness preservation effect on fresh-cut lettuce, indicating that FCCP is a natural food preservative that can extend the shelf life of food by preventing oxidation and deterioration. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author. ## Author contributions KF, CL, and WH: formal analysis. KF, WT, WZ, SL, and WH: funding acquisition. KF, XF, CL, SL, and WH: investigation. QZ, WT, XF, KF, YL, and WH: methodology. KF, WT, XF, WZ, and YL: project administration. QZ and WT: resources. KF: software and writing—original draft. KF and WH: supervision. 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--- title: Prevalence of diabetes in the USA from the perspective of demographic characteristics, physical indicators and living habits based on NHANES 2009-2018 authors: - Ling Fang - Huafang Sheng - Yingying Tan - Qi Zhang journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10028205 doi: 10.3389/fendo.2023.1088882 license: CC BY 4.0 --- # Prevalence of diabetes in the USA from the perspective of demographic characteristics, physical indicators and living habits based on NHANES 2009-2018 ## Abstract ### Objective To determine differences in DM in the U.S. population according to demographic characteristics, physical indicators and living habits. ### Methods 23 546 participants in the 2009 to 2018 National Health and Nutrition Examination Survey (NHANES) who were 20 year of age or older and not pregnant. All analyses used weighted samples and considered the stratification and clustering of the design. Specific indicators include length of leg (cm), BMI (kg/cm2), TCHOL (mg/dL), fasting plasma glucose (mg/dL) and comparison of means and the proportion of participants with DM. ### Results The prevalence of DM in the USA has been rising modestly in the past decade, and were consistent and robust for the observed differences in age, sex, and ethnicity. Compared with white participants, black participants and Mexican-American were both more likely ($P \leq 0.001$) to have diabetes: $14.6\%$ (CI, $13.6\%$ to $15.6\%$) among black participants, $10.6\%$ (CI, $9.9\%$ to $11.3\%$) among white participants, and $13.5\%$ (CI, $11.9\%$ to $15.2\%$) among Mexican-American participants. The prevalence of diabetes is increasing with age, males peaked around the 60s, and women around the 70s. The overall mean leg length and TCHOL was lower in diabetics than in non-diabetics (1.07 cm, 18.67 mg/dL, respectively), while mean BMI were higher in diabetics than in non-diabetics (4.27 kg/cm2). DM had the greatest effect on decline of TCHOL in white participants (23.6 mg/dL), less of an effect in black participants (9.67 mg/dL), and the least effect in Mexican-American participants (8.25 mg/dL). Notably, smoking had great effect on percent increment of DM in whites ($0.2\%$), and have little effect on black and Mexican-Americans. ### Conclusions DM is more common in the general population than might be clinically recognized, and the prevalence of DM was associated to varying degrees with many indicators of demographic characteristics, physical indicators, and living habits. These indicators should be linked with medical resource allocation and scientific treatment methods to comprehensively implement the treatment of DM. ## Introduction Diabetes mellitus (DM) is a chronic metabolic disease with a series of metabolic disorders (glucose, protein, fat, water, electrolyte, etc.) and chronic deficiency, or dysfunction of blood glucose level or dysfunction caused by a variety of pathogenic factors, which has been an important public health problem in the whole world [1]. In the past 4 decades, doctors have mainly observed the changes of blood glucose through fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), 2h postprandial blood glucose (2hPG), to provide a basis for the diagnosis of diabetes [2, 3]. Diabetes symptoms can be observed in individuals of different gender, race and age. South Asia ethnicity with diabetes have higher mortality compared with white Europeans, South Asian women were in particular affected [4, 5]. Obesity poses a huge risk on diabetes independently in the United States [6]. Previous studies were mainly limited to differences in the type of diabetes by ethnic groups, and comparative analysis in aspects of fatality. The more representative judgment indicators of diabetes include leg length, BMI (Body Mass Index), and total cholesterol (total cholesterol) [7]. Studies on the differences of these indicators under different races, age, gender and other factors are still lacking and not systematic enough [6, 8]. Studies have shown that leg length has been associated with diabetes prevalence [7], which urgently needs to be tested by quantitative analysis of large samples. We are unaware of a systematic analysis regarding the population-based prevalence of DM. The National Diabetes Data Group (NDDG) established a classification based mainly on diabetes treatment requirements, which was widely recognized and still used today. This classification included insulin dependent diabetes mellitus (IDDM), non-insulin dependent diabetes mellitus (NIDDM), gestational diabetes and diabetes secondary to other diseases like pancreatic cancer and other endocrine diseases [9]. In our study, diabetes in pregnant women was excluded from the study to ensure general applicability [7]. We used data from the 2009 to 2018 National Health and Nutritional Examination Survey (NHANES) to describe the prevalence of DM in the USA from the perspective of demographic characteristics, physical indicators and living habits, including age-, sex-, ethnicity-, smoking-, alcohol drinking-related differences in leg length, BMI and TCHOL in the United States, and focusing on the fasting plasma glucose (FPG) in black persons. ## Methods Details of NHANES have been described elsewhere [10]. In short, NHANES uses a complex, multistage, probabilistic sampling method to collect nationally representative health related data on the U.S. population, regular before 1999 and continued thereafter. Data were obtained through face-to-face interview, mobile physical examination and laboratory test. Since 2007, some changes have been made to the over-sampled domain. The main change was an oversampling of the entire Hispanic population, not just Mexican Americans (MA). Similar to those aged 60 and over in the previous cycle, blacks and low-income people were over-sampled to ensure accurate estimates for these groups [11]. Of the 49 693 persons 1 year of age or older who were asked to come to the mobile examination centers, 16 676 did not participate in body measures and TCHOL tests or had missing data. 8848 persons were excluded because our study focused on people over 20 years old and no pregnant women, and 623 were excluded for DM data missing. Finally, 23 546 participants were included with complete data for quantitative study (Figure 1). Figure 1: Flow diagram of participants inclusion for study. For the data sources and the selection criteria, three aspects should be explained. Firstly, leg length and BMI data from body measures of NHANES were collected in the Mobile Examination Center (MEC), by trained health technicians, in order to examine the associations between body weight and the health and nutritional status of the U.S. population [12]. The laboratory method used to measure the value of TCHOL is an enzymatic assay, in which esterified cholesterol is converted to cholesterol by cholesterol esterase [13]. Secondly, diabetes as diagnosed according to the standards of the American Diabetes Association [14] and participants’ self-reported questionnaires. Each of the following conditions was diagnosed as diabetes: fasting plasma glucose>=7 mmol/L (equal to 125 mg/dL), self-reported physician diagnosis of diabetes, or current use of diabetes medication to lower blood glucose level. Thirdly, current smokers were defined as participants who were age 20 years or older and reported smoking occasionally or daily during the past 7 days, or over 100 cigarettes [15]. For alcohol drinking group, current heavy alcohol use (≥3 drinks per day for females, ≥4 drinks per day for males, or binge drinking [≥4 drinks on same occasion for females, ≥5 drinks on same occasion for males] on 5 or more days per month); current moderate alcohol use (≥2 drinks per day for females, ≥3 drinks per day for males, or binge drinking ≥2 days per month); all other cases are mild [16]. All analyses used weighted samples and considered the stratification and clustering of the design to derive estimates that were applicable to the U.S. population [12, 13]. To provide estimates for the entire 10 years, a 10-year, weight-variable sample was created by taking one fifth for the 2-year weight for each person who was sampled in 2009 to 2018. All analyses were conducted in RStudio, version 2022.07.0 for Windows (RStudio, PBC) and R, version 4.2.1 (The R Foundation for Statistical Computing). Non-adjusted frequency distribution of body measures and TCHOL and non-adjusted prevalence of diabetes regarding age groups, sex, and ethnicity were obtained. These age groups were commonly used in highly stratified NHANES analyses [12, 13]. We obtained means and comparisons of age-adjusted and sex-adjusted body measures by using linear regression, in which age is modeled as a continuous variable. Adjusted means in leg length and BMI in smokers and nonsmokers were compared. Logistic regression adjusting for age group and sex was then used to generate predictive marginals for having diabetes. More complex models, which involved interaction between ethnicity and sex as well as between ethnicity and age (as a continuous variable), were also tested and different categorizations of age were considered. These models did not substantially add to the explanatory power of the model. ## Results The 23 546 NHANES participants with valid body measures and TCHOL value represented 308 million non-institutionalized residents of the United States. Among 26 147 participants with missing data, $30\%$ were white, $24\%$ were black, and $20\%$ were Mexican American. Over the past decade, the prevalence of diabetes has been steadily rising, as is the trend in different races, gender, and age groups (Figure 2). Figure 2: The trend of diabetes incidence from 2009 to 2018 by age groups and year. ## Demographic characteristics Firstly, the prevalence of diabetes differed by age, sex, and ethnicity (Figure 3; Appendix Table 1). A total of 3 621 participants had diabetes. The weighted prevalence was $11.6\%$ ($95\%$ CI, $11.0\%$ to $12.1\%$), which represented an estimated 23.1 million persons in the United States. Diabetes was present in $14.6\%$ (CI, $13.6\%$ to $15.6\%$) of black participants, $10.6\%$ (CI, $9.9\%$ to $11.3\%$) of white participants, and $13.5\%$ (CI, $11.9\%$ to $15.2\%$) of Mexican-American participants. Across ethnic groups, white and Mexican-American males were more likely to have diabetes: $12.2\%$ for white males versus $9.1\%$ for white females and $13.7\%$ for Mexican-American males versus $13.4\%$ for Mexican-American females; black men had roughly the same odds of developing diabetes as black women, with only slight differences: $14.5\%$ for black males versus $14.7\%$ for black females. The prevalence of diabetes is increasing with age, males peaked around the 60s, compared to women around the 70s. For every age and sex category, black participants were more likely than white participants to have diabetes; in most age and sex categories, diabetes was more common among Mexican-American participants than among white participants. Figure 3: Percentage of females (top) and males (bottom) with DM. Secondly, multivariate logistic regression analysis was done to estimate the prevalence of diabetes adjusted for age, sex, and ethnicity (Appendix Table 2). Compared with white participants, black participants and Mexican-American were both more likely ($P \leq 0.001$) to have diabetes: $14.6\%$ among black participants, $10.6\%$ among white participants, and $13.5\%$ among Mexican-American participants. The prevalence of diabetes was lower in adolescence and was less than $8\%$ from age 20 to 45 years onward. Males were also more likely than females to have diabetes ($P \leq 0.001$). There was significant interaction between ethnicity and sex ($P \leq 0.001$) and between ethnicity and age as a continuous variable ($$P \leq 0.00129$$). Thirdly, the prevalence of diabetes with 3 categories fasting plasma glucose (FPG) values was also seen across most age ranges in black participants (Appendix Table 3). Most participants with diabetes had fasting plasma glucose (FPG) of more than 125 mg/dL. $67\%$ of black participants, $80\%$ of white participants, and $78\%$ of Mexican-American participants. ## Physical indicators Firstly, mean age-adjusted and sex-adjusted leg length, BMI and TCHOL were compared for diabetics and non-diabetics (Table 1, Figure 4). The respective numbers of participants who had DM and those who did not have DM were 870 and 3 960 for black, 1 213 and 8 226 for white, and 656 and 2 863 for Mexican-American participants. The overall mean leg length and TCHOL was lower in diabetics than in non-diabetics (1.07 cm, 18.67 mg/dL, respectively), while mean BMI were higher in diabetics than in non-diabetics (4.27 kg/cm2). DM had the greatest effect on decline of TCHOL in white participants (23.6 mg/dL), less of an effect in black participants (9.67 mg/dL), and the least effect in Mexican-American participants (8.25 mg/dL). Secondly, for participants with DM, we compared age-adjusted and sex-adjusted mean leg length, BMI and TCHOL across the major ethnic groups by using linear regression. The numbers of males younger than age 50 years were 1 150, 2 220, and 954among black, white, and Mexican-American participants, respectively, and the numbers of males age 50 years or older were 1 238, 2 490, and 764, respectively. The numbers of females younger than age 50 years were 1 275, 2 248, and 1 0351among black, white, and Mexican-American participants, and the numbers of females age 50 years or older were 1 167, 2 481, and 766, respectively. Relative to white participants, black participants had higher mean leg length (1.28 cm; $P \leq 0.001$) and higher mean TCHOL (7.96 mg/dL; $$P \leq 0.0025$$). Mexican-American participants had lower mean leg length (1.74 cm; $P \leq 0.001$), lower mean BMI (0.64 kg/m2; $$P \leq 0.11$$) and apparently higher mean TCHOL (13.61 mg/dL; $P \leq 0.001$). In addition, mean leg length and TCHOL were higher, and BMI were lower in males than in females ($p \leq 0.001$) (Appendix Table 4). Appendix Table 5 summarize these 3 variables in the 3 major ethnic groups for sex and for the 12 age groups. Thirdly, for participants with DM, the distribution of leg length, BMI and TCHOL suggested that, black participants have an advantage in leg length, followed by white participants and Mexican-American participants and there was a downward shift for leg length and a obviously upward shift for BMI among Mexican-American participants, but little difference for TCHOL (Appendix Table 6; Figure 5). Figure 5: Distribution of leg length, BMI and TCHOL in persons with DM age 20 years or older from 3 ethic groups. (A) Leg length; (B) BMI; (C) TCHOL. ## Living habits Firstly, mean age-adjusted and sex-adjusted percentage of DM were compared for smokers and nonsmokers (Appendix Table 7, Figure 6). The respective numbers of participants who had smoked and those who did not smoke were 2 084 and 2 746 for black, 4 880 and 4 559 for white, and 1 272 and 2247 for Mexican-American participants. There was no significant difference in the overall mean percentage of DM between smokers and non-smokers ($$P \leq 0.13$$). Meanwhile, smoking had great effect on percent increment of DM in white participants ($0.2\%$), and have little effect on black and Mexican-American participants. In a separate logistic regression analysis that was adjusted for age group, sex, and ethnicity, smokers had a statistically significant lower percentage of DM (CI, 0.07 to 0.28; $$P \leq 0.002$$) relative to nonsmokers. Figure 6: Analysis on the Mean percentage of participants with DM in smokers and nonsmokers of different races. * P<0.01 vs Smokers* Secondly, mean age-adjusted and sex-adjusted percentage of DM were compared for alcohol-drinking groups (Appendix Table 8, Figure 7). The respective numbers of participants who were heavy, moderate, mild alcohol-drinkers were 8 51, 9 88 and 2 991 for black, 1 709, 2 044 and 5 686 for white, and 709, 880 and 1 930 for Mexican-American participants. Interestingly, heavy and moderate alcohol-drinkers had lower mean prevalence of DM than mild alcohol-drinkers. The association of moderate drinking with diabetes was significantly different from mild drinking ($$P \leq 0.004$$), otherwise, the differences among the alcohol-drinking groups were not obvious. Figure 7: Analysis on the Mean percentage of participants with DM in alcohol-drinking groups of different races. ## Discussion For demographic characteristics, reports have confirmed that the biological factors affecting the pathophysiology of diabetes differ from race or nationality, and may be influenced by social factors, such as different cultural backgrounds (17–24). Data from the Centers for Disease Control and Prevention shows that the probability of some racial and ethnic groups being diagnosed with diabetes in the USA is much higher than that of whites, which was reviewed in Lancet [25]. At the moment of the COVID-19 pandemic, the risk of diabetes among the black population has further increased, and is inextricably linked with income, education, occupation, housing, food security, social support and other factors [25]. Our analysis overcame the problem of insufficient data in the past to extend population-based observations that the prevalence of diabetes differ by sex and age. Males are slightly more likely to have diabetes than female counterparts, which was also confirmed by a recent study on the mechanism of gender difference inducing diabetes [26]. In addition, prevalence of diabetes were lower in younger persons. These sex- and age-related differences in percentage of participants with diabetes were consistently seen in all 3 major ethnic groups. Among the basic 3 factors, ethnicity is more closely related with diabetes and is one of the factors that increase its incidence. The current analysis shows that DM is most common among male black elderly, and certain reports have described similar findings [26, 27]. This population-based report suggests broad genetic influences. The findings in the current analysis were consistent and robust for the observed differences in age, sex, and ethnicity (24, 28–31). In addition, most participants with DM had fasting plasma glucose (FPG) of more than 125 mg/dL. Due to the different criteria for diabetes (7, 14, 32–35), the standard we adopted [14] is consistent with AACE/ACE standard [32], which is widely recognized. Although it is tempting to suggest that the NHANES database should collect data on diabetes with reference to a given standard, such a recommendation may be premature. The detailed clinical data of NHANES have its own considerations, with considerable independence and objectivity, therefore, the research reference criteria of different researchers may vary, and a reasonable and valid judgment criterion needs to be further confirmed. For physical indicators, our study shows that people with diabetes have shorter legs than those without diabetes, which confirms the findings from a cross-sectional analysis of data about simple-measured leg muscle strength and the prevalence of diabetes among Japanese males [36]. Some studies have shown that stretching the leg can reduce the incidence of diabetes (37–39). BMI is a comprehensive embodiment of weight and height, which has certain reference value for studying the influence of obesity and overweight on diabetes. Our finding of a higher BMI being associated with diabetes prevalence is consistent with the known effectiveness of weight control in treating diabetes [30, 31]. Interestingly, in the study TCHOL tends to be lower in diabetic patients, and little research on this area [40]. In addition, for participants with DM, the distribution of leg length, BMI and TCHOL varies among ethnic groups, which also illustrates the association of race with diabetes. With regard to living habits, smoking had a modest association with diabetes prevalence. This finding was also seen in a prospective study published [41]. Smoking may increase the risk of diabetes through a variety of mechanisms, with tobacco-induced insulin resistance (IR) and hyperinsulinism (HIS) as the main mechanisms. Currently the mechanism of smoking causing and aggravating IR is not completely concluded. It is quite consistent that chronic smoking may lead to lipid metabolism disorders, increased abdominal obesity and vascular endothelial dysfunction (41–46). Smoking statistically had a large effect on the increased percentage of DM among white participants and had minimal effect on black and Mexican-American participants. To our knowledge, this may be the first report of such an observation, and its significance is unknown. As for alcohol, interestingly, heavy and moderate drinkers had lower mean prevalence of DM than mild drinkers and the association of moderate drinking with DM was significantly different from mild drinking. A large meta-analysis illustrated a similar effect that reductions in risk of DM among moderate alcohol drinkers may be confined to women and non-Asian populations [47]. There are some limitations in our study. Firstly, the study focuses mainly on the prevalence of diabetes and did not explore associated complications. Secondly, the data on fasting plasma glucose is relatively limited, resulting in a large reduction in the sample size in the classification of statistics. Thus, the results of the analysis based on these data may be exaggerated. However, the prevalence of diabetes among ethnic groups is probably not affected, because participants with missing data were evenly distributed among all 3 groups. Third, the observed relationships were limited to adults of 3 races in the US and not include other races, particularly Asian-Americans, which may have limited the extrapolation of the results. Given these limitations, a well-designed prospective cohort trial is needed to validate our results. ## Conclusions It can be concluded that DM is more common in the general population than might be clinically recognized, and the prevalence of DM was associated to varying degrees with many indicators of demographic characteristics (race, gender, age), physical indicators (leg length, BMI, TCHOL), and living habits (smoking, alcohol-drinking). When deciding whether a thorough diagnostic assessment of diabetes is needed, not only the clinical symptoms should be considered, but also these indicators. They should be linked with medical resource allocation and scientific treatment methods to comprehensively implement the treatment of DM. ## Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm. ## Author contributions LF was involved in all parts of the study, design, acquisition of data, analysis, interpretation, drafting of paper, and final approval. HS and YT participated in data analysis and helped to revise the manuscript. QZ coordinated and supervised data collection, and critically reviewed the manuscript for important intellectual content. All authors approved the final version as submitted, and agree to be accountable for all aspects of the work. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1088882/full#supplementary-material ## References 1. Lu X, Zhao C. Exercise and type 1 diabetes. Adv Exp Med Biol (2020.0) 1228. DOI: 10.1007/978-981-15-1792-1_7 2. 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--- title: 'Causal effects for genetic variants of osteoprotegerin on the risk of acute myocardial infarction and coronary heart disease: A two-sample Mendelian randomization study' authors: - Peng Chao - Xueqin Zhang - Lei Zhang - Xinyue Cui - Shanshan Wang - Yining Yang journal: Frontiers in Cardiovascular Medicine year: 2023 pmcid: PMC10028206 doi: 10.3389/fcvm.2023.1041231 license: CC BY 4.0 --- # Causal effects for genetic variants of osteoprotegerin on the risk of acute myocardial infarction and coronary heart disease: A two-sample Mendelian randomization study ## Abstract Although since the 1980s, the mortality of coronary heart disease(CHD) has obviously decreased due to the rise of coronary intervention, the mortality and disability of CHD were still high in some countries. Etiological studies of acute myocardial infarction(AMI) and CHD were extremely important. In this study, we used two-sample Mendelian randomization(TSMR) method to collect GWAS statistics of osteoprotegerin (OPG), AMI and CHD to reveal the causal relationship between OPG and these two diseases. In total, we identified 7 genetic variants associated with AMI and 7 genetic variants associated with CHD that were not found to be in linkage disequilibrium(LD; r2 < 0.001). Evidence of a positive effect of an OPG genetic susceptibility on AMI was discovered(IVW OR = 0.877; $95\%$ CI = 0.787–0.977; $$p \leq 0.017$$; 7 SNPs) and CHD (IVW OR = 0.892; $95\%$ CI = 0.803–0.991; $$p \leq 0.033$$; 7 SNPs). After removing the influence of rs1385492, we found that there was a correlation between OPG and AMI/CHD (AMI: weighted median OR = 0.818;$95\%$ CI = 0.724–0.950; $$p \leq 0.001$$; 6SNPs;CHD: weighted median OR = 0.842; $95\%$ CI = 0.755–0.938; $$p \leq 1.893$$ × 10−3; 6SNPs). The findings of our study indicated that OPG had a tight genetic causation association with MI or CHD. This genetic causal relationship presented us with fresh ideas for the etiology of AMI and CHD, which is an area of research that will continue in the future. ## Introduction Although coronary intervention and the management of risk factors such as arterial hypertension, hyperlipidemia, diabetes, and smoking have resulted in a marked decline in CHD mortality rates since the 1980s, CHD remains a leading cause of death and disability in many parts of the world [1]. In 2019, there were 56.5 million deaths worldwide, of which $32.9\%$ (18.6 million deaths) were caused by cardiovascular diseases [2]. As part of the Sustainable Development Goals (SDGs), the United Nations aims to reduce premature mortality due to noninfectious chronic diseases (NCDs) by one third. Reducing in cardiovascular diseases, especially ischemic heart disease (IHD), might partially achieve this goal (2–4). As a result, it is necessary to keep an eye on the risk factor of CHD. Pathologically, AMI is considered as irreversible necrosis of myocardial cells caused by acute ischemia. [ 5]. Every year, more than 8 million Americans are hospitalized for signs and symptoms that suggest AMI. About 1,700,000 people were finally diagnosed with myocardial infarction(MI) [6]. Thus, etiological studies of AMI and CHD are extremely important. MI patients may benefit from the use of bone-related proteins for early risk stratification and prognosis evaluation. In patients with AMI, OPG levels are correlated with the complexity of coronary artery disease(CAD) [7, 8]. Healthy subjects with low or high coronary artery calcification cannot be distinguished by using OPG. A single OPG measurement is limited to the diagnosis of angina pectoris (AP) in patients with suspected CAD [9, 10]. OPG is identified as a new biomarker of cardiovascular mortality and clinical events in patients with AMI complicated with heart failure. These findings are consistent with the hypothesis that there may be a connection between bone homeostasis mediators and cardiovascular diseases [10]. However, the causal relationship between OPG and AMI or CHD has not been systematically tested due to the existence of potential deviations such as confounding factors or reverse causality, and the causal relationship between OPG and AMI or CHD is still unclear. In the traditional epidemiology, observational research is used to explore the causes of diseases, but the whole exploration process lacks the content of causal inference, which is often considered unreliable. Randomized controlled study (RCT) is considered to clearly explain factors that contribute to disease outcomes and their causal relationships. However, due to its ethical limitation and its colossal human resources and material resources, RCT research has not been widely implemented in the clinics. In recent years, Mendelian randomization (MR) design has been considered as one of the best ways to make up for RCT. To solve the above dilemma, GWAS database tool variables and genetic variation can be taken as exposure factor tool variables [11]. Analysis of TSMR is one of the most commonly used methods in MR has the following advantages. First of all, with the publication of a large number of GWAS, we can obtain a large number of GWAS data. Secondly, a two-sample study would have included subjects from both cohorts by using previous observational study cohorts, which can significantly expand the sample size and improve detection effectiveness. Finally, with the increasing amount of GWAS data, we can obtain a huge number of tool variables, which also increases the genetic explanation of the causal relationship between the tool variables related to exposure factors and the outcome, making the obtained results more reliable. In this study, we used the TSMR method to collect GWAS statistics of OPG, AMI or CHD to reveal the relationship between OPG and these two diseases. ## Study design We used the TSMR method to investigate the causal relationship between OPG and cardiovascular diseases (including AMI and CHD). This study adopted the published summary data in the GWAS database, so it had no use for ethical approval. It is important to note that this study has not been preregistered and should therefore be considered exploratory in nature. Our basic design is shown in Figures 1, 2. [ 1] The instrumental variables (IVs) have nothing to do with confounding factors; [2] The IVs have something to do with the exposure factor. [ 3] IVs are not directly related to the ending variable, but can only be related to the outcome variable through the exposure factor. The study was conducted using the two-sample MR package (version 0.5.4) and the ‘Mendelian Randomization’ package (version 0.5.1) of the R program (version 4.0.0). Figure 1: Graphical abstract. Figure 2: Workflow of MR. IVW, Inverse variance weighted; MR Pleiotropy RESidual Sum and Outlier; SNP, single-nucleotide polymorphisms. CHD was defined as a compound definition including MI, acute coronary syndrome, chronic stable angina, or coronary stenosis >$50\%$, and AMI included in the original GWAS database was all myocardial ischemia-related myocardial infarction, and non-ischemic myocardial infarction was not included in the study. ## Data sets The summary-level data of OPG was provided by the GWAS summary statistics for Olink CVD-I proteins were collected from 13 European ancestry populations, which consists of 21,758 patients with OPG and 13,138,400 SNPs. All patients were from the European population [12]. Summary data of AMI came from Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) plus The Coronary Artery Disease (C4D) Genetics(CARDIoGRAMplusC4D) and CHD also came from CARDIoGRAMplusC4D [13]. The MI data set included 43,676 patients with MI and 128,199 controls, all of whom are from Europe [12]. The CHD data set included 60,801 patients with CHD and 123,504 controls [13]. According to the queuing information reported in the initial GWAS analysis, our investigation identified no sample overlap between OPG and AMI or CHD. ## IVs selection and validation IVs must be closely related to OPG. In order to ensure the relationship between OPG and IVs, we chose $p \leq 5$×10−8 as the IVs in the GWAS database. Furthermore, PLINK 1.9015 method was applied to remove disequilibrium in the linkage effect of IVs to ensure the independence of the selected IVs. We should guarantee that r2 < 0.001 in IVs, and those who do not fulfill the criteria should be eliminated. F value was used to assess the IVs’ capacity to predict exposure. The chosen IVs must be independent of AMI and CHD, as well as several confounding variables. Besides, it needed to be closely related to OPG. To begin, we used the aforementioned criteria to choose just the IVs that would be most helpful. [ 14]. Secondly, MR-egger was used for horizontal pleiotropy test [15]. Subsequently, palindromic SNPs which were defined as having minor allele frequencies greater than 0.3 were removed to make sure the effect of SNP on plasma OPG corresponded to alleles with the same genotype due to its effect on CHD or AMI. Then, the GWAS catalog of1 was used to check the connection of the chosen IVs with adjusted potential confounders. Finally, we produced F statistics utilizing the online application2 to discover the correlation between the specified IVs and OPG. ## Statistical analysis In the present study, the follow-up sensitivity was evaluated using the weighted median, simple median, maximum likelihood, and penalized weighted median methods. The weighted median, simple median, maximum likelihood, and penalized weighted median methods are more robust than the inverse-variance weighted (IVW) for individual genes with highly outlying causal estimations and produce a consistent estimate of the causative influence when valid IVs surpass $50\%$. Firstly, our main analysis method was IVW in order to investigate causal relationships between exposure factors and outcomes. To determine whether the MR hypothesis was violated, a sensitivity analysis was conducted. We used Cochran Q-test and I2 statistics to detect the heterogeneity of the IVW model [16]. In the Cochran Q test, when I 2 > $25\%$ and $p \leq 0.05$, potential heterogeneity was regarded as existing. Excessive heterogeneity indicated that modeling assumptions were violated or invalid instruments were included, leading to horizontal pleiotropy [17]. Due to its inherent robustness to heterogeneous outliers, weighted median models were recommended for causal inference in this case. They offered a slightly lower estimation accuracy, but provided a somewhat higher estimation accuracy [18]. By using the MR-Egger intercept, a directional pleiotropy was detected. In order to determine whether any single SNP was responsible for the combined IVW estimate, we performed a leave-one-out analysis. Observed causal estimates were filtered using Steiger filtering to determine whether reverse causality affected the observed association [19]. TSMR analysis was also repeated, but rs1385492 related to OPG was removed for genome-wide significance analysis ($p \leq 5$ × 10–8), and leave-one-out analysis was performed to assess causality. As a next step, we checked in the GWAS catalog whether any of the remaining 6 SNPs have a secondary phenotype associated with cardiovascular disease. MR-PRESSO was performed prior to MR analysis in order to identify and exclude any SNPs that might be pleiotropic. ## Genetic variants selection and validation Overall, We obtained 7 AMI genetic variants and 7 LD-independent CHD genetic variants (r2 < 0.001). These genetic variants reached genome-wide significance in the dataset of genetic variants of OPG ($p \leq 5$10–8). There were several SNPs that were not found directly in the CHD or AMI datasets, however. Table 1 showed all independent genetic variants analyzed through the TSMR approach. Consequently, we calculated the exposure from MR-Egger regression using the intercept term (Table 1) and found no horizontal pleiotropic pathway. F statistics were analyzed to determine the relationship strength between genetic variants and exposure. Statistical values of F over 10 were considered to be strong enough to eliminate all biases in causal genetic variant estimation. The F statistic value of the gene variants we selected was 1,120 for CHD and 1,073 for AMI, which is enough to alleviate any bias in the assessment of causal genetic variants. Table 1* \| SNP \| OPG \| OPG.1 \| OPG.2 \| OPG.3 \| OPG.4 \| AMI \| AMI.1 \| AMI.2 \| CHD \| CHD.1 \| CHD.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SNP \| E/o allele \| Eaf \| Beta \| Se \| P \| Beta \| Se \| P \| Beta \| Se \| P \| \| rs114165349 \| C/G \| 0.038 \| −0.240 \| 0.029 \| 2.69 × 10−16 \| 0.022 \| 0.033 \| 0.509 \| 0.032 \| 0.030 \| 0.286 \| \| rs1385492 \| G/A \| 0.443 \| 0.175 \| 0.009 \| 6.07 × 10−76 \| −0.003 \| 0.010 \| 0.783 \| 0.000 \| 0.009 \| 0.959 \| \| rs17600346 \| C/T \| 0.038 \| 0.175 \| 0.028 \| 3.01 × 10−10 \| −0.057 \| 0.028 \| 0.043 \| −0.051 \| 0.025 \| 0.047 \| \| rs2515001 \| T/C \| 0.144 \| −0.082 \| 0.013 \| 4.35 × 10−10 \| 0.001 \| 0.015 \| 0.912 \| 0.003 \| 0.013 \| 0.804 \| \| rs28929474 \| T/C \| 0.023 \| 0.285 \| 0.037 \| 9.91 × 10−15 \| −0.148 \| 0.052 \| 0.004 \| −0.146 \| 0.045 \| 0.001 \| \| rs3761472 \| G/A \| 0.184 \| 0.0700 \| 0.012 \| 2.95 × 10−09 \| −0.022 \| 0.013 \| 0.090 \| −0.017 \| 0.011 \| 0.145 \| \| rs704 \| A/G \| 0.473 \| −0.147 \| 0.009 \| 1.55 × 10−55 \| 0.029 \| 0.010 \| 0.005 \| 0.024 \| 0.009 \| 0.008 \| ## Analyzed by TSMR and sensitivity analysis We found evidence that OPG genetic predisposition is beneficial to AMI (IVW OR = 0.877; $95\%$ CI = 0.787–0.977; $$p \leq 0.017$$; 7 SNPs) and CHD (IVW OR = 0.892; $95\%$ CI = 0.803–0.991; $$p \leq 0.033$$; 7 SNPs). There was wide consistency among different models of MR in terms of causal estimates (Figures 3–5). Using Cochran’s Q-test, the IVW model appears to have no heterogeneity. Based on the MR-Egger intercept, there was no evidence of directional pleiotropy. Steiger filtering did not detect SNPs in the genome associated with reverse causation, and the association’s causal direction was reliable (Table 2; Figure 6). Additionally, we found that pooled IVW estimations were independent of any single SNP according to the leave-one-out analysis (Figure 7). Figure 3: TSMR of plasma OPG and risk of AMI and CHD. We used the genome-wide association study (GWAS) of increasing plasma OPG level unit to summarize the statistics. The result was normalized to increase exposure by one unit. IVW, Inverse variance weighted; MR Pleiotropy RESidual Sum and Outlier; SNP, single-nucleotide polymorphisms. Figure 4: Results of the SNP analyses for the SNP effect of plasma OPG level on outcomes. (A) AMI (B) CHD. Figure 5: Scatterplot of MR estimates of genetic risk of OPG on AMI and CHD. Scatter-plot of genetic effects on OPG versus their effects on AMI (A) and CHD (B) with corresponding standard errors denoted by horizontal and vertical lines. The slope of each line corresponds to the estimated MR effect from different methods. Figure 6: Funnel plot MR estimates of genetic risk of OPG on AMI and CHD. The funnel plot of genetic effect of OPG relative to its effect on AMI (A) and CHD (B), and the distribution of corresponding points reflects the heterogeneity. TABLE_PLACEHOLDER:Table 2 Figure 7: Sensitivity analyses using the leave-one-out approach for the association of plasma OPG level with outcomes. (A) AMI; (B) CHD. Interestingly, leave-one-out analysis displayed that in AMI and CHD, the null estimates of the weighted median method are driven by rs1385492. Observed significant estimates in the weighted median method support the idea that IVW is biased toward causal inference by outliers (Table 3). As a result, rs1385492 might bias MR estimations of SNPs with seven genome-wide significance. That is to say, after removing the influence of rs1385492, we found that there was a correlation between OPG and AMI/CHD (AMI: weighted median OR = 0.818;$95\%$ CI = 0.724–0.950; $$p \leq 0.001$$; 6SNPs;CHD:weighted median OR = 0.842; $95\%$ CI = 0.755–0.938; $$p \leq 1.893$$×10−3; 6SNPs; Table 3; Figure 8). Neither heterogeneity nor pleiotropy was detected in the sensitivity analysis (Supplementary Table S1; Figures 9, 10). No high-impact points were found by Leave-one-out analysis (Figure 11). ## Discussion Consistent with most previous literature, our research found that the genetic tendency of OPG, as a polypeptide associated with the tumor necrosis factor receptor (TNF), is related to the reducing of the risk of AMI and CHD. As a member of the superfamily, OPG is a receptor for nuclear factor κB ligand receptor (RANKL), which is also an avid receptor for TNF-linked apoptosis-inducing ligand (TRAIL). OPG is commonly expressed in bone cells, vascular smooth muscle cells and endothelial cells. It is widely believed that it can be used as a sensitive biomarker of vascular calcification [7]. Considering the rising frequency of CHD and AMI each year, the incidence of these conditions is growing, the mortality and disability rate are also high, and more and more scholars are interested in the causes of CHD and AMI. As early as 2004, Toshiki Nagasaki and other scholars believed that OPG was closely related to MI and vascular injury of CHD [20]. This study aimed to determine whether OPG correlate with AMI or CHD by using GWAS on a large scale. It was revealed that the OPG had a causal link to CHD or AMI. More recently, Mieczysław Dutka [21] also observed the connection between OPG and AMI or CHD, and showed that OPG was related to the onset of AMI or CHD. The result indicated that OPG, as a single pathogenic factor in patients with AMI and CHD, has a substantial impact on prognosis and considerably affects the development of AMI and CHD. OPG may be the leading participant, not the bystander. And so far, no variant has been found within or near the OPG gene associated with circulating OPG levels. The presence of calcification in atherosclerotic plaques has been confirmed in a growing number of studies as a factor in the pathogenesis of atherosclerosis, as well as associated with atherosclerosis morbidity. Calcification in this area is affected by the exact regulatory mechanism as that in bone tissue, so OPG and OPG/RANKL axis were initially studied in relation to cardiovascular disorders. The first study to knock out the OPG gene in mice found that along with severe osteoporosis, the aortic wall calcified more rapidly than before. However, opposite results were shown in later clinical research [22]. It was found that the high cardiovascular risk in patients with CHD was closely related to high OPG levels [21, 23]. RANKL and TRAIL contribute to this association through their mutual interactions with OPG (24–26). Although several clinical experiments have proved that OPG is closely related to CHD or AMI [9, 21, 27], and this relationship is positive, there is still controversy about this relationship in academic circles. In addition, other studies have found an association between OPG and other cardiovascular diseases, including congestive heart failure, aortic stenosis, and aortic valve calcification [8, 28]. At the same time, it is not only related to the occurrence of cardiovascular diseases, but also firmly to the prognosis of related cardiovascular diseases (29–33). However, these observational studies are limited to the sample size and experimental design. The causal relationship between OPG and CHD or AMI cannot be obtained. RCT research is the highest level of epidemiological evidence, and it is also a research design that can best explain causality. However, due to the difficulty of its development, few researchers have done RCT research, plus the considerable human resources and material resources it needs, it is tough to conduct an RCT study. Empirical applications of mendelian randomization in traditional epidemiology skillfully make up for the deficiency of research on epidemiology traditionally in determining the cause of disease; at the same time, it can also make up for the weaknesses of previous observational study, including unavoidable confounding factors and inability to explore causality. At present, it has become one of the best epidemiological means to make up for RCT research [34]. Because offspring inherit their genotypes from their parents randomly, SNPs are an excellent tool for analyzing the causal relationship between two factors [35]. Our research enriched the literature on the relationship between OPG and MI or CHD. Firstly, we used the MR method to provide evidence for the causal relationship between OPG and AMI or CHD. Especially the causal relationship among them, IVW, weighted median and MR-Egger were provided to clarify the causal relationship among them. Although the weighted median method confirmed no causal relationship between OPG and AMI or CHD at the beginning of the study, with the deepening of the research, we found that rs1385492 was an important tool variable that affected the overall research results. After removing the influence of rs1385492, a reliable causal relationship among the three factors was obtained. Secondly, rs1385492 is related to TNF by consulting the GWAS catalog. It is well known that TNF plays a crucial important role in causing and developing the disease in cardiovascular medicine (36–38). Our research also has limitations, mainly in the following aspects. First of all, we used the dataset of GWAS, not a single raw data, which caused many inconveniences in the analysis, the most significant of which is that subgroup analysis is impossible. AMI and CHD have many subtypes, for example, AMI including 5 types (Thrombosis in coronary artery caused by rupture, crack or dissection of coronary plaque leads to spontaneous myocardial infarction is designated as a type 1 MI; type 2 MI is the pathophysiological process that leads to ischemic myocardium damage in the setting of an imbalance between oxygen supply and demand; because death has occurred, patients suspected of sudden cardiac death due to myocardial ischemia, or suspected of cardiac death due to new ECG ischemic changes or new LBBB have no time to collect blood samples for myocardial marker determination, which is defined as type 3 MI; type 4 is defined as myocardial infarction related to PCI; type 5 is defined as AMI related to coronary artery bypass grafting.) [ 39]. Secondly, we solely analyzed the link between OPG and AMI or CHD in terms of genetic determinants and did not take into account a number of environmental confounding variables. Thirdly, In the absence of a comprehensive knowledge of the biological function of the chosen SNP, a pleiotropy hypothesis cannot be ruled out completely. In spite of this, it is pleasing that the effect estimation was robust across different MR models, and the IVW sensitivity analysis array failed to detect any pleiotropy when applied to our research. Finally, Population stratification, dynastic mating, and assortative mating should be considered, since they may lead to confusion as they introduce false causality. ## Conclusion Generally speaking, our results supported that OPG is a casual risk factor for CHD or AMI. This causal relationship provided us with new ideas in the future research field of the etiology of AMI and CHD. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: https://www.ebi.ac.uk/gwas/. ## Ethics statement Ethical approval was not required because only summary-level data were used in our study. ## Author contributions PC and YY: writing—original draft preparation. LZ, XZ, and XC: validation. PC and SW: writing—review and editing. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: 'Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments' authors: - Ming Zhong - Enyi Zhu - Na Li - Lian Gong - Hai Xu - Yong Zhong - Kai Gong - Shan Jiang - Xiaohua Wang - Lingyan Fei - Chun Tang - Yan Lei - Zhongli Wang - Zhihua Zheng journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10028207 doi: 10.3389/fendo.2023.1134325 license: CC BY 4.0 --- # Identification of diagnostic markers related to oxidative stress and inflammatory response in diabetic kidney disease by machine learning algorithms: Evidence from human transcriptomic data and mouse experiments ## Abstract ### Introduction Diabetic kidney disease (DKD) is a long-term complication of diabetes and causes renal microvascular disease. It is also one of the main causes of end-stage renal disease (ESRD), which has a complex pathophysiological process. Timely prevention and treatment are of great significance for delaying DKD. This study aimed to use bioinformatics analysis to find key diagnostic markers that could be possible therapeutic targets for DKD. ### Methods We downloaded DKD datasets from the Gene Expression Omnibus (GEO) database. Overexpression enrichment analysis (ORA) was used to explore the underlying biological processes in DKD. Algorithms such as WGCNA, LASSO, RF, and SVM_RFE were used to screen DKD diagnostic markers. The reliability and practicability of the the diagnostic model were evaluated by the calibration curve, ROC curve, and DCA curve. GSEA analysis and correlation analysis were used to explore the biological processes and significance of candidate markers. Finally, we constructed a mouse model of DKD and diabetes mellitus (DM), and we further verified the reliability of the markers through experiments such as PCR, immunohistochemistry, renal pathological staining, and ELISA. ### Results Biological processes, such as immune activation, T-cell activation, and cell adhesion were found to be enriched in DKD. Based on differentially expressed oxidative stress and inflammatory response-related genes (DEOIGs), we divided DKD patients into C1 and C2 subtypes. Four potential diagnostic markers for DKD, including tenascin C, peroxidasin, tissue inhibitor metalloproteinases 1, and tropomyosin (TNC, PXDN, TIMP1, and TPM1, respectively) were identified using multiple bioinformatics analyses. Further enrichment analysis found that four diagnostic markers were closely related to various immune cells and played an important role in the immune microenvironment of DKD. In addition, the results of the mouse experiment were consistent with the bioinformatics analysis, further confirming the reliability of the four markers. ### Conclusion In conclusion, we identified four reliable and potential diagnostic markers through a comprehensive and systematic bioinformatics analysis and experimental validation, which could serve as potential therapeutic targets for DKD. We performed a preliminary examination of the biological processes involved in DKD pathogenesis and provide a novel idea for DKD diagnosis and treatment. ## Introduction Diabetes kidney disease (DKD) is a chronic kidney disease caused by diabetes. About $40\%$ of type 2 diabetes patients and $30\%$ of type 1 diabetes patients present with DKD [1, 2]. With the increasing prevalence of diabetes, the number of DKD patients has also increased [3, 4]. DKD patients present different forms of kidney damage, which is characterized by continuous increase of albuminuria excretion and/or a progressive decrease in glomerular filtration rate (GFR), eventually developing into end-stage renal disease (ESRD) [5]. DKD is the main cause of ESRD, and about $30\%$ to $50\%$ of worldwide ESRD is caused by DKD [6]. Therefore, it is urgent to explore early effective diagnosis and intervention targets for exploring new diagnosis and treatment strategies to improve the clinical DKD outcome. The pathogenesis of DKD is complex and multifactorial. Generally, DKD is mainly caused by hemodynamic changes and metabolic disturbances [7]. These changes subsequently lead to activation of the renin-angiotensin-aldosterone system (RAAS) [8], increases in metabolites and pro-inflammatory factors, and dysregulation of many intracellular signaling cascades associated with oxidative stress (9–11). In the state of diabetes, on one hand, the self-oxidation of glucose causes mitochondrial overload and excessive production of reactive oxygen species (ROS). On the other hand, the body’s antioxidant capacity decreases, and the amount of intracellular antioxidant (nicotinamide adenine dinucleotide phosphate [NADPH]) is insufficient [12], resulting in an imbalance between oxidants and antioxidants. In addition, oxidative stress is also closely related to inflammatory cells, which often coexist and activate each other. Excessive oxidative stress and inflammatory responses lead to damage to the renal interstitium, glomeruli, and renal podocytes, thereby impairing renal function. Therefore, finding diagnostic and therapeutic targets for oxidative stress and inflammatory response is expected to block the process of renal injury in DKD and restore renal function. With the popularization of gene chips and high-throughput sequencing, many disease databases have gradually been improved, and more and more effective data can be used to reveal the pathogenesis of diseases and new therapeutic targets. For example, Ma used a bioinformatic approach to analyze gene expression profiles and underlying functional networks in cardiac tissue from patients with dilated cardiomyopathy [13]. Huang analyzed the correlation of serum 25-hydroxyvitamin D levels in the progression of proteinuria in DKD and its underlying mechanisms [14]. Yang explored seven immune-related genes that can predict the progression of atherosclerotic plaque based on machine learning [15]. However, existing studies have some deficits, such as analysis based on a single dataset, limited number of patients, and no multi-faceted validation of bioinformatics methods, which affects prediction capability or reliability. This study integrated DKD datasets from different sources, used a variety of biological information methods to screen diagnostic markers related to oxidative stress and inflammatory response in DKD, and thoroughly examined the biological functions and potential mechanisms of diagnostic markers. This discovery may provide a promising direction for clarifying the diagnosis and pathogenesis of DKD. ## Data sources and processing DKD gene expression profiling data were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), including seven datasets, GSE111154, GSE142025, GSE162830, GSE163603, GSE96804, GSE1009, and GSE30122. Table 1 presents more details concerning the above datasets. Excluding the samples irrelevant to this study, 214 samples were finally obtained, including 101 normal samples and 113 DKD samples. The “sva” R package was applied for removing batch effects from different datasets [16]. A Principal component analysis (PCA) was utilized to assess the effect of batch effect removal and visualize the distribution of DKD and normal patient samples. Subsequently, we obtained 458 oxidative stress-related genes from the Gene Ontology (GO) knowledgebase (http://geneontology.org/) and 200 inflammatory response related genes from MsigDB (HALLMARK_INFLAMMATORY_RESPONSE) (http://www.broad.mit.edu/gsea/msigdb/) as shown in Supplementary Table 1. Table 1 \| Datasets \| Platforms \| Organism \| DKD \| Normal \| References \| Status \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| GSE111154 \| GPL17586 \| Homo sapiens \| 4 \| 4 \| PMID: 30253844 \| Public on Jul 03, 2018 \| \| GSE142025 \| GPL20301 \| Homo sapiens \| 27 \| 9 \| PMID: 32086290 \| Public on Dec 14, 2019 \| \| GSE162830 \| GPL20301 \| Homo sapiens \| 10 \| 9 \| PMID: 33537765 \| Public on Dec 09, 2020 \| \| GSE163603 \| GPL16791 \| Homo sapiens \| 9 \| 6 \| PMID: 35675394 \| Public on Dec 22, 2020 \| \| GSE96804 \| GPL17586 \| Homo sapiens \| 41 \| 20 \| PMID: 29242313 \| Public on Jul 31, 2018 \| \| GSE1009 \| GPL8300 \| Homo sapiens \| 3 \| 3 \| PMID: 15042541 \| Public on Apr 01, 2004 \| \| GSE30122 \| GPL571 \| Homo sapiens \| 19 \| 50 \| PMID: 21752957 \| Public on Aug 03, 2011 \| ## Identification of DEGs and functional enrichment analysis The “limma” R package was used for differential analysis (\|log2FC\|>0.5, padj < 0.05) [17]. The”ggplot2” R package was applied for drawing volcano plots showing the distribution of differentially expressed genes (DEGs). The up- and down-regulated genes were analyzed using an Over-Representation Analysis (ORA). The R package “msigdbr” was used to provide a reference gene set. The enrichment analysis results from the “C2,” “C5,” and “H” gene sets were selected for visual display. ## Consensus clustering analysis of DEOIGs The R package “ConsensusClusterPlus” was used for consensus unsupervised clustering analysis [18], and separated patients into different molecular subtypes based on the expression levels of differentially expressed oxidative stress- and inflammatory response-related genes (DEOIGs). A consensus matrix plot, consensus cumulative distribution function (CDF) plot, relative alterations in area under the CDF curve, and tracking plot to find the optimal number of clusters were used. The “clusterProfiler” R package was used for performing gene set enrichment analysis (GSEA) [19]. The single sample gene set enrichment analysis (ssGSEA) analysis was utilized to quantify pathways related to DKD [20]. ## Weighted gene co-expression network analysis Based on the expression similarity of 113 DKD samples, genes were divided into different modules using the weighted correlation network analysis (WGCNA) method [21]. According to the importance assessment of genes in the module and the correlation analysis between modules and subtypes, a module highly related to DKD was found, and the genes in this module were used for subsequent research. ## Screening and validation of diagnostic markers for DKD The module genes obtained from WGCNA analysis were uploaded to the String database (https://cn.string-db.org/) for protein interaction analysis, and the protein–protein interaction (PPI) network file was exported for further analysis based on the Cytoscape 3.9.0 software. The CytoHubba plug-in of the Cytoscape 3.9.0 software was applied for screening core genes. The plug-in utilized 12 algorithms, including MCC, dmnc, MNC, degree, EPC, bottleneck, eccentricity, closeness, radiology, betweenness, stress, and clustering efficiency to score the genes and screen the genes that satisfied the 12 algorithms as candidate genes. Next, we utilized the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression (analyzed by the “glmnet” R package [22]), Random Forest (RF) as analyzed by the “randomForest” R package, and Support Vector Machine_Recursive Feature Elimination (SVM_RFE)) [23] to screen candidate genes, and the overlapping genes of the three algorithms were regarded as diagnostic markers. The expression data and clinical information data, such as creatinine and GFR of DKD were downloaded from the Nephroseq v5 online database (http://v5.nephroseq.org/) to verify the selected diagnostic markers. A receiver operating characteristic (ROC) curve was employed for evaluating the diagnostic efficacy of diagnostic markers [24]. ## Establishment and validation of a nomogram scoring system The “RMS” R package was used to develop a diagnostic model of DKD based on diagnostic markers. Using a nomogram scoring system, each variable was assigned a score, and the scores for all variables were then summed to obtain a total score for each sample. The calibration curves were used to assess the accuracy of the nomogram, and the Decision Curve Analysis (DCA) was chosen for evaluating the clinical utility of the nomogram [25]. ## GSEA enrichment analysis of biological functions and pathways of diagnostic markers The ssGSEA analysis was applied for quantifying 28 immune-related gene sets. The samples were divided into high and low expression groups according to the gene expression of each diagnostic marker, and a GSEA analysis was used to explore the biological functions and pathways associated with each gene. The “h.all.v7.5.1.symbols.gmt” and “c5.all.v7.5.1.symbols.gmt” were used as reference genomes. ## Animal experiments Twenty-four male BKS-DB mice (Strain NO. T002407, 12 each of 6 weeks old and 12 weeks old) and twenty-four age-matched non-diabetic mice were purchased from GemPharmatech (Guangdong, China). Blood and urine samples were collected, and the mice were then sacrificed to obtain kidney samples, some of which were partially stored in $4\%$ paraformaldehyde, and the rest were immediately stored at –80°C for subsequent studies. All animal experiments were approved by the Ethics Committee of Sun Yat-sen University (Approval No. SYSU-IACUC-2022-001575), and the entire experimental procedure was carried out in strict compliance with the Guide for the Care and Use of Laboratory Animals. Blood was drawn via the tail vein and blood glucose levels were measured using an ACCU-CHEK® Performa glucometer (Roche, Manheim, Germany). Blood urea nitrogen (BUN), serum creatinine (Scr), glycosylated hemoglobin (HbA1c), and urine creatinine were detected using an automatic biochemical analyzer (Chemray 800, Shenzhen, China). Urine albumin was measured using a turbidimetric inhibition immunoassay (Nanjing Jiancheng Bioengineering Institute, Nanjing, China, No. E038-1-1). Urinary albumin-to-creatinine ratio (UACR) was calculated as urine albumin/urine creatinine (μg/mg). Mouse kidney tissue fixed in $4\%$ paraformaldehyde was rinsed, dehydrated, routinely paraffin embedded, sectioned (5 µm), and stained by Hematoxylin-eosin staining (H&E), Periodic Acid-Schiff stain (PAS), and Masson. Meanwhile, mouse kidney sections were deparaffinized, hydrated, and incubated in 10 mM sodium citrate buffer at 98°C for 20 min for antigen retrieval. The sections were incubated with primary antibody against TNC (Cat:67710-1-Ig, Proteintech, USA), PXDN (Cat: FNab10858, FineTest, China), TPM1(Cat: A1157, Abclonal, China), and TIMP1 (Cat: 106164-T08, Sinobiological, China) overnight at 4°C. Then, the sections were incubated with the secondary antibody for 1 h at room temperature. The sections were then incubated with 3,3’-diaminobenzidine for 20 min at room temperature. Stained sections were visualized and imaged with a light microscope (Olympus, Tokyo, Japan). Next, a double-sandwich enzyme-linked immunosorbent assays (ELISA) for mouse TNC and TIMP1 (Elabscience, Wuhan, China) and PXDN (SAB, Maryland, USA) were performed to determine their protein concentrations. We also performed quantitative real-time polymerase chain reaction (qRT-PCR) according to the manufacturer’s instructions (ACCURATE BIOLOGY, Changsha, China). The relative quantification of mRNA levels was calculated based on the 2−ΔΔCt method, and actin beta (ACTB) was used as an internal control. The PCR primer sequences are shown in Supplementary Table 2. ## Statistical analyses The Unpaired t-Test and Wilcoxon Rank-Sum Test were used to analyze the differences between the two groups. The differences among multiple groups were analyzed by the Kruskal-Wallis test. Pearson or Spearman correlation test was used to analyze the correlation between variables. R software (version 4.0.3) and Adobe Illustrator (version 25.0) was utilized for statistical analysis and drawing. P value < 0.05 was considered statistically significant. ## Data processing The workflow of this study was shown in the flowchart (Supplementary Figure 1). We downloaded seven datasets from the GEO database with a total of 214 samples and used the “ComBat” function of the “sva” R package to remove batch effects of data from different sources. The PCA chart showed the data distribution before and after (Figures 1A, B, respectively) removing the batch effect, and the results indicated that the batch effect had been effectively corrected. After the data were merged, the DKD and normal samples could be accurately distinguished (Figure 1C). Using the “limma” R package for differential analysis, we identified a total of 772 DEGs of which 381 and 391 were up- and down-regulated, respectively, as shown in the volcano map (Figure 1D). Next, we performed an ORA enrichment analysis on the resulting differential genes. It can be seen from the circle network diagram that these genes were enriched in pathways, such as “INFLAMMATORY_RESPONSE,” “EPITHELIAL_MESENCHYMAL_TRANSITION,” “APOPTOSIS,” and “TNFA_SIGNALING_VIA_NFKB” (Figure 1E). The TreeMap revealed that up-regulated genes were mainly involved in biological processes, such as immune activation, T-cell activation, and cell adhesion, while down-regulated genes were mainly enriched in biological functions related to metabolic regulation (Figure 1F). These findings were correspondingly verified by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (Figure 1G). Figure 1: Differentially expressed gene (DEG) identification of diabetic kidney disease (DKD) and enrichment analysis. (A, B) Principal component analysis (PCA) showing the expression distribution of seven DKD datasets before (A) and after (B) batch effect removal. (C) PCA analysis revealed significant differences in transcriptome levels between DKD samples and normal samples. (D) Volcano plot of differentially expressed genes (DEGs) between DKD samples and normal samples. The DEGs in the pathways related to the occurrence and development of DKD reported in the literature are displayed in different colors. Hallmark gene sets (E), gene ontology (GO) (F), KEGG (G) enrichment landscape of the reference gene set. ## Identification of distinct subgroups in DKD First, we intersected oxidative stress and inflammatory response-related genes (OS_Infla) with previously obtained DEGs and obtained 84 DEOIGs (Figure 2A). Next, we used the R package “ConsensusClusterPlus” to classify DKD patients into different subgroups based on these 84 DEOIGs. When the consensus matrix k value was 2, the crossover among DKD samples was the smallest, which met the selection standards (Figures 2B-E). Consequently, 113 DKD samples were divided into two distinct clusters, DKD subtypes 1 and 2 (C1 and C2, respectively). The heatmap showed that most DEOIGs were up-regulated in the C1 subtype, while they were down-regulated in the C2 subtype and normal samples (Figure 2F). The GSEA enrichment analysis indicated that ECM-receptor interactions were enriched in the C1 subtype, while metabolic pathways were enriched in the C2 subtype (Figure 2G). We quantified the ssGSEA enrichment scores of different immune cell subgroup to be used for investigating the relationship between DKD subtypes and immune cells. The results indicated that the C1 subtype was enriched in more immune-related cells, such as regulatory T-cells, macrophages, activated B-cells, and plasmacytoid dendritic cells (Supplementary Figure 2). We then found the pathways that have been reported to be closely related to DKD in recent years by consulting the literature and quantifying the resulting pathways using a ssGSEA analysis. The mountain map showed the pathway ssGSEA score of the two subtypes and normal samples, which revealed that the Wnt, Notch, and apoptosis pathway were high in C1 subtype, and peroxiding proliferator-activated receptor (PPAR), peroxisome, mammalian target of rapamycin (mTOR), autophagy, AMPK, and other pathways were lower in the C1 subtype (Figure 2H). Figure 2: Identification of DKD subtypes. (A) Venn diagram showing the intersection of oxidative stress and inflammation-related genes (OS_Infla) and DEGs. (B) Consensus matrix when k was 2. (C) Consensus distribution function (CDF) when K was between 2 and 9. (D, E) Relative changes in the area under the CDF curve for k = 2 to 9. (F) Expression heatmap of genes related to oxidative stress and inflammatory response among C1 and C2 subtypes and normal samples. (G) A gene set enrichment analysis (GSEA) of the status of biological pathways in two DKD subtypes. (H) The mountain graph showing the differences in DKD characteristic pathway scores among C1 subtype, C2 subtype, and normal samples. (Kruskal–Wallis test, *P < 0.001, P < 0.005, P < 0.05).* ## Construction of WGCNA and identification of key modules We used 113 DKD samples from seven different datasets to screen the top 5000 genes using the median absolute deviation for the WGCNA analysis. Subsequently, we evaluated the scale-free fitting index and average connectivity of various soft threshold powers on the basis of the scale-free R2. Our study selected the soft-threshold power of β = 6 and scale-free R2 = 0.8744133 to construct a standard scale-free network with the Pick Soft Threshold function (Figure 3A). Ultimately, we identified six modules (Figure 3B). A correlation heatmap was used to explore the correlation of each module with diabetic kidney disease, and we found the MEblue module with the highest correlation with C1 and C2 subtypes (Figure 3C). The gene significance score was applied for analyzing the association between genes and DKD subtypes, which showed that MEblue had the highest gene significance score (Figure 3D). The correlation scatterplot further demonstrated that the genes in MEblue module not only strongly correlated with the MEblue module but also significantly correlated with the diabetic kidney disease subtypes (Figure 3E). Thus, we extracted genes in the MEblue module for subsequent analysis. Figure 3: Weighted Gene Co-Expression Network Analysis (WGCNA). (A) Scale-free fit index and network connectivity under different soft thresholds. (B) The gene hierarchical clustering dendrogram. The modules corresponding to the branches are marked with color represented by the color band under the tree. (C) Heatmap of the correlation between gene modules and DKD subtypes. (D) Absolute value comparison of the correlation between genes within each module and DKD subtypes. (E) Scatter plot of module eigengenes in blue module. ## Identification of diagnostic markers in diabetic kidney disease We obtained 473 differential genes (\|log2FC\| > 1, padj < 0.05) through a differential analysis of the two subtypes in diabetic kidney disease. A Venn diagram revealed that after intersecting with the 1458 genes in the MEblue module, 347 intersecting genes were found (Supplementary Figure 3). The PPI network diagrams of the above-described 347 genes were constructed using the STRING online network tool, and the exported results were analyzed in Cytoscape software. The Upset plot was used to pick the intersecting genes that satisfied the 12 algorithms of the CytoHubba plugin, and finally we obtained 279 genes (Supplementary Figure 4). Based on these 279 genes, we further screened diagnostic markers using different bioinformatic methods. Using the LASSO regression algorithm, 12 genes were picked as potential biomarkers (Figures 4A, B). The random forest (RF) algorithm identified 15 candidate genes (Figures 4C, D). The SVM–RFE algorithm showed that when the number of eigengenes genes was 64, the accuracy was the highest up to 0.956 (Figure 4E). Ultimately, we obtained four genes as diagnostic markers for DKD (Figure 4F). Figure 4: Identification of diagnostic markers. (A, B) A least operator shrinkage and selection operator (LASSO) logistic regression was used to screen characteristic variables. (C) The relationship between the number and the error of random forest. Red, green, and blue represent the error of C1 subtype, C2 subtype and all samples. (D) Ordination plot of gene importance scores. (E) The accuracy curve of characteristic variables for the first 200 genes using the support vector machine–recursive feature eliminator (SVM–RFE) algorithm. The red circle indicates the position with the highest accuracy. (F) Venn diagram showing the intersection feature variables filtered by the three algorithms. ## Diagnostic value and validation of four diagnostic markers The boxplot showed the expression of the four signature genes in the seven combined GEO datasets (Figure 5A). It can be seen that expression of the four genes in DKD samples was higher than that in normal samples. The samples in the Nephroseq v5 online database also verified their high expression (Figure 5B), indicating their potential roles during the occurrence and development of DKD. In the combined GEO dataset, we found that the area under the curve (AUC) of the ROC curve was 0.808 when all four genes were fitted into one variable, which yielded a better result than when they were used alone as diagnostic variables (Figure 5C). We also assessed the diagnostic efficacy of these four genes in an independent patient cohort from the GSE142025 dataset. The AUC values of the ROC curves for each gene were all greater than 0.8 showing that these four genes could diagnose DKD (Figure 5D). Correlation analysis showed that the expression of four genes positively correlated with creatinine (Figure 5E) and negatively correlated with GFR (Figure 5F). Figure 5: Diagnostic efficacy and external verification of diagnostic markers. (A, B) The 4 diagnostic markers expression in seven DKD pooled datasets (A) and external datasets (B). (C, D) Receiver operating characteristic (ROC) curves assessing the diagnostic efficacy of four diagnostic markers in seven DKD combined datasets (C) and GSE142025 dataset (D). (E, F) Correlation analysis of gene expression levels with creatinine (E) and glomerular filtration rate (GFR) (F). *P < 0.001, P < 0.005, P < 0.01, P < 0.05.* ## Nomogram construction of DKD diagnosis model based on characteristic genes Based on the expression of the four diagnostic markers, we constructed a diagnostic model based on logistic regression and drew a nomogram (Figure 6A). In the nomogram, each gene involved in the construction of the diagnostic model corresponded to a score, and their scores were added to obtain a total score, which corresponded to different diagnostic effects of DKD. The calibration curve showed that the nomogram could reliably diagnose DKD (Figure 6B). The ROC curve indicated that the AUC value of this model was 0.801 (Figure 6C). DCA results showed the net benefit (NB) evaluating the DKD patients’ outcomes through the four individual genes or a combination of them. The results illustrated that the combined nomogram model could lead to a significant increase in the NB (Figure 6D). Figure 6: Construction of the DKD diagnostic model. (A) Nomogram of the DKD diagnostic model on the basis of 4 diagnostic markers. (B, C) The calibration (B) and ROC curves (C) were used to evaluate the diagnostic efficacy of the DKD diagnostic nomogram. (D) DCA illustrating the NB assessing the outcome. *P < 0.001, P < 0.005, P < 0.01, P < 0.05.* ## Functional enrichment analysis of diagnostic markers To explore the biological processes involved in diagnostic markers, we analyzed the correlation of these four diagnostic markers with immune cells. The results indicated that they positively correlated with most immune cell infiltration (Figure 7A), such as activated CD4 T-cells, activated dendritic cells, regulatory T-cells, macrophages and others. Next, we divided the DKD samples into high and low expression groups based on gene expression. The differentially expressed genes in the high and low expression groups were subject to GSEA analysis to explore the possible signal pathways involved, and it was found that the pathway enrichment of the four genes was consistent. As a result, all were significantly enriched in TNFA_SIGNALING_VIA_NFKB, KRAS_SIGNALING_UP, INTERFERON_GAMMA_RESPONSE, INFLAMMATORY_RESPONSE, EPITHELIAL_MESENCHYMAL_TRANSITION (Figure 7B). Functional enrichment showed that the high expression groups of the four genes were all enriched in ADAPTIVE_IMMUNE_RESPONSE, T_CELL_ACTIVATION, IMMUNE_RESPONSE_REGULATING_CELL_SURFACE_RECEPTOR_SIGNALING_PA THWAY. The low expression group was enriched in biological processes, such as SMALL_MOLECULE_CAT ABOLIC_PROCESS, FATTY_ACID_CATABOLIC_PROCESS, INNER_MITOCHONDRIAL_MEMBRANE_PROTEIN_COMPLEX (Figure 7C). Figure 7: Biological function enrichment of diagnostic markers. (A) Heatmap of the correlation between diagnostic markers and immune cells. (B, C) GSEA enrichment analysis when the reference gene sets are hallmark gene sets (B) and ontology gene sets (C). ## Validation in animal models To further verify the diagnostic value of the four markers in the diagnosis of early DKD, we utilized 12-week-old db/db mice as a model of spontaneous DKD. We found that body weight, blood glucose, HbA1c, serum creatinine, blood urea nitrogen, and urine albumin/creatinine levels were significantly increased in DKD group mice compared with normal control mice (Figure 8A, Supplementary Figure 5). Pathological staining also showed mesangial cell proliferation, mesangial matrix expansion, and irregular thickening of glomerular and tubular basement membranes in the kidney tissue of DKD group mice (Figure 8B), indicating that the spontaneous DKD model had been successfully established. Next, we detected the mRNA expression levels of four biomarkers, including TNC, PXDN, TIMP1, and TPM1. The results showed that TNC, TPM1, and PXDN were significantly elevated in the mouse model. Unfortunately, TIMP1 had an upward trend and no difference between the two groups was found (Figure 8C). We also detected three secreted proteins among four biomarkers in mouse blood and urine. The results showed that TNC and PXDN were consistently elevated in blood and urine while TIMP1 was significantly elevated in urine but not significantly different in blood (Figure 8D). Correlation analysis showed that whether in blood samples or urine samples, these markers had obvious positive correlation with UACR. As for blood glucose and HbAc1, the markers were not significantly correlated with them (Supplementary Figures 6, 7). Immunohistochemical results showed that the expression levels of TNC, TPM1, TIMP1, and PXDN were elevated in the DKD mouse model (Figure 8E). To further verify that the above changes are related to DKD rather than diabetes, our study also added two groups of 6-week-old db/db mice and normal mice. We found that body weight, blood glucose, HbA1c were significantly increased in DM mice compared with normal control mice, but there were no differences in serum creatinine, blood urea nitrogen, and urine albumin/creatinine levels between the two groups of mice (Supplementary Figures 8A, B). Meanwhile, no significant difference was found in renal pathological staining (Supplementary Figure 8C). The results of qRT-PCR showed that there was no statistical difference in the mRNA expression levels of TPM1 and TIMP1 between the two groups. The expression of TNC and PXDN increased in the DM group (Supplementary Figure 8D). In addition, the expression levels of three secreted proteins were detected in the blood and urine samples of mice in the DM group and 6-week-old normal mice, and we found that only TNC in the blood samples was significantly increased in the DM mice. For urine samples, there were significant differences in the elevation of TNC and TIMP1 in DM mice (Supplementary Figure 8E). Figure 8: Validation of diagnostic markers in animal experiments. (A) The levels of body weight, blood glucose, serum creatinine, blood urea nitrogen, and urine albumin-creatinine ratio in mice. (B) Hematoxylin and eosin (H&E), periodic acid Schiff (PAS), Masson staining of mouse kidney. (C) mRNA expression levels of four diagnostic markers in kidney tissue. (D) Expression levels of markers in blood and urine. (E) Representative immunohistochemical staining of the kidneys for the four markers. ns, P < 0.05. ## Discussion DKD is a common complication of diabetes [26] and the leading cause of ESRD, which imposes a heavy burden on people and has a noteworthy influence on health and quality of life [27]. Finding and mining DKD clinical biomarkers may effectively slow down or even stop the progression of DKD. Recently, a large number of studies have made many efforts to explore new targets of DKD. Diao identified eight hub genes of DKD, such as Scd5, Coasy, and Idi1, by constructing a PPI network [28]. Han used machine learning to obtain two diagnostic markers of protein kinase cAMP-dependent type II regulatory subunit beta and transforming growth factor beta 1 (PRKAR2B and TGFBI, respectively) [29] in glomerular injury in diabetic nephropathy. Wei screened and identified biomarkers in early DKD based on WGCNA, and initially explored the biological functions of candidate markers [30]. However, the existing biomarkers are not enough to address the common DKD. At present, the number of samples included in most bioinformatics analysis studies is too small, and no research exploring early diagnostic markers based on the pathogenesis of DKD is available. Therefore, we still urgently need to uncover potential biomarkers with high specificity and sensitivity for pathogenesis. In this study, we downloaded multiple DKD datasets from the GEO database, included a larger number of samples, merged the datasets by removing batch effects, and obtained differential genes between DKD patients’ kidney tissues and normal kidney tissues through differential analysis. The enrichment analysis of differential genes showed that biological processes such as immune activation, T-cell activation, and cell adhesion were enriched in DKD, which was consistent with previous reports [31, 32] in which immune regulation was found to be involved in the occurrence and progression of DKD, and many pro-inflammatory cytokines and chemokines played a vital role in the DKD pathogenesis. It was reported that oxidative stress and inflammatory response was an important pathogenesis of DKD [33, 34]. For further examination of the DKD pathogenesis, we divided DKD patients into C1 and C2 subtypes based on DEOIGs. Through a GSEA analysis, it was found that extra cellular matrix (ECM)-receptor interaction was enriched in the C1 subtype with high DEOIGs expression, while metabolism-related pathways were enriched in the C2 subtype with low DEOIGs expression. As reported in related studies, ECM organization and ECM structural components could lead to accelerated extracellular matrix deposition and renal fibrosis in DKD [35], and metabolic disorders were found to play a key role in the development of DKD [36]. Machine learning is often used to find the key genes of diseases. Wang et al. found the key genes of chronic kidney disease through the WGCNA method [37]. Liu et al. found the trait-related module through the WGCNA method and identified the key gene FCER1G [38]. Their research revolves around the WGCNA method and the PPI interaction network to find markers and conduct experimental verification. On this basis, our research adds bioinformatics methods for screening markers, such as Lasso, RF, SVM_RFE. Ultimately, we obtained four potential DKD diagnostic markers, namely TNC, PXDN, TIMP1, and TPM1. TNC is a large hexameric extracellular matrix glycoprotein expressed in most normal adult tissues [39]. TNC is significantly up-regulated in damaged and inflamed tissue [40], and it has also been reported to be independently associated with increased cardiovascular adverse events and death in patients with type 2 diabetes [41]. However, few studies on the role of TNC in the progression of DKD are available. PXDN encodes a heme-containing peroxidase secreted into the extracellular matrix, which is involved in extracellular matrix formation and may play a role in the physiological and pathological fibrotic responses of the fibrotic kidney. TIMP1 belongs to the TIMP gene family, and the protein encoded by this gene family is a natural inhibitor of matrix metallopeptidase, which can regulate cell differentiation, migration, and cell death. It has been reported that plasma levels of TIMP1 are associated with early diabetic neuropathy and nephropathy in patients with type 1 diabetes [42]. TPM1is a member of the highly conserved tropomyosin family, widely distributed actin-binding proteins that are involved in the contractile system of striated and smooth muscles and the cytoskeleton of non-muscle cells. No previous studies have reported the role of TPM1 in DKD pathogenesis. We found that these four genes had excellent diagnostic value in DKD (AUC > 0.8) and were positively associated with creatinine and negatively associated with GFR in DKD patients. Targeting the four genes identified by our analysis may be a promising approach for DKD treatment. Most notably, we have also developed a nomogram combining four diagnostic markers with high AUC values and good calibration that showed excellent accuracy and reliability in the diagnosis of DKD. It will hopefully be applied in the clinic and contribute to the early diagnosis of DKD. It has been reported that immune regulation correlates with the occurrence and development of DKD [31, 32]. To further explore the role of these four diagnostic markers in immune regulation, we found that these four genes may be involved in TNFA_SIGNALING_VIA_NFKB,KRAS_SIGNALING_UP, INTERFERON_GAMMA_RESPONSE, INFLAMMATORY_RESPONSE and other signaling pathways through GSEA analysis thus providing a theoretical basis for our further research. It is worth noting that any bioinformatics analysis needs to be validated experimentally, so we constructed a model of spontaneous DKD and DM. Through a variety of experiments, the results showed that the mRNA and protein levels of TNC, PXDN, and TPM1 in the kidneys of the DKD model mice were consistently elevated. In the mice of DM model, their expression either had no significant difference, or the increase was not obvious. We also detected significant differences in blood and urine compared to the control group, suggesting that the three biomarkers we selected deserve further investigation. It is undeniable that the experimental results of TIMP1 are questionable. This discrepancy may be related to the type of DKD model that we constructed or to species differences. After all, our model is based on mice, and our bioinformatics analysis is based on human sample analysis. Combined with previous studies, we can see that the markers we found were elevated in DKD samples as were the previous biomarkers. In contrast, our biomarkers were significantly elevated in blood and urine with good sensitivity. In addition, the biomarkers we found are easy to detect, are fairly low-cost, and have good clinical applicability. It is worth mentioning that the spontaneous DKD model we constructed can reflect the early manifestations of DKD. The increase of markers means that DKD can be detected earlier, providing a new idea for clinical diagnosis. Our study also has some limitations in which the deeper mechanism exploration requires a large number of experiments to verify the results, which is our subsequent experimental plan in the future. In conclusion, we identified TNC, PXDN, TIMP1, and TPM1 as potential diagnostic markers for DKD using a comprehensive and systematic bioinformatics analysis and experimental validation, established a nomogram containing these four diagnostic markers, and preliminarily explored their possible biological functions in the occurrence and development of DKD. These findings will provide a novel idea for the early diagnosis and treatment of DKD. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material. ## Ethics statement All animal experiments were approved by the Ethics Committee of Sun Yat-sen University, and the entire experimental procedure was carried out in strict compliance with the Guide for the Care and Use of Laboratory Animals. ## Author contributions MZ analyzed and interpreted the data. MZ, EZ, NL and LG wrote the manuscript. HX, YZ, KG, SJ, XW, LF, CT and YL edited the manuscript. ZZ and ZW designed and edited the manuscript. All authors contributed to the article and approved the manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1134325/full#supplementary-material ## References 1. Bikbov B, Purcell CA, Levey AS, Smith M, Abdoli A, Abebe M. 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--- title: Different cell compositions and a novel somatic KCNJ5 variant found in a patient with bilateral adrenocortical adenomas secreting aldosterone and cortisol authors: - Liling Zhao - Jinjing Wan - Yujun Wang - Wenjun Yang - Qi Liang - Jinrong Wang - Ping Jin journal: Frontiers in Endocrinology year: 2023 pmcid: PMC10028271 doi: 10.3389/fendo.2023.1068335 license: CC BY 4.0 --- # Different cell compositions and a novel somatic KCNJ5 variant found in a patient with bilateral adrenocortical adenomas secreting aldosterone and cortisol ## Abstract ### Introduction This study aimed to explore the possible pathogenesis of a rare case of co-existing Cushing’s syndrome (CS) and primary aldosteronism (PA) caused by bilateral adrenocortical adenomas secreting aldosterone and cortisol, respectively. ### Methods A 41-year-old Chinese woman with severe hypertension and hypokalemia for 5 and 2 years, respectively, was referred to our hospital. She had a Cushingoid appearance. Preoperative endocrinological examinations revealed autonomous cortisol and aldosterone secretion. Computed tomography revealed bilateral adrenal adenomas. Subsequently, adrenal vein sampling and sequential left and right partial adrenalectomy indicated the presence of a left aldosterone-producing tumor and a right cortisol-producing tumor. Pathological examination included immunohistochemical analysis of the resected specimens. Secretions of aldosterone and cortisol were observed both in vivo and in vitro. Further, whole-exome sequencing was performed for DNA that was extracted from peripheral blood leukocytes and bilateral adrenal adenomas in order to determine whether the patient had relevant variants associated with PA and CS. ### Results Immunohistochemical staining revealed that the left adenoma primarily comprised clear cells expressing CYP11B2, whereas the right adenoma comprised both eosinophilic compact and clear cells expressing CYP11B1. The mRNA levels of steroidogenic enzymes (including CYP11B1 and CYP17A1) were high in the right adenoma, whereas CYP11B2 was highly expressed in the left adenoma. A novel somatic heterozygous missense variant—KCNJ5 c.503T > G (p.L168R)—was detected in the left adrenal adenoma, but no other causative variants associated with PA and CS were detected in the peripheral blood or right adrenocortical adenoma. In the primary cell culture of the resected hyperplastic adrenal adenomas, verapamil and nifedipine, which are two calcium channel blockers, markedly inhibited the secretion of both aldosterone and cortisol. ### Conclusion We present an extremely rare case of bilateral adrenocortical adenomas with distinct secretion of aldosterone and cortisol. The heterogeneity of the tumor cell compositions of aldosterone- and cortisol-producing adenoma (A/CPA) and somatic mutation of KCNJ5 may have led to different hormone secretions in the bilateral adrenal adenomas. ## Introduction Primary aldosteronism (PA) is considered the most common cause of secondary hypertension, and it has been reported to be present in at least $5\%$–$10\%$ of patients with hypertension [1]. PA is caused by excessive production of aldosterone and inhibition of renin activity, resulting in hypertension and hypokalemic alkalosis. Moreover, PA is commonly caused by aldosterone-producing adenomas (APAs), idiopathic adrenal hyperplasia, or rare glucocorticoid-remediable aldosteronism. In 1977, Hogan et al. reported the first case of PA with significant cortisol auto-secretion, which was considered to be caused by an aldosterone- and cortisol-producing adenoma (A/CPA) [2]. Currently, A/CPA is recognized as a subtype of PA because it is frequently detected when screening for PA. Recent studies have reported that the prevalence of subclinical Cushing’s syndrome (CS) may be high ($21\%$–$26.8\%$) in patients with APA [3, 4], and $5\%$–$21\%$ of adrenal tumors are A/CPA [3, 5, 6]. As reported previously, PA associated with cortisol autonomous secretion can be classified into the following types: 1) a single ipsilateral adrenal adenoma secreting both cortisol and aldosterone simultaneously; 2) two adenomas on ipsilateral adrenal glands secreting aldosterone and cortisol separately; and 3) two adenomas, one on each side of the adrenal gland, secreting aldosterone and cortisol separately [3]. Most patients had adenomas secreting both cortisol and aldosterone simultaneously, and the cases of bilateral adenomas secreting different hormones independently are extremely rare. Over the past few years, somatic mutations have been reported to be associated with the development of A/CPA. To date, more than eight genes have been reported to be associated with APAs, including KCNJ5, CACNA1D, ATP1A1, ATP2B3, CACNA1H, CLCN2, CTNNB1, and/or GNAQ/11 [7, 8], representing >$50\%$ of sporadic APAs. Of these, mutations in KCNJ5 are the most common, with the KCNJ5 mutation rate in APA reportedly being approximately $40\%$ in Western countries [9] and $60\%$–$70\%$ in Asian countries [10, 11]. A recent study reported that among Chinese patients with APAs, the mutation rates of KCNJ5, ATP1A1, ATP2B3, and CACNA1D are $77\%$, $2\%$, $0.5\%$, and $0.5\%$, respectively Page: 4 [11]. Similarly, somatic gene mutations have been identified in approximately $50\%$ of the cortisol-producing adenomas (CPAs). The affected genes include PRKACA, GNAS, PRKAR1A, and CTNNB1, with PRKACA being the most frequently mutated gene [12, 13]. Furthermore, somatic KCNJ5 and PRKACA mutations have been found in patients with A/CPA [14, 15]. In this study, we aimed to explore the possible pathogenesis of a patient with bilateral adrenal adenomas secreting aldosterone and cortisol, respectively. ## Ethics statement This study was approved by the Institutional Ethics Committee of the Third Xiangya Hospital. After obtaining written informed consent, we collected peripheral blood samples as well as resected adrenocortical adenomas from the patient. The studies involving human participants were reviewed and approved by the institutional review board of Third Xiangya Hospital, Central South University, China. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. ## Adrenal vein sampling (AVS) Bilaterally simultaneous AVS techniques with cosyntropin (synthetic adrenocorticotropic hormone 1–24) stimulation were performed as described in a previous study [16]. The detailed steps were as follows: after successful placement of bilateral adrenal veins, blood samples were collected at baseline and at 10 minutes and 5 minutes before cosyntropin stimulation as well as at 10 minutes and 20 minutes after the stimulation. Overall, 125 μg of cosyntropin was intravenously injected; subsequently, 125 μg of cosyntropin was infused continuously for 1 hour. Further, cortisol and aldosterone levels were measured using the blood samples. ## Whole-exome sequencing To determine whether the patient had relevant gene variants associated with PA and CS, we performed whole-exome sequencing of DNA extracted from peripheral blood leukocytes and resected bilateral adrenocortical adenomas. The isolated DNA was sheared on a Bioruptor UCD-200 (Diagenode) with a size distribution peak of approximately 200 bp. The samples were diluted, loaded, and sequenced on the HiSeq2500 platform (Illumina, San Diego, CA). Further, exome data processing and variant annotation were performed as described in a previous study [17]. The variants were interpreted according to the standards of the American College of Medical Genetics (ACMG) and categorized as follows: pathogenic, likely pathogenic, variants of uncertain significance, likely benign, or benign. ## In silico analysis The effects of single nucleotide variants were predicted using SIFT (http://sift.jcvi.org), PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2), MutationTaster (http://www.mutationtaster.org), and PROVEAN (http://provean.jcvi.org/index.php) programs. Further, KCNJ5 amino acids across different species were aligned using AlignX software (Invitrogen). ## Immunohistochemical analysis Immunohistochemical analysis was performed using an En Vision detection kit (Dako). The antibodies and dilutions were as follows: Ki-67 (1:500, OriGene), CYP11B1 (1:200, EMD Millipore), and CYP11B2 (1:500, EMD Millipore). Normal adrenal medulla was used as negative tissue control. Pathological examination revealed that the left adrenal mass was a golden-yellow adenoma (Figure 1B) mainly composed of clear cells on hematoxylin/eosin stained sections (Figure 1D). Immunohistochemical analysis revealed cytoplasm immunoreactivity for aldosterone synthase (CYP11B2) with no expression of 11beta-hydroxylase 1 (CYP11B1; Figures 1E, F). Pathological examination revealed that the right adrenal tumor was brownish yellow and relatively solid (Figure 1C). This mass was composed of both eosinophilic compact and clear cells, with the former accounting for $60\%$ (Figure 1G). Immunohistochemical analysis also revealed cytoplasm immunoreactivity for CYP11B1 with no expression of CYP11B2 (Figures 1H, I). These results confirmed that the patient had a co-existing left aldosterone-producing adenoma and right cortisol-producing adrenal adenoma, which was consistent with the results of AVS. ## Primary cell culture of the adrenal tumors The resected bilateral adrenal adenoma tissues (wet weight, 1 g) were washed twice with Dulbecco’s modified eagle medium (DMEM), cut into small pieces, and dispersed by incubation in DMEM containing $2\%$ collagenase I for 20 minutes at 37°C. Primary cell culture of the adrenal adenomas was performed as described previously [18]. On day 4, when the cells grew to $80\%$ confluence, they were treated with verapamil (10 μM, Sigma-Aldrich) and nifedipine (10 μM, Sigma-Aldrich) or with the vehicle for 24 hours. The culture medium was collected for measuring aldosterone and cortisol, and the concentrations of aldosterone (Sinbe Diagnostic, China) and cortisol (Roche Diagnostics GmbH, Germany) in the medium were directly measured via chemiluminescence. ## RNA isolation and real-time quantitative PCR To clarify the expression of steroidogenic enzymes for the synthesis of aldosterone and cortisol in bilateral adrenal adenomas, we examined the mRNA levels of steroidogenic enzymes, including steroidogenic acute regulatory protein (STAR), 17α-hydroxylase (CYP17A1), 11β-hydroxylase (CYP11B1), and aldosterone synthase (CYP11B2), in the bilateral adrenal adenomas and other adrenal adenomas, including five APAs, five CPAs, and three nonfunctional adenomas. Further, the cells and tissues were lysed using TRIzol Reagent (Sigma-Aldrich), and total RNA was isolated in accordance with the standard protocol provided by the manufacturer. The cDNA was synthesized from 1 μg of total RNA using a high-capacity cDNA reverse transcription kit (Applied Biosystems). PCR amplification for the expression of STAR, CYP17A1, CYP11B1, and CYP11B2 was conducted using the LightCycler 480 SYBR Green I Master (Roche Applied Science) on a LightCycler 480 PCR system. Moreover, relative gene expression levels were determined with the cycle threshold value and were normalized against the expression of the human glyceraldehyde-3-phosphate de-hydrogenase (GAPDH) gene. The primers were used in accordance with a previous study [18]. ## Statistical analysis The data were presented as mean ± standard deviation for primary cell culture and qPCR tests. Two groups were compared using the Student t-test, and multiple groups were analyzed by one-way analysis of variance (GraphPad Prism, La Jolla, CA, USA). $P \leq 0.05$ was considered statistically significant. ## Clinical characteristics A 41-year-old Chinese woman with severe hypertension and hypokalemia for 5 and 2 years, respectively, was referred to our hospital. She was diagnosed with hypertension 5 years ago and was administered telmisartan (40 mg qd) and amlodipine besylate (5 mg qd), but her blood pressure remained poorly controlled. Two years ago, she was diagnosed with hypokalemia (2.1 mmol/L) because of fatigue and was administered potassium intermittently. She was also diagnosed with new-onset diabetes during that time, but she was not taking any medication. Ten days ago, she was referred to our hospital because of severe shortness of breath after experiencing cold. Based on her physical examination, her blood pressure (BP) was $\frac{187}{130}$ mmHg, height was 148 cm, weight was 64 kg, and body mass index was 29.2 kg/cm2. Notably, she had a Cushingoid appearance with a moon face, a buffalo hump, and centripetal obesity; however, no purplish abdominal striae were found. Laboratory examination at admission revealed marked hypokalemia with a high urinary potassium level (80.25 mmol/24 h). Moreover, she had elevated levels of aldosterone, suppressed renin activity, and a significantly high aldosterone-to-renin ratio (ARR) of 1623.3 (Table 1). Furthermore, the captopril challenge test failed to suppress aldosterone secretion. Additionally, she presented with elevated levels of cortisol and 24-hour urinary free cortisol (UFC), loss of normal diurnal rhythm, and suppressed levels of adrenocorticotropic hormone (ACTH) (Table 1). Moreover, serum cortisol remained unsuppressed after overnight 1-mg dexamethasone suppression test (DST) and high-dose (8 mg) dexamethasone suppression test (Table 1). Based on these results, the patient was diagnosed with co-existing ACTH-independent CS and primary aldosteronism. Moreover, serum catecholamine, metanephrine, methoxynorepinephrine, follicle-stimulating hormone, luteinizing hormone, estradiol, testosterone, and dehydroepiandrosterone sulfate levels were normal. Furthermore, glycated hemoglobin (HbA1c) level was $7.7\%$, creatinine level was 113 μmol/L (RR, 41–85 μmol/L), urinary microalbumin-to-creatinine ratio was 590.7 mg/g (RR, 0–30 mg/g), and brain natriuretic peptide level was 6850.8 pg/ml (RR, 0–500 pg/ml). ECG findings indicated sinus tachycardia with left ventricular high voltage. Chest X-ray revealed an enlarged heart, and the cardiothoracic ratio was 0.74. Fundus examination demonstrated 1:2 arteriovenous pressure traces and a small amount of fundus exudates, indicating changes in the fundus of patients with hypertension. Further, the results of echocardiography indicated a large left atrium and thickened left ventricular wall; moreover, the percentages of ejection fraction and fractional shortening were $50\%$ and $32\%$, respectively. Computerized tomography scan revealed bilateral adrenocortical adenomas, with the left adenoma of 38 × 28 mm and the right adenoma of 25 × 21 mm (Figure 1A). Bilateral AVS revealed that the aldosterone concentration in the left adrenal vein (LAV) was remarkably higher than that in the right adrenal vein (RAV) and the inferior vena cava (IVC), indicating aldosterone overproduction from the left side. Moreover, the cortisol concentration in RAV was 3.1–9.7 times higher than that in the LAV, indicating cortisol overproduction in the left side (Table 2). Furthermore, significant adrenal-to-IVC gradient of cortisol enabled successful catheterization on both sides. Table 2 \| Unnamed: 0 \| Aldosterone (pg/mL) \| Aldosterone (pg/mL).1 \| Aldosterone (pg/mL).2 \| Aldosterone (pg/mL).3 \| Aldosterone (pg/mL).4 \| Aldosterone (pg/mL).5 \| Cortisol (μg/dL) \| Cortisol (μg/dL).1 \| Cortisol (μg/dL).2 \| Cortisol (μg/dL).3 \| Cortisol (μg/dL).4 \| Cortisol (μg/dL).5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| LAV \| RAV \| IVC \| L/R \| L/I \| R/I \| LAV \| RAV \| IVC \| R/L \| L/I \| R/I \| \| −10’ \| 93590.0 \| 1600.7 \| 1094.6 \| 58.5 \| 85.5 \| 1.5 \| 285.0 \| 1032.4 \| 24.8 \| 3.6 \| 11.5 \| 41.6 \| \| −5’ \| 94576.0 \| 1426.3 \| 1149.6 \| 66.3 \| 82.3 \| 1.2 \| 295.0 \| 932.8 \| 36.6 \| 3.2 \| 8.1 \| 25.5 \| \| 10’ post ACTH \| 132587.0 \| 5716.0 \| 1202.9 \| 23.2 \| 110.2 \| 4.8 \| 427.0 \| 4141.7 \| 36.9 \| 9.7 \| 11.6 \| 112.2 \| \| 20’ post ACTH \| 152230.0 \| 5863.0 \| 1336.4 \| 26.0 \| 113.9 \| 4.4 \| 1390.0 \| 4297.2 \| 30.6 \| 3.1 \| 45.4 \| 140.4 \| ## Treatment and follow-up The patient was treated with antihypertensive agents (spironolactone 100 mg qd, nifedipine 60 mg qd, metoprolol 47.5 mg qd, prazosin 2 mg q8h, and sacubitril valsartan sodium 50 mg bid) and hypoglycemic therapy (insulin degludec and insulin aspart injection 16 IU qd, linagliptin 5 mg qd, and acarbose 50 mg tid). As the left adenoma was larger than the right adenoma and there was a possibility of hypersecretion of aldosterone from the left tumor, the patient underwent left laparoscopic adrenalectomy. One month after the left partial adrenalectomy, her BP decreased significantly, and she only needed to take 5 mg amlodipine daily to maintain BP at 130–$\frac{140}{90}$ mmHg. Meanwhile, she stopped receiving insulin therapy and took only one oral hypoglycemic agent. Further, her aldosterone level significantly decreased, renin activity increased, and ARR and serum potassium levels normalized (Table 1). However, serum cortisol and 24-hour UFC still exceeded the normal range. Further, serum cortisol remained unsuppressed after 1-mg DST, indicating that cortisol over-secretion from the right adrenal adenoma persisted. Subsequently, she underwent right laparoscopic adrenalectomy. One month after the second surgery, her BP, blood glucose level, and serum potassium level were found to be normalized without any medication. Replacement therapy with prednisone (5 mg daily) was provided to avoid adrenal insufficiency. Three months later, the patient stopped taking prednisone, her BP and blood glucose levels remained normal, cortisol and ACTH levels returned to the normal ranges, and her condition could be suppressed with a low-dose DST (Table 1). ## mRNA expression of STAR, CYP17A1, CYP11B1, and CYP11B2 The mRNA level of the steroidogenic enzyme STAR was high in both adenomas. CYP11B1 and CYP17A1 were highly expressed in the right adenoma and CPAs, whereas CYP11B2 was highly expressed in the left adenoma and APAs compared with other adrenal diseases and nonfunctional adenoma (Figure 2). Figure 2: mRNA expression of steroidogenic enzymes STAR, CYP17A1, CYP11B1, and CYP11B2 in the bilateral adrenal adenomas of the patient and other adrenal adenomas. (A) STAR was highly expressed in both bilateral adenomas, (B, C) CYP11B1 and CYP17A1 were highly expressed in right adenoma and CPAs, (D) CYP11B2 was highly expressed in the left adenoma and APAs compared with other adrenal diseases and nonfunctional adenoma. P < 0.05 compared with other groups. NFA, nonfunctional adenomas; left ACA, left adrenocortical adenoma of the index patient; right ACA, right adrenocortical adenoma of the index patient; APA, aldosterone-producing adenomas; CPA, cortisol-producing adenomas.* ## Calcium channel involved in the secretion of aldosterone and cortisol The bilateral adenoma cells were cultured in vitro, and the calcium channel blockers nifedipine and verapamil were administered as an intervention (classified into three groups: control, nifedipine, and verapamil groups). We further detected the concentrations of aldosterone and cortisol in the supernatant of the cultured cells. Aldosterone secreted by cultured cells of the left adenoma was significantly higher than that secreted by the cells of the right side. In contrast, cortisol secreted by the cultured cells of the right adenoma was significantly higher than that secreted by the cells of the left side. Moreover, verapamil and nifedipine remarkably inhibited the secretion of bilateral aldosterone and cortisol (Figure 3). Figure 3: Effects of nifedipine and verapamil on aldosterone and cortisol secretions, respectively, in the primary cells of bilateral adenomas. (A, B) Effects of verapamil and nifedipine on the secretion of aldosterone and cortisol in the left adenoma. (C, D). Effects of verapamil and nifedipine on the secretion of aldosterone and cortisol in right adenoma. P < 0.05 compared with the control. left ACA, left adrenocortical adenoma of the index patient; right ACA, right adrenocortical adenoma of the index patient.* ## Germline and somatic variant analysis A novel somatic heterozygous missense variant, KCNJ5 c.503T > G (p.L168R), was detected in the left adrenal adenoma, whereas no germline and somatic variants associated with PA and CS were found in the peripheral blood samples or right adrenocortical adenoma (Figures 4A–C). The amino acid alignment of KCNJ5 across different species revealed that the leucine residue mutated in our case was conserved across all examined species (Figure 4D). According to the ACMG guidelines, the KCNJ5 c.503T > G variant is considered pathogenic because: 1) this variant was found in an extremely low frequency (PM2), 2) it was predicted to be damaging and disease-causing by SIFT, PolyPhen-2, and MutationTaster (PP3), and 3) it was consistent with the patient’s phenotype (PP4). Figure 4: Sequencing chromatograms of the KCNJ5 variant identified in peripheral blood samples and bilateral adenomas. (A) The germline KCNJ5 variant was not found in peripheral blood samples. (B) Heterozygous KCNJ5 c.503T > G somatic variant was identified in the left adenoma. (C) Normal sequence of the KCNJ5 gene in the right adenoma. (D) Multiple alignment of the KCNJ5 protein sequence in different species, indicating conservation of the residues KCNJ5 c.503T > G affected by this variant. ## Discussion PAs are rarely associated with subclinical or clinical CS. Spath et al. reviewed the clinical features of 34 patients with A/CPA and reported that the average age of participants was 52 years and more than two-thirds of the participants were females [19]. The majority of patients with A/CPA presented with severe hypertension and electrolyte disturbances, $14\%$ of A/CPA patients presented with typical symptoms of hypercortisolism, and $74\%$ presented with preclinical CS. Among the 34 A/CPA cases, 29 had a single adenoma and only 5 had multiple lesions. In our study, the female patient had co-existing PA and clinical Cushing syndrome as well as bilateral adrenocortical adenomas of >2 cm. She developed diabetes mellitus and multiple target organ damage (including cardiomegaly, heart failure, proteinuria, and impaired renal function). This finding was consistent with that of previous reports on patients with A/CPA presenting with an increased risk for cardiovascular events, glucose intolerance, and postoperative adrenal insufficiency [19, 20]. Given the adverse metabolic risk in A/CPA, the presence of an aldosterone and cortisol co-secreting adrenocortical tumor should be considered if a patient has PA and an adenoma of >2 cm or cortisol that is non-suppressible with overnight 1-mg DST. The majority of the reported A/CPA tumors secreted aldosterone and cortisol simultaneously in the same tumor. Here we report an extremely rare case of bilateral adrenocortical adenomas secreting cortisol and aldosterone from each side. To date, only six cases of bilateral adenomas with different functions have been reported (Table 3) (21–26). Moreover, it is still challenging to detect the lateralization of A/CPA. Currently, the AVS lateralization index (aldosterone/cortisol of bilateral adrenal vein) has been widely used for the lateralizing functioning of primary aldosteronism. However, the secretion of cortisol is uneven in A/CPA tumors, which may lead to false-negative aldosterone-to-cortisol ratios. Thus, epinephrine levels in bilateral adrenal vein and peripheral vein were recommended to be used as the denominator for correcting aldosterone from the predominant side. Among the six reported cases of bilateral functioning adenomas (Table 3), only two cases underwent testing for epinephrine for correction in AVS. The remaining four cases did not undergo testing for epinephrine during AVS and were further confirmed by postoperative clinical manifestations, changes in hormone levels, immunohistochemical staining for steroidogenic enzymes, etc. The limitation of our study was the absence of epinephrine correction in interpretation the results of AVS. Nevertheless, bilateral adenomas with different function were confirmed by subsequent immunohistochemical analysis and in vitro experiments after the sequential removal of adrenal adenomas. Therefore, further studies with larger samples are required for more accurate localization. Table 3 \| Study ID \| Age \| Gender \| Hypokalemia \| ACTH(pg/ml) \| COT(μg/dl) \| ALD(pg/ml) \| PRA(ng/mL/h) \| CS \| Left lesion (mm) \| Right lesion (mm) \| Operation choice \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Nagae A. (21) \| 55 \| F \| Y \| 14.1 \| 17.3 \| 242.0 \| 1.4 \| Y \| two CPAs 8, 20 \| APA 9 \| Right partial adrenalectomy, left total adrenalectomy \| \| Oki K. (22) \| 50 \| M \| Y \| 21.6 \| 16.5 \| 203.0 \| 1.1 \| N \| CPA 10 \| APA 10 \| Left partial adrenalectomy, right total adrenalectomy \| \| Onoda N. (23) \| 43 \| F \| Y \| ND \| 29.4 \| 305.0 \| 1.5 \| Y \| APA 20 \| CPA 30 \| Bilateral partial adrenalectomy \| \| Morimoto R. (24) \| 54 \| F \| N \| ND \| 24.0 \| 128.0 \| 0.1 \| Y \| APAs, 2 micro nodules \| CPA 30 \| Right partial adrenalectomy, left total adrenalectomy \| \| Seung-Eun Lee. (25) \| 31 \| F \| Y \| 20.1 \| 6.8 \| 244.0 \| 0.76 \| N \| CPA 20 \| APA 16 \| Right partial adrenalectomy, left total adrenalectomy \| \| Ren K. (26) \| 30 \| M \| Y \| 17.8 \| 12.5 \| 259.0 \| 2.0 \| N \| CPA 25 \| APA 19 \| Bilateral partial adrenalectomy \| The exact mechanisms of tumorigenesis in A/CPA remain to be fully elucidated. As reported previously, APAs composed of pure adrenal cortical zonal cells are very rare, and most APAs are composed of different cell types. Thus, APA tumors have the potential to co-secrete aldosterone and cortisol. Previous studies have reported the capacity of APA cells to produce cortisol [27, 28]. Spath et al. reported two different morphological cell types in A/CPA tumors: zona glomerulosa-like cells and zona fasciculate-like cells. Immunohistochemical analysis indicated that A/CPA tumors could express CYP11B1 and CYP11B2—the key enzymes for synthesizing aldosterone and cortisol—simultaneously, but the composition ratio of these two cells and the expression of the two synthases were different in addition to the presence of marked heterogeneity in the tumors [19]. In our case, immunohistochemical analysis and mRNA levels of steroidogenic enzymes revealed a left adenoma that primarily consisted of clear cells and mainly expressed CYP11B2, whereas the right adenoma was composed of both eosinophilic compact and clear cells and mainly expressed CYP11B1. The different cell composition of the bilateral adrenocortical adenomas may be an important reason for the secretion of different hormones in each adrenal gland. A recent Chinese study reported that the KCNJ5 mutation was detected in 9 of 11 A/CPA cases, and PRKACA gene mutation was detected in two A/CPA cases. Another study detected 17 samples harboring KCNJ5 mutations among the 22 patients with A/CPA, but PRKACA, CACNA1D, CACNA1H, ATP1A1, or ATP2B3 mutations were not detected [5]. Thus, the authors of that study suggested that A/CPA was more similar to APA than CPA [5, 29]. In our case, we identified a novel somatic KCNJ5 c.503T > G mutation in the left adrenocortical adenoma. However, no germline and somatic variants associated with PA and CS were found in the peripheral blood samples or right adrenal adenoma. This observation may also explain significantly higher aldosterone levels in the left adrenal adenoma than those in the right adrenal adenoma. Elevated cytoplasmic Ca2+ concentrations have been reported to be critical in the pathogenesis of APA with a mutant KCNJ5, and Ca2+ channel blockers can reduce aldosterone secretion in H295R cells expressing mutant KCNJ5 [30, 31]. Meanwhile, blockers of voltage-gated Ca2+ channels can inhibit ACTH-stimulated Ca2+ influx and consequent cortisol secretion [32]. In the present study, bilateral adenoma cells were cultured in vitro, and the L-type Ca2+ channels blockers nifedipine and verapamil were administered for intervention. We found that both verapamil and nifedipine reduced the secretion of bilateral aldosterone and cortisol, suggesting that hypersecretion of aldosterone and cortisol in our patient was mediated by voltage-gated Ca2+ channels. ## Conclusions We report an extremely rare case of bilateral adrenocortical adenomas with distinct secretion of aldosterone and cortisol, as confirmed by clinical findings and pathological studies. The heterogeneity of the tumor cell compositions of A/CPAs and somatic mutation of KCNJ5 may have led to different hormone secretions in the bilateral adrenal adenomas. ## Data availability statement The data presented in the study are deposited in the BioProject repository, and the BioProject ID is PRJNA923486. ## Author contributions Study concepts were prepared by PJ. The study was designed by PJ and LZ. Data acquisition was performed by LZ, JJW, and YW. Statistical analysis was done by LZ and WY. Data analysis and interpretation was done by LZ and PJ. AVS was performed by QL. Bilateral partial adrenal resection was performed by JRW. The manuscript was prepared and edited by LZ and PJ. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1068335/full#supplementary-material ## References 1. 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--- title: 'Biogenic silver nanoparticles (AgNPs) from Tinosporacordifolia leaves: An effective antibiofilm agent against Staphylococcus aureus ATCC 23235' authors: - Sreejita Ghosh - Somdutta Mondol - Dibyajit Lahiri - Moupriya Nag - Tanmay Sarkar - Siddhartha Pati - Soumya Pandit - Abdullah A. Alarfaj - Mohamad Faiz Mohd Amin - Hisham Atan Edinur - Muhammad Rajaei Ahmad Mohd Zain - Rina Rani Ray journal: Frontiers in Chemistry year: 2023 pmcid: PMC10028272 doi: 10.3389/fchem.2023.1118454 license: CC BY 4.0 --- # Biogenic silver nanoparticles (AgNPs) from Tinosporacordifolia leaves: An effective antibiofilm agent against Staphylococcus aureus ATCC 23235 ## Abstract Medicinal plants are long known for their therapeutic applications. Tinospora cordifolia (commonly called gulancha or heart-leaved moonseed plant), a herbaceous creeper widely has been found to have antimicrobial, anti-inflammatory, anti-diabetic, and anti-cancer properties. However, there remains a dearth of reports regarding its antibiofilm activities. In the present study, the anti-biofilm activities of phytoextractof T. cordifolia and the silver nanoparticles made from this phytoextract were tested against the biofilm of S.taphylococcus aureus, one of the major nosocomial infection-producing bacteria taking tetracycline antibiotic as control. Both phytoextract from the leaves of T. cordifolia, and the biogenic AgNPs from the leaf extract of T. cordifolia, were found successful in reducing the biofilm of Staphylococcus aureus. The biogenic AgNPs formed were characterized by UV- Vis spectroscopy, Field emission Scanning Electron Microscopy (FE- SEM), and Dynamic light scattering (DLS) technique. FE- SEM images showed that the AgNPs were of size ranging between 30 and 50 nm and were stable in nature, as depicted by the zeta potential analyzer. MIC values for phytoextract and AgNPs were found to be 180 mg/mL and 150 μg/mL against S. aureusrespectively. The antibiofilm properties of the AgNPs and phytoextract were analyzed using the CV assay and MTT assay for determining the reduction of biofilms. Reduction in viability count and revival of the S. aureus ATCC 23235 biofilm cells were analyzed followed by the enfeeblement of the EPS matrix to quantify the reduction in the contents of carbohydrates, proteins and eDNA. The SEM analyses clearly indicated that although the phytoextracts could destroy the biofilm network of S. aureuscells yet the biogenicallysynthesizedAgNPs were more effective in biofilm disruption. Fourier Transformed Infrared Radiations (FT- IR) analyses revealed that the AgNPs could bring about more exopolysaccharide (EPS) destruction in comparison to the phytoextract. The antibiofilm activities of AgNPs made from the phytoextract were found to be much more effective than the non-conjugated phytoextract, indicating the future prospect of using such particles for combatting biofilm-mediated infections caused by S aureus. ## 1 Introduction Development of biofilm is a natural tendency of bacteria, to survive adversities (Donlan, 2002). It is a syntrophic cluster of sessile bacterial cells, remaining shielded by aself-secreted extracellular polymeric substance (EPS). With the help of such polymeric substances, the bacterial cells can spread over biotic and abiotic surfaces and their binding with such surfaces plays a significant role in spreading the pathogenicity and virulence of the respective bacteria (Haiko and Westerlund-Wikström, 2013). The diversity of the biochemical constitution of the EPS matrix leads to different capacities of surface adherence with the substratum, which in turn causes a wide range of adaptations to adverse environmental conditions (Kostakioti et al., 2013). The EPS prevents the penetrance of antibiotics and other drugs resulting in the development of antibiotic resistance and multi-drug resistance. In today’s clinical point of view, biofilm-associated chronic and acute infections are the most challenging to treat, and day by day these infections start to threaten human health globally Staphylococcus aureusis one of the predominant bacteria responsible for causing community-acquired and nosocomial infections and such infections often become life-threatening (Silva- Santana et al., 2020). S. auruesis a Gram-positive bacterium and it is a human commensal that continuously colonizes the frontal niches of about $20\%$–$25\%$ of fit adult communities and $60\%$ remain occasionally colonized (Ellis et al., 2014). The attachment of S. aureusto the surfaces of medical implants and host tissues results in the development of matured biofilm resulting in the persistence of chronic infections (Foster et al., 2014). The development of biofilm and their residence in the EPS matrix lessens their susceptibilities to different antibiotics and host immunity, which makes these infections difficult to eradicate (Chatterjee et al., 2014). The biofilms of Staphylococcus aurues are known to cause infections such as bacteremia, endocarditis, multiple sclerosis and sepsis (Sheykhsaran et al., 2022). Various parameters lead to the development of biofilms. These factors include particular gene expressions and communication among proteins that help in adherence of the biofilm to the substrate. During more advanced stages of the infections caused by Staphylococcus aureus biofilms, bacterial cells get dispersed from the biofilms and get spread to the secondary sites thereby worsening the infection. The World Health Organization (WHO) has classified a list of antibiotic-resistant pathogens, based on the priority of finding new alternative therapies for treating those infections, which can no longer be treated with the available antibiotic therapeutic strategies. There are three stages of priority list pathogens: Priority 1 (critical), priority 2 (high) and priority 3 (medium). Among the priority list of pathogens, WHO has considered S. aureus in the second priority group (high priority) because S. aureus is resistant against a wide class of antibiotics including methicillin and vancomycin. Vancomycin was one of the last resort antibiotics to eradicate the methicillin-resistant S. aureus (MRSA) infections. Prolonged or suboptimal exposure to vancomycin has led to the development of decreased vulnerability of the S. aureus associated infections. This decreased susceptibility can be also due to the thickening of the EPS matrix and various antibiotic inactivating enzymes present in the EPS matrix (Al-Marzoqi et al., 2020). Silver is an antimicrobial substance with antiseptic, antibacterial, and anti-inflammatory properties (Fong et al., 2005). When silver is in a soluble state, such as Ag+1 or Ag0 clusters, it is physiologically active. Ag+ is the ionic version of silver in silver sulfadiazine, silver nitrate, as well as other ionic silver compounds. Silver is used as an ingredient in various ointments, creams, medicines, and medical instruments. Nanoparticles of silver have unique characteristics attributed to their very small size, which helps in enhancing their bioavailability and efficacy. Silver nanoparticles (AgNPs) possess inhibitory actions on bacterial, fungal, and viral growth (Jeong et al., 2005). AgNPs are increasingly explored by researchers because of their cytotoxic and antibacterial potential due to their easy attachment with the bacterial cell walls. This adherence impacts the cellular respiration and permeability leading to cell death. Moreover, AgNPs can easily enter the cells and bind with the biomolecules, including protein and DNA through their phosphorous and sulfur groups, respectively (Aabed and Mohammed, 2021). Besides, their antimicrobial properties, their biosynthesis process is comparatively cost-effective (Capek, 2004). Physical and chemical methods of AgNPs synthesis involve the use of toxic reagents and result in a very low yield of AgNPs. However, the biogenic method of AgNPs synthesis is environment-friendly, readily-scalable, simple and involves only natural reagents, so they are free from potential toxicities and also biocompatible (Emeka et al., 2014). Hence, the biological methods for the preparation of nanoparticles have more advantages over chemical and physical methods of AgNP synthesis, via processes like ultrathin film procedure, thermal evaporation, synthesis through diffusion flame, lithographic process, electrodeposition, sol-gel technique, chemical solution and vapor deposition, catalytic process, hydrolysis and method of co-precipitation (Hu et al., 2020). However, the exact mechanism of anti-biofilm activities of AgNPs is not yet clearly understood. It is being presumed that their extremely small sizes lead to the increase in oxidative stress within the bacterial cells through the generation of reactive oxygen species (ROS) or through denaturation of the fatty acids present inside the cell membrane and increasing peroxidation of lipids. Once, the AgNPs penetrate the cells they destabilize the intracellular biomolecules and structures leading to the death of the bacterial cells (Rozhin et al., 2021). Table 1 below describes about the potential applications of AgNPs in different fields. TABLE 1 \| Sl No \| Rt \| Name of the compounds \| Molecular formula \| Molecular Wt (g/mol) \| \| --- \| --- \| --- \| --- \| --- \| \| 1 \| 4.94 \| Isopinocarveol \| C10H16O \| 152.23 \| \| 2 \| 8.6 \| Caryophyllene \| C15H24 \| 204.35 \| \| 3 \| 9.38 \| Benzene, 1-(1,5-dimethyl-4-hexenyl)-4-methyl \| C15H22 \| 202.34 \| \| 4 \| 10.45 \| (+)-Sativen \| C15H24 \| 204.35 \| \| 5 \| 10.78 \| Methyl 4,7,10,13-hexadecatetraenoate \| C17H26O2 \| 262.4 \| \| 6 \| 11.7 \| 7-epi-cis-sesquisabinene hydrate \| C15H26O \| 222.37 \| \| 7 \| 12.13 \| 2,5-Octadecadiynoic acid, methyl ester \| C19H30O2 \| 290.4 \| \| 8 \| 12.71 \| Phenol, 2-methyl-5-(1,2,2-trimethylcyclopentyl)-, (S)- \| C15H22O \| 218.33 \| \| 9 \| 13.33 \| Phenol, 2,4-bis(1,1-dimethylethyl) \| C22H30O \| 310.0 \| \| 10 \| 14.97 \| 5-Isopropyl-2,8-dimethyl-9- oxatricyclo [4.4.0.0 (2,8)]decan-7-one \| C14H22O2 \| 222.32 \| \| 11 \| 17.79 \| 17-Octadecynoic acid \| C18H32O2 \| 280.4 \| \| 12 \| 17.96 \| Z,Z,Z-4,6,9-Nonadecatriene \| C19H34 \| 262.5 \| \| 13 \| 20.51 \| n-Propyl cinnamate \| C12H14O2 \| 190.24 \| \| 14 \| 26.71 \| Dasycarpidan-1-methanol, acetate (ester) \| C20H26N2O2 \| 326.4 \| \| 15 \| 28.86 \| Piperine \| C17H19NO3 \| 285.34 \| In this study, we threw light on the green synthesis of AgNP from the leaf extracts of T. cordifoliaand tested the efficacies of the phytoextract alone as well as biogenic AgNPs as antibiofilm agents against the biofilms formed by S. aureus. This study also investigated the antibacterial properties of the AgNP nanoconjugates biogenically synthesized from T. cordifolia leaf extract along with investigation of anti-biofilm activity. The leaf extracts of T. cordifolia are generally enriched with diterpenoids, sterols, aliphatics and alkaloids. These bioactive compounds can potentially act as reducing agents in the biosynthesis of AgNPs. Pertinent to the increased resistance against the available antibiotics, AgNP nanoconjugates have been synthesized to overcome the problem of antibiotic resistance as well as to punctuate the persistence of acute and chronic biofilm-associated infections caused by S. aureus since biofilms are extremely impermeable to the penetration of antibiotics or other antibacterial agents due to the presence of multiple drug efflux pumps and antibiotic inactivating enzymes. The nanoconjugates of silver synthesized from the leaf extract of T. cordifolia can penetrate deeper and reach the sessile bacterial cells residing within the biofilms of S. aureus due to their small sizes. ## 2.1 Preparation of Tinospora cordifolia leaf extract The leaves of T. cordifoliawere collected from the local gardens of West Bengal under the guidance of Botanist, India and they were washed with double distilled water and dried. After that, the leaves were crushed with $95\%$ methanol followed by incubation for 16–24 h at room temperature (Quave et al., 2012). Then the phytoextract was sieved using a gauge cloth followed by centrifugation at 5000 rpm for 10 min and the supernatant was collected and stored at 4°C for further use. The methanol used was of analytical grade and purchased from HiMedia. ## 2.1.1 Synthesis of biogenic AgNP preparation using leaf extract of Tinospora cordifolia $10\%$ w/v of properly washed and dried T. cordifolialeaves were crushed in 20 mL of double distilled water. The aqueous extract was then sieved with a gauge cloth followed by centrifugation at 5,000 rpm for 10 min and the supernatant was collected. 90 mL of 1 mM silver nitrate solution was prepared, to which, 10 mL of phytoextract was added dropwise while stirring with a magnetic stirrer (Jalal et al., 2016). The biosynthesis of AgNPs was confirmed by the change in color from white to reddish brown. This reaction mixture was further centrifuged at 5000 rpm for 10 min and the pellets were collected followed by washing twice with distilled water and re-suspended in phosphate buffered saline (PBS). ## 2.2 Characterization of the biologically synthesized AgNPs The biogenically synthesized AgNPs were characterized using various techniques such as UV-Vis spectroscopy, Field Emission Scanning Electron Micrograph (FE- SEM), Dynamic Light Scattering (DLS) and measurement of zeta potential, which are all listed below. ## 2.2.1 Characterization using UV-Vis spectroscopy The synthesis and stability of biogenic AgNPs can be detected by the UV-Visible spectra of the AgNP solution (Sharma et al., 2020). Double distilled water was used as blank. The absorbance spectra of the reddish brown AgNP solution were recorded at wavelengths ranging from 300 to 700 nm by using Lasany LI- $\frac{294}{296}$ Microprocessor single beam UV-Vis spectrophotometer. ## 2.2.2 Characterization by using Field Emission Scanning Electron Microscope (FE- SEM) The biogenically synthesized AgNPs were dropped cast on a cover slip and oven-dried (Jalal et al., 2016). Then the dried sample of AgNPs was visualized under JEOL JSM- 7600F Field Emission Scanning Electron Microscope at a voltage of 15 kV to determine the surface morphology of the biogenic AgNPs. ## 2.2.3 Characterization by using Dynamic Light Scattering (DLS) and zeta potential measurement The hydrodynamic diameter, zeta potential (surface charge), and particle distribution intensity (polydispersity index/PdI) of AgNPs can be examined using DLS by the process of measuring the dynamic variations of the intensity of light scattering caused by Brownian motion of the particles (Elamawi et al., 2018). All the measurements were performed in triplicate with 1min of equilibration time at 25°C temperature in a ZetasizerNanoseries (Nano- ZS). The mode of data processing was set to high multi-modal resolution. ## 2.3 Cultivation of the Staphylococcus aureus ATCC 23235 working strain and development of biofilm Biofilm forming strain of Staphylococcus aureus ATCC 23235 was grown in Luria Bertani broth (SRL) overnight at a temperature of 37°C at pH 7.4. The bacterial strains that were used in this study were cultured within an Erlenmeyer flask of 100 mL containing 50 mL of Luria Bertani broth at pH seven and were incubated for 24 h at 37°C. The biofilm formation by the working bacterial strain was analyzed by the use of the microplate assay method. Biofilm growth depends on the synergistic activities of the concentrations of sugar and salt. The optimal concentration for the formation of biofilms was found by adding various concentrations of glucose ($0.25\%$–$10\%$ w/v) and NaCl ($0.5\%$–$7\%$ w/v) in the culture broth. All the experimental setups were incubated at 37°C for a period of 72 h. ## 2.4 Study of antimicrobial activities of phytoextract and biogenic AgNPs Antimicrobial activities of phytoextract from T. cordifoliaand biogenic AgNPs and standard antibiotics were detected by analyzing the diameter (in millimeters) of the inhibition zones obtained by agar well diffusion method. Various agar plates containing the test bacterial strain were treated with phytoextracts, AgNPs, and antibiotics at different concentrations in wells, punctured on the plates. The plates were incubated at 37°C for 24 h and observed for inhibition zones in accordance with the specifications made by the National Committee for Clinical Laboratory Standards (Patra et al., 2014). ## 2.5 Determination of minimum inhibitory concentration (MIC) The MIC values of the phytoextract from T. cordifoliaand the biogenic AgNPs were determined against S. aureus ATCC 23235 by the technique of microdilution (Jeyaseelan and Jashothan, 2012). 10 µL of S. aureus ATCC 23235 was inoculated in 5 mL of LB broth and treated with phytoextract (concentrations ranging from 10 to 50 μg/mL) and biogenic AgNPs (concentrations ranging between 1 and 50 μg/mL), except in the control tubes. The test tubes were incubated at 37°C for a period of 24 h and the bacterial growth intensity was measured spectrophotometrically at 660 nm. ## 2.6 Determination of minimum biofilm eradication concentration The minimum biofilm eradication concentration was calculated using an MTT assay (Baishya et al., 2016). In 96-well plates, added100 µl of LB broth and 2 µL of S. aureus ATCC 23235 was added to each well and incubated at 37°C for 72 h. The LB broth was discarded after 72 h at 37°C to remove the planktonic cells, and 20 µL of phytoextract and biogenic AgNPs was added to all of the wells except the control (untreated microbe) followed by the addition of MTT reagent and mixing it well with the help of cyclomixer including control and incubated at 37°C for 4 h and the absorbance was measured at 550 nm using Thermo Scientific Multiskan Sky ELISA plate reader. ## 2.7 Determination of reduction of biofilm formation by Staphylococcus aureus ATCC 23235 on treatment with phytoextract and biogenic AgNPs The microtiter plate technique was used to estimate biofilm formation quantitatively (Lahiri et al., 2021b). In this method, six-well plates were used and each well was filled with 5 mL LB Broth a single coverslip was dropped in each of the wells, and 20 µL of S. aureus ATCC 23235 cells was added to all the wells. After 72 h of incubation at 37°C, 100 µL of treatment was added and kept in the incubator for 24 h at 37°C. The planktonic cells were gently removed and the coverslip was taken out and stained with crystal violet dye ($0.1\%$ w/v in acetic acid) for 1 min. After discarding the excess crystal violet dye, the stained cells were treated with acetic acid for 1 min and absorbance of the acetic acid from the coverslip was measured at 540 nm spectrophotometrically. A sterile growing medium only and a functioning solution were employed as negative and positive controls, respectively, in the assay. The following formula was used to compute the percentage of biofilm inhibition: Percentage % of biofilm inhibition=OD at 540 of non−treated cells−OD at 540nm of treated cellsOD at 540nm of non−treated cellsX 100 [1] ## 2.8 Detection of viability and revival count of the sessile bacterial cells The bacterial cells were grown in an LB broth containing chitin flakes ($0.1\%$ w/v) for a period of 72 h and then the broth was discarded to remove the planktonic cells and the chitin flakes were washed with sterile double distilled water. Followed by washing, fresh LB broth was added followed by the addition of 240 µL of phytoextract, AgNPs, and standard antibiotic (tetracycline). The bacterial growth was spectrophotometrically determined at 660 nm at regular time intervals of 2 h (Ding et al., 2015). After measuring the viability count of the sessile biofilm cells, the remaining broth from the test tubes was discarded and the sessile cells, which were attached to the chitin flakes were washed with sterilized double distilled water to eliminate any planktonic cells before being refilling with 5 mL of fresh LB broth in each test tube. The density of these cells was spectrophotometrically measured at 660 nm to detect any revival of the biofilm-forming cells after withdrawing the treatment. ## 2.9 Enfeeblement of extracellular polymeric substances (EPS) The mechanism of extraction of EPS associated with the biofilm-forming bacterial cells, 30 µL of S. aureus ATCC 23235 inoculum was added to 15 mL of LB broth containing chitin flakes and grown for a period of 72 h at a temperature of 37°C. After 72 h, the LB broth was discarded to eliminate the planktonic cells and the chitin flakes were washed with sterile double-distilled water. Then 240 µL of phytoextract and AgNPs were added to the respective conical flasks, except for the control, and incubated for a period of 1 h. 5 mL of phosphate-buffered saline (PBS) at pH 7.2 was added in each conical flask and cyclospinned for 2–3 min for breakage of the biofilm by hindering the interactions and keeping the EPS components together within the matrix. The PBS was then transferred to 15 mL flacon tubes followed by centrifugation at 6000rpm for 15 min s at 4°C. The pellet was re-suspended in 2.5 mL of 10 mM TrisHCl (at pH 7.8). 20mM beta-mercaptoethanol (BME) and 1 mM phenylmethylsulfonyl fluoride (PMSF) was added to the above suspension in a ratio of 1:1. The cell suspension was provided with heat shock by placing it between hot water and ice for 5 min each and the process was repeated for 4–5 times for all the samples. The suspensions were then centrifuged at 5000 rpm for 30 min at 4°C and the supernatant was transferred in fresh tubes followed by the addition of 1 mL of $10\%$ Trichloroacetic acid (TCA) in acetone and incubated for at least 72 h at 4°C. After 72 h, the suspensions were again centrifuged at 5000 rpm for 30 min at 4°C followed by the washing of the protein pellet with $90\%$ acetone and air drying. The pellet was dissolved in 500 µL of rehydration buffer (Teanpaisan et al., 2017). ## 2.9.1 Reduction in total carbohydrate content of EPS The content of carbohydrates present in the EPS was quantified using the Anthrone method (Meade et al., 1982). In this method, to each of the EPS samples, 50 µL of $80\%$ phenol was added and mixed completely for 2 mins using vortex followed by the addition of 2 mL of concentrated sulfuric acid and the color turned to deep red. The mixture was incubated at room temperature for 10 min before being measured spectrophotometrically for a reduction in carbohydrate content in EPS at 490 nm. ## 2.9.2 Reduction in total protein content of EPS Protein content was estimated by using Bradford assay, which is the shortest sensitive method of simple dye binding assay and was developed by Marion M. Bradford in 1976. In this method, 2 µL of samples were loaded in a 96-well plate with 100 µL of Bradford reagent followed by incubation at room temperature for 5 min, and the absorbance of protein content in the EPS was measured using Thermo Scientific Multiskan Sky ELISA plate reader at 595 nm. ## 2.9.3 Reduction in total eDNA content of EPS For estimating the eDNA content of EPS, the EPS samples were diluted with cold $100\%$ ethanol in a 1:3 ratio followed by incubation at 4°C for 2 h and spectrophotometrically measured at a wavelength of 560 nm. ## 2.10 FTIR analysis of the EPS matrix after treatment Biofilm produced by S. aureus ATCC 23235 on chitin flakes in LB broth for 72 h at 37°C was treated separately with phytoextract of T. cordifolia and the biogenic AgNPs followed by drying in a hot air oven. The FT-IR spectra were recorded in the range between 450 and 4000 cm-1 with a PerkinElmer FT- IR Spectrometer (Frontier) (Pati et al., 2020). ## 2.11 Detection of biofilm reduction by scanning electron microscopy (SEM) Biofilms developed on chitin flakes in LB broth after incubation for 72 h at 37°C, were treated with the phytoextract as well as with the biogenic AgNPs and incubated for 2 h at 37°C. Thereafter, the broth was discarded and the chitin flakes were washed with $0.9\%$ (w/v) NaCl for removing any leftover planktonic cells. The samples were then suspended in $2.5\%$ glutaraldehyde for 20 mins followed by repeated dehydration using upgraded ethanol. The dried chitin flakes containing the sessile colonies were visualized under a ZEISS EVO- MA 10 scanning electron microscope (Lahiri et al., 2021a). ## 2.12 Reagents and chemicals All chemicals and reagents used in the experimentation are of analytical grade and were purchased from HiMedia and SRL. ## 2.13 Statistical analyses All the experiments were performed in triplicate and the results were depicted as mean ± SD (standard deviation). ## 3.1 Identification of the bioactive compounds from the phytoextract of Tinospora cordifolia It was observed that the phytoextract of T. cordifolia comprised of different chromophoric groups such as -OH, phenolic, unsaturated carbonyls, etc. Mass spectrum interpretation through GC-MS of unidentified bioactive compounds and comparing them with the database stored in National Institute Standard and Technology (NIST) library verified the biochemical identity of 15 compounds from T. cordifolia extract. The molecular formula, name of the compounds, peak area, molecular weight, and bioactivity of the experimental materials were determined. The relative percentage composition of each bioactive compound was calculated by comparison with the average peak area with respect to the total area (Table 1). ## 3.2.1 Determination of UV- Vis spectra of the biogenically synthesized AgNPs UV-Vis spectroscopy acts as an important technique for the purpose to detect the synthesis of AgNPs with the monitoring of electronic structures and optical properties of the synthesized NPs. The electron clouds undergo oscillation on the surface of the NPs possessing the ability to absorb the electromagnetic waves possessing a particular frequency. This mechanism is termed surface plasmon resonance (SPR) which in turn is being recorded by the use of a UV-Vis spectrophotometer (Smitha et al., 2008). The UV- Visible spectra of the biogenically synthesized AgNPs using the leaf extract of T. cordifolia presented a peak at 430 nm (Figure 1) in correspondence with the surface plasmon response of AgNPs. The peak was similar to the work performed by Marhaby and Seoudi 2016 which also depicted the AgNPs synthesized by 4-Nitrophenol fetched at a peak at 423 nm (Almarhaby and Seoudi, 2016). Another work showed the peak of biogenic AgNPs from Clinacanthus nutans at 450 nm (Mat Yusuf et al., 2020). Biogenic synthesis of AgNPs is based on conditions like the type of solvent being used, the reducing agent, and the non-toxic substance being used for the purpose of stabilizing the nanoparticles (Raveendran et al., 2003). The change in the intensity of the wavelength is based on the increase in the number of NPs that are formed as a result of the reduction of silver ions along with the biomolecules that are present within the system. It is usually observed that the SPR bands become sharper and undergoes a shift to shorter wavelengths with the rise in temperature indicating a decrease in the size of the particles. The reduction in the size of the NPs is due to an enhancement in the reduction time during the mechanism of synthesis. Consumption of silver ions takes place during the process thereby blocking the phenomenon of secondary reduction taking place on the surface of AgNPs (Yang and Li, 2013). It has been observed that NPs that absorb wavelengths between 400 and 900 nm are spherical in shape (Yusuf et al., 2020). FIGURE 1: Graphical representation UV- Visible absorption spectra of AgNPs from leaf extract of Tinospora cordifolia showing absorbance peak at the wavelength of 430 nm. ## 4 Particle size distribution and surface charge analysis of the biogenic AgNPs Dynamic light scattering (DLS) is the technique used for the purpose of measuring the average size of the NPs within liquid suspension requiring fewer volumes of samples. The measurement of the size is based on the Brownian motion theory which denotes the random movement of the particles randomly in suspension or gas. The dynamic fluctuation from the intensity of light scattering is used for measuring the average size of the NPs (Murdock et al., 2008). The size of the biogenically synthesized AgNPs ranged between 43.82 ± 1.023 nm- 91.28 ± 1.12 nm (Figure 2). A considerable number of peaks appeared below 100 nm (Table 2). FIGURE 2: Particle size distribution of the biogenic AgNP solution synthesized from the leaf extract of Tinospora cordifolia. TABLE_PLACEHOLDER:TABLE 2 It was observed that the calculated PDI was 0.201 ± 0.005 for the green-synthesized NPs which is within the range from 0–1 in which 0 signifies monodisperse and one is polydisperse (Murdock et al., 2008). Thus the result signifies that the synthesized AgNPs were present in the monodisperse phase and aggregations of particles were minimum. Experimental conditions have a direct influence on morphology, size, and stability (Ghorbani et al., 2011). The agglomeration of NPs sometimes occurs due to the presence of bioactive compounds present within the solution (Shameli et al., 2012). The charges of the moving particles under the impact of the electric field can be calculated with the help of Zeta Potential (Bhattacharjee, 2016). Negative charges were observed around the particle which does not represent the actual surface charge (Figure 3). The presence of the negative charge is due to the absorption of bioactive compounds on the surface of AgNPs(Moldovan et al., 2016). Temperature plays a vital role in the regulation of the stability of the NPs and thereby increases the value of zeta potential. The high amount of zeta potential results in the development of repulsive forces thereby preventing aggregation of the particles (Priyadarshini et al., 2013). FIGURE 3: Zeta potential of AgNPs biosynthesized from the leaf extract of Tinospora cordifolia. ## 4.1 FE- SEM analyses of the AgNPs Green-synthesized AgNPs were studied under FE-SEM and it was observed that the NPs were spherical in shape (Figure 4) that was as per the SPR peak being observed in the UV-spectroscopy. The peak was observed at 430 nm which indicated the spherical nature of the NPs. In the presence of a protective agent, the sides of the NPs showed slightly elliptical or oval (Ahila et al., 2016), High surface tension and energy resulted in the agglomeration of the NPs (Wang et al., 2020). FIGURE 4: FE- SEM images of biogenic AgNPs from T. cordifolialeaves. ## 4.2 Antimicrobial activity determination of the phytoextract and AgNPs against Staphylococcus aureus ATCC 23235 Amongst the phytoextract and the biogenic AgNPs, the biogenic AgNPs depicted zones of inhibition of 12–18 mm while that of the phytoextract was 10–15 mm (Figure 5A). A control setup was arranged using ethanol and tetracycline that did not show significant antimicrobial activity against S. aureus ATCC 23235 proving that the test bacteria developed resistance against ethanol as well as a tetracycline antibiotic. The biogenic AgNPs possessed a MIC value of as low as 10 μg/mL (Figure 5B) while the phytoextract showed a MIC value of 15 μg/mL (Figure 5C), which is quite higher in comparison to the AgNPs. This determines that the phytoextract along with AgNPs can have better effects against Staphylococcus aurues ATCC 23235 than phytoextract alone. This may be due to the higher penetration capacities of the AgNPs, which can penetrate deep within the bacterial cells and bring about their destruction. This observation was similar to the previously published work where the AgNPs exhibit an inhibitory effect within the range of 4–64 μg/mL (Attallah et al., 2022). FIGURE 5: (A) Inhibitory zones on addition of AgNPs (B) Antimicrobial efficacy of biogenic AgNPs from Tinospora cordifolia and (C) Antimicrobial efficacy of the phytoextract of Tinospora cordifolia. ## 4.3 Antibiofilm property determination of phytoextract and AgNPs Both the phytoextract as well as the biogenic AgNPs demonstrated antibiofilm properties against S. aureus ATCC 23235 (Figure 6). However, the biogenic AgNPs showed a percentage reduction of more than $83.14\%$ ± $0.56\%$ of the biofilm, while that of the phytoextract and tetracycline were around $60.12\%$ ± $1.23\%$ and $50.25\%$ ± $0.87\%$ respectively. The reduction of biofilm by AgNPs were found to be statistically significant ($p \leq 0.01$) Figure 1. The biosynthesized AgNPs exhibited greater efficacy of action due to the doped bioactive compounds from the plant source (Miškovská et al., 2022). FIGURE 6: Anti-biofilm efficacies of biogenic AgNPs, phytoextract and antibiotic Tetracycline against Staphylococcus aureus ATCC 23235. ## 4.4 Reduction in viability and revival count of the sessile cells after treatment It was found that the viability count of the sessile colonies of Staphylococcus aurues ATCC 23235 demonstrated the highest reduction in the presence of biogenic AgNP than the phytoextract and tetracycline (Figure 7A). The efficacy of the biofilm eradication was further validated by the negligible revival of the cells after the withdrawal of the respective treatments for 24 h (Figure 7B). FIGURE 7: (A): Viability count reduction followed by treatment with phytoextract, AgNPs, and tetracycline and (B) Revival of the cells after withdrawal of the treatment. ## 4.5 Reduction in the contents of the biofilm matrices of Staphylococcus aureus ATCC 23235 by biogenic AgNPs and phytoextract Structural composition disruption of the biofilm matrices, namely carbohydrates, proteins, and eDNA leads to biofilm destabilization. It was observed that the maximum reduction of carbohydrates, proteins, and eDNA of the EPS matrix was brought about due to the activity of the biogenically synthesized AgNPs than the phytoextract alone or tetracycline. Carbohydrate content was reduced by about $90.12\%$ ± $0.56\%$ by the biogenic AgNPs while that with phytoextract and tetracycline were about $65\%$ ± $1.03\%$ and $50\%$ ± $0.98\%$ (Figure 8A). The content of protein was significantly reduced by around $85\%$ ± $1.26\%$ by the biogenic AgNPs while that with phytoextract and tetracycline were around $65\%$ ± $0.75\%$ and $45\%$ ± $1.02\%$ (Figure 8B). The eDNA content was reduced to about $80\%$ ± $1.23\%$ by the biogenic AgNPs in comparison to the phytoextract, which reduced the eDNA content by $65\%$ ± $0.85\%$, and tetracycline, which decreased the content of eDNA by $50\%$ ± $0.45\%$ (Figure 8C). FIGURE 8: Probable mode of action involved in the eradication of biofilm matrix by biogenically synthesized AgNPs from Tinospora cordifolia. ## 4.6 FTIR analysis of the EPS modification by the biogenic AgNPs and phytoextract FT- IR was performed for analyzing the modifications in the functional groups of the EPS matrix of S. aureus ATCC 23235 after treating with the biogenically synthesized AgNPs and the phytoextract (Figure 9). Remarkable modifications in the spectral regions of polysaccharides (890–1175 cm-1), lipids (3,000–2800 cm-1), proteins (1700–1500 cm-1), and nucleic acids (1,300–900 cm-1) were analyzed in the FT- IR spectroscopy. The biogenic AgNPs brought about the highest reduction in peak intensities, shape alterations, and shifts in wavelengths of S. aureus ATCC 23235 in comparison with the control sample and sample treated with phytoextract. This suggests that the biogenic AgNPs could directly interact and decrease the concentration of various EPS constituents such as polysaccharides, lipids, and nucleic acids as evidenced by the reduced peak intensity data. FIGURE 9: Comparative FT-IR spectra showing the effect of treatment by phytocompound and AgNPnanoconjugate. ## 4.7 Photomicrographic analyses of the removal of biofilm by treatment with biogenic AgNPs and phytoextract The best anti-biofilm activity was observed with the biogenic AgNPs than the phytoextract and this indicates that the phytoextract acts synergistically well when combined with NPs than phytoextract alone. AgNP- treated (Figure 10B) and phytoextract-treated (Figure 10C) bacterial sessile cells were observed under SEM, which depicted clear biofilm disruption of the sessile cells after treatment as compared with the control samples (Figure 10A). FIGURE 10: Scanning electron micrograph of Staphylococcus aureus (A) before and (B) after treatment with biogenic AgNPs (C) Staphylococcus aureus ATCC 23235 after treatment with phytoextract seen at a magnification of × 15000. ## 5 Conclusion The application of natural products for human welfare is time immemorial and their usage are getting enhanced with every passing day. Tinospora cordifolia is a widely available weed possessing numerous health-beneficial activities (Ahmad et al., 2021) and can be used successfully for irreversible disruption of the biofilm-associated cells of S. aureus. T. cordifoliacan also be effectively used in the green synthesis of biogenic AgNPs from silver nitrate. Such kind of NP synthesis can be deemed to be environmentally friendly since it is free from any type of harmful chemicals or reducing substances since the entire NP synthesis process is biogenic. However, the exact mode of action of AgNPs on bacterial cells is yet to be known in detail. Some of the experimental results indicated that these NPs mainly interact with the cell surfaces of several bacteria (Mikhailova, 2020). On the surfaces of cells, the AgNPs get adhered to the cell wall and cell membrane of bacteria thereby penetrating deep inside the intracellular organelles and modifying the biomolecular signal transduction pathways. In the case of Gram-positive bacteria such as S. aureus, the AgNPs find their way to the cytoplasm by membrane property modification leading to the dissipation of proton motive force (PMF) and lead to the damage in the bacterial cell due to membrane destruction (Durán et al., 2016). The penetration of AgNPs lead to the development of oxidative stress within the cells leading to the generation of reactive oxygen species (ROS), which oxidize the double bonds of the membrane fatty acids allowing the production of free radicals and damage to the cell membrane (Vega-Baudrit et al., 2019). The preliminary step for the formation of biofilms gets inhibited by the presence of AgNPs. This is because the AgNPs can bind with the cellular surface thereby altering the adhesive compounds such as extra polymeric matrices, which are involved in the aggregation of bacterial cells and biofilm formation (Gurunathan et al., 2014). The biologically synthesized AgNPs have demonstrated good anti-biofilm efficacy, suggesting that they could be employed as an antibiofilm weapon against the biofilm-associated infections caused by S. aureus. Experimental observations clearly indicated that biofilm removal is accomplished through irreversible denaturation of EPS matrices and subsequent inhibition of biofilm formation by S. aureus. The mode of action of these biogenic AgNPs synthesized from the leaf extract of T. cordifoliais mainly by EPS matrix denaturation. Hence, these NPs can act as potential drug candidates for controlling chronic and persistent infections caused by the biofilms of S. aureus. ## Data availability statement The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors. ## Author contributions Conceptualization, SG, DL, MN, TS, SiP, SM, SoP, HE, and RR; writing—original draft preparation, DL, MN, TS, and RR; writing—review and editing, AA, SG, DL, MN, TS, SiP, SM, SoP, HE, and RR. All authors have read and agreed to the published version of the manuscript. ## Conflict of interest Author SiP was employed by the company NatNov Private Limited. 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--- title: 'Association of red cell distribution width with the risk of 3-month readmission in patients with heart failure: A retrospective cohort study' authors: - Fang Gu - Han Wu - Xiaoli Jin - Cheng Kong - Wenyan Zhao journal: Frontiers in Cardiovascular Medicine year: 2023 pmcid: PMC10028279 doi: 10.3389/fcvm.2023.1123905 license: CC BY 4.0 --- # Association of red cell distribution width with the risk of 3-month readmission in patients with heart failure: A retrospective cohort study ## Abstract ### Background In recent years, red cell distribution width (RDW) has been found to be associated with the prognosis of patients with heart failure (HF) in Western countries. However, evidence from Asia is limited. We aimed to investigate the relationship between RDW and the risk of 3-month readmission in hospitalized Chinese HF patients. ### Methods We retrospectively analyzed HF data from the Fourth Hospital of Zigong, Sichuan, China, involving 1,978 patients admitted for HF between December 2016 and June 2019. The independent variable in our study was RDW, and the endpoint was the risk of readmission within 3 months. This study mainly used a multivariable Cox proportional hazards regression analysis. Smoothed curve ﬁtting was then used to assess the dose-response relationship between RDW and the risk of 3-month readmission. ### Results In the original cohort of 1,978 patients with HF ($42\%$ male and $73.1\%$ aged ≥70 years), 495 patients ($25.0\%$) were readmitted within 3 months after discharge. Smoothed curve ﬁtting showed a linear correlation between RDW and the risk of readmission within 3 months. In the multivariable-adjusted model, every $1\%$ increase in RDW was associated with a $9\%$ increased risk of readmission within 3 months (hazard ratio = 1.09, $95\%$ confidence interval: 1.00–1.15; $P \leq 0.005$). ### Conclusions A higher RDW value was significantly associated with a greater risk of 3-months readmission in hospitalized patients with HF. ## Introduction Heart failure (HF) is one of the major global public health issues and a common cause of hospitalization in middle-aged and older adults [1]. Traditional and innovative therapies have been used to treat and manage HF, leading to a significant reduction in HF mortality [2, 3]. However, the high rate of readmission of HF patients within 3 months of discharge is an emerging problem that can cause a clinical and economic burden. Readmission of HF patients within 3 months after discharge may be seen as a warning sign of a worse subsequent prognosis for the patient. A study from Japan concluded that patients with HF who were readmitted within 3 months had a higher risk of death at long-term follow-up [4]; therefore, early identification of this group of patients with HF could optimize treatment more quickly, improve symptoms, increase patient survival, reduce readmission rates, and reduce the economic burden on families and society. Several variables have been associated with readmission in patients with HF. Apostolos et al. conducted a propensity score-matched observational study that included HF patients with ejection fraction use from the U.S. Medicare-related OPTIMIZE-HF registry from March 2003 to December 2004 and found that systolic blood pressure (SBP) <120 mmHg at discharge was associated with an increased risk of readmission in these patients [5]. However, this study included only patients with HF with an ejection fraction of ≥$50\%$; therefore, it is not possible to infer whether an SBP of <120 mmHg at discharge is associated with an increased risk of readmission in all HF patients. Ioanna et al. also studied 671 HF patients from the OPTIMIZE-HF registry [6]. They found that psychosocial factors were associated with 1-year readmission risk in HF patients. Still, their study sample size was small, and the questionnaire for assessing psychosocial factors was too cumbersome and complex for clinical replication. Therefore, we need to find an earlier and easier indicator to detect the risk of readmission and better manage this group of inpatients with HF. Red cell distribution width (RDW), an indicator of changes in red blood cell (RBC) size and shape, is easily obtained from a complete blood count and is a simple, inexpensive measure [7]. The RDW typically ranges from $11\%$ to $15\%$, and a higher RDW value indicates greater variability in size [8]. Traditionally, RDW has been used to differentiate the cause of anemia [9]. Recent clinical evidence suggests that changes in RBC size are associated with the development and adverse outcomes of non-hematologic diseases, such as stroke, osteoporosis, dementia, and cardiovascular disease (10–13). Especially in patients with HF, RDW has been shown to be associated with first hospitalization and poor prognosis in HF [14, 15]. However, most of the available evidence is focused on the United States and Europe, and there are few studies related to Asia, especially China. Thus, this study aimed to investigate the relationship between RDW and the risk of 3-month readmission in hospitalized Chinese HF patients. ## Study population This study used a single-center database included in PhysioNet [16]. This dataset collected information on a total of 2,008 adult HF patients from December 2016 to June 2019 at the Fourth People's Hospital in Zigong, Sichuan, China, to understand the characteristics of the Chinese HF population [17]. The data collected in this dataset included demographic data, baseline clinical characteristics, co-morbidities, laboratory test results, medications, and outcomes. This study followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement and was designed to investigate whether elevated RDW is correlated with an increased risk of rehospitalization. The Ethics Committee of the Fourth People's Hospital of Zigong approved this study (approval number:2020-010). The requirement for informed consent was waived owing to the retrospective design of this study. This study was conducted in strict accordance with the Declaration of Helsinki. HF was defined according to the criteria of the European Society of Cardiology [18]. ## Study variables In this study, the exposure variable was RDW, and the primary outcome variable was the risk of readmission within 3 months. We also collected additional data from the database, including age, sex, Charlson Comorbidity Index (CCI) score, New York Heart Association (NYHA) cardiac function classification, type of HF (right, left, both), systolic blood pressure, diastolic blood pressure, body mass index, brain natriuretic peptide, white blood cell count, RBC count, hematocrit, hemoglobin, mean cell volume, mean cell hemoglbin content, mean cell hemoglobin concentration, platelet, total protein, albumin, alanine aminotransferase, aspartate transaminase, lactate dehydrogenase, alkaline phosphatase, γ-glutamyltranspeptidase, and estimated glomerular filtration rate (eGFR, ml/min per 1.73 m2)). ## Statistical analysis Categorical variables were analyzed using percentages, and normally continuous variables were expressed as mean ± standard deviation. Creatinine levels were included in the Chronic Kidney Disease Epidemiology Collaborative Study formula to estimate the glomerular filtration rate. The patients were divided into three groups according to the tertiles of RDW. First, this study used linear regression models and χ2 tests to compare the baseline characteristics of the patients in different groups. Second, univariate and multivariate Cox proportional hazards regression analyses were applied to estimate the correlation between RDW and the probability of readmission within 3 months. Three models were simultaneously calculated as follows: a non-adjusted model, not adjusted for potential confounders; a minimally adjusted model, adjusted for age and sex; a multivariable-adjusted model, adjusted for confounders in the minimally adjusted model + body mass index, CCI score, NYHA cardiac function classification, type of HF, brain natriuretic peptide, RBC, HB, total protein, albumin, alanine aminotransferase, lactate dehydrogenase, and eGFR. The covariates selected for adjustment were based on the fact that their addition to the model changed the regression coefficient by at least $10\%$. $95\%$ confidence intervals (CIs) and hazard ratios (HRs) were estimated for all models. Third, sensitivity analyses were conducted in this study to test the robustness of the results. The RDW was converted into a categorical tertile variable. The P for the trend was calculated. We examined whether the results were consistent with those of RDW as a continuous variable. Stratiﬁed analyses and interactions were implemented according to age, sex, CCI score, NYHA cardiac function classification, HF type, eGFR, and absolute iron deficiency. Absolute iron deficiency referred to an RDW > $15\%$ with: either a mean cell volume <80 fl or a mean cell hemoglobin content <27 pg or a mean cell hemoglobin concentration <32 g/dl [19]. We used smooth curve ﬁttings (penalized spline method) to evaluate the dose-response relationship between RDW and the risk of 3-month readmission. A cumulative 3-month readmission-free probability analysis was performed using Kaplan–Meier curves with log-rank statistics according to the different groups. All tests were two-sided, and $P \leq 0.05$ was considered statistically significant. Data were analyzed using the R statistical package (R Foundation for Statistical Computing, Vienna, Austria; http://www.r-project.org; version 3.6.3) and Free Statistics software version 1.7. ## Patient selection The database included 2008 patients with HF, 30 of whom were excluded because of missing RDW values on admission. Therefore, 1,978 patients were included in the analysis. ## Baseline characteristics The selected patient characteristics are shown in Table 1. Of these patients, $73.1\%$ were older than 70 years, and $42\%$ were male. Compared with the patients in the lower RDW groups (tertile 1 and tertile 2), patients with a higher RDW (tertile 3) were more likely to be male, have total HF, have higher CCI scores, NYHA class, brain natriuretic peptide, lactate dehydrogenase, alkaline phosphatase, γ-glutamyltranspeptidase, and have lower body mass index, RBC count, mean cell volume, mean cell hemoglobin content, mean cell hemoglobin concentration, hematocrit, HB, total protein, albumin, and eGFR ($P \leq 0.01$). Table 1 \| Characteristics \| Red cell distribution width (%) \| Red cell distribution width (%).1 \| Red cell distribution width (%).2 \| Red cell distribution width (%).3 \| P-value \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Characteristics \| Total \| Tertile1 (11.8–13.8) \| Tertile 2 (13.9–15.0) \| Tertile 3 (15.1–29.9) \| P-value \| \| Characteristics \| n = 1,978 \| n = 634 \| n = 669 \| n = 675 \| P-value \| \| Age, year \| \| \| \| \| <0.001 \| \| <70 \| 533 (26.9%) \| 207 (32.6%) \| 158 (23.6%) \| 168 (24.9%) \| \| \| ≥70 \| 1,445 (73.1%) \| 427 (67.4%) \| 511 (76.4%) \| 507 (75.1%) \| \| \| Male \| 830 (42.0%) \| 267 (42.1%) \| 276 (41.3%) \| 287 (42.5%) \| \| \| CCI score \| \| \| \| \| 0.074 \| \| <3 \| 1,503 (76.2%) \| 491 (77.9%) \| 519 (77.6%) \| 493 (73.1%) \| \| \| ≥3 \| 470 (23.8%) \| 139 (22.1%) \| 150 (22.4%) \| 181 (26.9%) \| \| \| NYHA cardiac function classification \| NYHA cardiac function classification \| \| \| \| <0.001 \| \| Class II \| 341 (17.2%) \| 124 (19.6%) \| 113 (16.9%) \| 104 (15.4%) \| \| \| Class III \| 1,027 (51.9%) \| 357 (56.3%) \| 337 (50.4%) \| 333 (49.3%) \| \| \| Class IV \| 610 (30.8%) \| 153 (24.1%) \| 219 (32.7%) \| 238 (35.3%) \| \| \| Type of heart failure \| \| \| \| \| 0.004 \| \| Right \| 50 (2.5%) \| 17 (2.7%) \| 20 (3.0%) \| 13 (1.9%) \| \| \| Left \| 464 (23.5%) \| 169 (26.7%) \| 168 (25.1%) \| 127 (18.8%) \| \| \| Both \| 1,464 (74.0%) \| 448 (70.7%) \| 481 (71.9%) \| 535 (79.3%) \| \| \| SBP, mmHg \| 131.2 ± 24.8 \| 132.8 ± 23.9 \| 131.2 ± 24.8 \| 129.6 ± 25.4 \| 0.058 \| \| DBP, mmHg \| 76.6 ± 14.5 \| 76.6 ± 14.1 \| 77.3 ± 14.7 \| 75.8 ± 14.7 \| 0.172 \| \| BMI, kg/m2 \| 21.8 ± 13.7 \| 23.2 ± 23.1 \| 21.2 ± 6.0 \| 21.1 ± 3.9 \| 0.006 \| \| BNP, pg/ml \| 1,285.2 ± 1,353.4 \| 953.3 ± 1,201.3 \| 1,321.4 ± 1,308.8 \| 1,562.9 ± 1,463.1 \| <0.001 \| \| WBC, 109/L \| 7.3 ± 3.5 \| 7.5 ± 3.3 \| 7.4 ± 3.3 \| 7.1 ± 3.8 \| 0.076 \| \| RBC, 1012/L \| 3.9 ± 0.8 \| 3.9 ± 0.6 \| 3.9 ± 0.7 \| 3.8 ± 0.9 \| <0.001 \| \| MCH, pg \| 29.9 ± 3.4 \| 31.5 ± 1.8 \| 30.6 ± 2.5 \| 27.8 ± 4.2 \| <0.001 \| \| MCV, fl \| 92.0 ± 8.8 \| 94.9 ± 4.9 \| 93.6 ± 7.1 \| 87.8 ± 11.2 \| <0.001 \| \| MCHC, g/L \| 324.8 ± 14.0 \| 332.2 ± 10.0 \| 326.8 ± 10.2 \| 316.0 ± 15.6 \| <0.001 \| \| HCT, % \| 0.4 ± 0.1 \| 0.4 ± 0.1 \| 0.4 ± 0.1 \| 0.3 ± 0.1 \| <0.001 \| \| HB, g/L \| 115.1 ± 24.5 \| 122.9 ± 19.9 \| 119.3 ± 21.6 \| 103.6 ± 26.9 \| <0.001 \| \| PLT, 109/L \| 145.2 ± 65.0 \| 150.7 ± 57.2 \| 140.9 ± 61.1 \| 144.3 ± 74.6 \| 0.021 \| \| Total protein, g/L \| 65.1 ± 7.4 \| 65.8 ± 6.7 \| 64.9 ± 7.0 \| 64.6 ± 8.2 \| 0.007 \| \| ALB, g/L \| 36.5 ± 5.0 \| 37.7 ± 4.6 \| 36.9 ± 4.7 \| 35.0 ± 5.2 \| <0.001 \| \| ALT, U/L \| 54.2 ± 205.2 \| 43.8 ± 166.3 \| 56.1 ± 237.8 \| 62.1 ± 202.8 \| 0.278 \| \| AST, U/L \| 63.6 ± 299.2 \| 52.1 ± 284.0 \| 52.6 ± 168.7 \| 85.9 ± 399.3 \| 0.088 \| \| LDH, IU/L \| 274.1 ± 254.6 \| 247.6 ± 175.5 \| 260.8 ± 161.4 \| 313.4 ± 367.5 \| <0.001 \| \| ALP, U/L \| 89.6 ± 45.2 \| 81.6 ± 33.7 \| 89.3 ± 37.2 \| 97.3 ± 58.6 \| <0.001 \| \| GGT, U/L \| 61.3 ± 68.8 \| 52.8 ± 53.0 \| 64.5 ± 69.6 \| 65.9 ± 79.6 \| 0.001 \| \| eGFR, ml/min/1.73 m2 \| 68.6 ± 36.7 \| 73.3 ± 36.8 \| 67.6 ± 34.6 \| 65.2 ± 38.0 \| <0.001 \| ## Association between RDW and the risk of readmission within 3 months Univariate analysis suggested that age, CCI score, cardiac function class, NYHA cardiac function classification, systolic blood pressure, diastolic blood pressure, RBC, hematocrit, HB, eGFR, and RDW were associated with readmission risk within 3 months ($P \leq 0.05$) (Table 2). Table 2 \| Covariate \| HR (95% CI) \| P-value \| \| --- \| --- \| --- \| \| Age, year \| Age, year \| Age, year \| \| <70 \| Ref \| \| \| ≥70 \| 1.299 (1.053, 1.604) \| 0.015 \| \| Sex \| Sex \| Sex \| \| Female \| Ref \| \| \| Male \| 1.051 (0.880, 1.256) \| 0.582 \| \| CCI score \| CCI score \| CCI score \| \| <3 \| Ref \| \| \| ≥3 \| 1.495 (1.234, 1.812) \| 0.000 \| \| NYHA cardiac function classification \| NYHA cardiac function classification \| NYHA cardiac function classification \| \| Class II \| Ref \| \| \| Class III \| 1.496 (1.124, 1.990) \| 0.006 \| \| Class IV \| 1.947 (1.450, 2.615) \| <0.001 \| \| Type of heart failure \| Type of heart failure \| Type of heart failure \| \| Right \| Ref \| \| \| Left \| 0.885 (0.459, 1.706) \| 0.715 \| \| Both \| 1.460 (0.780, 2.735) \| 0.237 \| \| SBP \| 0.991 (0.987, 0.994) \| <0.001 \| \| DBP \| 0.992 (0.985, 0.998) \| 0.007 \| \| BMI \| 1.001 (0.994, 1.007) \| 0.831 \| \| BNP \| 1.000 (1.000, 1.000) \| 0.002 \| \| WBC \| 0.992 (0.966, 1.018) \| 0.523 \| \| RBC \| 0.837 (0.747, 0.938) \| 0.002 \| \| HCT \| 0.191 (0.055, 0.664) \| 0.009 \| \| HB \| 0.996 (0.992, 0.999) \| 0.023 \| \| PLT \| 0.999 (0.998, 1.001) \| 0.456 \| \| Total protein \| 0.995 (0.983, 1.008) \| 0.448 \| \| ALB \| 1.003 (0.985, 1.022) \| 0.750 \| \| ALT \| 1.000 (0.999, 1.000) \| 0.420 \| \| AST \| 1.000 (0.999, 1.000) \| 0.528 \| \| LDH \| 1.000 (1.000, 1.000) \| 0.690 \| \| ALP \| 1.001 (0.999, 1.003) \| 0.311 \| \| GGT \| 1.001 (0.999, 1.002) \| 0.341 \| \| eGFR \| 0.995 (0.992, 0.998) \| 0.000 \| \| RDW \| 1.065 (1.024, 1.107) \| 0.001 \| Table 3 shows that 495 ($25.0\%$) participants were readmitted within 3 months. In the RDW tertile 1–3 groups, 132 ($20.8\%$), 175 ($26.2\%$), and 188 ($27.9\%$) patients with HF, respectively, were readmitted within 3 months. The results of the multivariable Cox proportional hazards regression analysis suggested that an elevated RDW was associated with an increased risk of readmission within 3 months. In the multivariable-adjusted model, every $1\%$ increase in RDW was associated with a $9\%$ increased risk of readmission within 3 months (HR = 1.09, $95\%$ CI: 1.00–1.15, $$P \leq 0.003$$). We also analyzed RDW as a categorical variable one more time. Compared with participants with a RDW lower than $13.8\%$ (tertile 1), the probability of readmission within 3 months increased by $37\%$ in those with RDW levels of $15.1\%$–$29.9\%$ (tertile 3) in the multivariable-adjusted model. P-values for the trend tests were all less than 0.05, indicating that our findings were robust. Table 3 \| RDW, % \| No. of events \| The risk of 3-month readmission, HR (95% CI), P-value \| The risk of 3-month readmission, HR (95% CI), P-value.1 \| The risk of 3-month readmission, HR (95% CI), P-value.2 \| \| --- \| --- \| --- \| --- \| --- \| \| RDW, % \| No. of events \| Non-adjusted model \| Minimally adjusted model \| Multivariable adjusted model \| \| Per 1% increment \| 495(25.0%) \| 1.07 (1.02, 1.11) 0.0015 \| 1.06 (1.02, 1.11) 0.0025 \| 1.09 (1.03, 1.15) 0.0039 \| \| Tertile \| Tertile \| Tertile \| Tertile \| Tertile \| \| Tertile 1 (11.8–13.8) \| 132(20.8%) \| Ref \| Ref \| Ref \| \| Tertile 2 (13.9–15.0) \| 175(26.2%) \| 1.29 (1.03, 1.62) 0.0276 \| 1.27 (1.01, 1.59) 0.0392 \| 1.19 (0.92, 1.54) 0.1952 \| \| Tertile 3 (15.1–29.9) \| 188(27.9%) \| 1.38 (1.10, 1.72) 0.0049 \| 1.35 (1.08, 1.68) 0.0090 \| 1.37 (1.03, 1.83) 0.0307 \| \| P for trend \| \| 0.006 \| 0.010 \| 0.035 \| ## Dose-response relationship between RDW and the risk of readmission within 3 months The correlation between RDW and the risk of readmission within 3 months was evaluated on a continuous scale using smoothed curve fitting (restricted cubic spline method) based on the Cox proportional hazards models. The fully adjusted smoothed curve fitting showed a linear association between RDW and the risk of readmission within 3 months, with a P-value for non-linearity of 0.993 (Figure 1). When the RDW was greater than $14.4\%$, the HR of readmission within 3 months for patients with HF was greater than 1. Figure 1: Dose-response relationships between RDW and the hazard ratio of readmission within 3 months. The black solid line represents the estimated risk of readmission within 3 months, with dashed lines showing 95% confidence intervals. The blue histogram at the bottom represents the distribution of RDW. Analyses were adjusted for age, sex, BMI, CCI score, NYHA cardiac function classification, type of heart failure, BNP, RBC, HB, total protein, ALB, ALT, LDH, and eGFR. RDW, red cell distribution width; BMI, body mass index; CCI, charlson comorbidity index; NYHA, new york heart association; BNP, brain natriuretic peptide; RBC, red blood cell, HB, hemoglobin; ALB, albumin; ALT, alanine aminotransferase; LDH, lactate dehydrogenase; eGFR, estimated glomerular filtration rate. ## Subgroup analysis The stratiﬁcation and interaction analyses of the correlation between RDW and the risk of readmission within 3 months for patients with HF are shown in Figure 2. The results of the subgroup analysis were highly consistent with those of the multivariate Cox regression analysis. Interaction analysis results showed no interactive roles in the subgroup. Figure 2: Association between RDW and the hazard ratio of readmission within 3 months according to subgroup. Analyses were adjusted for age, sex, BMI, CCI score, NYHA cardiac function classification, type of heart failure, BNP, RBC, HB, total protein, ALB, ALT, LDH, eGFR, and absolute ID, except for the stratification variable. RDW, red cell distribution width; BMI, body mass index; CCI, charlson comorbidity index; NYHA, new york heart association; BNP, brain natriuretic peptide; RBC, red blood cell, HB, hemoglobin; ALB, albumin; ALT, alanine aminotransferase; LDH, lactate dehydrogenase; eGFR, estimated glomerular filtration rate. ## Kaplan–Meier survival curve Patients with a lower RDW (tertile 1) had a significantly higher 3-month readmission-free probability than those with a higher RDW (tertiles 2 and, 3) ($$P \leq 0.014$$), as shown in Figure 3. Figure 3: Kaplan–Meier curves for 3-month readmission-free probability in the patients with heart failure for categories of RDW tertiles. RDW, red cell distribution width. ## Discussion This retrospective cohort study focused on determining the association between RDW and the risk of readmission within 3 months in patients with HF. It suggested that RDW was linearly and positively associated with the risk of readmission within 3 months in middle-aged and elderly patients with HF in China after adjusting for several major confounding factors. In the multivariable-adjusted model, we found that every $1\%$ increase in RDW was correlated with a $9\%$ increased risk of readmission within 3 months (HR = 1.09, $95\%$ CI: 1.03–1.15, $P \leq 0.005$) and the highest RDW group (tertile3) had a higher risk of 3-month readmission compared with the lowest group (tertile1). These results were consistent for each subgroup. RDW is strongly associated with the prognosis of patients with HF. Consistent with our results, previous studies have shown that the RDW at admission is associated with a multifactorial adjusted risk of rehospitalization. Remo et al. showed that patients with high RDW had a risk ratio of 1.55 ($95\%$ CI: 1.08–2.22) for death or reoccurrence of HF from any cause compared with patients with a low RDW [20]. Kyoung et al. found that readmission rates for older patients were higher in the highest group of the RDW category ($7.4\%$ in the lowest group and $15.8\%$ in the highest group, P-trend < 0.001) [21]. However, some studies have reported inconsistent results. Tan et al. included patients with HF in China by using multivariate logistic regression models to predict the risk of 3-month readmission and concluded that simple observation of RDW from clinical blood test reports could not be used to visually determine the risk of readmission [22]; the area under the red cell volume distribution width curve also confirmed its weaker discriminatory ability (C statistic = 0.61); however, their study included was only 350 patients, and the study population was limited to the HF population in central China. Our study had a larger sample size, and the patients were not limited to central China to further investigate the relationship between RDW and the risk of readmission within 3 months. RDW is a common clinical measure of heterogeneity in RBC size [23]. A higher RDW indicates a defect in the maturation or degradation of blood cells [24]. An elevated RDW has been shown to be associated with anemia, inflammatory diseases (e.g., infections and tumors), chronic kidney disease, diabetes mellitus, and cardiovascular diseases (e.g., HF, hypertension, stroke, and atrial fibrillation) (25–30). However, the specific mechanisms underlying the association between RDW and poor prognosis in chronic diseases, including HF, are not fully understood. Multiple interrelated pathological mechanisms, including nutritional deficiency, ineffective erythropoiesis, reduced iron mobilization, oxidative stress, chronic inflammation, adrenergic stimulation, and enhanced immune system activation, are thought to be associated with an increase in RDW and poor prognosis in HF patients [31, 32]. The main role of erythrocytes is to transport oxygen and carbon dioxide from the lungs to the tissues and to maintain the acid-base balance of the body [33]. Recent studies suggest that erythrocytes are involved in nitric oxide metabolism and have secretory functions (release of nitric oxide, nitric oxide metabolites, and adenosine triphosphate) [33]. The mediators released by erythrocytes play a key role in cardiovascular regulation and have a direct impact on the heart. Nutritional deficiencies lead to inadequate erythropoiesis and increased RDW [34]. Nutritional factors are critical for improving the medium-to-long-term prognosis of patients with HF. Chronic inflammation and oxidative stress can lead to increased RDW by suppressing effective bone marrow erythropoiesis and increasing RBC variability [35]. On the one hand, inflammatory markers (interleukin-6 and tumor necrosis factor-α) disrupt erythropoiesis by directly inhibiting erythroid precursors, affect iron metabolism, and reduce erythropoiesis; it also decreases erythropoietin secretion from the kidney [36, 37]. On the other hand, oxidative stress decreases erythrocyte survival and leads to increased circulating premature erythrocytes, resulting in anisocytosis and increased erythrocyte lysis, which leads to increased free radicals and adverse effects on the heart [38, 39]. Thus, higher oxidative stress is another potential mechanism linking RDW to a poor prognosis in patients with HF. Based on the subgroup analysis, we found that the association between RDW and the risk of readmission within 3 months was stronger in men than in women (HR: 1.18 vs. 1.06). Sex differences exist in almost all aspects of HF. First, there are differences in cardiac anatomy between men and women, with women without heart disease having a higher left ventricular ejection fraction than men [40]. Women with HF with preserved ejection fraction have smaller, stiffer hearts and more frequent concentric remodeling than men with HF with preserved ejection fraction [41]. Second, from a pathophysiological point of view, women with HF with reduced left ventricular ejection fraction have better adaptation of the myocardium to stress and a lower risk of ventricular tachycardia, atrial fibrillation, and sudden cardiac death than men [42, 43]. Third, several neurohormonal modulators have been shown to have better therapeutic effects in women with HF, but even before the recent introduction of neurohormonal modulators into HF treatment, clinical outcomes for women with HF with preserved or reduced ejection fraction were consistently better than for men [44]. Mechanistic studies to explain these sex-related differences in HF treatment are lacking, and further research is required to verify these phenomena. An analysis of PARADIGM-HF suggested that mortality and hospitalization rates are lower in women for reasons that are not yet clear [45]. Moreover, future consideration of gender-based differences in treatment is needed to reduce the risk of readmission. In the subgroup analysis, we found that the association between RDW and the risk of readmission within 3 months was stronger among patients with more co-morbidities (CCI score ≥3) and poor cardiac function (NYHA cardiac function class IV). Previous studies have demonstrated that co-morbidities are common in hospitalized elderly patients and are associated with long-term prognosis [46]. Moreover, other studies have documented co-morbidities and cardiac function class as independent adverse prognostic factors in elderly patients with acute HF [47], consistent with our results. Our study has the following strengths. First, this study had a larger sample size than previous studies. Second, the effect modification factor analysis allowed better use of the data to draw stable conclusions across different subgroups. However, this study has some limitations. First, because this was an observational study, we could not confirm a causal relationship between RDW and the risk of readmission within 3 months. Second, this study only assessed RDW values at the first admission. Therefore, it is impossible to evaluate the influence of dynamic changes in RDW on the risk of readmission within 3 months. Third, the subjects of this study were all patients with HF in China; therefore, there are some deficiencies in the extrapolation and generalizability of the study. In conclusion, we found that RDW was positively and linearly related to the risk of readmission within 3 months in patients with HF. Patients with HF with higher RDW values also had a higher risk of readmission within 3 months. Further studies on the ability of RDW to predict the risk of readmission are needed. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/$\frac{10.13026}{8}$a9e-w734. ## Ethics statement The studies involving human participants were reviewed and approved by the Ethics Committee of Zigong Fourth People's Hospital (approval number:2020-010). The patients/participants provided their written informed consent to participate in this study. ## Author contributions FG and HW: writing—original draft preparation. XJ: validation. CK and WZ: writing—review and editing. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Rethy L, Petito LC, Vu THT, Kershaw K, Mehta R, Shah NS. Trends in the prevalence of self-reported heart failure by race/ethnicity and age from 2001 to 2016. JAMA Cardiol (2020) 5 1425-9. DOI: 10.1001/jamacardio.2020.3654 2. Packer M, Anker SD, Butler J, Filippatos G, Pocock SJ, Carson P. 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--- title: Supporting mental health self-care discovery through a chatbot authors: - Joonas Moilanen - Niels van Berkel - Aku Visuri - Ujwal Gadiraju - Willem van der Maden - Simo Hosio journal: Frontiers in Digital Health year: 2023 pmcid: PMC10028281 doi: 10.3389/fdgth.2023.1034724 license: CC BY 4.0 --- # Supporting mental health self-care discovery through a chatbot ## Abstract Good mental health is imperative for one’s wellbeing. While clinical mental disorder treatments exist, self-care is an essential aspect of mental health. This paper explores the use and perceived trust of conversational agents, chatbots, in the context of crowdsourced self-care through a between-subjects study ($$n = 80$$). One group used a standalone system with a conventional web interface to discover self-care methods. The other group used the same system wrapped in a chatbot interface, facilitating utterances and turn-taking between the user and a chatbot. We identify the security and integrity of the systems as critical factors that affect users’ trust. The chatbot interface scored lower on both these factors, and we contemplate the potential underlying reasons for this. We complement the quantitative data with qualitative analysis and synthesize our findings to identify suggestions for using chatbots in mental health contexts. ## Introduction Good mental health is imperative for one’s general wellbeing. Conversely, mental disorders cause tremendous social [1] and economic [2] burdens worldwide. Higher education students are especially vulnerable, as they are typically at the peak onset of many mental disorders, such as depression and anxiety [3]. However, a staggering number of students suffering from symptoms never seek help, and many seek help far too late in the process [4]. To this end, support from one’s community has been identified as a valuable avenue to explore as a complementary mechanism to traditional healthcare and clinical interventions [5]. However, knowledge is often sparsely shared within the community due to stigma [6]. Novel research approaches and support mechanisms with a lower barrier for participation are required to address this. In addition to helping people with existing mental health conditions, it is important to maintain healthy mental wellbeing for those not feeling particularly ill. Support mechanisms have proved effective for preventive approaches as well [7]. One approach currently investigated for mental health is self-care. Self-care, in general, refers to how people take care of their wellbeing or a mental health condition on their own, either using the information found online or as instructed by their caretakers [8]. A community sharing a similar burden can be an excellent resource for self-care methods. While various other means of serving these methods exist, researchers are currently actively looking into the affordances of chat-based conversational agents, chatbots, due to their inherent relatability and rapidly increasing interaction capabilities (see, e.g., [9,10]). In our earlier work (unpublished in academic venues), we have crowdsourced an extensive list of self-care methods among the higher education community to uncover how students maintain and improve their mental health. These methods include, for example, meditation, spending time with others, volunteering, and working out at a gym, with additional methods presented in Figure 1. The students have also cross-evaluated each other’s contributions across a set of specific criteria. In this paper, we used this data to bootstrap a decision support system (DSS) that allows for discovering suitable self-care methods through an online user interface (UI) and by using the same criteria that were used to bootstrap the DSS (see Figure 1). To explore the potential use of chatbots in serving the DSS and trust in the system, we offered the DSS UI to 80 higher education students in a between-subjects study. The study groups consist of two groups of 40 participants through A) a standalone online DSS, and B) the DSS embedded in a narrative served by a conversational interface (see Figure 1). Figure 1: Snippets of our two platforms used in the study. (A) The system for mental health self-care discovery with sliders for different criteria (A1) and recommended methods (A2), based on the slider selections. (B) The chatbot with examples of asking found methods and presenting the user with a new task. In this work, we set out to find factors which affect the formed trust between a mental health chatbot and the user. We offer the users hundreds of crowdsourced methods for mental health self-care in an interface embedded in a chatbot conversation and compare that to a traditional web interface. We hypothesize that providing the users with clear instructions and interactive conversation alongside the method discovery could lead to improved trust towards the system. Our findings highlight that the participants interacting with the chatbot report lower perceived system security and integrity but no significant difference in the overall trust between the DSS group. The human-like behaviour of the chatbot also appears to affect trust for individual participants. Based on our findings, we argue that improving the (perceived) security and integrity of the chatbot will help design more effective chatbots for mental health. Furthermore, we find that using a chatbot for mental health self-care method discovery shows promise, with several participants stating their fondness towards the chatbot. While research in mental health chatbots and their trust is plentiful, we provide contributions to direct comparisons of two systems and how to further improve the trust towards them. In addition, we provide information on how viable these kind of crowdsourced methods are in digital healthcare. ## Related work Chatbots mimic human conversation using voice recognition, natural language processing, and artificial intelligence. Initial versions of chatbots operated purely through text-based communication, aiming to provide intelligent and human-like replies to its users [11]. Over the past decade, chatbots have grown in popularity, and together with voice-activated conversational agents such as Apple’s Siri, they have become part of everyday life [11]. ## Chatbots in mental health Mental health has been defined by the World Health Organization (WHO) as “a state of wellbeing in which the individual realizes his or her abilities, can cope with the everyday stresses of life, can work productively and fruitfully, and can make a contribution to his or her community” [12]. Mental health is a suitable context for chatbots due to their ability to provide dynamic interaction without relying on a professional’s availability [13], and the potential for chatbots to provide empathic responses [14]. In this article, we specifically focus on self-care for mental health. Self-care is used both to manage long-term conditions and to prevent future illnesses and has been identified as a critical approach to supporting independence, providing control to the patient rather than solely relying on a clinician, and reducing reliance on an overburdened healthcare system [15]. The application of chatbots as self-care tools is a relatively under-explored opportunity, with many open questions regarding identifying, monitoring, and evaluating self-care methods. Here, we focus on using a chatbot as a tool for discovering self-care solutions in mental health. Using chatbots in mental health care has grown in popularity in recent years [16,17] and point to the opportunity to support users in long-term self-care development and effectively communicate goals in response to prior and new user needs. While chatbots are not suitable to provide the users with actual clinical intervention, they are an excellent way to provide mental health counselling, such as presenting the users with various self-care methods to help them improve their mental health [18]. While most research for chatbots offering self-care focuses on young people, it has been shown to be effective for older adults, as well, as is shown by Morrow et al., who present a framework for the design of chatbots on health-related self-care for older adults [9]. As is found in the review by Abd-Alrazaq et. al. [ 17], using chatbots for these kind of purposes can improve their mental health, but should commonly be used as an adjunct to intervention with a healthcare professional. In addition, using chatbots in mental health is not without its risks. One of the most crucial things to be taken into consideration when designing a mental health chatbot is its ability to reply accordingly to the user’s messages; for example, poorly managing the responses to suicidal behaviour might lead to serious consequences [16]. Various factors affect the overall effectiveness of a chatbot, but in this research we focus specifically on the user’s trust towards the chatbot. A vital prerequisite to offering mental health through chatbots is to build a level of trust between the user and the chatbot. Müller et al. find that a lack of trust in chatbots results in reduced uptake of these digital solutions [19]. Furthermore, there are several factors to be taken into account to further enhance the perceived trust for chatbots, most notably, the chatbot’s personality, knowledge and cognition have shown to increase trust [20]. Previous research shows promise in building trust between a chatbot offering counselling and the user [21] and that the use of conversational interfaces as compared to a conventional web interface can lead to better performance and user experience [22]. Recent work by Gupta et al. shows a similar setting to ours and an increase to trust when using a chatbot as opposed to a traditional web interface for housing recommendations [23]. ## Trust in computer systems Trust and its formation have been important topics in automated [24,25] and online systems [26,27], as well as specifically in chatbots [28]. The definition of trust varies, but for this paper, we define it as “the attitude that an agent will help achieve an individual’s goals in a situation characterized by uncertainty and vulnerability” [25]. An agent can refer to a human individual but also a chatbot. Zhang & Zhang highlight the many factors that affect trust, stating that trusting behaviour is formed from an individual’s trust beliefs, attitude towards trust, and trust intention, which are further influenced by, for example, external environmental factors [26]. Work by Tolmeijer et al. shows that trust develops slowly, with the user’s initial trust impression having a large anchoring effect [29]. To measure trust, we used the “trust in the automation” scale by Jian et al. [ 24], which is one of the most widely used scales for measuring trust. Several other scales for measuring trust exist, but as most revolve around the same core topics and the scale by Jian offers easily interpretable results, we deemed this scale suitable for our purposes. Extensive research by Nordheim [30] shows that trust in chatbots is formed with factors such as risk, brand, and expertise, which are covered in our survey questions, presented in Table 1. In addition, the concept of trust seems to be similar for both human-human and human-machine situations [24]. Table 1 \| # \| Question \| \| --- \| --- \| \| Q1 \| The system is deceptive \| \| Q2 \| The system behaves in an underhanded manner \| \| Q3 \| I am suspicious of the system’s intent, action, or outputs \| \| Q4 \| I am wary of the system \| \| Q5 \| The system’s actions will have a harmful or injurious outcome \| \| Q6 \| I am confident in the system \| \| Q7 \| The system provides security \| \| Q8 \| The system has integrity \| \| Q9 \| The system is dependable \| \| Q10 \| The system is reliable \| \| Q11 \| I can trust the system \| \| Q12 \| I am familiar with the system \| ## Crowdsourcing decision support system We used a publicly available lightweight crowdsourcing tool developed by Hosio et al. [ 31] to collect and assess mental health self-care methods. The tool is implemented using HTML, Javascript, PHP, and MySQL, and can be deployed on any website using a standard HTML iFrame tag. We will refer to this tool as the Decision Support System or DSS. The DSS has three main components; users can search for methods, rank existing methods, and input new methods to the system. A similar system framework has been adapted to other studies, e.g., for crowdsourcing treatments for low back pain [32] and personalized weight-loss diets [33]. In the context of this work, the study participants use the search component. Using the decision support interface, as depicted in Figure 1A, participants can search for self-care methods through a configuration of six different sliders that adjust familiarity, effectiveness, affordability, required level of sociality, the time required to get started, and ease of getting started. After the sliders have been adjusted, the tool presents the participant with the mental health self-care methods that best match the criteria configuration. Each of the six characteristics was rated by the users of the tool during the data collection. Data collection of the shown methods was conducted before this study. The tool was made publicly available, and it was used to collect new mental self-care methods from its users and requested users to rate and validate pre-existing methods in the system. Methods were collected from over 900 participants, and over 30 000 individual ratings for hundreds of different self-care methods were obtained during the study. In addition to these methods, the participants were asked for open feedback on where, how, and why they seek self-care-related information. As these components of the tool are not the focus of this article, we point the reader to [32] for more information about its functionalities. ## Chatbot implementation In this study, we were interested in exploring whether wrapping the tool in a conversational interface where participants could converse with an agent would affect the perceived trust or other aspects of the system. We purchased a license to BotStar1 to use as the chatbot. BotStar supports opening external URLs in a full-screen modal popup as part of the conversation flow, which is how we embedded the DSS among the scripted conversation. A snippet of the used conversation script can be seen in Figure 1B. The chatbot was fully implemented via BotStar and was launched on a remote WordPress page. ## Post-task survey After completing the three tasks of using the DSS either through the web interface and instructions, or while interacting with the chatbot, each participant responds to a final questionnaire through Google Forms. The final questionnaire contains the trust in automation scale items (see Table 1) using a 7-point Likert Scale (1 = Strongly Disagree, 2 = Disagree, 3 = Somewhat Disagree, 4 = Neutral, 5 = Somewhat Agree, 6 = Agree, 7 = Strongly Agree) and three open-ended follow-up questions; •F1: We asked you to search for mental health self-care methods with criteria of your own choice. What are your thoughts on the results?•F2: What kind of support do you expect from a system offering mental health self-care techniques?•F3: What features affected your trust in the system? ## System overview The full study system setup consists of the following components, and is presented in detail in Figure 2: •Prolific: The study is deployed on the Prolific2 crowdsourcing platform, where participants are given instructions and links to proceed to their study tasks.•DSS Platform: The decision support system is deployed as a web interface on our remote server.•Chatbot: The chatbot is self-hosted using BotStar on our remote server. For the chatbot participant group, the DSS platform is opened inside the chatbot using embedded web views.•Final Questionnaire: The final questionnaire for the participants is deployed using Google Forms. Figure 2: Flow diagram of the study design for the two study groups. The WEB group does every part of the study in the DSS, while the CB group switches from the chatbot to embedded DSS only for task completion. The chatbot briefly asks, what methods the user found from the CB group. ## Experimental setup and protocol For this article, the two study groups are named and referred to as follows: •CB group: Group using the chatbot that wraps the online DSS•WEB group: Group using only the online DSS. Participants were recruited from Prolific, an online crowdsourcing platform. The participants were pre-filtered to higher education students using the platform’s quality control mechanisms. Participants were rewarded USD2.03–USD2.54 based on a task duration of 11–15 min. Participants are anonymous; thus, no approval for human subject research was needed beyond our project-wide approval from our University’s ethics board. Participants were asked to give consent at the beginning of the study and could terminate their participation at any point of the study. We asked participants from both groups to look for self-care methods three times; first to explore methods that are the most affordable, then methods requiring only a little time to get started but which are at the same time the most effective, and finally methods that would suit the participants’ own needs the best. In the CB group, these instructions were given by the chatbot, and in the WEB group, the instructions were given on top of the web page in a simple notice box. In the CB group, we focused on making the narrative realistic and neutral tone. To this end, the chatbot walks the participants through the process of discovering self-care methods, providing them with detailed instructions on what to do next (Figure 1B). To add a level of human-like conversation between the chatbot and the participant, the participant can be called by a nickname. The chatbot greets them at the beginning and expresses their gratitude at the end of the session. In the middle of the conversation, the participants are prompted to complete the same tasks as in the other experimental condition. At the end of each task, the chatbot asks what methods the participant found during this round. To ensure comparability between conditions, participants use the same tool for identifying a suitable self-care method. After completing the three aforementioned tasks of identifying self-care methods, participants were directed to the final questionnaire. To evaluate the trust and credibility in both conditions, we use the 12-item questionnaire for trust between people and automation proposed by Jian et al. [ 24] presented in Table 1. This scale has been created using large amounts of empirical data and helps us understand how different system characteristics affect users’ trust. In addition, we ask three open-ended questions to determine what the users think of the methods recommended for them, what kind of support they expect from a system offering mental health self-care methods, and which factors affected their trust in the system. This study setup is presented in Figure 2 ## Participant demographics 43 of the participants identify themselves as male and 37 female. The average age was 23.26 (SD = 4.59) years. 72 of the participants reside in Europe, the two most represented countries being Portugal ($$n = 20$$) and the UK ($$n = 13$$). 52 participants were undergraduate students, 24 graduate students, and 5 doctoral students. The mean age for the two groups was 24.1 (SD = 5.89) years for the CB group versus 22.43 (SD = 2.44) for the WEB group. ## Quantitative analysis Scores for the trust in automation scale are presented in Figure 3. The CB group reports approximately half a point lower values for both the security and integrity question than the WEB group with a significant difference ($p \leq 0.05$). For the other questions, the difference between the two groups is similar, but with $p \leq 0.05$, so these findings are not statistically significant. In addition, we found a significant difference in positive trust, with the CB group reporting approximately 0.5 lower scores compared to the WEB group ($p \leq 0.05$). Figure 3: Scores of the trust in automation scale items for both study groups. Student’s t-test shows significant difference for Q7 (The system provides security): CB: 3.93 (SD = 1.58), WEB: 4.6 (SD = 1.34), t = −2.07, p = 0.042, Cohen’s d = 0.46, and Q8 (The system has integrity): CB: 4.25 (SD = 1.46), WEB: 5 (SD = 1.18), t = −2.53, p = 0.014, Cohen’s d = 0.57. As the performed t-tests were independent of each other, there was no need for Bonferroni corrections. ## Trust scores Trust scores for the trust in automation survey were determined by taking the combined means of the positively worded items (Q6-Q12) and reverse-scored negatively worded items (Q1-Q5). Similarly, trust scores for positive and negative items were determined. For the overall and positive trust scores, a higher value indicates a bigger trust, whereas, for the negative trust scores, a lower value signifies a bigger trust [24]. Positive trust: CB: 4.07 (SD = 1.25), WEB: 4.57 (SD = 0.95). t = −1.98, $$p \leq 0.03$$, Cohen’s d: 0.45. Negative trust: CB: 2.56 (SD = 1.20), WEB: 2.7 (SD = 1.33). t = −0.51, $$p \leq 0.31$$, Cohen’s d: 0.11. Overall trust: CB: 4.64 (SD = 1.12), WEB: 4.87 (SD = 0.98). t = −0.96, $$p \leq 0.17$$, Cohen’s d: 0.22. ## Qualitative analysis To obtain a more in-depth understanding of the underlying reasons behind participants’ differences in trust, we conducted a thematic analysis of the participants’ feedback responses F1–F3 as collected directly after using the system. This analysis was conducted by using conventional content analysis [34] in which two of the paper’s authors tagged the feedback responses in a shared online document with key themes present in the given response. In case of disagreement in categorizing participant responses, a third author was included in the discussion. We present the three themes that we identified to affect participant trust; “personal experiences,” “perceived reliability,” and “presentation of results.” These themes are present in both two study groups and we found no significant difference to how often each theme appeared between the CB and WEB groups. ## Personal experiences The ability to relate presented results to one’s own experiences strongly affected participants’ trust. Being presented with familiar results leads to participants being more receptive to methods they had not seen before. One participant discusses this notion in terms of establishing initial trust in the system; “I found that my trust was established when I saw many techniques that I use, like mindfulness, running, etc. I think it was how familiar I was with the techniques shown that established my trust.” ( P21). On the other hand, one participant that had poor experiences with some of the methods suggested by the system expressed that the inclusion of these methods negatively affected their trust level; “disagreeing with some of the suggestions made me question how good it was” (P25). In addition to relying on their own experience in assessing and evaluating presented methods, several participants also expressed their wish for a future system to incorporate their own experiences to provide more accurate suggestions. “ I expect the system to be able to assist me in choosing the most suitable techniques for me, giving me the right information and listening to my needs” (P14). Participants’ responses indicate that they are willing to put in the additional effort required to provide this data if it would result in more valuable suggestions; “A short questionnaire done previously so that the system can make more accurate suggestions.” ( P05). Such an approach may also help present the mental health self-care methods a participant already has experienced differently. ## Perceived reliability While our quantitative results indicate that participants typically trusted the results presented by the system, our analysis of the open-text responses also highlights why some participants indicated a lower level of trust in the presented results. A couple of participants highlighted a lack of information on the people who contributed to the system’s data as an obstacle to building trust. “ Not knowing much about the ‘community’ behind the system.” ( P26) and “You cannot know precisely who is suggesting what.” ( P47) highlight these perspectives. In line with the aforementioned theme of personal experiences, our participant responses indicate a tight balance between novel suggestions to which participants might express some uneasiness and well-known, established ideas that participants could dismiss as too obvious. For example, one participant highlighted that they had high trust in the presented results but that the methods were not extremely helpful to them; “I thought the techniques would show me something ‘new’ that I have not heard of yet, but I have already tried most of them. I also think the techniques are too focused on depression and anxiety, which I guess is what most people suffer from, but I was expecting something more…groundbreaking?” ( P75). On the other hand, other participants commented that the lack of novelty did not affect their perceived usefulness or trust in the system; “I think the results were rather expected, but that does not make them worse in any way. The system provided simple solutions and great ideas overall. ( P27), indicating that being familiar with the suggestions increases the perceived reliability of the overall system. Even as participants may have experience with some of the presented self-care methods, this did not necessarily deter them from trying out any of the other methods; “Some of the techniques do not work for me, but some I have not thought about it and might be good to give them a try.” ( P31). ## Presentation of results Lastly, a large part of the participant sample showed how results were presented as a trust-affecting factor. Several participants mentioned a lack of uniformity in presenting self-care methods as having a negative effect. While this lack of uniformity directly results from the crowdsourced nature of the self-care methods and our deliberate choice not to edit participant contributions, some of these issues can be addressed relatively quickly in future iterations. For example, one participant commented on the capitalization of the methods “The results […] were not written uniformly (some lacked capitalization, other did not, which is fine but weird)” (P10). Similarly, another participant highlighted grammatical errors, as well as inconsistency in capitalization, as a trust-impeding factor in “poor capitalization/non-standard grammar” (P18). Although participants identified these errors as problematic, this lack of ‘strict’ grammar and spelling also highlighted to the participants that these methods were contributed by other users; “it seems that a lot of the options were submitted by users, the spelling was wrong in some places too” (P34) Without explicitly being asked to do so, several participants commented positively on how the chatbot presented the system to the user; “The fact that the system used a very humane, calm and warm language. The fact that it called me by nickname also affected my trust in the system.” ( P14). Similarly, another participant mentioned that their trust was positively affected by “[…] the aesthetic and the reassuring phrases” (P42). While not made explicit by the participants, the sensitive nature of mental health can play a significant role in this expressed sentiment. While participants typically found the presented discovery interface useful, they also highlighted that additional information would be helpful. In particular, a couple of participants highlighted that the tool could do more to help them on their way once a method had been selected; “Other than listing the options, as the tool had greatly done, I would also love a description on how to get started with each technique.” ( P21). ## Using chatbots in a self-care discovery system Chatbots are forecast to ease the looming resource crisis in healthcare and automate many customer service functions in general. Mental health is a high-impact and sensitive domain. Thus, any interactions must be safe, secure, and confidential. To this end, crowdsourcing systems can complement ‘official’ clinical care: a repository of user-contributed self-care methods is simply another way to structure people’s ideas and content, much like an online forum. However, before deploying this kind of crowdsourced system into real use, significant work has to be done to ensure it works as intended. In our research we did not receive any major feedback on potentially harmful or undesired methods, due to researches having gone through the list of methods beforehand. To take this into account in similar systems, we believe, that a moderator or automated recognition could increase the system’s safety to be viable in mental healthcare. Our study compared a standalone decision support interface to a setup wrapped in a conversation with an artificial agent. We found that the version wrapped within a conversation with a chatbot led to a lower trust for the system’s security and integrity. While the exact reasons for this cannot directly be identified, we speculate that one explanation might be the privacy concerns that are frequent among online mental health services [20, 35]. Participants could not be identified from the conversations with the chatbot, but we asked the users whether they would like to be called by a nickname to make the chatbot more humane and empathetic and act like most chatbots online do. Participants might have felt this makes them more identifiable and interferes with their privacy needs. This might also be due to the study design, as the nickname was asked only from the CB group. Other possible explanations for the lower scores could be the preference to use such a system without additional guidance and the general uncertainty towards the chatbot. Subsequent studies, perhaps using a within-subjects design, could help determine why the chatbot seemed to degrade these scores. This study gives a clear direction to create a broadly accessible system for helping people to maintain and improve their mental health. With an active user base, new self-care methods could be introduced and ranked as they continuously interact with the system. Integrating a self-care discovery system within the conversation can make it more approachable and easily accessible [36]. We believe that interaction with the chatbot can also improve its overall usability and performance, thus potentially increasing the effect it can have on the user’s mental health. To achieve this, the trust towards the system using the chatbot needs to be improved. ## Transparency, integrity, and security One of the other factors affecting chatbot trust is transparency, i.e., sharing the limitations of the chatbot with the users to help them predict different outcomes and conversations, with other affecting factors including dialogue, interface, expressions, and conversational styles [28, 37]. Here, transparency was also clearly an issue with the content itself: Users wanted more details about who articulated, assessed, and helped build the knowledge base of self-care methods. As our qualitative analysis shows, this negatively affected the trust for both of the two groups. Much uncertainty comes from the recommended self-care methods and how those have been added to our system. Second, the difference between the integrity and security scores might also come from the fact that the CB group uses two different systems instead of one for the WEB group. Participants’ experienced that if the service they are using is fragmented to multiple interfaces, this could potentially create more points for attack for privacy intrusions. When the number of used systems, applications, websites, and similar increases, the risk of being exposed to data breaches increases. Some might feel uncomfortable sharing private information if it is required to share it with multiple sources. Thus, we believe that implementing this method discovery directly in the conversation could lead into better trust, without the need to do that in a separate system. ## Chatbot behaviour and humanity Chatbot behaviour and human likeness are essential factors in forming trust in chatbots [9, 20, 38]. There is evidence that the personality of the user makes a difference in how trust between the user and the chatbot is formed [19, 37]. In line with our findings, previous studies have frequently mentioned human likeness as a key aspect of trust [9, 38]. Indeed, in our study, six participants directly mentioned how the chatbot and its human-likeness positively affected their trust in the system. Some contradicting evidence emerged in a study by Folstad et al. [ 38] on chatbots in customer service, where the majority of participants preferred human-likeness and personalized chatbots for building trust. However, some participants referred to the uncanny valley effect. To conclude, it is essential to keep a chatbot identifiable as non-human when developing trustworthy systems. Our chatbot was designed to be identifiable as non-human from the beginning with qualities such as its introduction, speech patterns, and name, CareBot. A We believe our results might have been significantly different with a different kind of bot personality, but having the chatbot play as neutral of a role as possible could yield the best results in the mental health context. Naturally, chatbots with varying personalities are excellent avenues to explore in future work. ## Chatbot as a factor affecting trust Although some participants did mention that the chatbot affected their trust in the systems, the amount was lower than expected. Most participants focused on describing the factors affecting their trust in the self-care discovery tool while disregarding the chatbot. While we made sure to keep the connections between the two systems seamless by, for example, implementing the self-care discovery system within the chatbot window instead of a separate web-browser tab, this is something to focus on more in the future. An excellent way to handle this would be to have the chatbot ask for the criteria and give the recommended methods to the user directly, without needing a tool of its own. This could make the system easier to use while also giving more possibilities to explain the methods and their practical usage to the participants, one feature requested by the users and discussed in the presentation of the results section. Transparency should remain, and the sources of the recommendations offered should always be explained and referred to. As it stands, the created chatbot might not yet be trustworthy enough for self-care method discovery, as the standalone DSS version enjoys larger overall trust. While we got significant results only for the security and integrity between the two conditions, those are crucial factors for a successful chatbot and might lead to users not being comfortable using the chatbot due to, for example, security concerns. Neglecting these factors can lead to significantly lower trust compared to standalone systems. This gives us a clear direction for improving the chatbot. ## Limitations and future research To gain a more in-depth understanding of how users form trust in the specific DSS used here, a larger sample could be beneficial. We also compared results only between two groups; one group using the WEB interface wrapped in conversation and another group using only the WEB interface but were missing a study condition where the full interaction is conducted through the chatbot. The DSS interface was included in both groups. Even though the DSS interface was presented to the CB group embedded in the chatbot interface, it remains a web interface at its core. In future studies, it would be important to see how a conversational agent performs without the need of external interfaces outside of the chatbot. For this study, due to technical limitations, this feature was not yet implemented. The original research by Jian et al. [ 24] shows, that trust, and distrust (positive and negative trust) have a negative correlation, and thus there is no need to develop separate scales for the two conditions. The scale used is their proposed way to measure overall trust, but their findings also suggest that calculating the positive and negative trust scores might be unnecessary. We are also aware of other existing measurements and standardized surveys for trust. In our work we decided to use a widely accepted and used survey to gain first insights to how trust might change when using a conversational agent for mental health self-care intervention. However, in future research, it might be beneficial to use surveys made to measure trust in a health or web-based context instead of a more generalized one [39,40]. As mentioned before, previous research shows that the human-likeness of the chatbot might significantly affect the formed trust between them and the user. This could be especially important in mental health applications. To best compare the use of chatbots within self-care discovery systems, chatbots with differing personalities and levels of anthropomorphism could be used. ## Conclusion We presented an exploration of using a chatbot for self-care method discovery, specifically focusing on perceived trust in the system. The use of a chatbot was compared to a traditional standalone web interface. We found significant differences between security, integrity, and the positive trust between the two conditions, and that trust is affected mainly by personal experiences, perceived reliability, and presentation of results. To improve the trust for the chatbot further, more attention is needed for its security and integrity, which could be done by, for example, implementing the self-care method discovery directly within the conversation. Although the results for the trust survey showed lower trust in all categories for the CB group, several students mentioned the chatbot to positively affect their trust in the system. We believe improvements to the chatbot, especially to increase its security and integrity, could indeed increase the trust to the same level as the WEB group. Using a chatbot for self-care method discovery could make this kind of system more easily accessible, easier to use, and overall increase the user experience if the overall trust towards it is high enough. ## Data availability statement The datasets presented in this study can be found at https://doi.org/10.6084/m9.figshare.22193803.v1. ## Ethics statement Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study. ## Author contributions All authors contributed substantially to the conception and design of the study. JM and AV contributed to the data analysis of the results. JM and NvB contributed substantially to the writing of the paper. UG, WvdM and SH provided critical reviews and significant contributions to the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Boardman J. Ment Health Soc Incl (2011.0) 15 112-21. DOI: 10.1108/20428301111165690 2. Soeteman DI, Roijen LH, Verheul R, Busschbach JJ. The economic burden of personality disorders in mental health care. J Clin Psychiatry (2008.0) 69 259. DOI: 10.4088/JCP.v69n0212 3. Kruisselbrink Flatt A. 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--- title: PD-1 and CTLA-4 exert additive control of effector regulatory T cells at homeostasis authors: - Joseph A. Pereira - Zachary Lanzar - Joseph T. Clark - Andrew P. Hart - Bonnie B. Douglas - Lindsey Shallberg - Keenan O’Dea - David A. Christian - Christopher A. Hunter journal: Frontiers in Immunology year: 2023 pmcid: PMC10028286 doi: 10.3389/fimmu.2023.997376 license: CC BY 4.0 --- # PD-1 and CTLA-4 exert additive control of effector regulatory T cells at homeostasis ## Abstract At homeostasis, a substantial proportion of Foxp3+ T regulatory cells (Tregs) have an activated phenotype associated with enhanced TCR signals and these effector Treg cells (eTregs) co-express elevated levels of PD-1 and CTLA-4. Short term in vivo blockade of the PD-1 or CTLA-4 pathways results in increased eTreg populations, while combination blockade of both pathways had an additive effect. Mechanistically, combination blockade resulted in a reduction of suppressive phospho-SHP2 Y580 in eTreg cells which was associated with increased proliferation, enhanced production of IL-10, and reduced dendritic cell and macrophage expression of CD80 and MHC-II. Thus, at homeostasis, PD-1 and CTLA-4 function additively to regulate eTreg function and the ability to target these pathways in Treg cells may be useful to modulate inflammation. ## Introduction At homeostasis, Foxp3+ regulatory T cells (Treg) [1], have a critical role in prevention of auto-immunity and can limit the intensity and duration of inflammatory responses (2–4), Treg cells can originate from the thymus (nTreg), or naïve CD4+ T cells that receive TCR stimulation combined with signals from transforming growth factor beta (TGFβ) and IL-2 can lead to Foxp3 expression and the formation of induced Treg cells (iTreg) [5, 6]. Treg cells differ from conventional CD4+ and CD8+ T cells (Tconv), in that the majority of them have a TCR that recognizes self-antigens and are specialized to preserve tolerance (7–9). It is now appreciated that Treg cells require ongoing TCR activation and costimulation to retain Foxp3 expression, suppressive capacity [10], and survival (11–13). This is illustrated by the spontaneous immunopathology in experimental models when Treg cells are absent (14–17). The clinical relevance of Treg cell mediated control of adaptive responses is illustrated by X-linked immunodysregulation polyendocrinopathy and enteropathy (IPEX). In these patients, the Foxp3 gene is mutated and has an impaired ability to drive Treg formation, resulting in autoimmune diseases such as neonatal type 1 diabetes, hemolytic anemia, eosinophilia, and hyper IgE production [18]. Given the role of Treg cells in limiting immune responses there is considerable interest in promoting their activities to limit inflammation while the ability to antagonize Treg cells is one approach to augment anti-tumor responses (19–21). There is considerable heterogeneity in Treg cell populations associated with development (iTreg versus nTreg), activation status and their responses to inflammation [22, 23]. This is illustrated by the description of central Treg (cTreg) and effector Treg cells [11, 22] (eTreg) as distinct populations defined based on activation status [11]. There is emerging evidence that the relative ratio of effector Tconv cells: Treg cells is an important determinant for the outcome of immunotherapy in cancer [24]. However, too many Treg cells can be deleterious and lead to reduced effector responses in the context of infection or cancer [21, 25]. Consequently, there need to be processes to balance Treg cell activities and IL-2 availability is one mechanism involved in modulation of the Treg cell pool [26, 27]. There is also evidence that the inhibitory receptors PD-1 [28, 29] and CTLA-4 [20] restrict Treg cell activities in the setting of cancer, autoimmunity and infection [20, 21, 29]. PD-1 and CTLA-4 are expressed by activated T cells and most studies on these pathways have focused on their impact on effector responses which has formed the basis for checkpoint blockade in cancer. In this context, there is evidence that PD-1 and CTLA-4 act in cis and engage SHP2 phosphatases (30–33) which antagonize TCR signals (34–36), and thus blunt the response of effector T cells [37]. In addition, the ability of the extracellular domain of CTLA-4 to sequester CD$\frac{80}{86}$ provides an additional trans mechanism to limit professional antigen presenting cell (APC) function required for optimal effector T cell activities [34]. A subset of Treg cells also express these receptors [21, 29], and several reports have highlighted that effector Treg(eTreg) cells express the highest levels of PD-1 and CTLA-4 [28, 29]. It appears that while eTreg cells receive continuous TCR signals, constitutive signals through PD-1 constrain the size of the eTreg cell pool [28, 29]. In contrast to PD-1, Treg cell expression of CTLA-4 provides an effector mechanism that can limit autoimmune inflammation, but total loss of CTLA-4 results in enhanced Treg cell populations [38, 39] and lineage specific deletion of CTLA-4 in Treg cells results in enhanced Treg cell activities in models of autoimmunity [20]. Interestingly, while CTLA-4 is a relevant target to enhance effector responses during cancer in some tumor models [33, 40], blockade of CTLA-4 results in enhanced costimulatory signals and hyperproliferation of Treg cells which drove increased immune tolerance [41]. Since a subpopulation of Treg cells co-express PD-1 and CTLA-4 [21, 29], the finding that even short-term blockade of PD-L1 result in increased eTreg cell population at homeostasis raises questions about the relationship between the PD-1 and CTLA-4 pathways. For example, it is unclear if these pathways are both constitutively active, act together or separately or are functionally redundant at stasis and whether mitigation of these checkpoint proteins would impact the ratio of cTreg: eTreg cell populations. The studies presented here reveal at homeostasis that the combined blockade of PD-1 and CTLA-4 have an additive effect on expansion of eTreg cell populations associated with reduced APC function. Thus, PD-1 and CTLA-4 have distinct but complementary roles in the tonic regulation of Treg cell homeostasis. ## Mice All mice used were housed in the University of Pennsylvania Department of Pathobiology vivarium with 12 hour light and dark cycles, maintained at temperature ranges of 68°F - 77°F and humidity ranges from $35\%$ - $55\%$ humidity in accordance with institutional guidelines. C57BL/6 mice were purchased from Taconic (Rensselaer, NY, USA) at 6 weeks of age and housed in the University of Pennsylvania Department of Pathobiology vivarium for 2 – 4 weeks until used. Ethical oversight of all animal use in this study was approved by the University of Pennsylvania Institutional Animal Care and Use Committee. ## Homeostatic in vivo combination checkpoint blockade In vivo blockade antibodies: Details of antibodies and reagents in blockade can be found in Supplementary Table 1. Inhibition of PD-1/PD-L1 signaling was performed by intraperitoneal injection of 1mg/dose of αPD-L1 (clone: 10F.9G2, BioXcell) supplemented with 500μg/dose of polyclonal hamster IgG isotype (clone: polyclonal Armenian hamster, BioXcell). Inhibition of CTLA-4 signaling was performed by intraperitoneal injection of 500μg/dose of αCTLA-4 (clone: UC10-4F10-11, BioXcell) supplemented with 1mg/dose of IgG2b isotype (clone: LTF-2, BioXcell) while control mice were treated with 1mg/dose IgG2b isotype supplemented with 500μg/dose of polyclonal hamster IgG isotype. Mice were sacrificed 72 hours following treatment and splenocytes were analyzed via flow cytometry. ## Vaccine-induced immune responses during checkpoint blockade 8-week-old C57BL/6 mice were treated with either αPD-L1, αCTLA-4, combination αPD-L1 and αCTLA-4, or combination isotype antibody mixes (same dosages/combinations/antibody clones used in the homeostatic blockade above). After 72 hours, congenically labeled CD45.1+ OTI cells were isolated from healthy donor spleen using an Easysep Mouse CD8+ T cell isolation kit (19853, STEMCELL Technologies). 5,000 OTI cells were injected intraperitoneally into the antibody-blockade treated hosts. After 24 hours following the transfer of OTI cells, we intraperitoneally vaccinated these mice with 200,000 tachyzoites of a non-replicating vaccination-strain of T. gondii that expresses OVA (CPS-OVA). Previous studies have shown that CPS alone does not lead to activation of OTI or P14 TCR transgenic CD8+ T cells, and expression of OVA is essential for activation and expansion of the OTI T cells [42]. At 24 hours after vaccination, we re-dosed these groups of mice with the original blocking antibody they had previously received to maintain the blockade treatment. At 7 days post-vaccination, the spleen, peritoneal exudate cells (PEC), and draining lymph nodes (mediastinal LN) were analyzed via flow-cytometry to assess the impact of blockade on the formation of an OTI response, endogenous responses to the parasite itself, and the phenotypes of the Tregs in these tissues. ## Tacrolimus treatment FK506 (F4679-5MG, Sigma-Aldrich, MO, USA) was reconstituted in DMSO to 25mg/ml, and then the reconstituted stock was diluted in 1xDPBS to achieve a working concentration of 2.5mg/ml. 8-week-old C57BL/6 mice were subcutaneously injected with 50µl of FK506 at 2.5mg/ml to deliver 125µg of FK506 per dose daily of either FK506 or PBS vehicle control every 24 hours over a 96 hour period. Following 96 hours of treatment, splenocytes were then harvested and analyzed via flow cytometry. ## Isolation of tissues for analysis Tissue Preparation: Single cell suspensions were prepared from spleen for flow cytometry analysis. Spleens were mechanically processed and passed through a 70µm nylon filter and then lysed in 1ml of $0.846\%$ solution of NH4Cl for red blood cell lysis. The cells were then washed in cRPMI and stored on ice. ## Analysis by flow cytometry Staining antibodies and staining reagents: Antibody, viability dye, Fc block, dilutions, and buffer reagent details can be found on Supplemental Table 1. T cell staining: Aliquots consisting of 5e6 cells were washed with ice cold 1xDPBS in a 96 well round bottom plate, then incubated in in 50µl volume of viability stain reconstituted in 1xDPBS for 20 minutes on ice and then washed in $0.2\%$ FACS buffer. The cells were then incubated in 50µl volume of Fc block for 30 minutes on ice. In the event of vaccination, the cells were washed in $0.2\%$ FACS buffer and then stained with in 50µl volume of $0.2\%$ FACS buffer supplemented with tetramer loaded with the parasite-specific peptide AS15 [43] for 30 minutes on ice, in non-vaccination studies this step was skipped. The cells were washed in $0.2\%$ FACS buffer, and then incubated for 30 minutes on ice in 50µl volume of antibody cocktail composed of surface-stain antibodies in $0.2\%$ FACS buffer supplemented with brilliant stain buffer (Supplemental Table 1). The cells were washed in $0.2\%$ FACS buffer and re-suspended in 100µl Foxp3 Perm-fix cocktail (00-5523-00, Thermo Fisher Scientific) for 4 hours at 4°C. The cells were then washed twice in 1X permeabilization buffer, and then re-suspended in an intracellular staining cocktail composed of intracellular-stain antibodies diluted in 1x permeabilization buffer supplemented with normal goat serum of for 2 hours at 4°C. The cells were then washed with 1x permeabilization buffer twice, and then resuspended in 50µl of Goat α-Rabbit detection antibody diluted in 1X permeabilization buffer for 2 hours at 4°C. The cells were washed in 1x permeabilization buffer and resuspended in 500µl $0.2\%$ FACS buffer for flow cytometric analysis. Cytokine staining: To detect intracellular cytokines on T cells, cells were re-suspended in a 1X dilution of Cell Stimulation Cocktail Plus Protein Transport Inhibitors (Invitrogen, #00-4975-93, CA) in cRPMI for 2 hours at 37°C and $5\%$ CO2. Cells were then washed, surface stained, and permeabilized as described above in the T cell panel. The cytokine stain prepped cells were then intracellularly stained with a cytokine detection panel for 2 hours on ice. The cells were washed and then resuspended in 500µl $0.2\%$ FACS buffer for analysis. Myeloid staining:Aliquots of 5e6 cells were washed in ice cold $0.2\%$ FACS buffer in a 96 well and then viability stained and Fc-blocked as described in the T cell panel. The cells were surface stained in 50µl of antibody cocktail consisting diluted in $0.2\%$ FACS buffer supplemented with brilliant stain buffer on ice for 30 minutes. The cells were washed and fixed in with $2\%$ PFA (15710-S, Electron Microscopy Sciences) diluted in $0.2\%$ FACS buffer for 15 minutes at room temperature. The cells were then washed and then re-suspended in 500µl $0.2\%$ FACS buffer for analysis. Phos-flow: Splenocyte-derived CD4+ T cells were isolated using Easysep Mouse CD4+ T cell isolation kit (19852, STEMCELL Technologies), and then 2e5 cells/well were plated in a 96 well plate, and viability stained as described above using sterile 1xDPBS. Cells were blocked for PD-1, CTLA-4, or combination of PD-1 and CTLA-4 using anti-PD-1 (clone: RMP1-14, BioXcell), anti-CTLA-4 (clone: UC10-4F10-11, BioXcell) or isotype control antibodies (clone: 2A3, BioXcell, and clone: polyclonal Armenian hamster IgG, BioXcell). The cells were blocked in 100µl of PD-1/CTLA-4 blocking cocktails in sterile MACS buffer ($2\%$ FCS, 2mM EDTA, in 1xDPBS) at a concentration of 10µg/ml of antibody on ice for 20 minutes. The cells were washed with sterile MACS and were then resuspended in 100µl sterile RPMI containing $0.5\%$ BSA, and then transferred to a 96 well plate that had been coated overnight at 4°C with 5µg/ml αCD3 (BE0001-1, BioXcell), 5µg/ml CD80-Fc (555404, Biolegend), and 2µg/ml PD-L1-Fc (758206, Biolegend). The cells were either incubated at 37°C for 30 minutes or one hour, and then mixed with 100µl of $5\%$ PFA (15710-S, Electron Microscopy Sciences) diluted in ice cold 1xDPBS and incubated on ice for 20 minutes (direct exvivo phos-flow assessments were directly fixed without incubation). The cells were washed 2x in 1xDPBS, and permeabilized in 100µl Foxp3 Perm-fix cocktail (00-5523-00, Thermo Fisher Scientific) for 2 hours, and then washed as described above. The cells were re-suspended in an intracellular staining cocktail composed of intracellular-stain antibodies diluted in 1x permeabilization buffer for 2 hours at 4°C. The cells were washed twice in 1x permeabilization buffer and resuspended in $0.2\%$ FACS buffer for flow cytometric analysis. Data acquisition: The cells were analyzed on a FACS Symphony A5 (BD Biosciences) using BD FACSDiva v9.0 (BD Biosciences) and analysis was performed with FlowJo (10.8.1, BD biosciences). Statistics: Statistical analysis was performed using Prism 9 for Windows (version 9.2.0). For comparison of means between two groups, either a two-tailed unpaired, or paired student’s t test was utilized with a $95\%$ CI depending on separate treatment groups or treatments within groups. Analysis for univariate statistics comparing multiple means was performed using a one-way ANOVA (family-wise significance and confidence level of $95\%$ CI), with post-hoc analysis consisting of Fisher’s LSD test for direct comparison of two means within the ANOVA, or Tukey’s multiple comparisons test for comparisons of all means within the test group for multiple-comparison correction. For multi-group multivariate analysis, a two-way ANOVA with post-hoc analysis utilizing Sidak’s multiple comparisons test for comparisons across two groups with two variables, or Tukey’s multiple comparisons test for comparisons across multiple groups for multiple variables (also with a $95\%$ CI). Probability for p values <0.05 or lower were considered statistically significant. All error bars in the figures indicate standard error of the mean (SEM). UMAP analysis: Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) analysis was performed using the UMAP plug-in using the Euclidean distance function with a nearest neighbor score of 20, and a minimum distance rating of 0.5 (version: 1802.03426, 2018, ©2017, Leland McInness) for Flowjo (Version 10.8.1). All stained parameters were included in UMAP analysis except for: Live Dead (gated out), CD4 (pre-gated), PD-L1 and CTLA-4 (avoiding grouping bias), Foxp3 (avoiding grouping bias or already pre-gated). The heatmap overlay figures for UMAP analysis presented are based on median fluorescence of each labeled stain in each figure and generated within Flowjo (Version 10.8.1). Data availability statement: The data that support the findings of this study are available on request from the corresponding author C.A. Hunter. ## Preferential expression of PD-1 and CTLA-4 by eTreg cells To compare the relative activation state of CD8+ T cells, CD4+ Foxp3- T cells (Tconv) and CD4+ Foxp3+ cells (Tregs) at homeostasis, the levels of CD69, CD11a, and CD44 (markers associated with TCR activation) were assessed. Treg cells had highest expression of CD69, CD11a, and CD44 (Figure 1A; Supplemental Figure 1A), and the highest proportion of CD11ahi CD44hi cells (Figure 1B). Likewise, Treg cells also had the largest proportion of Ki67+ and cMyc+ cells, two markers associated with proliferation [44, 45] (Figure 1C). These markers of activation and proliferation correlated with the preferential co-expression of PD-1 and CTLA-4 by Treg cells compared to non-Treg T cells (Figure 1D). Next, Treg cells were divided into PD-1- CTLA-4low and PD-1+ CTLA-4hi Treg cells (Supplemental Figure 1B), that correlate with cTreg and eTreg subsets [11, 29] respectively. Based on this division, eTreg cells had significantly greater expression of CD69, CD11a, CD44, and Helios (Figure 2A) and the eTreg subset was enriched for cells that co-expressed elevated levels of CD11a and CD44 (Figure 2B), Ki67 and cMyc (Figure 2C). In addition, this eTreg subset had an increased ability to produce IL-10 (Figure 2D). We also noted that the proportion of these proliferative eTreg cells increased with age and could be as high as $40\%$ of the Treg cells in older mice (Supplemental Figure 1C). Figure 1: Treg cells are the most active and proliferative T cells at homeostasis, yet express PD-1 and CTLA-4. Splenocytes from naïve 8-week old male C57BL/6 mice were analyzed via high-parameter flow cytometry to compare the expression of activation, proliferation, and PD-1/CTLA-4 proteins CD8+, CD4+ Foxp3- (CD4 Tconv), and CD4+ Foxp3+ (Treg) cells for the following figures. (A) Histogram comparisons of gMFI of CD69, CD11a, and CD44 expression between the CD8/CD4+ Tconv and Treg compartments (n = 5/group, 1-way ANOVA with Tukey’s multiple comparisons test, p < 0.01, *p < 0.0001, 4 experimental replicates). (B) Flow plots of ex-vivo CD11a and CD44 staining comparing the proportion of CD11ahi CD44hi cells within each subset (n = 5/group, 1-way ANOVA with Tukey’s multiple comparisons test, p < 0.001, 4 experimental replicates). (C) Plots of depicting comparisons of the proportion of Ki67+ cMychi cells across these subsets (n = 5/group, 1-way ANOVA with Tukey’s multiple comparisons test, p < 0.0001, 4 experimental replicates). (D) Plots demonstrating proportions of PD-1+ and CTLA-4hi cells between the Tconv and Treg compartments (n = 5/group, 1-way ANOVA with Tukey’s multiple comparisons test, p < 0.001, **p < 0.0001, 4 experimental replicates). All data presented are means +/- SD and show individual data points. ns, not significant. Figure 2: PD-1+ CTLA-4+ eTreg cells express Helios and have activated Treg effector phenotypes compared to PD-1- CTLA-4- cTreg cells. Splenocytes from naïve 8-week old male C57BL/6 mice were analyzed via high-parameter flow cytometry and then pre-gated ( Supplemental Figure 1B ) on PD-1+ CTLA-4hi (eTreg) vs PD-1- CTLA-4low (cTreg) subsets. Phenotypes were compared between the c/eTreg subsets based on the expression of proteins associated with activation, proliferation, and IL-10 production. Additionally, TCR-downstream phosphorylation potential in response to activation between Treg subsets was also evaluated. (A) Comparative histograms of CD69, CD11a, CD44, and Helios between eTreg and cTreg subsets demonstrating greater expression of activation associated proteins and Helios on eTreg cells (n = 5/group, 2-way ANOVA with Sidak’s multiple comparisons test, p < 0.01, *p < 0.0001, 4 experimental replicates). (B) Ex-vivo flow-plots comparing the proportion of CD44hi CD11ahi populations and Ki67+ cMychi populations (C), between cTreg and eTreg subsets (n = 5/group, two-tailed unpaired student’s t-test, p < 0.0001 4 experimental replicates). (D) Flow plots following PMA/Ionomycin stim comparing the proportion of IL-10+ CD11ahi cells between cTreg and eTreg subsets (n = 5/group, two-tailed unpaired student’s t-test, p < 0.0001 4 experimental replicates). (E) Histogram comparisons of gMFI of p-ZAP70, p-AKT, pERK1/2, and p-mTOR of cTreg and eTreg cells exvivo, demonstrating a greater magnitude of phospho-protein presence in eTreg cells comparatively (n = 5/group, 2-way ANOVA with Sidak’s multiple comparisons test, *p < 0.0001, 2 experimental replicates). (F) Histogram comparisons of gMFI of p-SHP2 for Y580 and Y542 residues exvivo on PD-1+ CTLA-4hi and PD-1- CTLA-4low CD4+ Tconv subsets, in addition to cTreg, and eTreg cells (n = 5/group, 2-way ANOVA with Tukey’s multiple comparisons test, p < 0.05, p < 0.01, p < 0.001, **p < 0.0001, 2 experimental replicates). All data presented are means +/- SD and show individual data points. Next, eTreg and cTreg cells, directly isolated from spleens, without any additional TCR activation, were stained for phosphorylation of TCR-associated proteins (ZAP70, PI3k, AKT, ERK$\frac{1}{2}$, and mTOR) and the SHP2 tyrosine sites Y542 and Y580, [of which Y542 can dephosphorylate Y580 - the active tyrosine site associated with inhibition of TCR signals [32, 46]]. As expected, in this setting, cTreg cells had minimal signs of TCR activity when compared to eTreg cells (Figure 2E). Regarding SHP2-Y542 and SHP2-Y580, comparisons of phospho-protein were made between PD-1+ CTLA-4hi and PD-1- CTLA-4low subsets for both the CD4+ Tconv (Foxp3-) and Treg (Foxp3+) T cell populations (Figure 2F). For this analysis, the lowest levels of pY542 and pY580 were detected in cTreg and naïve Tconv cells whereas eTreg cells had the highest levels of pY542 and pY580 SHP2. This was apparent even when comparing effector PD-1+ CTLA-4hi CD4+ Tconv cells to eTreg cells (also defined as PD-1+ CTLA-4hi) (Figure 2F). These results suggest that at homeostasis eTreg cells receive increased constitutive TCR activation while experiencing ongoing SHP2 mediated restriction of these signals. ## Homeostatic blockade of PD-L1 and CTLA-4 enhances the eTreg compartment Previous studies showed that blockade of PD-L1 at homeostasis resulted in enhanced Treg cell responses within three days [29] that was apparent for as long as 5 days (data not shown). To determine whether CTLA-4 also plays a similar role and how it relates to PD-1, cohorts of 8-week-old C57BL/6 mice were treated with a single intraperitoneal injection of control antibodies alone or in combination with α-PD-L1, α-CTLA-4, or a combination of α-PD-L1 and α-CTLA-4. Splenocytes from these hosts were harvested 72 hours later and analyzed via flow cytometry. The blockade of PD-L1 or CTLA-4 resulted in a significant enrichment in the proportion and total number of Treg cells, yet when these blocking antibodies were combined there was an additive increase in the number of Treg cells (Figure 3A). This was accompanied by a concurrent increase in the proportion and total number of activated (CD11ahi CD44hi) eTreg-associated cells, which correlated with the observed total increase in Treg cells (Figure 3B). This short-term blockade of the PD-1 and CTLA-4 pathways did not impact the non-Treg subsets (CD4+ Tconv, and CD8+ T cells) but resulted in increases in activated (CD11ahi CD44hi) Treg cells with further increases in the co-blockade treated hosts (Figure 3B). The enrichment of activated eTreg cells correlated with increases in PD-1+ CTLA-4hi Treg cells with either blockade and when PD-1 and CTLA-4 were simultaneously blocked there was an additive increase in the ratio of eTreg cells to cTreg cells (Figure 3C). Figure 3: PD-1 and CTLA-4 additively restrict activated eTreg cells at homeostasis. Cohorts of 8 week-old male C57BL/6 mice were given a single intraperitoneal injection of either αPD-L1, or αCTLA-4, or combination αPD-L1 and αCTLA-4, or Isotype control antibody. Splenocytes were harvested for analysis 72 hours later and analyzed via high parameter flow-cytometry. (A) Flow plots of bulk CD4+ T cells demonstrating increases in the proportion and number of Treg cells following either blockade, with greatest enrichments occurring with combination blockade (n = 5/group, 1-way ANOVA with Tukey’s multiple comparisons test, p < 0.05, p < 0.01, p < 0.001, *p < 0.0001, 3 experimental replicates). (B) Comparison of the proportions and number of CD44hi CD11ahi populations between CD8+ Tconv, CD4+ Tconv, and Treg cells following 72 hours of single or combination α-PD-L1/CTLA-4 checkpoint blockade treatment (n = 5/group, 1-way ANOVA with Tukey’s multiple comparisons test, p < 0.01, *p < 0.001, p < 0.0001, 3 experimental replicates). (C) Flow plots of cTreg (PD-1- CTLA-4low), and eTreg (PD-1+ CTLA-4hi) cells following blockade treatment, demonstrating enrichment of PD-1+ CTLA-4hi cells with either blockade, with the greatest enrichment occurring when both pathways were blocked (n = 5/group, 1-way ANOVA with Tukey’s multiple comparisons test, p < 0.01, *p < 0.001, *p < 0.0001, 3 experimental replicates), and subsequent ratio of eTreg to cTreg cells that were shifted with treatment (n = 5/group, 1-way ANOVA with Tukey’s multiple comparisons test, p< 0.05, p < 0.01, p < 0.001, **p < 0.0001, 3 experimental replicates). (D) Enriched bulk CD4+ T cells were treated with either αPD-1, or αCTLA-4, or combination αPD-1 and αCTLA-4, or Isotype control antibody, and then stimulated with plate-bound α-CD3, PD-L1-Fc, and CD80-Fc and phospho-stained. Depicted are histogram comparisons of the Treg subset (CD4+ Foxp3+) comparing gMFI of p-SHP2 at tyrosine residues Y542 and Y580 on Treg cells (n = 5/group, 1-way ANOVA with Fisher’s LSD individual comparisons test, p < 0.05, p < 0.01, p < 0.001, **p < 0.0001, 2 experimental replicates). All data presented are means +/- SD and show individual data points. Next, the impact of combination blockade on phosphorylation of suppressive SHP2 tyrosine phosphatases during activation was considered. SHP2 tyrosine phosphatase activity restricts CD28-mediated co-stimulation [32], and there are two tail tyrosine residues; Y542, which mitigates SHP2 phosphatase activity and Y580, which stimulates suppressive SHP2 phosphatase activity associated with signals from PD-1 and CTLA-4 [46]. To evaluate whether PD-1 and CTLA-4 blockade would affect the immediate response to TCR associated SHP2 phosphorylation splenocyte-derived MACS enriched CD4+ T cells from naïve mice (Supplemental Figure 2A) were treated ex vivo with either an isotype control, α-PD-1, α-CTLA-4, or α-PD-1 plus α-CTLA-4. These cells were then transferred to plates coated with PD-L1-Fc, CD80-Fc, and α-CD3 in serum-free media. After incubating the cells for only 1 hour, to avoid complications associated with long term activation, the cells were fixed and phosphorylation of SHP2 tyrosine residues Y542 and Y580 were measured via flow cytometry. Firstly, Treg cells stimulated with plate-bound PD-L1-Fc, CD80-Fc, and α-CD3, did not demonstrate any clear differences in the amount of phosphorylated SHP2 Y542 (pY542), but did have an increase in phosphorylated SHP2 Y580 (pY580) (Figure 3D). Interestingly, cells that were pre-treated with individual blockades of α-PD-1 or α-CTLA-4 did not yield any differences in the amount of pY580 observed but, when both PD-1 and CTLA-4 were blocked, the levels of pY542 remained constant but the amount of pY580 was significantly reduced (Figure 3D; Supplemental Figure 2B). These data sets indicate that for Treg cells that PD-1 and CTLA-4 can simultaneously contribute to the phosphorylation of TCR-suppressive Y580 that is independent of changes to the Y580-disabling Y542 residue. Another approach to depict how these treatments impacted the Treg cell populations was to utilize Uniform Manifold Approximation and Projection (UMAP) analysis of the concatenated data sets generated using an extensive panel of proteins expressed by Treg cells from each of the treated groups, excluding the expression of PD-L1 and CTLA-4 from analytical algorithms (Supplemental Figure 3A). Following UMAP analysis, the samples were then unmixed into respective treatment groups and changes in distribution density within the UMAP analysis depicted across the different treatment groups (Figure 4A). Thus, comparison of the isotype treated with the combination treatment shows a marked shift in the heat map associated with expansion of eTreg cells. The inclusion of staining for Nur77, a protein expressed proximally to TCR activation [47], allowed these events to be overlaid on respective UMAPs. This analysis illustrates how individual, or combination PD-L1/CTLA-4 blockade led to enrichment of Nur77 expression associated with eTreg cells (Figure 4B). Then, using the original concatenated UMAP (Figure 4C), median fluorescence expression heatmaps were created to show comparative expression of proliferation-associated proteins (cMyc and Ki67) (Figure 4D), and co-stimulation associated proteins (Figure 4E). Compared to isotype treated hosts, the PD-L1 and CTLA-4 blockade treated hosts had increased enrichment in regions that overlap with Foxp3 and Helios, yet no clear enrichment over the CD25hi regions of the UMAP while combination blockade hosts had even further enrichment over the Foxp3hi and Helios+ regions and a comparative reduction of CD25hi cells in addition to enrichment of CD73hi Treg cells (Supplemental Figure 3B). Likewise, either blockade resulted in enrichment in regions of the UMAP associated with activation (Figure 4B) or proliferation (Figure 4D), or expression of B7-family co-stimulation proteins (Figure 4E), with the greatest enrichments occurring in the cohort treated with the combination blockade. Figure 4: Activated and proliferative Treg compartment phenotypic shifts following checkpoint blockade. Cohorts of 8 week-old male C57BL/6 mice were given a single intraperitoneal injection of either αPD-L1, or αCTLA-4, or combination αPD-L1 and αCTLA-4, or Isotype control antibody. Splenocytes were harvested for analysis 72 hours later and analyzed via high parameter flow-cytometry (3 experimental replicates). (A) Bulk Treg sample data from each treatment group (n=5/group, 20 individuals total) was concatenated into a single sample and then evaluated using Uniform Manifold Approximation and Projection (UMAP) analysis ( Supplemental Figure 3A for description) to produce 2-dimensional plots containing the measured parameters excluding PD-L1 and CTLA-4 from analysis to portray qualitative trends that emerged following treatment. (A) The individual UMAP analysis was then sub-divided into treatment specific UMAP sub-plots from the concatenated analysis depicting pseudo-color density distribution of Treg cells amongst each treatment group within the UMAP. (B) Nur77+ cells (red) overlaid Nur77- cells (blue) amongst the reference UMAP plots for each treatment group depicting enrichment of Nur77+ Treg cells with individual and combination blockade treatment. (C) Representation of the cumulative UMAP density plot depicting the assimilation of concatenated UMAP data from the 4 treated groups in figure (A, D, E) Median heatmap expression of proteins based on the total concatenated UMAP analysis, depicting expression of the protein labeled in each plot, allowing qualitative comparison to population density shifts demonstrated in (A). (D) Heatmap expression of proliferation associated proteins cMyc and Ki67, with extensive overlap with enriched regions following individual or combination blockade, with more activated and proliferative cells accumulating in the upper right region of the UMAP plots, and more quiescent cells in the bottom left region of the UMAP plots. (E) Heatmap expression of B7-family costimulatory proteins, PD-1, PD-L1, ICOS, and CTLA-4, with enrichment in the regions correlating to blockade treatment. To compare the impact of inhibitory receptor blockade on the proliferative responses of conventional and Treg cells, expression of Ki67 and cMyc was assessed. In these experiments, short term blockade did not lead to increased proliferation of CD8+ T cells (Figure 5A). For CD4+ Tconv cells, a modest increase in the percentage of proliferative cells (from 2 to $4\%$) was only observed with treatments that included α-CTLA-4. In contrast, Treg cells demonstrated a marked increase of the Ki67+ cMychi population with either PD-L1 or CTLA-4 blockade, with the most prominent increase observed when both were blocked (Figure 5A). This observation was consistent with the increased number of the PD-1+ Treg subsets (PD-1low, PD-1hi) (Figure 5B). Additionally, TCR stimulation of Treg cells is associated with maintenance of Foxp3 expression [10], and antagonism of TCR activity by treatment of mice for 4 days with tacrolimus (FK506) [48] resulted in a reduced MFI of Foxp3 in Treg cells (Figure 5C). In contrast, the blockade of PD-L1 or CTLA-4 resulted in an overall increase in the MFI of Foxp3 amongst the bulk Treg compartment, with the combination blockade having the greatest enhancement (Figure 5D; Supplemental Figure 3C for individual eTreg blockade comparisons). Combined with the numerical, phenotypic and phos-data sets, these results highlight that PD-1 and CTLA-4 additively contribute to restrict the population of TCR-driven eTreg cells. Figure 5: Blockade of PD-L1 and CTLA-4 additively drive enrichment and proliferation of the eTreg compartment. Cohorts of 8 week-old male C57BL/6 mice were given a single intraperitoneal injection of either αPD-L1, or αCTLA-4, or combination αPD-L1 and αCTLA-4, or Isotype control antibody. Splenocytes were harvested for analysis 72 hours later and analyzed via high parameter flow-cytometry. (A) Flow plots comparing the proportion and number of Ki67+ cMychi T cells cells following individual or combination PD-L1/CTLA-4 blockade treatment, subdivided into CD8+, CD4+ Tconv, and Treg cells (n = 5/group, 1-way ANOVA with Fisher’s LSD individual comparisons test, p < 0.05, p < 0.01, p < 0.001, **p < 0.0001, 3 experimental replicates). (B) Flow plots comparing the proportions and number of PD-1-, PD-1low, and PD-1hi Treg cells following individual or combination blockade treatment, with enrichments occurring within the PD-1+ eTreg associated subsets following blockade, the greatest of which occur with combination blockade treatment (2-way ANOVA with Sidak’s multiple comparisons test, p < 0.05, *p < 0.001, p < 0.0001, 3 experimental replicates). (C) Cohorts of 8 week-old male C57BL/6 mice were treated once daily for 4 days with subcutaneous injections of PBS/vehicle or Tacrolimus (FK506), and splenocytes were harvested and analyzed via flow cytometry. Comparative histograms of Treg cells from vehicle control and FK506 treated mice demonstrating decreases in the gMFI of Foxp3 in Treg cells in FK506 treated hosts (n = 10/group two-tailed unpaired student’s t-test, p < 0.0001, 2 experimental replicates). (D) Comparative histograms of Treg cells from isotype and αPD-L1/αCTLA-4 combination blockade treated mice demonstrating increases in the gMFI of Foxp3 in Treg cells from blockade treated hosts (n = 5/group two-tailed unpaired student’s t-test, *p < 0.0001, 3 experimental replicates). All data presented are means +/- SD and show individual data points. To directly assess whether the enhanced eTreg cell populations observed after blockade of PD-L1 and/or CTLA-4 at homeostasis would impact the ability to generate de novo T cell responses, these pathways were blocked in naïve mice that were then immunized with a non-replicative form of Toxoplasma gondii that expresses OVA. This vaccine strain provides a system to assess the activities required to generate effector T cell responses [42, 49]. In these studies, mice were treated with isotype, α-PD-L1 or α-CTLA-4 and three days later were recipients of OTI T cells. A day later mice were vaccinated with CPS parasites and then re-treated with the relevant antibodies. Seven days post-vaccination mice were assessed for Treg cell populations, parasite specific CD4+ T cell responses as well the OTI T cells. At this time point (11 days after initial treatment), enhanced eTreg cell responses were still obvious, indicating that the effects of CTLA-4 and PD-L1 blockade were sustained (Supplemental Figures 5A, B). However, despite this enhanced eTreg cell activity the magnitude of the CPS-induced T cell responses were not reduced (Supplemental Figures 5C, D) but they did profoundly skew the eTreg to Tconv Teff ratios (Supplemental Figures 5E, F). However, it is relevant to note that in these experiments the use of α-CTLA-4 alone resulted in heightened OTI and endogenous CD4+ T cell responses (Supplemental Figures 5C, D) but this was antagonized by the inclusion of anti-PD-L1. This antagonism of the T cell responses correlated with conditions that resulted in the presence of the highest numbers of eTreg cells across multiple experiments. Nevertheless, to further assess the impact of IR blockade on conventional T cells and Treg cell function at homeostasis, splenocytes from naïve treated hosts were stimulated with PMA and ionomycin and the ability to produce cytokines was assessed. The Foxp3- CD4+ and CD8+ T cells readily produced TNFα, and there was a small proportion of these cells that co-expressed TNFα and IFNγ. Following solo, or combined PD-L1 and CTLA-4 blockade there were no significant increases in the production of these cytokines (Figure 6A). Likewise, a small proportion of the CD4+ T cell population produces IL-2, but this was not altered by these treatments (Figure 6B). Thus, consistent with the data in Figure 4, these short term blockades did not appear to lead to any obvious enhancement of the incipient T cell response or levels of IL-2 that might contribute to the enhanced eTreg population observed. Figure 6: Combo-blockade of PD-L1 and CTLA-4 drives a myeloid-suppressive Treg environment. 8 week-old male C57BL/6 mice were given a single intraperitoneal injection of either αPD-L1, or αCTLA-4, or combination αPD-L1 and αCTLA-4, or Isotype control antibody. At 72 hours following treatment, their splenocytes were harvested, and stimulated with PMA/Ionomycin for cytokine staining and analyzed via flow cytometry. (A) Plots depicting the expression of IFNγ and TNFα on CD4+ Tconv and CD8+ cells from single and combo blockade treated hosts (n = 5/group, 1-way ANOVA with Fisher’s LSD individual comparisons test, 2 experimental replicates). (B) Plots depicting the expression of IL-2 on CD4+ Tconv cells from single and combo blockade treated hosts (n = 5/group, 1-way ANOVA with Fisher’s LSD individual comparisons test, 2 experimental replicates). (C) Plots depicting the expression of IL-10 and CTLA-4 on bulk Treg cells from each treatment group, with increases in IL-10+ CTLA-4hi Treg cells from single and combo blockade treated hosts (n = 5/group, 1-way ANOVA with Fisher’s LSD individual comparisons test, p < 0.05, p < 0.01, *p < 0.0001, 2 experimental replicates). (D) Ex-vivo staining of splenocytes evaluating the expression of MHC-II on cDC1s (CD3-, B220-, CD19-, NK1.1-, Ly6G-, CD64-, CD11c+, MHC-II+, XCR1+), cDC2s (CD3-, B220-, CD19-, NK1.1-, Ly6G-, CD64-, CD11c+, MHC-II+, SIRPα+), and macrophages (CD3-, B220-, CD19-, NK1.1-, Ly6G-, CD64+, CD11b+, MHC-II+, Ly6Clow) ( Supplemental Figure 4 for description) following blockade treatments, with decreasing trends MHC-II with combo blockade (n = 5/group, 2-way ANOVA with Fisher’s LSD individual comparisons test, p < 0.05, p < 0.01, p < 0.001, **p < 0.0001, 3 experimental replicates) (E) Plots comparing CD80 expression on cDCs and Macrophages, demonstrating changes to surface CD80 based on blockade treatment (n = 5/group, 2-way ANOVA with Fisher’s LSD individual comparisons test, p < 0.05, p < 0.01, *p < 0.0001, 3 experimental replicates) All data presented are means +/- SD and show individual data points. Treg cell production of IL-10 is one important function of these cells, and this cytokine can act on APCs and limit their expression of MHC class II and CD80. In contrast, the ability of CTLA-4 to bind to and strip CD80 from these cells can reduce costimulation [34]. In these in vivo studies, blockade of PD-L1 or CTLA-4 resulted in an increase in the number of IL-10+ Treg cells, with the combination blockade resulting in the greatest increase (Figure 6C). Evaluation of the splenic DC and macrophages compartments ex vivo (Supplemental Figure 4) showed that cDC2s and macrophages had varied expression of MHC class II and CD80. The blockade of PD-L1 alone resulted in modest reductions in MHC-II expression, particularly amongst macrophages (Figure 6D). Comparatively, solo CTLA-4 blockade drove reductions in MHC-II particularly on DCs while the combined blockade of both PD-L1 and CTLA-4 had consistent trends of decreasing MHC class II expression in cDCs and macrophages (Figure 6D). In context of co-stimulatory CD80, PD-L1 blockade alone reduced CD80 on cDC2s and macrophages, but not cDC1s (Figure 6E). In comparison and consistent with the ability of CTLA-4 to strip CD80 [34], CTLA-4 blockade resulted in increased CD80 expression on cDCs and macrophages (Figure 6E). When blockade treatments were combined, the effects of anti-PD-L1 were dominant with reduction in the expression of CD80. This result established that not only do these treatments favor the expansion of the eTreg compartment, but this correlates with reduced APC functions of other cell types that are known to be impacted by Treg cells. ## Discussion The focus on the role of PD-1 and CTLA-4 in limiting effector T cell responses has revealed that the expression of these molecules is a byproduct of repeated TCR stimulation over time (50–52). The studies presented here focus on the impact of these potentially overlapping pathways on Treg cell homeostasis and in particular on the differences between eTreg and cTreg cell populations. In this context, short term homeostatic blockade of PD-L1 or CTLA-4 did not result in appreciable activation of Tconv CD4+ or CD8+ T cells. Subsequent evaluation of the possible impact of these enhanced eTreg populations on the formation of T cell responses to vaccination with OVA-expressing parasites did not antagonize the expansion of transferred OTI populations or endogenous effector CD4+ T cells during this challenge. However, these vaccination studies do not distinguish the effects of blockade of CTLA-4 or PD-L1 on the Treg cell populations versus an impact on the expansion of the parasite specific effectors. For example, CTLA-4 blockade treatment alone led to an expansion in the formation of effector T cell responses. However, the observation that CTLA-4 blockade in combination with PD-L1 blockade treatment resulted in an even greater expansion of eTreg cells and antagonized the effects of solo CTLA-4 blockade on endogenous and effector T cells and transferred OTI cells, of which would be consistent with a role for these heightened eTreg populations to limit effector responses. Nevertheless, these studies need to be interpreted with care and additional studies that allow the isolation of the effects of PD-1 and CTLA-4 on activated CD4+ and CD8+ T cells versus Treg cells in the same environment would be required. In considering these findings, Treg cells receive ongoing TCR signaling which is required to maintain expression of Foxp3 and their suppressive capacity [10]. When PD-1 and CTLA-4 signaling is mitigated, there is increased overall expression of Foxp3 on Treg cells, which is directly contrasted by short-term blockade of TCR activation which results in reduced Treg Foxp3 expression. Earlier reports suggested that PD-1 and CTLA-4 are associated with the suppressive functions of Treg cells [53, 54], but a consensus is emerging that these inhibitory receptors can individually restrict Treg capacity and suppressive function during autoimmune disease [20, 28], cancer [21, 41, 55] and infection [29]. Thus, co-blockade of PD-L1 and CTLA-4 resulted in increased Treg cell proliferation, percentage (but not relative levels) of cells that produced IL-10 and expressed CD73, CTLA-4, and PD-L1, and a reduction in markers of APC activation. Together, these results suggest that targeting PD-1 and CTLA-4 does not result in an increase in Treg suppressive activity per se, but rather that these pathways act to limit the size of the effector Treg pool. We now appreciate that while the cTreg compartment makes greater use of STAT5 signaling cytokines such as IL-2 for maintenance [11, 13, 56] the eTreg compartment is more dependent on TCR-mediated activation and co-stimulation to survive. This is suggested by the finding that the eTreg subset has higher basal levels of pZAP70, pAKT, and pmTOR, than the cTreg compartment. However, eTreg cells also express PD-1 which interacts with SHP2 to antagonize T cell activation [57]. In comparing PD-1 to CTLA-4 which is also expressed on eTreg cells, PD-1 has been directly implicated in binding SHP2, while CTLA-4 is missing a motif that would allow recognition of SHP2, but CTLA-4 does function as a negative regulator of T cell activation [31, 40, 58]. SHP2 associates with CTLA-4 and the TCR [59, 60] and Schneider & Rudd, [2000] postulated that this activity is mediated via its impact on PI3K, of which CTLA-4 signaling does have impacts on PI3K. Notably, CTLA-4 does associate with SHP2 in T cells, and possibly has indirect interactions with SHP2 mediated by an intermediate which is still unclear [31]. Here, there is an observation that the eTreg subset may have an enhanced capacity to respond to TCR signals while simultaneously being sensitive to negative SHP2-associated signals from PD-1 or CTLA-4. There are multiple possible mechanisms whereby blockade of these IR may lead to enhanced Treg cell activities. Several imaging studies have highlighted that when compared to activated CD4+ T cells, Treg cell interactions with DC are characterized by less stable short term contacts [61, 62], and a recent report highlighted that Treg cell use CTLA-4 to disrupt these interactions [41]. Whether blockade of CTLA-4 leads to enhanced DC-Treg interactions remains to be tested. Likewise, previous studies have deployed strategies to evaluate the impact of SHP2 via T cell specific SHP2-/- mice and highlighted that this pathway is redundant in exhaustion [63]. However, in that report, during LCMV infection the loss of SHP2 resulted in enhanced expansion (almost 3 fold) of virus-specific effector CD8+ T cells and in a tumor model the ability of PD-1 blockade to enhance the percentage of total and IFNγ positive intra-tumoral CD8+ T cells was SHP2-dependent. While there may be SHP2-independent pathways that contribute to the activities of PD-1, these data sets remain consistent with the idea that PD-1 mediated engagement of SHP2 limits T cell activation. Indeed, this is reflected in our own data sets in which eTreg cells had enhanced levels of pSHP2 and that mitigation of both PD-1 and CTLA-4 signaling pathways reduces suppressive pSHP2-Y580, which correlated with increased Foxp3 expression and numbers of eTreg cells. Given the ubiquitous expression of SHP2 by cells of the immune system, whether this reduction in SHP2 activity in eTreg cells accounts for their expansion will require the use of lineage-specific approaches to directly address this question. While PD-1 and CTLA-4 are related B7 family members, engage SHP2 signaling, and seem to additively limit eTreg proliferation and function, the current literature indicate that there is still distinction to their suppressive mechanisms. For example, PD-1 accumulates on the cell surface and is accessible to PD-L1 ligation [64]. and thereby act in cis to limit T cell activation. For Treg cells, blockade of this pathway resulted in enhanced numbers and IL-10 production and was associated with reduced APC expression of CD80 and MHC class II. In contrast, the majority of CTLA-4 is stored intracellularly and is translocated to the surface upon TCR stimulation [65, 66] where it can provide negative costimulatory signals [67]. In addition, the ability of CTLA-4 to bind with high affinity to CD80 means that it can outcompete the ability of CD28 to provide costimulation and can actively restrict APC function through CTLA-4 mediated trogocytosis of CD80 [34]. Thus, CTLA-4 is an invoked off switch which can act in cis and trans to limit eTreg cells. Interestingly, this complex biology is apparent in the studies presented here: α-PD-L1 treatment alone drove a reduction in CD80 expression by cDC2s and macrophages, while α-CTLA-4 treatment still drove an enrichment of Treg cells yet resulted in a significant increase in myeloid expression of CD80 (consistent with reduced trogocytosis). Nevertheless, that combination blockade of PD-L1 and CTLA-4 resulted in a reduction of myeloid CD80 expression suggests that the increased number of Treg cells and their production of IL-10 is sufficient to exceed the effects of CTLA4 on CD80 levels. The past twenty years has witnessed an increased utilization of immunotherapeutic drugs to enhance immune mediated control of certain cancers or to limit autoimmune inflammation. The blockade of PD-1 or CTLA-4 or the use of CTLA4-Ig are all examples of clinical interventions to impact effector T cell responses that are directly relevant to eTreg cells and the pathways that we show here. However, these treatment strategies do not always prove effective, and their impact of Treg cells may in part explain some of this heterogeneity in clinical outcome [21, 68, 69]. Perhaps, the ability to specifically target these pathways (either to agonize or block) on eTreg cells can be used as an immunotherapeutic strategy to enhance Treg function to treat immunopathological diseases or select against Treg mediated suppression in the context of infection or cancer. ## Data availability statement The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author. ## Ethics statement The animal study was reviewed and approved by University of Pennsylvania Institutional Animal Care and Use Committee. ## Author contributions JP conceptualized the project, designed/executed all experiments, performed data analysis, figure production, and authored the paper. ZL, JC, AH, BD, LS and KO aided in data collection, provided conceptual feedback regarding experimental design, data analysis, and manuscript editing. DC directly supervised experimental execution, interpretation, and presentation of data. CH supervised the project in entirety. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.997376/full#supplementary-material ## References 1. Fontenot JD, Gavin MA, Rudensky AY. Foxp3 programs the development and function of CD4+CD25+ regulatory T cells. Nat Immunol (2003) 4. DOI: 10.1038/ni904 2. 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--- title: Association between Indicators of Inequality and Weight Change following a Behavioural Weight Loss Intervention authors: - Jack M. Birch - Julia Mueller - Stephen J. Sharp - Simon J. Griffin - Michael P. Kelly - Jason C.G. Halford - Amy L. Ahern journal: Obesity Facts year: 2022 pmcid: PMC10028366 doi: 10.1159/000528135 license: CC BY 4.0 --- # Association between Indicators of Inequality and Weight Change following a Behavioural Weight Loss Intervention ## Abstract ### Introduction Weight loss through behavioural weight management interventions can have important health benefits for people with obesity. However, to maximise the health benefits, weight loss must be maintained. Evidence suggests that behavioural weight loss interventions do not exacerbate inequalities in the short term. However, no study has yet considered whether inequalities exist in long-term weight change following intervention. We aimed to investigate if there are inequalities in weight change following weight loss intervention. ### Methods We conducted a cohort analysis of data from the Weight Loss Referrals for Adults in Primary Care (WRAP) trial ($$n = 1$$,267). WRAP randomised participants to receive a brief intervention information booklet or vouchers for 12-weeks or 52-weeks of WW (formerly WeightWatchers) and followed them for 5 years. Multiple linear regression estimated the association between exposures (indicators of inequality) and outcomes (change in weight between 1- and 5-years). Each model was adjusted for the intervention group, baseline weight, weight change between baseline and 1-year, research centre, and source of the 5-year weight data. ### Results Of the 1,267 participants in WRAP, 708 had weight change data available. Mean weight change between 1- and 5-years was +3.30 kg (SD 9.10 kg). A 1 year difference in age at baseline was associated with weight change of 0.11 kg (($95\%$ CI 0.06, 0.16), $p \leq 0.001$). We did not find evidence of associations between ethnicity, gender, education, indices of multiple deprivation, household income, or other family members participating in a weight loss programme and weight change. ### Conclusion Except for age, we did not find evidence of inequalities in weight change following a behavioural intervention. Findings further support the use of behavioural weight management interventions as part of a systems-wide approach to improving population health. ## Background and Rationale Overweight and obesity are associated with an increased risk of several non-communicable diseases such as type 2 diabetes, cardiovascular disease, and some cancers (for example, bowel and post-menopausal breast cancers) [1, 2]. People living with overweight or obesity also experience higher rates of premature all-cause mortality compared to those within a healthy weight range [3]. In England in 2019, it is estimated that $68\%$ of men and $60\%$ of women live with overweight or obesity [4]. Inequalities in health outcomes and processes (such as intervention uptake and adherence) are known to occur across many measures summarised by the PROGRESS-Plus criteria − place of residence, race/ethnicity, occupation, gender/sex, education, socioeconomic status (SES), social capital, plus other factors for which discrimination could occur such as age and sexual orientation [5]. Inequalities are known to exist in the prevalence of overweight and obesity, such as by education and SES (those who have received fewer years of education or are more deprived are more likely to live with overweight or obesity) [6, 7]. These inequalities in obesity were particularly highlighted by the COVID-19 pandemic, where having overweight or obesity was associated with an increased risk of hospitalisation and mortality [8]. A common, effective intervention to help manage overweight and obesity is behavioural weight management [9], often following a referral from a general practitioner. Weight loss through behavioural weight management interventions can have important health benefits for people with obesity and can be cost-effective if weight loss is maintained over the long term [10]. Due to the high level of personal agency required for behavioural weight management interventions to be effective (i.e., commitment of personal resource, such as time), it is suggested that such interventions may exacerbate health inequalities by being less effective in more disadvantaged groups [11, 12]. We have previously conducted a systematic review that considered inequalities in the uptake of, adherence to, and effectiveness of behavioural weight management interventions [13]. The review noted that most trials did not find evidence of inequalities. Where inequalities were observed, trial uptake, intervention adherence, and trial attrition generally those considered “more advantaged” were favoured. Findings were more mixed for weight loss outcomes. Few trials of behavioural weight management interventions have followed participants up for more than 12 months. It is important to consider long-term weight change post-intervention as the maintenance of weight loss sustains the positive health effects associated with initial clinically significant weight loss and improves cost-effectiveness [10]. However, given that very few trials have long-term follow-up of participant weight, there is a lack of evidence on associations between characteristics of inequality (such as those outlined in the PROGRESS-Plus framework) and long-term maintenance of attained weight loss (i.e., at 5-year follow-up). Consequently, it is not known if particular sociodemographic groups demonstrate different patterns of weight change in the period following a weight loss intervention. This is important to understand as certain sociodemographic groups may benefit from additional support to maintain weight loss achieved through a behavioural intervention. The Weight Loss Referrals for Adults in Primary Care (WRAP) trial has completed follow-up data collection at the 5-year time point, allowing for a rare opportunity to study inequalities in weight change following weight loss intervention over an extended time period. All participants in the WRAP trial received a behavioural weight loss intervention (brief intervention information booklet or vouchers for 12- or 52-weeks of WW (formerly WeightWatchers)). Previous analyses of this trial have found that trial uptake (the number of invited vs. recruited participants) was higher in older participants, people from less deprived areas, and women (although the proportion of males in WRAP was much higher than seen in similar trials or routine primary care referral) [14]; intervention attendance was higher in older participants, but there was no evidence of inequalities in attendance by any other PROGRESS-Plus criteria [15]; and there was no evidence to suggest that the greater weight loss outcomes observed in the WW groups were affected by gender, education, or income level [16]. In this current study, we investigated if there were inequalities in weight change following participation in a weight loss intervention by analysing data from the WRAP trial as a cohort. ## Methods This study analysed data from the Weight Loss Referrals for Adults in Primary Care (WRAP) trial as a cohort. ## The WRAP Trial The WRAP trial is a three-group randomised controlled trial of behavioural weight loss interventions. Full information about the trial design has been published elsewhere [17]. Briefly, participants were recruited through their general practice and randomised to a brief intervention, 12-week commercial weight loss programme, or a 52-week commercial weight loss programme. WRAP was registered with Current Controlled Trials on October 15, 2012 (trial registration: ISRCTN82857232). Ethical approval for WRAP was received from the NRES Committee East of England Cambridge East and local approvals from the NRES Committee North West Liverpool Central and the NRES Committee South Central Oxford. The WRAP trial was registered with Current Controlled Trials (ISRCTN82857232). ## Participants Participants were required to be ≥18-years-old, residing in the UK, and have a BMI of ≥28 kg/m2. Participants were not eligible if they were pregnant or had planned pregnancy in the subsequent two years; had previous or planned bariatric surgery; were following a weight loss programme; did not speak English or had additional communication needs that would preclude them from understanding the study requirements and materials; or the participant's general practitioner considered them ineligible for inclusion (such as history of eating disorders or severe/terminal illness). ## Interventions Participants in the behavioural programmes were given vouchers and asked to attend local WW weekly meetings and access WW web tools at no cost for the duration of the intervention (12-weeks or 52-weeks). Participants allocated to the brief intervention were given a 32-page booklet from the British Heart Foundation that comprised advice and strategies on how to lose weight. Research staff read a scripted introduction that drew attention to each section of the booklet. There were no restrictions on participants in any group accessing other weight management interventions during follow-up. ## Outcomes Participants completed outcome assessments at baseline, 3-months, 1-year, 2-years, and 5-years. The primary outcome for this analysis was change in weight between 1-year and 5-years. Weight measurements were made at participants' primary care practice or at the research centre by trained clinical or research staff, in line with standard operating procedures and with informed consent. Participants also reported their self-measured weight, and we collected weight data from primary care records. At the 5-year time point, if clinic-measured weight data were unavailable, the self-reported weight or weight from GP records was used ($$n = 239$$). Participant demographics were collected via self-report questionnaire at the baseline assessment. The exposure variables considered for possible association with change in weight between 1- and 5-years were: [1] ethnic group (white/ethnic minorities (excluding white minorities)); [2] employment status (employed/self-employed/unemployed/student/retired/unable to work/other (carer, home-maker, voluntary work)); [3] sex (female/male); [4] level of education attained (university degree or equivalent, or higher/post-secondary education/A-levels or equivalent/GCSEs or equivalent/no formal qualifications attained); [5] indices of multiple deprivation (IMD) quintile (1 (most deprived)/$\frac{2}{3}$/$\frac{4}{5}$ (least deprived)); [6] household income (<£20,000/£20,000 to £39,999/>£40,000); [7] member of household participating in a weight loss programme (yes/no); and [8] age (years). ## Statistical Analysis We analysed data from the WRAP trial as a cohort rather than consider intervention versus control arms separately to estimate intervention effects. We conducted data analyses using Stata v16 (StataCorp. 2019, Stata Statistical Software: Release 16. StataCorp LLC., College Station, TX, USA). Mean (standard deviation) weight change was calculated within each exposure category. We defined weight maintenance as having a weight change of $3\%$ or less from the 1-year time point [18]. We used multiple linear regression to estimate the association between each exposure and weight change from 1- to 5-years. Each model was adjusted for the intervention group, baseline weight, weight change between baseline and 1-year, research centre, and source of the 5-year weight data. A complete case analysis was performed. ## Sensitivity Analyses To investigate the impact of missing data on the findings, we performed a sensitivity analysis using Multiple Imputation by Chained Equations (MICE). MICE assumes that data are missing at random conditional on observed participant characteristics. Variables with ≥$5\%$ and <$25\%$ missing data had missing values imputed. The number of imputations was set to be the same as the percentage of missing data. We conducted a further sensitivity analysis to consider if the source of the 5-year weight measurement had an impact on the results. The analysis excluded participants whose weight at the 5-year time point was collected through GP records or self-reported information. The final sensitivity analysis, added following peer review feedback, was conducting a single regression model containing all PROGRESS-Plus inequality characteristic variables included in this study to account for potential confounding between them. Only one variable, education, met the conditions for performing MICE to impute missing data (between $5\%$ and $25\%$ missing data). The observed results using 11 imputed datasets were comparable to the primary analysis, and no associations were identified (online suppl. Table S3). In the sensitivity analysis using only study-measured weight at the 5-year follow-up (online suppl. Table S1), being older at baseline remained associated with lower weight regain or greater weight loss, but the effect size was smaller (coefficient −0.092 ($95\%$ CI: −0.15, −0.04), $$p \leq 0.001$$). Being retired, compared to being employed, was not associated with weight change when we included only study-measured weight, neither when not controlling for age (coefficient −0.95 ($95\%$ CI: −2.48, 0.59), $$p \leq 0.226$$) nor when controlling for age (coefficient 1.66 ($95\%$ CI: −0.46, 3.78), $$p \leq 0.125$$). The final sensitivity analysis was of all independent variables included in a single model, which also adjusted for the intervention group, baseline weight, weight change between baseline and 1-year, research centre, and source of the 5-year weight data (full results available in online suppl. Table S4). In this model, older age at baseline remained associated with lower weight regain or greater weight loss (coefficient −0.15 ($95\%$ CI: −0.25, −0.04), $$p \leq 0.008$$). The remaining results from this model were broadly consistent with our primary analyses, with the exception that being male was associated with greater weight regain or lower weight loss (coefficient 2.05 ($95\%$ CI: 0.02, 4.08), $$p \leq 0.048$$) and being unable to work (compared to being employed) was associated with lower weight regain or greater weight loss (coefficient −5.59 (−10.86, −0.32), $$p \leq 0.038$$). ## Participant Characteristics A total of 1,267 participants were randomised to one of the three groups (211 brief intervention, 528 12-week, 528 52-week intervention). The majority of the recruited participants were women ($67.8\%$) and of white ethnicity ($89.7\%$). Weight data were available for 823 participants at the 1-year follow-up. At 5-year follow-up, weight data were available for 871 participants in total. Study-measured weight was available for 632 participants; weight data were extracted from GP records for 146 participants ($11.5\%$) and collected by self-report from 93 participants ($7.3\%$). No weight values were available for 396 ($31.3\%$) of participants at the 5-year follow-up. Data were available at the 1- and 5-year follow-ups for 708 participants ($55.5\%$, Table 1). The participants included in the analyses were more likely to be retired ($35.3\%$ vs. $21.9\%$ not included), have a university degree ($41.6\%$ vs. $31.5\%$), and be older (mean age at baseline of participants included in analysis was 55.7-years-old vs. 50.0-years-old for those not included). Mean weight change between 1 and 5 years was 3.30 kg (SD 9.10 kg). Mean weight change for each exposure category is presented in Table 2. Given the overall mean weight change and standard deviation values, any negative coefficients gained from the regression models (Table 3) would suggest less weight regain, or greater weight loss, in that category. Where complete data were available, weight at the 1-year time point was maintained at the 5-year time point by $28.0\%$ of participants, >$3\%$ weight loss occurred in $16.5\%$ of participants, and >$3\%$ weight gain occurred in $55.5\%$ of participants (online suppl. Fig. S1; for all online suppl. material, see www.karger.com/doi/$\frac{10.1159}{000528135}$). ## Association between PROGRESS-Plus Criteria and Weight Change from 1- to 5-Years Being 1-year older compared to other participants at baseline was associated with experiencing 0.11 kg less weight regain, or greater weight loss (($95\%$ CI: 0.06, 0.16), $p \leq 0.001$). When considering occupation as the independent variable, being retired was associated with a 1.67 kg lower weight regain compared to being employed (($95\%$ CI: 0.27, 3.08), $$p \leq 0.020$$, Table 3). Subsequently, we performed a post-hoc analysis of occupation as the independent variable controlling for age, given the differences in age distribution between occupational categories. In this analysis, being retired was no longer associated with weight change between 1- and 5-year follow-up (coefficient 1.15 ($95\%$ CI: −0.79, 3.09), $$p \leq 0.246$$; full results presented in online suppl. Table S2). No other category of occupation, when compared to being employed, was associated with weight change between 1- and 5-years in either analysis. There was no evidence of association between ethnicity, gender, education, IMD, household income, and other family members participating in a weight loss programme and weight change between 1- and 5-years. ## Discussion In this study, we explored inequalities in weight change following participation in a weight loss intervention using data from the WRAP trial. Given that $55.5\%$ of participants regained weight between the 1- and 5-year follow-up time points, the coefficients produced from the regression analyses were interpreted as indicating either less weight regain or greater weight loss compared to the reference group. We found that age at baseline was correlated with weight change between 1- and 5-years, showing that older participants experienced less weight change, and this effect was consistent across models that only used study-measured data and a model controlling for all other PROGRESS-Plus characteristics. No association was observed in our primary analysis between weight change between 1- and 5-years and other PROGRESS-Plus characteristics (ethnicity, occupation, sex, education, IMD, household income) included in our study, although in a sensitivity analysis controlling for all PROGRESS-Plus characteristics, being male was associated with greater weight regain or lesser weight loss. ## Comparison with Existing Literature Inequalities have previously been considered in trial participation and intervention uptake, intervention adherence, and at 1-year follow-up both in the WRAP trial and other UK-based trials of behavioural weight management interventions [13]. Our study is the first to consider inequalities in weight in the longer term (i.e., at the 5-year time point). In the previous studies that considered differential weight outcomes at 12 months, most found no association between SES or gender and weight change outcome [13], mirroring our findings of no association between these factors and weight change between 1- and 5-year follow-up. One study did identify SES as a moderator of the intervention effect [19]; however, in one intervention group, more weight was lost in those who were less deprived, and in the other intervention group, more weight was lost in those who were more deprived, showing an inconsistent relationship between SES and intervention effect. For age, one study at the 12-month time point found that older participants lost more weight [20], supporting our finding that older people regained less weight between 1- and 5-year follow-up. However, the other study that considered this issue did not find an association [21]. This lack of observed inequalities in UK-based trials could indicate that behavioural weight management interventions are similarly effective across sociodemographic groups. However, cautious interpretation is necessary, as most of these studies were not designed to identify whether inequalities in weight outcome following intervention exist and may not have been designed to detect differences between subgroups. Future research could synthesise data on weight outcomes following intervention across multiple studies, leading to more robust conclusions. There may be several reasons why older participants regained less weight than younger participants. First, in WRAP, older participants tended to have better attendance at intervention sessions [15]. As higher levels of attendance are more likely to lead to clinically significant weight loss [22, 23], this increased level of attendance may lead to long-lasting effects of the intervention. Second, healthcare behaviour patterns of older people are generally different from those of younger people. Older adults experience fewer barriers in accessing primary care [24], meaning they are more likely to have regular consultation with healthcare professionals and may have greater healthcare need to focus on behaviours that could affect their weight. Further, specifically in terms of weight management, older people are more likely to be offered access to a weight management intervention in routine practice [25]. Third, biological factors related to ageing could affect long-term weight loss maintenance following participation in a weight loss trial. As participants reach an age of 65 years and older, a reduction in appetite and an increased rate of loss of muscle tissue have been observed [26, 27]. This may partly explain the lesser weight regain in older participants in the WRAP trial, especially as the mean age at baseline was 55.7 years old; at the 5-year follow-up, the average participant age is in the sixties. Finally, factors associated with being an older person may make it easier to attend or maintain behaviours associated with maintaining weight loss. For example, older adults may be less likely to be currently raising children or have a full-time job. ## Strengths and Limitations This study is the first to consider if there are inequalities in weight loss maintenance following participation in a weight loss trial at the 5-year follow-up time point. The demographics of the WRAP trial sample are similar to those of the UK population in terms of SES and ethnicity [15], whereas the demographic makeup of research trials is often more affluent than that of the population. A limitation of our study is the large amount of missing data for our primary outcome; complete outcome data were available for $55.5\%$ of the total sample [28]. This level of missing data is common in trials of weight management interventions − the estimated retained rate in these trials at the 1-year time point was $63\%$, and it was $65\%$ in WRAP [16]. Despite this, those with and without missing data were similar, indicating the sample was unlikely to have been biased by the missing data. Furthermore, our sensitivity analyses conducted to test the robustness of the data were consistent with the findings of our primary analyses. A further limitation of this study is the homogeneity of the sample, especially regarding ethnicity. The majority ($94\%$) of the participants were self-described as white British, which limited the extent to which we could explore inequalities in weight change by ethnicity. This is reflective of the issue of diversity in clinical trials; those of ethnicities other than white are typically underrepresented [29, 30, 31]. The sensitivity analyses conducted broadly supported the results of our primary analyses, with the exception that when all PROGRESS-Plus inequality characteristics included in the study were controlled for, associations between gender and a category of occupation (unable to work) were identified. ## Implications Despite obesity being socially patterned, with the exception of age, we did not find evidence of inequalities in weight change following weight loss intervention. Younger participants of behavioural weight management interventions may need additional support when maintaining weight loss following intervention. However, overall, our findings support the continued use of behavioural weight management interventions as part of a system-wide approach to reducing obesity and related diseases without widening existing health inequalities. Such an approach would also include population-level interventions that could support all people with obesity in maintaining weight after treatment. ## Conclusion Except for age, we did not find evidence of inequalities in weight change following a behavioural intervention, demonstrating that behavioural weight management interventions are unlikely to generate inequalities in weight change following intervention. ## Statement of Ethics Ethical approval for up to 2-year post-randomisation assessment was received by East of England Cambridge East and local approvals from the NRES Committee North West Liverpool Central and the NRES Committee South Central Oxford. Ethical approval for 5-years post-randomisation assessment was received from West Midlands-Coventry and Warwickshire Research Ethics Committee on December 8, 2017. The original trial (ISRCTN82857232) and 5-year follow-up (ISRCTN64986150) were prospectively registered with Current Controlled Trials on October 15, 2012, and February 01, 2018 (https://doi.org/10.1186/ISRCTN82857232; https://doi.org/10.1186/ISRCTN64986150). All participants provided written informed consent for their participation in the trial. ## Conflict of Interest Statement WRAP was a publicly funded, independent, investigator-led trial, in which WW provided the intervention at no cost and provided funds for blood sampling and analysis for the first 2-years via an MRC Industrial Collaboration Award. Neither the funders nor WW had any role in the study design, data collection, data analysis, data interpretation, or writing of the report. Amy L. Ahern is principal investigator on two publicly funded (NIHR, MRC) trials where the intervention is provided by WW at no cost. Simon J. Griffin is principal investigator on a publicly funded (NIHR) trial in which the intervention is provided by WW at no cost. Julia Mueller is a trustee for the Association for the Study of Obesity (unpaid role). Michael P. Kelly has undertaken consultancy for Slimming World and led the obesity and weight management guidelines development for NICE from 2005 until 2014. Jason C.G. Halford has undertaken consultancy from Dupont/iFF, Mars, and Novo Nordisk (all monies paid to the University of Leeds). ## Funding Sources Five-year follow-up of the WRAP trial was funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (RP-PG-0216-20010). The WRAP trial was funded by the National Prevention Research Initiative through research grant MR/J000493. Jack M. Birch, Amy L. Ahern, Simon J. Griffin, and Stephen J. Sharp are supported by the Medical Research Council (MRC) (Grant MC_UU_$\frac{00006}{6}$). The University of Cambridge has received salary support in respect of Simon J Griffin from the National Health Service in the East of England through the Clinical Academic Reserve. This work is funded by UKRI grant MC_UU_$\frac{00006}{6.}$ For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any author-accepted manuscript version arising. Jason C.G. Halford is currently supported by research funding from Horizon 2020 and the American Beverage Association via the University of Liverpool. ## Author Contributions Jack M. Birch planned and designed the study, wrote the statistical analysis plan, conducted the analysis, and led the writing and development of the final manuscript. Julia Mueller and Stephen J. Sharp planned and designed the study, provided statistical input, reviewed the statistical analysis plan, and reviewed and edited the final manuscript. Simon J. Griffin and Amy L. Ahern planned and designed the study, reviewed the statistical analysis plan, and reviewed and edited the final manuscript. Michael P. Kelly and Jason C.G. Halford planned and designed the study and reviewed and edited the final manuscript. ## Data Availability Statement The data cannot be made publicly available because of ethical and legal considerations. Non-identifiable data and code can be made available to bona fide researchers on submission of a reasonable request to datasharing@mrc-epid.cam.ac.uk. Further enquiries can be directed to the corresponding author. ## References 1. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K. Body fatness and cancer − viewpoint of the IARC working group. N Engl J Med (2016) 375 794-798. PMID: 27557308 2. Forouzanfar MH, Alexander L, Anderson HR, Bachman VF, Biryukov S. 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--- title: Dapagliflozin alleviates renal fibrosis in a mouse model of adenine-induced renal injury by inhibiting TGF-β1/MAPK mediated mitochondrial damage authors: - Jianhua Zeng - Hao Huang - Yan Zhang - Xin Lv - Jiawei Cheng - Si Jue Zou - Yuanyuan Han - Songkai Wang - Li Gong - Zhangzhe Peng journal: Frontiers in Pharmacology year: 2023 pmcid: PMC10028454 doi: 10.3389/fphar.2023.1095487 license: CC BY 4.0 --- # Dapagliflozin alleviates renal fibrosis in a mouse model of adenine-induced renal injury by inhibiting TGF-β1/MAPK mediated mitochondrial damage ## Abstract Renal fibrosis is a common pathological outcome of various chronic kidney diseases, and as yet, there is no specific treatment. Dapagliflozin has shown renal protection in some clinical trials as a glucose-lowering drug, but its role and mechanism on renal fibrosis remain unclear. In this study, we used a $0.2\%$ adenine diet-induced renal fibrosis mouse model to investigate whether dapagliflozin could protect renal function and alleviate renal fibrosis in this animal model. In vivo, we found that dapagliflozin’s protective effect on renal fibrosis was associated with 1) sustaining mitochondrial integrity and respiratory chain complex expression, maintained the amount of mitochondria; 2) improving fatty acid oxidation level with increased expression of CPT1-α, PPAR-α, ACOX1, and ACOX2; 3) reducing inflammation and oxidative stress, likely via regulation of IL-1β, IL-6, TNF-α, MCP-1, cxcl-1 expression, and glutathione (GSH) activity, superoxide dismutase (SOD) and malondialdehyde (MDA) levels; and 4) inhibiting the activation of the TGF-β1/MAPK pathway. In HK2 cells treated with TGF-β1, dapagliflozin reduced the expression of FN and α-SMA, improved mitochondrial respiratory chain complex expression, and inhibited activation of the TGF-β1/MAPK pathway. ## 1 Introduction Renal fibrosis is a common pathological outcome of various chronic kidney diseases (CKD), which affects about $13\%$ of the world’s human population (Zhu et al., 2021; Evans et al., 2022). Except for the management of existing comorbidities, such as diabetes, hypertension and obesity, there remains no effective therapy for CKD and renal fibrosis due to its complicated and ambiguous pathogenesis (Liu and Zhuang, 2019; Akinnibosun et al., 2022; Cao et al., 2022). Diabetic nephropathy (DN) is a major cause of CKD that accounts for $30\%$–$47\%$ cases, some new drugs such as DPP-4 Inhibitors (sitagliptin, vildagliptin, saxagliptin, alogliptin and linagliptin, and preclinical), GLP-1 analogues (exenatide, liraglutide), sodium glucose cotransporter-2 (SGLT2) inhibitor (dapagliflozin, canagliflozin and empagliflozin) and so on show renal protection in DN (Schernthaner et al., 2014; Sharma et al., 2017; Sharma et al., 2018a; Kelly et al., 2019). Dapagliflozin is a potent, reversible, and selective inhibitor of (SGLT2), which is currently approved for type 2 diabetes treatment by the European Union; it is also the first SGLT2 inhibitor approved for using in type 1 diabetes (Dhillon, 2019; Paik and Blair, 2019). Several well-designed clinical studies have shown that dapagliflozin has a significant protective effect on the renal and cardiovascular systems, in addition to robust control of blood sugar levels (Wiviott et al., 2019; Kurata and Nangaku, 2022). In a dapagliflozin and CKD outcome prevention (DAPA-CKD) trial, dapagliflozin also reduced the risk of progressive kidney disease in non-diabetic chronic kidney disease (Persson et al., 2021). In basic diabetes-related research, dapagliflozin was found to reduce renal fibrosis in a type I and type II diabetic nephropathy mouse model (Terami et al., 2014; Tang et al., 2017; Huang et al., 2019). Due to its protective effect in the kidneys of patients with non-diabetic chronic kidney disease, research into the use of dapagliflozin to treat renal fibrosis is underway. Studies have shown that dapagliflozin can reduce renal fibrosis in alcohol-induced kidney injury and unilateral ureteral obstruction (UUO) animal models (Wu et al., 2021; Xuan et al., 2021; Liu et al., 2022). Adenine, the basic material for DNA and RNA synthesis in the body, is widely used to treat leukopenia (Tomita et al., 2016). However, excessive intake of adenine can lead to high levels of accumulation in the kidneys, which can lead to severe nephrotoxicity, destruction of the renal structure, and ultimately renal fibrosis (Tamura et al., 2009; Boon et al., 2015). The present model simulates a genetic deficiency of human adenine phosphate glycosyltransferase (APRT), which causes 2,8-dihydroxy adenine (2,8-DHA) nephropathy, a rare disease characterized by formation of 2,8-dihydroxy adenine (2,8-DHA) in the renal tubules. The resulting crystals often damage the renal structure and impair renal function, leading to kidney failure (Runolfsdottir et al., 2016; Klinkhammer et al., 2020). This model has also been widely used to study renal fibrosis (Vázquez-Méndez et al., 2020; Yi et al., 2021; Ito et al., 2022), however, the effect of dapagliflozin on renal fibrosis induced by a high-adenine diet remains unclear. In this study, we aimed to investigate the protective effect of dapagliflozin in a renal fibrosis mouse model induced by a $0.2\%$ adenine diet and to explore the underlying pharmacological mechanisms, then provide a more adequate theoretical basis for dagliflozin in the treatment of renal fibrosis. ## 2.1 Reagents and materials Dapagliflozin was obtained from Apexbio (United States, #A5854), with a purity of ≥$99\%$; TGF-β1was obtained from PeproTech (United States). The western blot antibodies used were Collagen 1 (Abcam, United States, #ab270993), α-SMA (Sigma, United States, #A5691), vimentin (Proteintech, United States, #10366-1-AP), VDAC1(Proteintech, United States, #55259-1-AP), FN (Proteintech, United States, #15613-1-AP), p-JNK (CST, United States, #4668), JNK (CST, United States, #9252), p-ERK (CST, United States, #4370), ERK (CST, United States, #4695), P-P38 (CST, United States, #4631), p38 (CST, United States, #8690), total oxphos complexes (Thermo, United States, AB-2533835), CPT1-α (Proteintech, United States, #15184-1-AP), $0.2\%$ adenine diet (TP 1S002), and control diet (LAD 3001) obtained from Trophic Animal Feed High-tech Co., Ltd., China. The SOD activity kit (#A001-3), GSH content assay kit (#A006-2-1) and MDA content assay kit (#A003-1) were purchased from the Nanjing Jiancheng Institute of Bioengineering. ## 2.2 Animal model and treatment Eight-week-old male C57BL/6J mice were obtained from Silaike Laboratory (Hunan, China) and fed in the animal experiment laboratory of Central South University with approval from the Animal Care and Use Committee of Central South University. After strict adaptive feeding for 1 week, mice were divided into four groups: normal diet with vehicle control (oral gavage, 0.1 mL/10 g, $$n = 6$$), $0.2\%$ adenine diet with vehicle control (oral gavage, 0.1 mL/10 g, $$n = 8$$), $0.2\%$ adenine diet with dapagliflozin therapy at a dose of 1 mg/kg/day (oral gavage, 0.1 mL/10 g, $$n = 8$$) (Wang et al., 2021), and $0.2\%$ adenine diet with dapagliflozin therapy at a dose of 10 mg/kg/day (oral gavage, 0.1 mL/10 g, $$n = 8$$) (Huang et al., 2019). During this study, body weight and food consumption of the four groups were recorded. On day 21 of the experiment, the mice were anesthetized via intraperitonaeal injection of pentobarbital ($1\%$, 50 mg/kg) to harvest the kidneys and blood for further study. ## 2.3 Preparation of dapagliflozin Dapagliflozin was fully dissolved in DMSO (Sigma Aldrich, United States, #D2650), according to the manufacturer’s instructions, and stored at −20°C. When needed, it was diluted in normal saline to a final concentration of DMSO of $1\%$. Dapagliflozin was administered when mice were fed a $0.2\%$ adenine diet. ## 2.4 Serum creatinine, urea nitrogen, and uric acid Blood samples obtained from the previous animal experiments were placed in an incubator at 37°C for 1 h. After centrifugation at 4°C and 3,000 rpm for 10 min, the serum was aspirated and sent to the laboratory of Xiangya Hospital of Central South University for detection of serum creatinine and urea nitrogen. ## 2.5 Histological examination Tissue fixative was used to fix the isolated kidney, dehydrated, and embedded in paraffin. The paraffin sections were stained with hematoxylin-eosin to enable visualization of the kidneys pathological morphology, and the sections were stained with Masson’s trichrome to facilitate observation of the degree of renal fibrosis. Each kidney sample was randomly selected from 10 regions (200×) under a light microscope (Nikon, Ni Ci E100 E200, Japan) for semi quantitative analysis, and the grading was 0–4 (0: $0\%$; 1: <$25\%$; 2: $26\%$–$50\%$; 3: $51\%$–$75\%$; 4: ≥$76\%$ of the damaged renal tubules), and ImageJ software (Image-Pro Plus 6.0 software, Media Cybernetics, Bethesda, MD, United States) was used for measurement and calculation (Ren et al., 2021). Frozen sections were prepared, and the kidney tissues were stained with Oil Red O dye for the observation of lipid deposition in the kidney tissues, the samples were examined using microscope (Nikon, Ni Ci E100 E200, Japan), and the images were analyzed using ImageJ software (Image-Pro Plus 6.0 software, Media Cybernetics, Bethesda, MD, United States) (Chen et al., 2021). ## 2.6 RNA-Seq Transcriptomic assay Total RNA in kidney tissue was extracted using TRIzol (Invitrogen, United States, #15596026), and the quality of total RNA was evaluated with an RNA6000 Nano LabChip kit (Agilent, California, United States) using the Agilent biological analyzer 2100 system. RNA sequencing was conducted by Beijing Berry Hekang Biotechnology Co., Ltd. EdgeR was used to analyze the significance of the differential expression. The actual analysis parameters used in this analysis were log2 \| FoldChange \|>2.0 and a q value of <0.05. Gene Ontology (GO) software was used for GO enrichment analysis of differentially expressed genes, and the KOBAS (v3.0) software was used for Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. ## 2.7 Transmission electron microscopy The kidney tissue was first fixed with electron microscope fixative (Pinofet, S191102), followed by $1\%$ osmium tetroxide, and was then dehydrated in graded alcohol and acetone and prepared into ultrathin sections (60–80 nm) in pure 812 embedding agent (SPI, 90529-77-4). The sections were stained with uranyl acetate and lead citrate, dried. Using transmission electron microscope (Hitachi; HT7800/HT7700), the number and size of mitochondria were measured in 20 random unoverlapped proximal tubular cells of each sample (Xuan et al., 2021). ## 2.8 Cell culture and intervention Human proximal renal tubular epithelial cells (HK2) were purchased from Zhongqiao Xinzhou (China, ZQ0313). DMEM/F12 (Gbico, United States, #11330032) containing $10\%$ fetal bovine serum (FBS) (Gbico, United States, #10099-141) and $1\%$ penicillin-streptomycin was used to culture the cells in a cell culture box containing $5\%$ carbon dioxide at 37°C. When the cell density was above $90\%$, $0.05\%$ tryptase was used to digest the cells. The cell suspension obtained was terminated with DMEM/F12 containing FBS. Firstly, the cells from the 3×104/well were inoculated into a 96 well plate. When the cells adhered to the wall and grew to $70\%$ confluence, different concentrations of dapagliflozin were added. After 24 h of action, the CCK8 kit (Apexbio, United States, #K1018) instructions were strictly followed to explore the effects of the drugs on normal cells. Cells were selected in good growth conditions and inoculated 5×105 cells per well into a 12 well plate, then divided into a normal group, TGF-β1 (10 ng/mL) stimulation group, and dapagliflozin treatment group. TGF-β1 was given to both the stimulation group and the treatment group when the cell growth was $70\%$, and dapagliflozin was added to the treatment group. The principle behind selecting the treatment concentration was to select 1 or 2 concentrations that had no effect on normal cells. Cell proteins were obtained for subsequent analysis after 24 h of stimulation. ## 2.9 Western blotting Sodium dodecyl sulfate (SDS) contained $1\%$ protease inhibitor and $1\%$ phosphatase inhibitor to lyse an appropriate amount of each kidney sample and cell sample. Cells were centrifuged at 13000 rpm at 4°C for 10 min. The supernatant was collected after the completion of lysis, and bicinchoninic acid (BCA) protein detection kit was used to quantify the obtained protein samples (Sharma et al., 2020). According to the indicators to be analyzed, $8\%$–$12\%$ polyacrylamide gel to electrophoresis was added to the obtained protein samples, which were subsequently transferred to a polyvinylidene fluoride (PVDF) membrane under a condition of 260 mA current for 90 min. The rapid sealing solution was applied for 15 min and the corresponding primary antibody was incubated at 4°C overnight. The next day, after incubation with the corresponding secondary antibody for 1 h, the membrane was imaged with a super-sensitivity developer or an ordinary sensitivity developer. ImageJ software was used to analyze the strip produced by the development. ## 2.10 RNA extraction and real-time quantitative PCR Trizol was used to extract total RNA from kidney tissues. A Revert Aid First Strand cDNA Synthesis Kit (Thermo Scientific, MA, #K1622) was used to reverse-transcribe RNA into DNA. Real-time PCR was performed using the CFX96 Quantitative PCR Detection System (Bio-rad, United States, #1855200). The primer sequences used in this study are shown in Supplementary Table S1. ## 2.11 SOD, GSH, and MDA assays To detect SOD, GSH and MDA content in the kidney tissues of mice in each group, kidney tissues stored in liquid nitrogen with appropriate weight were weighed and ground into $10\%$ tissue homogenate with normal saline. We strictly followed the instructions to detect the activity of SOD the content of GSH and MDA in the kidney tissue (Sharma et al., 2018b; Fernandes et al., 2018; Sharma et al., 2019). ## 2.12 Intracellular ROS assay Intracellular ROS was determined by 2′,7′-dichlorofluorescein diacetate (DCFH-DA) (Solarbio, #4091-99-0) according to the manufacturer’s instructions. Briefly, HK-2 cells were seeded in 24-well plates. After 24 h incubation, cells were pretreated with or without various concentrations of dapaliglozin (20 and 40 μM) and coincubated with or without H2O2 (300 μmol/L) for 24 h (Zhang et al., 2019). Then HK-2 cells were loaded with 5 μmol/L DCFH-DA for 30 min at 37°C in the dark and washed with PBS three times. The cells were digested for analysis using a microplate reader at $\frac{485}{528}$ nm (Bio-tek Synergy HT, United States). ## 2.13 Statistical analysis All data are expressed as mean ± standard deviation. Comparisons between groups were analyzed using one-way ANOVA, and comparisons between two groups were analyzed using the least significant difference test. $p \leq 0.05$ was considered statistically significant. ## 3.1 Dapagliflozin protected renal function and alleviated renal fibrosis in mice fed a 0.2% adenine diet Serum levels of urea nitrogen (BUN) and creatinine in $0.2\%$ adenine diet-fed mice were significantly higher than those in control mice, the serum levels of urea nitrogen and creatinine were significantly decreased, and dapagliflozin at a dosage of 10 mg/kg was better than the dosage of 1 mg/kg (Figure 1A). During the period of the experiment, we found that the mice treated with $0.2\%$ adenine had a significantly reduced body weight, dapagliflozin treatment was effective in reducing their weight loss, and the dosage of 10 mg/kg was better than the dosage of 1 mg/kg (Figure 1B). Hematoxylin and eosin (HE) and Masson staining were used to evaluate tubulointerstitial injuries and interstitial extracellular matrix (ECM) deposition, respectively. HE and Masson staining showed that the tubules in the kidneys of mice fed with a $0.2\%$ adenine diet were markedly dilated and atrophied, and ECM deposition was observed in the renal interstitium, whereas mice treated with dapagliflozin showed a partial but significant decrease in both renal tubular injury and interstitial ECM deposition (Figure 1C). The HE and Masson pathological scores were higher in the $0.2\%$ adenine diet mice than control group. After dapagliflozin treatment, the pathological scores in dapagliflozin treatment group were lower than the $0.2\%$ adenine diet mice, indicating that dapagliflozin alleviated tubulointerstitial injuries and interstitial ECM deposition, and the higher dosage (10 mg/kg) was better than the lower dosage (1 mg/kg) (Figure 1D). FIGURE 1: Dapagliflozin reduced renal fibrosis in 0.2% adenine diet-fed mice. (A) Serum urea nitrogen (BUN) and creatinine levels in each group. (B) Body weight variation of mice in each grough. (C) HE and Masson staining of renal tissues from each group. (D) Renal HE and Masson scores of each group. Scale bars, 50 μm for all groups. Values are means ± SD. *p < 0.001 adenine diet group compared with control group; ## p < 0.01 dapagliflozin treatment group compared with adenine diet group; ### p < 0.001 dapagliflozin treatment group compared with adenine diet group; p < 0.001 1 mg/kg dapagliflozin treatment group compared with 10 mg/kg dapagliflozin treatment group. ## 3.2 Dapagliflozin reduces the expression of fibrotic molecules As the effect of dapagliflozin at a dosage of 10 mg/kg was better than the dosage of 1 mg/kg, we used this dosage in further studies. Western blotting showed that proteins such as α-SMA, Vimentin and Collagen 1 were significantly upregulated in the kidneys of mice fed a $0.2\%$ adenine diet, and significantly downregulated by 10 mg/kg dapagliflozin treatment (Figures 2A, B). Simultaneously, real-time Quantitative Polymerase Chain Reaction (PCR) confirmed that the expression of fibrotic genes, such as col1a1, col1a3, FN, α-SMA, and vimentin, was significantly upregulated in the kidneys of mice fed $0.2\%$ adenine, and their expression was significantly downregulated by dapagliflozin treatment (Figure 2C). FIGURE 2: Dapagliflozin reduced the expression of renal fibrosis-related markers in 0.2% adenine diet-fed mice. (A, B) Western blot showing the expression of Collagen 1, α-SMA, and vimentin in the renal tissue of each group. (C) Real time PCR showing the expression of col1a1, col3a1, FN, Vimentin, α-SMA, and MMP7 in the renal tissue of each group. Values are means ± SD. p < 0.01 adenine diet group compared with the control group; *p < 0.001 adenine diet group compared with the control group; # p < 0.01 dapagliflozin treatment group compared with the adenine diet group; ## p < 0.01 dapagliflozin treatment group compared with the adenine diet group; ### p < 0.001 dapagliflozin treatment group compared with the adenine diet group. ## 3.3 Analysis of renal transcriptome in 0.2% adenine diet-induced renal fibrosis mice RNA-seq analysis was performed to understand how dapagliflozin alleviates renal fibrosis. The volcano plot shows significantly different gene expression profiles among normal diet mice, $0.2\%$ adenine mice, and dapagliflozin (10 mg/kg)-treated mice. Among these differentially expressed genes, 2459 genes were upregulated, and 1357 genes were downregulated in the kidneys of $0.2\%$ adenine diet-fed mice compared with control mice ($p \leq 0.05$) (Figure 3A). However, dapagliflozin (10 mg/kg) reversed the changes in 494 downregulated and 323 upregulated genes ($p \leq 0.05$) (Figure 3B). Significant dapagliflozin-modulated genes are illustrated by a heatmap (Figure 3C). Genes related to fibrosis (col1a1, col1a3) and inflammation (TNF-α, IL-6, IL-1β) were upregulated in $0.2\%$ adenine diet mice, and were downregulated by dapagliflozin (10 mg/kg) (Figure 3C). The heatmap also showed that mitochondrial metabolism and fatty acid oxidation (PDH, Acox1, and Acox2)-related genes were downregulated in $0.2\%$ adenine diet-fed mice and upregulated by dapagliflozin (Figure 3C). Furthermore, GO (Figure 3D) and KEGG (Figures 3E, F) enrichment analyses suggested that these differentially expressed genes were involved in mitochondrial and lipid metabolism and inflammatory responses. FIGURE 3: Analysis of renal transcriptome in 0.2% adenine diet-induced renal fibrosis mice. (A) Volcano plot of gene expression difference between the control and 0.2% adenine diet groups. (B) Volcano plot of gene expression difference between the 0.2% adenine diet and 10 mg/kg dapagliflozin treatment groups. (C) Heatmap of gene expression difference among the control, 0.2% adenine diet, and dapagliflozin 10 mg/kg groups. (D) GO analysis of differentially expressed genes. (E, F) KEGG analysis of differentially expressed genes. N: control diet M: 0.2% adenine diet D 0.2% adenine diet + dapagliflozin (10 mg/kg). ## 3.4 Dapagliflozin increased mitochondrial metabolism and fatty acid oxidation in the kidneys of mice fed 0.2% adenine Transcriptomic GO and KEGG enrichment analyses suggested that both mitochondrial metabolism and fatty acid oxidation levels were significantly downregulated in the kidneys of mice fed a $0.2\%$ adenine diet compared with mice in the control group. Mitochondrial metabolism and fatty acid oxidation are important in the occurrence and development of renal injury and fibrosis (Quadri et al., 2019; Console et al., 2020), therefore, we explored the effect of dapagliflozin on these two aspects. First, we examined mitochondrial morphology using electron microscopy. In comparison to control mice, mitochondria with degenerative changes, including disruption of the mitochondrial membrane and matrix swelling, were observed in $0.2\%$ adenine diet-fed mice, and these changes were significantly improved by dapagliflozin treatment (Figure 4A). In addition to the structure of mitochondria, the area and number of mitochondria in renal tubular were decreased in $0.2\%$ adenine diet-fed mice, and the treatment of dapagliflozin could maintain the area and number of mitochondria in renal tubular cell (Figure 4B). Furthermore, mitochondrial respiratory chain complexes regulate mitochondrial oxidative phosphorylation (OxPhos), which is an important part of mitochondrial energy metabolism, like the tricarboxylic acid (TCA) cycle (Edmond, 2009). In comparison to the control mice, the expression of all complexes was decreased in $0.2\%$ adenine diet-fed mice, and dapagliflozin attenuated the reduction of all complexes (Figures 4C, D), partly due to the maintenance of the amount of mitochondria (Figure 4E). Moreover, we used real time PCR to confirm whether the mitochondrial metabolism-related genes, such as ND1, ND4, AKDGH, and PDH, the mitochondrial DNA that encode oxphos-related protein, were downregulated in $0.2\%$ adenine diet mice, and dapagliflozin significantly upregulated the mRNA levels of these genes (Figure 4F). Except for up-regulating the expression of mitochondrial DNA, the RNA-Seq Transcriptomic results shown dapagliflozin also upregulated the expression of nuclear DNA (Supplementary Figure S1). Fatty acid oxidation (FAO) mainly occurs in mitochondria; therefore, mitochondrial dysfunction often leads to the downregulation of fatty acid oxidation (Console et al., 2020). Oil red staining of the mouse kidney tissue showed that lipid deposition in the renal tubules of the mouse kidney was obvious in the $0.2\%$ adenine diet-fed mice compared with control mice, and dapagliflozin reduced lipid deposition in the tubules (Figure 4G). Given that CPT1-α is a key enzyme in fatty acid oxidation (Miguel et al., 2021), we investigated its expression further. Western blotting showed that the expression of CPT1-α was downregulated in the mice that were fed a $0.2\%$ adenine diet, and it was significantly upregulated by dapagliflozin treatment (Figure 4H). The results of real time PCR showed that genes related to fatty acid oxidation, such as CPT1-α, PPAR-α, Acox1, and Acox2, were downregulated in the $0.2\%$ adenine mice, and dapagliflozin attenuated these reductions (Figure 4I). FIGURE 4: Dapagliflozin increased the levels of mitochondrial metabolism and fatty acid oxidation in 0.2% adenine diet-fed mice. (A) Mitochondrial morphology of renal tubular epithelial cells was examined using transmission electron microscopy. Scale bars, 1 μm. (B) Mitochondrial areas and amount in renal tubular cell of each group. (C–E) Western blot showing the expression of mitochondrial respiratory chain complexes (oxphos) in the renal tissue of each group. (F) Real time PCR showing the expression of mitochondrial metabolism-related genes ND1, ND4, AKGDH, and PDH in the renal tissue of each group. (G) Renal oil red staining of each group. Scale bars, 20 μm. (H)Western blot showing the expression of CPT-1α of in the renal tissue of each group. (I) Real time PCR showing the expression of fatty acid oxidation-related genes CPT1, PPAR-α, ACOX1, and ACOX2 in the renal tissue of each group. Values are means ± SD.p < 0.05 adenine diet group compared with the control group; p < 0.01 adenine diet group compared with the control group; p < 0.001 adenine diet group compared with the control group; # p < 0.01 dapagliflozin treatment group compared with the adenine diet group; ## p < 0.01 dapagliflozin treatment group compared with the adenine diet group; ### p < 0.001 dapagliflozin treatment group compared with the adenine diet group. ## 3.5 Dapagliflozin relieved inflammation and oxidative stress in the kidneys of mice fed with 0.2% adenine Inflammation and oxidative stress are important factors in the development of renal fibrosis (Meng et al., 2014; Rayego-Mateos and Valdivielso, 2020). The transcriptomic results also showed that inflammation was significantly activated in the mice fed with a $0.2\%$ adenine diet, whereas it was alleviated when the mice were treated with dapagliflozin. Consistently, our real-time PCR results showed that the expression of inflammatory factors such as IL-1β, IL-6, cxcl-1, TNF-α, and MCP-1 was increased in the mice fed with a $0.2\%$ adenine diet compared with control mice, and dapagliflozin treatment reduced their expression (Figure 5A). We also detected SOD activity, GSH and MDA contents in the renal tissue of mice in each group. The results showed that the activity of SOD and content GSH in the kidneys of mice fed with $0.2\%$ adenine decreased, but significantly increased after treatment with dapagliflozin (Figure 5B). The content of MDA increased in the kidneys of mice fed with $0.2\%$ adenine decreased, but decreased after treatment with dapagliflozin (Figure 5C). FIGURE 5: Dapagliflozin alleviated inflammation and increased antioxidant proteins in 0.2% adenine diet-fed mice. (A) Real time PCR showing the expression of IL-1β, IL-6, TNF-α, cxcl-1, and mcp-1 in the renal tissue in each group. (B) The activity of SOD and content of GSH in the renal tissue of each group. (C) The content of MDA in the renal tissue of each group. Values are means ± SD. p < 0.01 adenine diet group compared with the control group; *p < 0.001 adenine diet group compared with the control group; ## p < 0.01 dapagliflozin treatment group compared with the adenine diet group; ### p < 0.001 dapagliflozin treatment group compared with the adenine diet group. ## 3.6 Dapagliflozin inhibited TGF-β1/MAPK signaling pathways in the kidneys of mice fed a 0.2% adenine diet TGF-β1 is considered a key driver of renal fibrosis (Meng et al., 2016). Downstream of TGF-β, the MAPK (ERK, JNK, and p38) signaling pathways are overactivated and promote the EMT process in renal fibrosis (Rhyu et al., 2005; Hung et al., 2016). Therefore, we explored whether dapagliflozin affected the TGF-β1/MAPK pathway. Real-time PCR confirmed that TGF-β1 was upregulated when the mice were fed a $0.2\%$ adenine diet, and dapagliflozin reduced its expression (Figure 6A). Western blotting showed that the levels of p-ERK and p-P38 were upregulated in mice fed a $0.2\%$ adenine diet and downregulated by dapagliflozin treatment (Figures 6B, C). FIGURE 6: Dapagliflozin inhibited the activation of TGF-β1/MAPK pathway in 0.2% adenine diet-fed mice. (A) Real time PCR showing the expression of TGF-β1 in the renal tissue of each group. (B, C) Western blot showing the expression of p-ERK and p-P38 in each group. Values are means ± SD. *p < 0.001 adenine diet group compared with the control group; ## p < 0.01 dapagliflozin treatment group compared with the adenine diet group; ### p < 0.001 dapagliflozin treatment group compared with the adenine diet group. ## 3.7 Dapagliflozin inhibits the expression of fibrotic proteins and relieved oxidative stress in vitro In order to determine the toxicity of dapagliflozin to normal cells, we incubated HK2 cells with dapagliflozin at differing concentrations (0–100 μM) for 24 h. Cell viability was tested using CCK8 reagent (Figure 7A). We then selected the concentrations (20 and 40 μM) that had no apparent effect on normal cell viability for further research. The results suggested that both concentrations of dapagliflozin could significantly inhibit the expression of FN and α-SMA in HK2 cells under stimulation by TGF-β1 (10 ng/mL), and a higher concentration (40 μM) had a more pronounced effect (Figures 7B, C). To further confirm the effects of dapagliflozin, we observed the effects of dapagliflozin in vitro model of kidney oxidative stress (H2O2-induced HK-2 cells). The results suggested the ROS level was increased in H2O2-induced HK-2 cells compared to the control group. Treatment with dapagliflozin (40 μM) lowered the level of ROS in HK-2 cells stimulated with H2O2 (Figure 7D). FIGURE 7: Dapagliflozin reduced the expression of renal fibrosis-related markers in HK2 cells stimulated by TGF-β1. (A) CCK8 was used to evaluate the toxicity of dapagliflozin to normal cells. (B, C) Western blot showing the expression of FN and α-SMA in HK2 cells in each group. (D) The ROS content in HK2 in each group. *p < 0.001 TGF-β1 stimulated group compared with the control group; Values are means ± SD. ### p < 0.001 dapagliflozin treatment group compared with TGF-β1 stimulation group; and p < 0.05 20 uM dapagliflozin treatment group compared with 40 uM dapagliflozin treatment group; and p < 0.01 20 uM dapagliflozin treatment group compared with the 40 uM dapagliflozin treatment group. ## 3.8 Dapagliflozin inhibited the TGF-β1/MAPK signaling pathway and attenuated the reduction of mitochondrial respiratory chain complexes in HK2 cells As for the therapeutic group, HK2 cells were incubated with 20 and 40 μM dapagliflozin for 24 h, and 10 ng/mL TGF-β1 was administered to both the model group and the therapeutic groups for 30 min. The cell protein was then harvested for further detection. As seen in Figures 8A, B, the levels of p-JNK, p-ERK, and p-P38 were higher than those in the control group, whereas when treated with dapagliflozin, their expression was much lower, and a higher concentration (40 μM) resulted in a greater reduction in expression. We also found that the expression of mitochondrial respiratory chain complexes decreased in HK2 cells stimulated by TGF-β1, and dapagliflozin (40 μM) increased the level of the complexes (Figures 8C–E). FIGURE 8: Dapagliflozin inhibited activation of the TGF-β1/MAPK pathway and attenuated the reductions of mitochondrial respiratory chain complexes in HK2 cells stimulated by TGF-β1. (A, B) Western blot showing the expression of p-JNK, p-ERK, and p-P38 in HK2 cells in each group. (C–E) Western blot showing the expression of mitochondrial respiratory chain complexes (oxphos) in HK2 cells in each group. Values are means ± SD. p < 0.01 TGF-β1 stimulated group compared with the control group; *p < 0.001 TGF-β1 stimulated group compared with the control group; #p < 0.05 dapagliflozin treatment group compared with the TGF-β1 stimulated group; ## p < 0.01 dapagliflozin treatment group with the TGF-β1 stimulated group compared; ### p < 0.001 dapagliflozin treatment group compared with the TGF-β1 stimulated group; and p < 0.01 20 μM dapagliflozin treatment group compared with the 40 μM dapagliflozin treatment group. ## 4 Discussion Renal fibrosis is a common pathological feature of progressive CKD (Meng et al., 2016), for which the clinical management remains challenging. This is partly due to the lack of effective drugs. DN as an important cause of CKD, and some drugs treated with diabetes like SGLT2 inhibitors have shown renal protective effects (Sharma et al., 2017; Kelly et al., 2019), as a typical drug of SGLT2 inhibitors, dapagliflozin’s renal protective effects on CKD have been demonstrated in patients with and without diabetes (Kurata and Nangaku, 2022), and it has also displayed antirenal fibrosis activities in animal models induced by unilateral ureter obstruction and alcohol damage (Wu et al., 2021; Xuan et al., 2021; Liu et al., 2022). In this study, we extended this model to the setting of renal fibrosis induced by a $0.2\%$ adenine diet. By protecting renal function, attenuating renal pathological changes, and reducing the expression of fibrosis-related proteins, including Collagen I, Vimentin, and α-SMA, we confirmed that dapagliflozin had an antifibrotic effect in a $0.2\%$ adenine diet model. Specifically, dapagliflozin sustained mitochondrial integrity and functions, maintained the amount of mitochondria, returned FAO levels, reduced inflammation and oxidative stress, and inhibited the TGF-β1/MAPK signaling pathway in renal tissue. TGF-β1 is considered a master regulator of epithelial-mesenchymal transition (EMT) and ECM accumulation and, consequently, a potential key driver of renal fibrosis (Meng et al., 2016). As additional downstream pathways of TGF-β, the MAPK (ERK, JNK, and p38) signaling pathways are overactivated and further promote the EMT process in renal fibrosis (Rhyu et al., 2005; Hung et al., 2016). There were some evidences show that in the body of mice fed with $0.2\%$ adenine diet, the excessive adenine would lead to its metabolites (2,8-DHA crystals) deposited in the renal tubules, resulting in tubular injury (Klinkhammer et al., 2020). Then the classic fibrotic pathway MAPK activated, the level of phosphorylation of p38, ERK and JNK increased in adenine-induced renal fibrosis animal model (Rhyu et al., 2012; Tang et al., 2021; Zhou et al., 2021; Ha et al., 2022; Kim et al., 2022). This study provides the first evidence that dapagliflozin could reduce the levels of p-ERK, and p-P38 in renal fibrosis in $0.2\%$ adenine diet-fed mice. In vitro, dapagliflozin reduced the levels of p-JNK, p-ERK, and p-P38 in HK2 cells stimulated by TGF-β1. These results suggest that dapagliflozin can inhibitor the activation of TGF-β1/MAPK pathway. Mitochondria plays an essential role in kidney health, and mitochondrial dysfunction is involved in the physiological process of renal fibrosis (Braga et al., 2022). Studies have shown the members of MAPK signaling pathway can interact with mitochondria to regulate mitochondrial metabolism (Javadov et al., 2014). The activation of ERK, P38, JNK can lead to mitochondrial dysfunction, attenuate pyruvate dehydrogenase activity in the TCA cycle in renal cells, thereby weakening mitochondrial respiration and ATP production, accumulative evidence reveals that insufficient energy supply can lead to renal damage and fibrosis (Nowak, 2002; Nowak et al., 2006; Zhou et al., 2008; Zhou et al., 2009; Simon and Hertig, 2015; Zhao et al., 2019). Thus, activation of the TGF-β1/MAPK pathway is usually accompanied by dysfunction of the mitochondrial function. Fatty acid oxidation mainly occurs in the mitochondria, and lower FAO levels appear to contribute to renal fibrosis development (Simon and Hertig, 2015). In the kidneys, fatty acids are important substrates of ATP production; they are converted to acyl-CoA by acyl-CoA synthetases and then transported to the mitochondria by FAO enzymes, such as CPT-1 and ACOX1 (Yang et al., 2017). During the TCA cycle, pyruvate, generated via glycolysis, is converted into acetyl-CoA by PDH, which is then metabolized to carbon dioxide by enzymes such as CS and AKGDH (Martínez-Reyes and Chandel, 2020). Electrons generated in the TCA cycle undergo oxidative phosphorylation to produce ATP (Martínez-Reyes and Chandel, 2020). Our results show that the mitochondrial respiratory chain, TCA cycle, and fatty acid oxidation may play a role in the progression of renal fibrosis induced by a $0.2\%$ adenine diet. In addition to sustaining mitochondrial integrity and maintained the amount of mitochondria, dapagliflozin preserved the expression of ND1, ND4, AKGDH, PDH and ATP5e, SDHA (Mitochondrial DNA and nuclei DNA encode OxPhos protein), along with the expression of mitochondrial respiratory chain complexes in the $0.2\%$ adenine diet model. Except for its protective effects on mitochondria, dapagliflozin preserved the expression of CPT1-α, PPAR-α, ACOX1, and ACOX2, the key enzymes of FAO in $0.2\%$ adenine diet-fed mice. These results suggest dapagliflozin sustained mitochondrial integrity and functions, maintained the amount of mitochondria, returned FAO levels in mice fed a $0.2\%$ adenine diet, and maybe partly due to the inhibition of the TGF-β1/MAPK pathway. Mitochondrial dysfunction activates inflammation through the mtDNA-cGAS-STING-NF-κB pathway in renal fibrosis, with higher levels of the proinflammatory cytokines IL-6, TNF-α and IL-1β (Chung et al., 2019). Our results showed that dapagliflozin treatment decreased the expression of IL-1β, IL-6, TNF-α, MCP-1, and CXCL-1 in the $0.2\%$ adenine diet-fed mice. Oxidative stress is not only a result of mitochondrial damage, but also a cause of mitochondrial damage. Morphological abnormalities and loss of function of mitochondria will lead to increased ROS production and decreased antioxidant GSH and SOD levels, whereas imbalance of oxidative products and antioxidants will lead to the development of oxidative stress, triggering the progression of renal fibrosis (Zhang et al., 2021; Braga et al., 2022). Inflammation and oxidative stress were critical aspects of renal fibrosis (Meng et al., 2014; Rayego-Mateos and Valdivielso, 2020). Dapagliflozin reduces inflammation and oxidative stress in the kidneys in UUO animal models (Xuan et al., 2021; Liu et al., 2022). Our study also showed that dapagliflozin increased content of GSH, improved activity of SOD, and decreased the content of MDA in $0.2\%$ adenine diet-fed mice. In vitro, we also shown that dapagloflozin reduce the ROS lever in H2O2-induced HK2-cell. Collectively, our research supports the notion that dapagliflozin alleviates inflammation and prevents oxidative stress as a part of its action in protecting against mitochondrial damage. SGT2 is a clear target of dapagliflozin, thus, we explored whether it plays a role in renal fibrosis. Our study is the first to identify reduced expression of SGLT2 in mice fed a $0.2\%$ adenine diet, and that dapagliflozin was capable of increasing SGLT2 expression (Supplementary Figure S2). Previous reports that the expression of SGLT2 is also decreased when renal tubules are injured by STZ and ischemia-reperfusion (Brouwers et al., 2013; Nespoux et al., 2020). Therefore, we hypothesized that the destruction of tubular structure by a high-adenine diet is responsible for the decreased expression of SGLT2, which reduces SGLT2 by protecting the structure of the kidney, meaning that the change in SGLT2 expression was secondary to the protective effect of dapagliflozin on the kidney. And the therapeutic effect of dapagliflozin on renal interstitial fibrosis is likely independent of SGLT2 inhibiting. Whether dapagliflozin has a specific target in renal fibrosis requires further investigation. ## 5 Conclusion This study is the first to demonstrate a significant protective effect of dapagliflozin on high adenine diet-induced renal fibrosis. The underlying mechanism was related to mitochondrial protection due to inhibition of the TGF-β1/MAPK pathway activation, then the mitochondrial protection reduced of inflammation and oxidative stress. This study provides new theoretical and experimental evidence of the therapeutic potential of dapagliflozin in renal fibrosis, and it is worth our further study. ## Data availability statement The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA009959) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa. ## Ethics statement The animal study was reviewed and approved by the Animal Care and Use Committee of Central South University. ## Author contributions ZP conceived and directed the project; JZ carried out experiments; HH, JC, YZ, and SZ analyzed the data; XL guided the article revision, experimental design and make the figures; YH, SW, and LG made the figures. ZP and JZ drafted and revised the paper with the help of all the other authors; all authors approved the final version of the manuscript. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## Supplementary material The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1095487/full#supplementary-material ## References 1. Akinnibosun O. A., Maier. M. C., Eales J., Tomaszewski M., Charchar F. J.. 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--- title: The Relationship Between Coronary Collateral Circulation and Serum Adropin Levels journal: Cureus year: 2023 pmcid: PMC10028480 doi: 10.7759/cureus.35166 license: CC BY 3.0 --- # The Relationship Between Coronary Collateral Circulation and Serum Adropin Levels ## Abstract Objective Coronary collateral circulation (CCC) are vascular structures that limit the infarct area, protect left ventricular function, and reduce the frequency of arrhythmia and mortality during myocardial ischemia and infarction. In this study, we examined the relationship between the development of CCC and serum adropin levels, which has been shown in previous studies to regulate endothelial functions and increase endothelial nitric oxide synthesis, in patients with acute myocardial infarction. Methods This study included 41 patients with insufficient CCC and 43 patients with well-developed CCC who were hospitalized for acute myocardial infarction and underwent coronary angiography. The Cohen-Rentrop classification was used to grade the CCC. The patients were divided into two groups according to Rentrop grades: those with a 0-1 stage were considered as insufficient and those with grades of 2-3 were considered as well-developed CCC. We took blood samples to measure the adropin levels within the first 24 hours of hospitalization. Results The mean age was 59.1±11.9 years and 62 ($73.8\%$) were male. The right coronary artery was the most frequently target vessel (n: 51, $60.7\%$), and the majority of the patients presented with ST-segment elevation myocardial infarction (STEMI) (n:58, $69\%$). The median interval between the severe chest pain and the intervention was significantly higher in patients with well-developed CCC ($$p \leq 0.042$$). The serum adropin levels in patients with insufficient CCC were significantly lower than in those with well-developed CCC (196.3 [131.5 - 837.0] pg/mL vs. 235.5 [171.9 - 1124.2] pg/mL, $p \leq 0.001$). Logistic regression analysis revealed that the circumflex artery as the target vessel, NSTEMI (non-STEMI) as the type of myocardial infarction, and serum adropin level were the independent risk factors for the prediction of poor coronary collateral vessel formation ($p \leq 0.05$). Conclusions In this study, we found that in patients with acute myocardial infarction, those with well-developed CCC had higher adropin levels. ## Introduction Coronary collateral circulation (CCC) are vessels that are potentially present but are non-functional in a healthy heart. Coronary collaterals have very important and beneficial functions such as reducing ischemia and the frequency of myocardial infarction, limiting the infarct area, preventing aneurysm formation by preserving left ventricular functions, antiarrhythmic effects, and reducing cardiovascular mortality [1-2]. Adropin is a peptide molecule that plays a role in maintaining energy homeostasis and insulin resistance [3]. In recent studies, it has been shown that adropin maintains endothelial function by regulating endothelial nitric oxide synthase and is protective against endothelial dysfunction [4]. An ischemia model was created in animal experiments, and it was observed that adropin treatment increased perfusion and capillary density [5]. There are many studies showing the protective role of adropin on endothelial structure and function. In a recent study, the relationship between adropin level and the development of CCC was shown in patients with chronic coronary syndrome [6]. In this study, we planned to examine the relationship between coronary collateral circulation, which is thought to develop rapidly in patients with acute myocardial infarction, and adropin levels. ## Materials and methods Methods This cross-sectional, prospective study included 84 patients, who were admitted to our hospital due to acute myocardial infarction and underwent coronary angiography between January 2019 and December 2020. Medical treatment was initiated in accordance with the treatment guidelines for patients hospitalized with the diagnosis of acute myocardial infarction (NSTEMI and STEMI) [7-8], and coronary angiography was performed with the conventional method. Patients with acute myocardial infarction with $90\%$ or more stenosis on coronary angiography were included in the study. Those who have previously undergone percutaneous coronary intervention (PCI) or coronary artery bypass grafting; patients with chronic total occlusion, severe kidney and liver disease, pregnancy, malignancy, and chronic inflammatory disease were excluded from the study. Patients with an interval between the last severe chest pain and intervention of more than 12 hours were excluded. This study was approved by the local ethics committee (Clinical Research Ethics Committee of Giresun University) (approval date - number: $\frac{04}{09}$/2018 - $\frac{01}{07}$); all patients were informed about the goals of the study and informed consent was obtained. The presence of CCC towards the artery of the culprit lesion was evaluated in patients with stenosis of $90\%$ or more in coronary angiography and who underwent coronary percutaneous intervention. Angiographic images were evaluated by two experienced cardiologists, and they made a joint decision in the case of borderline lesions. CCC was graded according to the Rentrop classification: 0: no significant collateral circulation, 1: collateral circulation to the lateral branches without reaching the epicardial artery, 2: partial filling of the epicardial artery, 3: full filling of the epicardial artery [9]. The patients were divided into two groups according to Rentrop grades: those with a Rentrop grade of 2-3were accepted as well-developed collateral circulation, and those with a grade of 0-1 were considered as insufficient collateral circulation. Forty-one patients with insufficient collateral circulation and 43 patients with well-developed collateral circulation were divided into two groups. In addition, the Thrombolysis in Myocardial Infarction (TIMI) flow grade was used to angiographically evaluate coronary perfusion after PCI. Flow grade in coronary arteries is classified as grade 0 (no flow), grade 1 (penetration without perfusion), grade 2 (partial perfusion), or grade 3 (complete perfusion) [10]. Venous blood samples for routine clinical chemistry and blood cell count analyses were collected in clot activators and EDTA (ethylenediaminetetraacetic acid) tubes, respectively. All analyses without adropin were performed at presentation. Routine clinical chemistry parameters (creatinine, total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein) were obtained on routine chemistry analyzers from Roche (Cobas; Roche Diagnostics, Basel, Switzerland). Blood cells count parameters (leukocytes, lymphocytes, platelets, granulocytes, and hemoglobin) were derived from a complete blood count (CBC) measured on a BC-6800 (Mindray, Shenzhen, China). For the adropin test, the samples were taken within the first 24 hours of admission in tubes with clot activator and gel and were centrifuged at 3500 rpm for 10 minutes and the serum samples obtained were portioned and stored at -80°C. Absorbance measurements for serum adropin levels using a commercial ELISA (enzyme-linked immunosorbent assay) kit (Shanghai Korain Biotech, Shanghai, China) were performed on a microplate reader (AccuReader, Metertech Inc., Taipei, Taiwan). Statistical analysis For descriptive statistics, mean ± standard deviation was used to give continuous data with normal distribution. A median with minimum-maximum values was applied for continuous variables without normal distribution. Numbers and percentages were used for categorical variables. The Shapiro-Wilk, Kolmogorov-Smirnov, and Anderson-Darling tests analyzed the normal distribution of the numerical variables. The Independent Samples t-test compared two independent groups where numerical variables had a normal distribution. For the variables without normal distribution, the Mann-Whitney U test was applied in comparing two independent groups. The Pearson Chi-Square and Fisher's Exact tests were used to compare the differences between categorical variables in 2x2 tables. The Fisher Freeman Haltontest was used in RxC tables. The receiver operating characteristic (ROC) analysis using the DeLong method with the Youden index was used to determine the optimum serum adropin cut-off value that predicts the development of insufficient CCC. The area under the characteristic (AUC) curve and the corresponding $95\%$ confidence interval (CI) were calculated. Based on the appropriate cut-off value of the serum adropin level, specificity, sensitivity, positive, and negative predictive values, positive, and negative likelihood ratios were also calculated for the parameters with AUC value. For statistical analysis, Jamovi (Version 2.2.5.0, The Jamovi Project, www.jamovi.org) and JASP (Version 0.16.1, https://jasp-stats.org/) were used. The significance level (p-value) was determined at 0.05 in all statistical analyses. ## Results There were 84 patients in the study. We randomized 41 ($48.8\%$) patients with insufficient CCC and 43 ($51.2\%$) patients with well-developed CCC. The demographic and clinical characteristics were similar in the groups ($p \leq 0.05$). We detected significant differences between the groups in white blood cell and neutrophil counts and serum adropin levels. The patients with insufficient CCC had significantly higher white blood cell (12.3 ± 3.7 vs. 10.4 ± 2.9, $$p \leq 0.009$$) and neutrophil (9.0 ± 3.8 vs. 7.1 ± 2.4, $$p \leq 0.004$$) counts than those with well-developed CCC. The serum adropin levels in patients with insufficient CCC were significantly lower than in those with well-developed CCC (196.3 [131.5 - 837.0] pg/mL vs. 235.5 [171.9 - 1124.2] pg/mL, $p \leq 0.001$) (Table 1). Table 1 \| Unnamed: 0 \| Unnamed: 1 \| Unnamed: 2 \| Patients with \| Patients with.1 \| Patients with.2 \| Patients with.3 \| Unnamed: 7 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| Overall (n=84) \| Overall (n=84) \| Insufficient CCC (n=41) \| Insufficient CCC (n=41) \| Well-developed CCC (n=43) \| Well-developed CCC (n=43) \| p-value \| \| Age (year) † \| 59.1 ± 11.9 \| 59.1 ± 11.9 \| 59.4 ± 11.8 \| 59.4 ± 11.8 \| 58.8 ± 12.1 \| 58.8 ± 12.1 \| 0.805** \| \| Sex ‡ \| \| \| \| \| \| \| \| \| Male \| 62 (73.8) \| 62 (73.8) \| 32 (78.0) \| 32 (78.0) \| 30 (69.8) \| 30 (69.8) \| 0.385* \| \| Female \| 22 (26.2) \| 22 (26.2) \| 9 (22.0) \| 9 (22.0) \| 13 (32.5) \| 13 (32.5) \| \| \| Smoking ‡ \| 27 (32.1) \| 27 (32.1) \| 13 (31.7) \| 13 (31.7) \| 14 (32.5) \| 14 (32.5) \| 0.999* \| \| Diabetes mellitus ‡ \| 20 (23.8) \| 20 (23.8) \| 12 (29.2) \| 12 (29.2) \| 8 (18.6) \| 8 (18.6) \| 0.447* \| \| Hypertension ‡ \| 44 (52.4) \| 44 (52.4) \| 27 (65.8) \| 27 (65.8) \| 17 (39.5) \| 17 (39.5) \| 0.058* \| \| Hemoglobin (g/dL) † \| Hemoglobin (g/dL) † \| 14.5 ± 1.9 \| 14.5 ± 1.9 \| 14.5 ± 1.5 \| 14.5 ± 1.5 \| 0.956* \| 0.956* \| \| White blood cell count (mm3) † \| White blood cell count (mm3) † \| 12.3 ± 3.7 \| 12.3 ± 3.7 \| 10.4 ± 2.9 \| 10.4 ± 2.9 \| 0.009* \| 0.009* \| \| Neutrophil count (mm3) † \| Neutrophil count (mm3) † \| 9.0 ± 3.8 \| 9.0 ± 3.8 \| 7.1 ± 2.4 \| 7.1 ± 2.4 \| 0.004* \| 0.004* \| \| Lymphocyte count (mm3) § \| Lymphocyte count (mm3) § \| 1.8 [0.6 – 7.3] \| 1.8 [0.6 – 7.3] \| 2.1 [0.7 – 7.3] \| 2.1 [0.7 – 7.3] \| 0.856* \| 0.856* \| \| Platelet count (mm3) † \| Platelet count (mm3) † \| 247.2 ± 73.7 \| 247.2 ± 73.7 \| 252.8 ± 59.0 \| 252.8 ± 59.0 \| 0.690* \| 0.690* \| \| Creatinine (mg/dL) § \| Creatinine (mg/dL) § \| 0.9 [0.6 – 1.4] \| 0.9 [0.6 – 1.4] \| 0.9 [0.7 – 1.7] \| 0.9 [0.7 – 1.7] \| 0.281* \| 0.281* \| \| Total cholesterol (mg/dL) † \| Total cholesterol (mg/dL) † \| 193.7 ± 40.1 \| 193.7 ± 40.1 \| 188.8 ± 46.1 \| 188.8 ± 46.1 \| 0.590* \| 0.590* \| \| Triglyceride (mg/dL) § \| Triglyceride (mg/dL) § \| 159.0 [55.0 – 474.0] \| 159.0 [55.0 – 474.0] \| 140.0 [39.0 – 852.0] \| 140.0 [39.0 – 852.0] \| 0.695* \| 0.695* \| \| High-density lipoprotein (mg/dL) † \| High-density lipoprotein (mg/dL) † \| 40.4 ± 9.9 \| 40.4 ± 9.9 \| 38.3 ± 10.6 \| 38.3 ± 10.6 \| 0.353* \| 0.353* \| \| Low-density lipoprotein (mg/dL) † \| Low-density lipoprotein (mg/dL) † \| 121.6 ± 36.3 \| 121.6 ± 36.3 \| 115.1 ± 36.4 \| 115.1 ± 36.4 \| 0.402* \| 0.402* \| \| Adropin (pg/mL) § \| Adropin (pg/mL) § \| 196.3 [131.5 – 837.0] \| 196.3 [131.5 – 837.0] \| 235.5 [171.9 – 1124.2] \| 235.5 [171.9 – 1124.2] \| <0.001* \| <0.001* \| The angiographic and interventional treatment characteristics of coronary artery disease (CAD) are given in Table 2. The right coronary artery was the most frequently target vessel ($60.7\%$) among all patients. The majority of the patients presented with STEMI (69 %). The TIMI thrombus grade and the outcomes of PCI are detailed in Table 2. Table 2 \| Unnamed: 0 \| Unnamed: 1 \| Patients with \| Patients with.1 \| Unnamed: 4 \| \| --- \| --- \| --- \| --- \| --- \| \| \| Overall (n=84) \| Insufficient CCC (n=41) \| Well-developed CCC (n=43) \| p-value \| \| Target vessel ‡ \| \| \| \| \| \| Right coronary artery \| 51 (60.7) \| 19 (46.3) \| 32 (74.4) \| 0.012* \| \| Left descending artery \| 26 (30.9) \| 16 (39.0) \| 10 (23.2) \| 0.012* \| \| Circumflex artery \| 7 (8.3) \| 6 (14.6) \| 1 (2.3) \| 0.012* \| \| Type of myocardial infarction § \| \| \| \| \| \| ST Elevation Myocardial Infarction (STEMI) \| 58 (69.0) \| 24 (58.5) \| 34 (79.0) \| 0.071* \| \| Non-ST Elevation Myocardial Infarction (NSTEMI) \| 26 (30.9) \| 17 (41.5) \| 9 (20.9) \| 0.071* \| \| Interval between last severe chest pain and intervention(hr) § \| 4.0 [1.0 – 10.0] \| 3.0 [2.0 – 6.0] \| 4.0 [1.0 – 10.0] \| 0.042** \| \| TIMI thrombus grades ‡ \| \| \| \| \| \| 1 \| 3 (3.6) \| 2 (4.8) \| 1 (2.3) \| 0.749* \| \| 2 \| 2 (3.3) \| 1 (2.4) \| 1 (2.3) \| 0.749* \| \| 3 \| 79 (94.0) \| 38 (92.6) \| 41 (95.3) \| 0.749* \| \| Outcome of PCI ‡ \| \| \| \| \| \| Complete recanalization (TIMI 3) \| 76 (90.4) \| 37 90.2) \| 39 (90.6) \| 0.999* \| \| Slow-flow/no-reflow (TIMI ≤2) \| 8 (9.5) \| 4 (9.7) \| 4 (9.3) \| 0.999* \| There were significant differences in the frequencies of the target vessel and the median interval between the last severe chest pain and intervention. The right coronary artery was the more frequently detected target vessel in patients with well-developed CCC ($74.4\%$ vs. $46.3\%$), and the circumflex artery was more frequent in patients with insufficient CCC ($14.6\%$ vs. $2.3\%$) ($$p \leq 0.012$$). The median interval between the last severe chest pain and the intervention was significantly higher in patients with well-developed CCC than those with insufficient CCC ($$p \leq 0.042$$). TIMI thrombus grade 3 was detected in $94.0\%$ of the patients. Successful PCI was obtained in $90.4\%$ of the patients. The comparison of the other characteristics revealed no significant difference between the groups ($p \leq 0.05$). The Receiver Operating Characteristics (ROC) curve analysis revealed that the cut-off value of serum adropin higher than 211 pg/mL had sensitivity and specificity values of $68.9\%$ and $75.6\%$, respectively, in predicting poor coronary collateral vessel formation (AUC=0.716, CI $95\%$: 0.611-0.806, $p \leq 0.001$) (Figure 1). Figure 1: The Receiver Operating Characteristics (ROC) curve analysis of serum adropin in predicting insufficient CCC (AUC: area under curve).ROC: receiver operating characteristics, AUC: area under curve, CCC: coronary collateral circulation Logistic regression analysis revealed that the circumflex and left descending coronary artery as the target vessel, NSTEMI as the type of myocardial infarction, and serum adropin level were the independent risk factors for the prediction of insufficient coronary collateral vessel formation ($p \leq 0.05$) (Table 3). Table 3 \| Unnamed: 0 \| Crude OR (95%CI) \| Crude p-value \| Adjusted OR (95%CI) \| Adjusted p-value \| \| --- \| --- \| --- \| --- \| --- \| \| Target vessel: ref.=right coronary \| \| \| \| \| \| Left descending coronary \| 2.7 (1.06 – 6.87) \| 0.037 \| 3.74 (1.21 – 11.59) \| 0.022 \| \| Circumflex coronary \| 11.55 (1.32 – 100.92) \| 0.027 \| 40.84 (2.63 – 634.1) \| 0.008 \| \| Type of myocardial infarction: NSTEMI vs. STEMI \| 2.56 (1.02 – 6.41) \| 0.045 \| 3.41 (1.07 – 10.88) \| 0.038 \| \| Adropin level \| 0.99 (0.99 – 1) \| 0.006 \| 0.99 (0.99 – 1) \| 0.022 \| \| White blood cell count \| 1.19 (1.04 – 1.36) \| 0.012 \| 1.04 (0.78 – 1.4) \| 0.766 \| \| Neutrophil count \| 1.23 (1.06 – 1.43) \| 0.007 \| 1.22 (0.89 – 1.69) \| 0.222 \| ## Discussion In this study, we found that there is a relationship between adropin levels and CCC in patients with acute myocardial infarction. The serum adropin levels in patients with insufficient CCC were significantly lower than in those with well-developed CCC. In addition, there were significant differences in the frequencies of the target vessel and the median interval between the last severe chest pain and intervention. The right coronary artery was the more frequently detected target vessel in patients with well-developed CCC. Logistic regression analysis revealed that the circumflex artery as the target vessel, NSTEMI as the type of myocardial infarction, and serum adropin level were the independent risk factors for the prediction of insufficient CCC. In the presence of severe coronary stenosis or occlusion, CCC helps maintain myocardium vitality by providing alternative blood flow to the ischemic myocardium. CCC has beneficial effects on infarct size, ventricular remodeling and functions, and mortality in patients with myocardial infarction [10-11]. The CCC are normally collapsed and dysfunctional. Coronary collateral vessels become functional when ischemic events develop. The development of CCC is thought to occur in two stages; angiogenesis and arteriogenesis. Angiogenesis is the development of new vessels by sprouting and invagination from a pre-existing plexus [12]. Severe stenosis of a coronary artery causes a decrease in post-stenotic pressure, and redistribution of blood to the ischemic area occurs by dilation of the vessels developed by angiogenesis. This is called arteriogenesis [13]. The development of CCC is promoted by various factors: hypoxia, hypoperfusion, shear stress, cytokines, and clinical factors such as time of occlusion [14], diabetes mellitus, severity of CAD vessels and coronary ectasia [15]. Adropin is a protein hormone secreted from the liver, which is important for maintaining energy homeostasis and insulin sensitivity [3]. Aydin et al. showed that adropin was expressed in the brain, cerebellum, kidney, heart, liver, pancreas and vascular tissues of diabetic rats [16]. In previous studies, an ischemia model was created in animal experiments and it was observed that adropin treatment increased perfusion and capillary density [5]. Adropin plays an important protective role in cardiovascular endothelial functions as increasing the level of endothelial nitric oxide synthetase (eNOS) by activating vascular endothelial growth factor receptor 2 (VEGFR2) -phosphatidylinositol-3-phosphate kinase pathway activation (P13K-Akt) and VEGFR2 extracellular signal-regulated kinase (ERK$\frac{1}{2}$) pathways [17]. We claim that the relationship between adropin and CCC is the result of adropin stimulating eNOS [18]. Another result of our study was that NSTEMI as a type of myocardial infarction was the independent risk factor for the prediction of insufficient CCC. We think that this result is due to the higher probability of complete occlusion and more severe stenosis in patients with STEMI. Moreover, the positive correlation between well-developed CCC and increased interval between severe chest pain and intervention is not surprising. Since closed and non-functional collateral arteries need time to become functional, it is expected to be more frequent as this period increases. This study had several limitations. The number of patients included in this study was limited; larger study groups are needed to confirm the relationship between adropin and CCC. Since the follow-up time is not long in our study, we do not know the effects of high adropin levels on clinical long-term outcomes. Furthermore, the development of CCC is a multifactorial process. We do not have data on many factors such as physical activity, presence of angina before infarction, genetic factors, etc. ## Conclusions In our study, we found that CCC secondary to ischemia as a result of coronary artery occlusion was associated with increased serum adropin levels. Larger studies are needed to investigate the effects of adropin on CCC. If this relationship can be demonstrated in larger studies, perhaps future studies on the therapeutic use of adropin to enhance the development of CCC can be planned. ## References 1. Habib GB, Heibig J, Forman SA, Brown BG, Roberts R, Terrin ML, Bolli R. Influence of coronary collateral vessels on myocardial infarct size in humans. Results of phase I thrombolysis in myocardial infarction (TIMI) trial. The TIMI Investigators. Circulation (1991) 83 739-746. PMID: 1900223 2. Meier P, Gloekler S, Zbinden R. Beneficial effect of recruitable collaterals: a 10-year follow-up study in patients with stable coronary artery disease undergoing quantitative collateral measurements. Circulation (2007) 116 975-983. PMID: 17679611 3. Kumar KG, Trevaskis JL, Lam DD. Identification of adropin as a secreted factor linking dietary macronutrient intake with energy homeostasis and lipid metabolism. Cell Metab (2008) 8 468-481. PMID: 19041763 4. Li L, Xie W, Zheng XL, Yin WD, Tang CK. A novel peptide adropin in cardiovascular diseases. Clin Chim Acta (2016) 453 107-113. PMID: 26683354 5. Lovren F, Pan Y, Quan A. Adropin is a novel regulator of endothelial function. Circulation (2010) 122 0-92 6. Akkaya H, Güntürk EE, Akkaya F, Karabıyık U, Güntürk İ, Yılmaz S. Assessment of the relationship between the adropin levels and the coronary collateral circulation in patients wıth chronic coronary syndrome. Arq Bras Cardiol (2022) 119 402-410. PMID: 35766616 7. Corrigendum to: 2020 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Eur Heart J (2021) 42 1908. PMID: 33197246 8. Ibanez B, James S, Agewall S. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: the task force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J (2018) 39 119-177. PMID: 28886621 9. Cohen M, Rentrop KP. Limitation of myocardial ischemia by collateral circulation during sudden controlled coronary artery occlusion in human subjects: a prospective study. Circulation (1986) 74 469-476. PMID: 2943529 10. Sianos G, Papafaklis MI, Serruys PW. Angiographic thrombus burden classification in patients with ST-segment elevation myocardial infarction treated with percutaneous coronary intervention. J Invasive Cardiol (2010) 22 6-14. PMID: 20048390 11. Kodama K, Kusuoka H, Sakai A. Collateral channels that develop after an acute myocardial infarction prevent subsequent left ventricular dilation. J Am Coll Cardiol (1996) 27 1133-1139. PMID: 8609332 12. Bigler MR, Seiler C. The human coronary collateral circulation, its extracardiac anastomoses and their therapeutic promotion. Int J Mol Sci (2019) 20 13. Weil BR, Canty JM. Coronary blood flow and myocardial ischemia. Essential Cardiology: Principles and Practice (2013) 387-403 14. Werner GS, Ferrari M, Betge S, Gastmann O, Richartz BM, Figulla HR. Collateral function in chronic total coronary occlusions is related to regional myocardial function and duration of occlusion. Circulation (2001) 104 2784-2790. PMID: 11733395 15. Hsu PC, Su HM, Lee HC. Coronary collateral circulation in patients of coronary ectasia with significant coronary artery disease. PLoS One (2014) 9 0 16. Aydin S, Kuloglu T, Aydin S. Expression of adropin in rat brain, cerebellum, kidneys, heart, liver, and pancreas in streptozotocin-induced diabetes. Mol Cell Biochem (2013) 380 73-81. PMID: 23620340 17. Kuloglu T, Aydin S. Immunohistochemical expressions of adropin and ınducible nitric oxide synthase in renal tissues of rats with streptozotocin-ınduced experimental diabetes. Biotech Histochem (2014) 89 104-110. PMID: 23957703 18. Matsunaga T, Warltier DC, Weihrauch DW, Moniz M, Tessmer J, Chilian WM. Ischemia-induced coronary collateral growth is dependent on vascular endothelial growth factor and nitric oxide. Circulation (2000) 102 3098-3103. PMID: 11120701
--- title: Development of Osteoarthritis in Adults With Type 2 Diabetes Treated With Metformin vs a Sulfonylurea authors: - Matthew C. Baker - Khushboo Sheth - Yuhan Liu - Di Lu - Rong Lu - William H. Robinson journal: JAMA Network Open year: 2023 pmcid: PMC10028483 doi: 10.1001/jamanetworkopen.2023.3646 license: CC BY 4.0 --- # Development of Osteoarthritis in Adults With Type 2 Diabetes Treated With Metformin vs a Sulfonylurea ## Key Points ### Question Is metformin use associated with incidence of osteoarthritis (OA)? ### Findings In this cohort study including 41 874 time-conditional propensity score–matched patients using metformin or a sulfonylurea, those treated with metformin had a lower estimated risk of developing OA. ### Meaning These findings suggest that metformin use was associated with a lower incidence of OA, and future interventional studies with metformin for preventing OA could be considered. ## Abstract This cohort study assesses the risk of osteoarthritis and joint replacement in individuals with type 2 diabetes treated with metformin vs a sulfonylurea. ### Importance Metformin may have a protective association against developing osteoarthritis (OA), but robust epidemiological data are lacking. ### Objective To determine the risk of OA and joint replacement in individuals with type 2 diabetes treated with metformin compared with a sulfonylurea. ### Design, Setting, and Participants This retrospective cohort study used claims data from the Optum deidentified Clinformatics Data Mart Database between December 2003 and December 2019. Participants included individuals aged 40 years or older with at least 1 year of continuous enrollment and type 2 diabetes. Individuals with type 1 diabetes or a prior diagnosis of OA, inflammatory arthritis, or joint replacement were excluded. Time-conditional propensity score matching was conducted using age, sex, race, Charlson comorbidity score, and treatment duration to create a prevalent new-user cohort. Data were analyzed from April to December 2021. ### Exposures Treatment with metformin or a sulfonylurea. ### Main Outcomes and Measures The outcomes of interest were incident OA and joint replacement. Cox proportional hazard models were used to calculate adjusted hazard ratios (aHRs) of incident OA and joint replacement. In a sensitivity analysis, individuals only ever treated with metformin were compared with individuals only ever treated with a sulfonylurea, allowing for longer-term follow up of the outcome (even after stopping the medication of interest). ### Results After time-conditional propensity score matching, the metformin and control groups each included 20 937 individuals (mean [SD] age 62.0 [11.5] years; 24 379 [$58.2\%$] males). In the adjusted analysis, the risk of developing OA was reduced by $24\%$ for individuals treated with metformin compared with a sulfonylurea (aHR, 0.76; $95\%$ CI, 0.68-0.85; $P \leq .001$), but there was no significant difference for risk of joint replacement (aHR, 0.80; $95\%$ CI, 0.50-1.27; $$P \leq .34$$). In the sensitivity analysis, the risk of developing OA remained lower in individuals treated with metformin compared with a sulfonylurea (aHR, 0.77; $95\%$ CI, 0.65-0.90; $P \leq .001$) and the risk of joint replacement remained not statistically significant (aHR, 1.04; $95\%$ CI, 0.60-1.82; $$P \leq .89$$). ### Conclusions and Relevance In this cohort study of individuals with diabetes, metformin treatment was associated with a significant reduction in the risk of developing OA compared with sulfonylurea treatment. These results further support preclinical and observational data that suggest metformin may have a protective association against the development of OA; future interventional studies with metformin for the treatment or prevention of OA should be considered. ## Introduction Osteoarthritis (OA) is the most common form of arthritis, affecting more than 32.5 million individuals in the United States, and it is one of the major contributors to global years lived with disability.1,2,3 Current therapeutic strategies for OA are focused on symptomatic management, and there are no effective disease-modifying treatments to halt, slow, or reverse the progression of OA.4 This represents a large unmet need. Metformin is a biguanide derivative that is used as first-line treatment of type 2 diabetes by inhibiting hepatic gluconeogenesis and increasing muscle insulin sensitivity.5 Metformin is generally considered safe in most patient populations and is available at a low cost.6 In addition to its primary role in the treatment of diabetes, metformin has been purported to have anti-inflammatory, antiaging, anticancer, pro–weight loss, and immunomodulatory effects.7 Emerging evidence suggests that metformin may be useful for the treatment or prevention of OA.8,9,10,11,12,13,14,15 Preclinical studies suggest that metformin has disease-modifying properties in OA models in mice, rats, and macaque monkeys.9,10 Observational studies in humans have also largely supported the use of metformin associated with preventing the development of OA or the need for joint replacement.8,13,14,15 However, these studies have predominately focused on progression of preexisting OA (as opposed to the development of incident OA); many have not accounted for concomitant antidiabetic medication use, thus failing to fully isolate the effects of metformin; and some have suffered from immortal time bias related to the comparison of metformin users and nonusers (as opposed to an active treatment control arm). Based on the available preclinical and observational human data, metformin use may prevent the development of OA. Therefore, we conducted a large, nationwide cohort study using time-conditional propensity score matching to evaluate the risk of developing OA and the need for joint replacement in individuals with diabetes who were treated with metformin compared with a sulfonylurea. ## Methods This cohort study was deemed exempt from review and informed consent by the Stanford University institutional review board because data were deidentified. This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies. ## Study Design and Data Source This was a retrospective cohort study using data from the Optum Clinformatics Data Mart Database (CDM), a deidentified database derived from a large, adjudicated claims data warehouse, from December 1, 2003, to December 31, 2019. This data set includes more than 15 million individuals annually from across the United States who are privately insured or Medicare Advantage Part D members. This provides a geographically representative sample; however, it does not include recipients of Medicaid, and thus the resulting study population has a higher socioeconomic status than the total population with diabetes at risk for OA. ## Study Population We included individuals aged 40 years or older with at least 1 year of continuous enrollment in the Optum CDM database before the first International Classification of Diseases, Ninth Revision (ICD-9) (before October 1, 2015) or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) (after October 1, 2015) diagnosis of diabetes and first prescription for either metformin or a sulfonylurea. The index date for both groups was the first fill date for the drug of interest. Individuals with diabetes were defined as having at least 2 ICD-9 or ICD-10 codes for type 2 diabetes separated by 14 days or more (eTable 1 in Supplement 1).16,17 We excluded individuals with type 1 diabetes, patients with the first diagnosis of diabetes occurring after the start date of metformin or the sulfonylurea, patients started on metformin and a sulfonylurea at the same time, and patients using combination metformin or sulfonylurea medications. Individuals with prior diagnoses of OA or any inflammatory arthritis or with joint replacement based on Current Procedural Terminology (CPT) codes prior to the index date or within the first 90 days of the index date were also excluded (eTable 1 in Supplement 1). The lookback period for these exclusion criteria included all available data. ## Exposure The exposed group included individuals with diabetes treated with metformin for at least 90 days, and the control group included individuals with diabetes who were treated with a sulfonylurea medication for at least 90 days. Those who were initially treated with metformin and then switched to a sulfonylurea could contribute data to both groups. People who switched to metformin after being treated with a sulfonylurea contributed data to the sulfonylurea group and were then censored when switching. This was done to maximize the number of individuals contributing data to the sulfonylurea group, which was substantially smaller in size compared with the metformin group. ## Outcome Ascertainment The primary end point was the time to diagnosis of incident OA starting 90 days after the index date. Ninety days was chosen as the minimum period of time in which a treatment effect was likely to be observed. OA was defined as 2 or more ICD-9 or ICD-10 codes for OA separated by 14 days or more.18 The secondary end point was the time to joint replacement starting 90 days after the index date. Joint replacement was defined as a documented CPT code for hip or knee joint replacement. Individuals were followed from 90 days after the index date until they received a diagnosis of OA, underwent knee or hip joint arthroplasty, stopped treatment with metformin or a sulfonylurea (defined as the start of a gap of 90 days or more with no treatment), received any diabetes treatment other than metformin or a sulfonylurea, were no longer present in the Optum CDM database, or until the end of the follow-up period (December 31, 2019) (eFigure 1 in Supplement 1). ## Statistical Analysis Given that sulfonylureas are often used as second-line agents after metformin, there may be inherent biases in the comparison of individuals treated with metformin and individuals treated with a sulfonylurea. For this reason, we compared these 2 groups using a prevalent new-user cohort design (eFigure 2 in Supplement 1).19 This allowed us to compare the first-line treatment (metformin) with the second-line treatment (sulfonylurea) using time-based exposure sets to identify matched individuals at the same point in the course of disease, thus helping to eliminate potential time-lagging bias.20 The cohort included all individuals treated with a sulfonylurea. For each person treated with a sulfonylurea, a matched person treated with metformin was identified based on time-based exposure sets defined as time intervals (±15 days) from the first prescription of metformin to the first dose of sulfonylurea. Individuals were matched 1:1 on time-conditional propensity scores using conditional logistic regression adjusting for age, sex, race and ethnicity (reported in the database as Asian, Black, Hispanic, White, or unknown), Charlson comorbidity score, and treatment duration to estimate the propensity to receive a sulfonylurea. Race and ethnicity were included in analyses because racial differences in OA prevalence and severity may exist.21 For time-conditional propensity score matching, we started chronologically with the first individual prescribed a sulfonylurea and selected the individual from the exposure set with the closest time-conditional propensity score. Once a person had been selected into the comparator group, they were no longer considered in subsequent exposure sets as potential comparators. Baseline characteristics of individuals in both groups after time-conditional propensity score matching were compared. We used the Quan-Deyo method to calculate the Charlson comorbidity score.22 Standardized mean differences (SMDs) were calculated using the tableone package in R software version 4.1.1 (R Project for Statistical Computing). Missing data for the categorical variables are reported as unknown. For the continuous variables, there were no missing data. Incidence rates (IRs) and $95\%$ Wald CIs were calculated for developing OA and undergoing joint replacement, and Cox proportional hazard models were used to assess the hazard ratio (HR) and $95\%$ CI of developing OA and joint replacement among individuals with diabetes treated with metformin compared with a sulfonylurea after adjusting for age, sex, race and ethnicity, geographical region, education, Charlson comorbidity score, and outpatient visit frequency. IRs were reported as the number of events per 1000 person-years. Kaplan-Meier curves were created to report the probability of developing OA over a certain time interval. A stratified analysis was conducted using the matched data to evaluate the treatment outcomes of metformin compared with a sulfonylurea. The results were stratified by the matched pairs for individuals treated with a sulfonylurea with prior metformin exposure and those without prior metformin exposure. All statistical analyses were conducted using SAS version 9.4 (SAS Institute) and R version 4.1.1. Charlson comorbidity scores were calculated using the icd package.23 All $95\%$ CIs and P values were based on 2-sided hypothesis tests, where $P \leq .05$ was considered statistically significant. The statistical analysis plan is presented in the eAppendix in Supplement 1. Data were analyzed from April to December 2021. The robustness of our results was examined through a sensitivity analysis comparing individuals treated with metformin with individuals treated with a sulfonylurea who were only ever treated with those medications (eFigure 1 in Supplement 1). This allowed for longer-term follow-up of the outcome even after people had stopped the medication of interest, since with this analysis, no new medications could subsequently be introduced that could influence the outcome of interest. For both groups, the index date was the first fill date for the drug of interest. For the exposed group, we included individuals with diabetes who were ever treated with metformin and never treated with any additional diabetes medications during their entire follow-up period. For the control group, we included individuals with diabetes who were ever treated with a sulfonylurea medication and never treated with any additional diabetes medications during their entire follow-up period. We excluded people who ever received any diabetes medication other than metformin or a sulfonylurea (including combination metformin or sulfonylurea medications). We conducted 1:1 propensity score matching of individuals in the exposed group and individuals in the control group using the same variables as in the primary analysis. We used a caliper of width equal to 0.2 of SD of the logit of the propensity score.24,25 ## Patient Characteristics After time-conditional propensity score matching, 20 937 individuals were included in each group (mean [SD] age 62.0 [11.5] years; 24 379 [$58.2\%$] males; mean [SD] Charlson comorbidity score 0.71 [1.35]) (eFigure 3 in Supplement 1; Table 1). In the metformin group, the mean (SD) duration of treatment was 12.34 (10.70) months, compared with 12.56 (12.37) months in the sulfonylurea group (Table 1). Table 1. \| Characteristic \| Patients, No. (%) \| Patients, No. (%).1 \| Patients, No. (%).2 \| SMD \| \| --- \| --- \| --- \| --- \| --- \| \| Characteristic \| Total cohort (N = 41 874) \| Metformin (n = 20 937) \| Sulfonylurea (n = 20 937) \| SMD \| \| Age, mean (SD), y \| 62.0 (11.5) \| 62.0 (11.1) \| 62.1 (11.9) \| 0.013 \| \| Sex \| \| \| \| \| \| Female \| 17 495 (41.8) \| 8747 (41.8) \| 8748 (41.8) \| <0.001 \| \| Male \| 24 379 (58.2) \| 12 190 (58.2) \| 12 189 (58.2) \| <0.001 \| \| Race and ethnicity \| \| \| \| \| \| Asian \| 1837 (4.4) \| 932 (4.5) \| 905 (4.3) \| 0.017 \| \| Black \| 5797 (13.8) \| 2860 (13.7) \| 2937 (14.0) \| 0.017 \| \| Hispanic \| 5507 (13.2) \| 2798 (13.4) \| 2709 (12.9) \| 0.017 \| \| White \| 28 733 (68.6) \| 14 347 (68.5) \| 14 386 (68.7) \| 0.017 \| \| Region \| \| \| \| \| \| North Central \| 9599 (22.9) \| 4635 (22.1) \| 4964 (23.7) \| 0.072 \| \| Northeast \| 3592 (8.6) \| 1872 (8.9) \| 1720 (8.2) \| 0.072 \| \| South \| 19 196 (45.8) \| 9431 (45.0) \| 9765 (46.6) \| 0.072 \| \| West \| 9434 (22.5) \| 4978 (23.8) \| 4456 (21.3) \| 0.072 \| \| Unknown \| 53 (0.1) \| 21 (0.1) \| 32 (0.2) \| 0.072 \| \| Education \| \| \| \| \| \| <12th Grade \| 376 (0.9) \| 165 (0.8) \| 211 (1.0) \| 0.140 \| \| High school diploma \| 14 184 (33.9) \| 6528 (31.2) \| 7656 (36.6) \| 0.140 \| \| <Bachelor’s degree \| 22 320 (53.3) \| 11 406 (54.5) \| 10 914 (52.1) \| 0.140 \| \| ≥Bachelor’s degree \| 4814 (11.5) \| 2748 (13.1) \| 2066 (9.9) \| 0.140 \| \| Unknown \| 180 (0.4) \| 90 (0.4) \| 90 (0.4) \| 0.140 \| \| Charlson comorbidity score \| \| \| \| \| \| 0 \| 29 048 (69.4) \| 14 493 (69.2) \| 14 555 (69.5) \| 0.029 \| \| 1-2 \| 10 017 (23.9) \| 5008 (23.9) \| 5009 (23.9) \| 0.029 \| \| 3-4 \| 2044 (4.9) \| 1017 (4.9) \| 1027 (4.9) \| 0.029 \| \| 5-6 \| 380 (0.9) \| 217 (1.0) \| 163 (0.8) \| 0.029 \| \| >6 \| 385 (0.9) \| 202 (1.0) \| 183 (0.9) \| 0.029 \| \| Charlson comorbidity score, mean (SD) \| 0.71 (1.35) \| 0.69 (1.35) \| 0.74 (1.35) \| 0.038 \| \| Outpatient annual visit frequency, mean (SD) \| 6.98 (7.08) \| 6.53 (6.32) \| 7.43 (7.75) \| 0.127 \| \| Treatment duration, mean (SD), mo \| 12.45 (11.56) \| 12.34 (10.70) \| 12.56 (12.37) \| 0.019 \| \| Follow-up time, mean (SD), mo \| 9.45 (11.56) \| 9.34 (10.70) \| 9.56 (12.37) \| 0.019 \| ## Primary Outcome Using a prevalent new-user cohort design and after time-conditional propensity score matching, the IR of OA for individuals treated with metformin was 27.5 events per 1000 person-years, compared with 39.6 events per 1000 person-years for individuals treated with a sulfonylurea (Table 2). After adjusting for age, sex, race and ethnicity, geographical region, education, Charlson comorbidity score, and outpatient visit frequency, individuals who received metformin were $24\%$ less likely to develop OA compared with those who were treated with a sulfonylurea (aHR, 0.76; $95\%$ CI, 0.68-0.85; $P \leq .001$) (Table 2). Table 2. \| Measure \| Metformin (n = 20 937) \| Sulfonylurea (n = 20 937) \| \| --- \| --- \| --- \| \| Incident OA \| Incident OA \| Incident OA \| \| Events, No. (%) \| 568 (2.7) \| 817 (3.9) \| \| Person-years, No. \| 20 653 \| 20 644 \| \| IR (95% CI), per 1000 person-years \| 27.5 (25.3-29.8) \| 39.6 (36.9-42.4) \| \| IRR (95% CI) \| 0.69 (0.62-0.77) \| 1 [Reference] \| \| HR (95% CI) \| \| \| \| Crude \| 0.69 (0.62-0.77) \| 1 [Reference] \| \| Adjusteda \| 0.76 (0.68-0.85) \| 1 [Reference] \| \| Joint replacement \| Joint replacement \| Joint replacement \| \| Events, No. (%) \| 31 (0.15) \| 45 (0.21) \| \| Person-years, No. \| 21 202 \| 21 553 \| \| IR (95% CI), per 1000 person-years \| 1.5 (1.0-2.1) \| 2.1 (1.5-2.8) \| \| IRR (95% CI) \| 0.70 (0.44-1.11) \| 1 [Reference] \| \| HR (95% CI) \| \| \| \| Crude \| 0.72 (0.46-1.14) \| 1 [Reference] \| \| Adjusteda \| 0.80 (0.50-1.27) \| 1 [Reference] \| In the stratified analysis, the risk of developing OA in people treated with metformin compared with those treated with a sulfonylurea with prior metformin use was no longer statistically significant (aHR, 0.92; $95\%$ CI, 0.76-1.12), but the risk of developing OA in individuals treated with metformin compared with individuals treated with a sulfonylurea with no prior metformin use was still significantly lower (aHR, 0.71; $95\%$ CI, 0.62-0.81) (eTable 2 in Supplement 1). The Kaplan-Meier plot demonstrated that individuals who received metformin were less likely to be diagnosed with OA over time, with a visual separation of the curves by 1 year from the index date, and this persisted during the follow-up period of more than 6 years (Figure). Figure.: Kaplan-Meier Curve of Time to Osteoarthritis (OA) Diagnosis in Patients Treated With Metformin Compared With Those Treated With Sulfonylurea After Time-Conditional Propensity Score Matching ## Secondary Outcome In the prevalent new-user cohort analysis, the IR of joint replacement for individuals treated with metformin was 1.5 events per 1000 person-years, compared with 2.1 events per 1000 person-years for individuals treated with a sulfonylurea. There was no statistically significant reduction in the risk of undergoing joint replacement in people treated with metformin vs a sulfonylurea (aHR, 0.80; $95\%$ CI, 0.50-1.27; $$P \leq .34$$) (Table 2). ## Sensitivity Analysis In the sensitivity analysis, a total of 104 471 individuals were treated with metformin only, and 8277 individuals were treated with a sulfonylurea medication only (eFigure 4 in Supplement 1). After propensity score matching, 8277 people remained in each group; both groups had a similar mean (SD) age (metformin: 66.1 [11.9] years; sulfonylurea: 66.3 [12.5] years) and Charlson comorbidity score (metformin: 1.3 [2.0]; sulfonylurea: 1.3 [2.1]), and a total of 10 325 ($62.4\%$) males (Table 3). The mean (SD) treatment duration for the metformin group was 18.4 (24.0) months compared with 19.8 (26.1) months for the sulfonylurea group (Table 3). Neither group received any additional diabetes medication after treatment with either metformin or a sulfonylurea ended. In the sensitivity analysis, after propensity score matching, the IR of OA for individuals who received metformin was 25.4 events per 1000 person-years, compared with 31.1 events per 1000 person-years for individuals treated with a sulfonylurea medication (Table 4). There was a $23\%$ reduction in risk for the development of OA in people treated with metformin compared with patients treated with a sulfonylurea (aHR, 0.77; $95\%$ CI, 0.65-0.90; $P \leq .001$) (Table 4). The protective association of metformin treatment compared with sulfonylurea treatment over time is visually represented in the Kaplan-Meier plot in eFigure 5 in Supplement 1. No difference in joint replacement was seen between groups. ## Discussion In this large retrospective cohort study, we found a $24\%$ reduction in the risk of developing OA in individuals with diabetes treated with metformin compared with time-conditional propensity score–matched individuals treated with a sulfonylurea. When stratified by prior exposure to metformin within the sulfonylurea group, the observed benefit associated with metformin compared with sulfonylurea was attenuated in the people treated with a sulfonylurea with prior exposure to metformin compared with those treated with a sulfonylurea with no prior exposure to metformin. One possible hypothesis for this finding is that individuals in the sulfonylurea group with prior exposure to metformin derived a degree of long-lasting protection associated with the metformin exposure. In a sensitivity analysis comparing individuals only ever treated with metformin with individuals only ever treated with a sulfonylurea, allowing for longer-term follow-up of the outcome (even after stopping the medication of interest), we found a similar $23\%$ reduction in the risk of developing OA in individuals treated with metformin. This study supports prior literature demonstrating benefit in OA associated with treatment with metformin.9,11,12 Several preclinical studies have suggested a protective association of metformin in OA through activating AMP-activated protein kinase signaling, decreasing the level of matrix metalloproteinase 13, increasing autophagy and reducing chondrocyte apoptosis, and augmenting chondroprotective and anti-inflammatory properties of mesenchymal stem cells.9,10,11,12 Human data also support the use of metformin for the treatment or prevention of OA. In an observational study,8 individuals with obesity and knee OA who were treated with metformin were found to have a lower rate of medial cartilage volume loss compared with individuals not treated with metformin. A population-based cohort study reported a reduced incidence of total knee arthroplasty in individuals with preexisting OA and diabetes who had received a combination of metformin and a cyclooxygenase-2 inhibitor compared with a cyclooxygenase-2 inhibitor alone.13 Additional cohort studies have found that individuals with diabetes treated with metformin had a significantly reduced risk of total knee arthroplasty.14,15 One cohort study found no association between metformin use and incidence of developing OA; however, a systematic review of 10 preclinical and 5 human studies of OA concluded that metformin had chondroprotective, immunomodulatory, and analgesic associations.26,27 Our study provides further, robust epidemiological evidence that metformin may be associated with protection in the development and progression of OA in individuals with type 2 diabetes. ## Strengths and Limitations Our study has several strengths. We used a large claims database covering individuals in a wide geographic area in the United States. We were able to exclude people with diabetes who were using additional treatments, thus reducing potential confounding from these medications and more effectively isolating the outcomes associated with metformin. We conducted our analysis using a prevalent new-user cohort design with time-conditional propensity score matching, which allowed us to compare persons using metformin users or sulfonylurea at the starting point of each medication, helping to avoid immortal time bias and time-lagging bias. We specifically selected individuals with type 2 diabetes who either required treatment with metformin alone or a sulfonylurea alone to create similar cohorts of people with mild diabetes. We were able to follow up individuals for up to 10 years to ascertain the outcome. We were also able to conduct a sensitivity analysis comparing individuals only ever treated with metformin with those only ever treated with a sulfonylurea, allowing for longer-term follow up for the outcome, which demonstrated similar results as our primary analysis. Our study has several limitations. First, as this is a retrospective study using claims data, there may be residual or unmeasured confounders. To balance the covariates, we used propensity score matching and adjusted for important covariables. Second, we did not have data on body mass index, which is associated with OA. It is possible that metformin use resulted in more weight loss than sulfonylurea use, and the reduction in OA we observed was mediated primarily by weight loss. However, studies have shown that weight loss induced by metformin is modest, and a prior randomized clinical trial of diet and exercise that resulted in a similar degree of weight loss did not significantly reduce the risk of developing OA.28,29,30 We believe metformin likely exerts protective associations beyond what can be attributed to weight loss alone. Third, we also lacked data on level of physical activity or history of trauma to the involved joints, both of which can be associated with OA. However, these factors should not have affected whether patients received metformin or a sulfonylurea for the treatment of diabetes and are thus likely nondifferential between groups. Fourth, our study only evaluates the association of metformin with the development of OA in patients with diabetes, thus limiting its generalizability. Given the underlying metabolic derangements in patients with diabetes, it is possible that the benefits we observed from metformin treatment would not be seen in patients without diabetes. Fifth, the Optum CDM data set is limited to individuals with commercial or Medicare Advantage coverage, and therefore may not be representative of the entire US population. Sixth, we included people who switched from metformin to sulfonylureas, but not vice versa, to maximize the number of individuals contributing data to the sulfonylurea group, which was substantially smaller in size than the metformin group. This may have created some form of bias; however, we wanted to isolate the associations of metformin alone without potential confounding by prior treatment with a sulfonylurea. Seventh, we used ICD-9, ICD-10, and CPT codes for identification of diseases and outcomes, which could have led to misclassification of variables and outcomes; however, we believe this is likely to be nondifferential between groups. Eighth, we could not determine the degree of medication adherence in the any of the treatment groups. ## Conclusions In our large, nationwide cohort study of individuals with diabetes, metformin treatment was associated with a significant reduction in the risk of developing OA compared with sulfonylurea treatment. Results from this study must be interpreted with caution due to the lack of data on body mass index, and the possibility that weight loss induced by metformin may have accounted for some of the benefit seen. Despite this limitation, this study further supports the preclinical and observational data that show metformin may have a protective association against the development of OA. Future interventional studies with metformin for the treatment or prevention of OA should be considered. ## References 1. 1Centers for Disease Control and Prevention. Osteoarthritis (OA). Accessed February 13, 2023. https://www.cdc.gov/arthritis/basics/osteoarthritis.htm 2. 2United States Bone and Joint Initiative. Musculoskeletal diseases and the burden they cause in the United States. Accessed April 22, 2021. https://www.boneandjointburden.org 3. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet (2018.0) 392 1789-1858. DOI: 10.1016/S0140-6736(18)32279-7 4. Hunter DJ, Bierma-Zeinstra S. Osteoarthritis. Lancet (2019.0) 393 1745-1759. DOI: 10.1016/S0140-6736(19)30417-9 5. Foretz M, Guigas B, Viollet B. Understanding the glucoregulatory mechanisms of metformin in type 2 diabetes mellitus. 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--- title: 'Aboriginal and Torres Strait Islander Women’s Access to and Interest in mHealth: National Web-based Cross-sectional Survey' journal: Journal of Medical Internet Research year: 2023 pmcid: PMC10028504 doi: 10.2196/42660 license: CC BY 4.0 --- # Aboriginal and Torres Strait Islander Women’s Access to and Interest in mHealth: National Web-based Cross-sectional Survey ## Abstract ### Background Health programs delivered through digital devices such as mobile phones (mobile health [mHealth]) have become an increasingly important component of the health care tool kit. Aboriginal and Torres Strait Islander women of reproductive age are likely to be caring for children and family members and needing health care, but little is known about their access to and interest in mHealth. ### Objective The objectives of this study were to investigate Aboriginal and Torres Strait Islander women’s ownership of digital devices, access to the internet, current mHealth use, and interest and preferences for future mHealth. We examined the factors (age, remoteness, caring for a child younger than 5 years, and level of education) associated with the ownership of digital devices, use of internet, and interest in using a mobile phone to improve health. This study also examines if women are more likely to use mHealth for topics that they are less confident to talk about face-to-face with a health professional. ### Methods A national web-based cross-sectional survey targeting Aboriginal and Torres Strait Islander women of reproductive age (16-49 years) was performed. Descriptive statistics were reported, and logistic regressions were used to examine the associations. ### Results In total, 379 women completed the survey; $89.2\%$ ($\frac{338}{379}$) owned a smartphone, $53.5\%$ ($\frac{203}{379}$) a laptop or home computer, $35.6\%$ ($\frac{135}{379}$) a tablet, and $93.1\%$ ($\frac{353}{379}$) had access to the internet at home. Most women used social media ($\frac{337}{379}$, $88.9\%$) or the internet ($\frac{285}{379}$, $75.2\%$) everyday. The most common modality used on the mobile phone for health was Google ($\frac{232}{379}$, $61.2\%$), followed by social media ($\frac{195}{379}$, $51.5\%$). The most preferred modality for future programs was SMS text messaging ($\frac{211}{379}$, $55.7\%$) and social media ($\frac{195}{379}$, $51.4\%$). The most preferred topics for future mHealth programs were healthy eating ($\frac{210}{379}$, $55.4\%$) and cultural engagement ($\frac{205}{379}$, $54.1\%$). Women who were younger had greater odds of owning a smartphone, and women with tertiary education were more likely to own a tablet or laptop. Older age was associated with interest to use telehealth, and higher educational attainment was associated with interest for videoconferencing. Most women ($\frac{269}{379}$, $70.9\%$) used an Aboriginal medical service and overall reported high rates of confidence to discuss health topics with a health professional. Overall, women showed a similar likelihood of selecting a topic in mHealth whether they were or were not confident to talk to a health professional about that. ### Conclusions Our study found that Aboriginal and Torres Strait Islander women were avid users of the internet and had strong interest in mHealth. Future mHealth programs for these women should consider utilizing SMS text messaging and social media modalities and including content on nutrition and culture. A noteworthy limitation of this study was that participant recruitment was web-based (due to COVID-19 restrictions). ## Introduction Aboriginal and Torres Strait Islander people experience inequitable health burden due to the continuing impacts of colonization, intergenerational trauma, and systemic racism experienced in Australia [1]. A number of health outcomes for Aboriginal and Torres Strait Islander people have remained steady or worsened over the past decade [2], including rates of mental illness [3], psychological distress [3], asthma [4], diabetes [5], cardiovascular disease [3], and chronic obstructive pulmonary disease [3], although gains have been made in other areas such as decreased smoking during pregnancy [6], antenatal visits [6,7], year 12 completion [8], and university attendance [8]. The life expectancy gap between Aboriginal and Torres Strait Islander women and non-Indigenous women is 7.6 years. Although the life expectancy of Aboriginal and Torres Strait Islander women has improved in recent decades, the gap still remains [9]. Addressing the social, cultural, and political determinants of health will lead to the greatest improvements in Aboriginal and Torres Strait Islander health outcomes [10]. A large-scale systemic reform that positions Aboriginal and Torres Strait Islander people as the decision makers for Aboriginal and Torres Strait Islander people is required [10]. Aboriginal and Torres Strait Islander women are “healers, storytellers, keepers of our kids, and truth-seekers” [11]. Aboriginal and Torres Strait Islander women have been instrumental in driving the change for Aboriginal and Torres Strait Islander people, including leading the mandate for Uluru Statement from the Heart [11]. Aboriginal and Torres Strait Islander women not only look after their own health but also the health of the collective: their community, grandchildren, parents, grandparents, children, and other family members [12]. The positive experiences of and role modeling by Aboriginal and Torres Strait Islander women to their children and others influence their development and behavior and often lead to better health outcomes for all of their community [13]. Health promotion programs targeting women’s health inevitably have important positive impacts on children and other community members. There is strong evidence that health promotion programs developed by and for Aboriginal and Torres Strait Islander women are the most successful [10,14,15]. Aboriginal and Torres Strait Islander women can seek health care from Aboriginal Community Controlled Health Organizations (ACCHOs) or a mainstream public health service. Specific services for Aboriginal and Torres Strait Islander women’s and children’s health exist in both ACCHOs and mainstream services, though ACCHOs tend to outperform mainstream services in health and well-being outcomes [16]. One such exemplar of a women’s and children’s health promotion program developed by an ACCHO is the Waminda Dead or Deadly program [17]. This program has been running for over 10 years and aims to enhance cultural connection and health and well-being through a range of activities, including cooking groups with local ingredients, exercise groups (prenatal and postnatal), yarning groups, and lifestyle medicine. This program was designed by and for local Aboriginal and Torres Strait Islander women and therefore operates in a flexible way to meet local women’s needs [16]. Locally developed programs achieve positive health outcomes for Aboriginal and Torres Strait Islander women [16]. To supplement these programs and to reach women who may not have access, alternative modes of delivery could be beneficial. The potential of technology in promoting health and well-being in general is significant, with low cost and wide reach, high acceptability, and equitability if digital inclusion is considered carefully. Telehealth, health websites, social media campaigns, web-based patient portals, health apps, SMS text messaging programs, and wearable devices are becoming important daily tools for health care nationally and internationally. The COVID-19 experience has reinforced how important it is to have alternatives to face-to-face health care. Global evidence suggests mobile health (mHealth) to be effective and acceptable to populations underserved by traditional primary health and public health campaigns [18,19]. mHealth may be particularly important for Aboriginal and Torres Strait Islander communities, given the high rate of mobile phone use [20,21] and barriers to accessing mainstream primary health care [22]. An important first step to developing and delivering effective mHealth interventions is gathering information about the population, including context, digital access, and interest in mHealth [23]. This information is critical to designing interventions that have sustained engagement [23], which many mHealth solutions fall short of [24]. To date, there is little information on the access, interest, and preferences in mHealth among Aboriginal and Torres Strait Islander women. The aims of this study were as follows: ## Study Design A web-based cross-sectional survey design was selected, as we were interested in the experiences and views of a large sample of women at one point in time and to compare different variables at that point in time. We had planned to complete a portion of the surveys face-to-face; however, due to COVID-19 restrictions, web-based data collection was the most feasible approach. This study is reported according to the Checklist for Reporting Results of Internet E-Surveys [25]. ## The Which Way? Study This study is part of a larger study, the Which Way? study, a co-designed and co-owned research study with urban and regional Aboriginal and Torres Strait Islander communities [26-31]. The Which Way? study aims to improve care relating to smoking cessation by developing an Indigenous-led evidence base for smoking cessation to support Aboriginal and Torres Strait Islander women to be smoke-free during pregnancy and beyond. Detailed information on the larger study research prioritizations, governance, relationships, and methodologies can be found in the protocol paper [30]. ## Study Participants Aboriginal and Torres Strait Islander women of reproductive age (16-49 years) who were smokers or ex-smokers (any level of consumption) were invited to participate in this study. Smokers and ex-smokers were the eligibility criteria, as this study is a substudy of a large study on Aboriginal and Torres Strait Islander women’s preference for nonpharmacological approaches to smoking cessation. ## Procedures Consent was obtained via a digital consent sheet using a tick box at the beginning of the survey. A copy of the Participant Information Sheet was provided via a hyperlink; progression through the survey was not granted until consent was provided. Participants were also informed of the approximate time required to complete the survey in the opening page of the survey. The survey was hosted on REDCap [32]. The database was accessible by authorized team members only. On completion of the survey, women were eligible to go in a draw for a chance to win an iPad. Women were recruited over a 3-month period between July 10, 2020 and October 10, 2020 inclusive. Participants were recruited via snowballing and targeted Facebook and Instagram paid advertising. The survey was promoted through social media by using both organic and paid advertisements. A Facebook page and an Instagram account were developed for the Which Way? study. The survey link was shared by the research team through professional contacts and by Aboriginal partner organizations via organizational social media pages and accounts. Paid advertising was used to increase reach on social media accounts. Advertising was specified for “location: Australia” and “Aboriginal peoples’ television network-Aboriginal title-smoking.” It was an open survey; all participants who accessed the link to the website were able to participate in the survey. ## Instrument Items The survey included 36 items, of which 17 items are reported here. Branching logic was used to present questions that were relevant for each participant based on their previous responses. Generally, there was 1 survey item per page. The full survey took 10 minutes to complete. Women were required to complete each response to progress through the survey and were unable to return to their responses. Survey items and questions were developed in partnership with the partnering services. A draft survey was discussed among the research team, and partners then pretested with 15 Aboriginal and Torres Strait Islander women and community members known to the research team. ## Participant Characteristics The characteristics that were analyzed were [1] Aboriginal and Torres Strait Islander status, [2] age, [3] smoking status, [4] rurality (Accessibility and Remoteness Index of Australia), [5] use of Aboriginal Health Services, [6] education, [7] pregnancy status, [8] number of children living in the household, and [9] number of children younger than 5 years. Participant demographics are presented in Table 1. The mean age of the women was 31 years. More than half of the women lived in cities ($\frac{194}{379}$, $51.2\%$), $42.7\%$ ($\frac{162}{379}$) in a regional area, and $6.1\%$ ($\frac{23}{379}$) in a remote area. Most women used an Aboriginal health service ($\frac{269}{379}$, $70.9\%$). Table 1 \| Characteristics \| Characteristics.1 \| Characteristics.2 \| Values \| \| --- \| --- \| --- \| --- \| \| Age (years) \| Age (years) \| Age (years) \| Age (years) \| \| \| <21, n (%) \| 43 (11.3) \| 43 (11.3) \| \| \| 21-34, n (%) \| 196 (51.7) \| 196 (51.7) \| \| \| >34, n (%) \| 140 (36.9) \| 140 (36.9) \| \| \| Mean (SD) \| 31.0 (7.7) \| 31.0 (7.7) \| \| \| Median (min, max) \| 32 (16, 49) \| 32 (16, 49) \| \| Remoteness, n (%) \| Remoteness, n (%) \| Remoteness, n (%) \| Remoteness, n (%) \| \| \| Major city \| 194 (51.2) \| 194 (51.2) \| \| \| Regional \| 162 (42.7) \| 162 (42.7) \| \| \| Remote \| 23 (6.1) \| 23 (6.1) \| \| Level of education, n (%) \| Level of education, n (%) \| Level of education, n (%) \| Level of education, n (%) \| \| \| Up to year 9 \| 37 (9.8) \| 37 (9.8) \| \| \| Year 10-11 \| 102 (26.4) \| 102 (26.4) \| \| \| Year 12 \| 73 (19.3) \| 73 (19.3) \| \| \| Current student at University/Technical and Further Education/apprentice \| 77 (20.3) \| 77 (20.3) \| \| \| Trade certificate \| 40 (10.6) \| 40 (10.6) \| \| \| University degree \| 50 (13.2) \| 50 (13.2) \| \| Aboriginal and Torres Strait Island status, n (%) \| Aboriginal and Torres Strait Island status, n (%) \| Aboriginal and Torres Strait Island status, n (%) \| Aboriginal and Torres Strait Island status, n (%) \| \| \| Aboriginal \| 357 (94.2) \| 357 (94.2) \| \| \| Torres Strait Islander \| 7 (1.8) \| 7 (1.8) \| \| \| Aboriginal and Torres Strait Islander \| 15 (3.9) \| 15 (3.9) \| \| Use of Aboriginal Health Service, n (%) \| Use of Aboriginal Health Service, n (%) \| Use of Aboriginal Health Service, n (%) \| Use of Aboriginal Health Service, n (%) \| \| \| Yes \| 269 (70.9) \| 269 (70.9) \| \| \| No \| 110 (29) \| 110 (29) \| \| Children living in household, n (%) \| Children living in household, n (%) \| Children living in household, n (%) \| Children living in household, n (%) \| \| \| 1-2 \| 159 (42) \| 159 (42) \| \| \| 3 or more \| 129 (34) \| 129 (34) \| \| \| \| 91 (24) \| 91 (24) \| \| Children living in household younger than 5 years, n (%) \| Children living in household younger than 5 years, n (%) \| Children living in household younger than 5 years, n (%) \| Children living in household younger than 5 years, n (%) \| \| \| \| 237 (63.5) \| 237 (63.5) \| \| \| 1 or more \| 142 (37.5) \| 142 (37.5) \| ## Exclusion Criteria Women were excluded from all analyses if they did not meet the inclusion criteria (ie, self-identifying as an Aboriginal or Torres Strait Islander woman, aged 16-49 years, and a current or ex-smoker) or if their survey was incomplete. ## Ethics Approval Ethics approvals were granted by Aboriginal Health and Medical Research Council New South Wales [14541662], University of Newcastle (H-2020-0092), and the local health district ethics committee (2020/ETH02095). ## Analyses The data were analyzed in SAS v9.4 (SAS Institute). Descriptive statistics are presented as count (%), mean (SD), and median (range). Age was categorized as <21 years, 21-34 years, and >34 years for descriptive statistics. Logistic regressions were used to examine the associations of age, remoteness, caring for a child younger than 5 years, and level of education with device ownership, mHealth modalities of interest, and mHealth topics of interest (top 3 selections only). A chi-squared test was used to examine the relationship between women’s interest in using mHealth for a health topic if they were not confident to discuss it in person with a health professional. An α level of.05 was specified for all tests and CIs. Logistic regressions are presented as odds ratio (OR) ($95\%$ CI). For the logistic regressions, age was treated as continuous and ORs reported for increments of 5 years. Rurality was dichotomized into “major cities,” “regional,” and “remote” Australia. “ Caring for a child younger than 5 years” was recoded as a binary response for the purposes of analysis. Level of education was collapsed into 3 categories and was presented as an overall Wald type-III P value and pairwise OR ($95\%$ CI) and P values for each level comparison with the reference (completed high school). “ Education” was recoded into “did not complete high school,” “completed high school,” and “completed tertiary education” for the purpose of analysis. Complete case analysis was used for this study, given the relatively low number of participants being excluded due to missing data. ## Results Data were collected for 865 women. Of these, 607 were eligible, 228 were excluded due to incomplete surveys, leaving 379 women included in the analyses. ## Access to Digital Devices and the Internet Approximately $89.2\%$ ($\frac{338}{379}$) of the women owned a smartphone, $53.6\%$ ($\frac{203}{379}$) a laptop, $35.6\%$ ($\frac{135}{379}$) a tablet, and $16.4\%$ ($\frac{62}{379}$) a mobile phone (calls and text only) (Table 2). Approximately $93.1\%$ ($\frac{353}{379}$) of the women had access to the internet at home, and $88.9\%$ ($\frac{337}{379}$) of the women used social media everyday. Table 2 \| Characteristics \| Characteristics.1 \| Values, n (%) \| \| --- \| --- \| --- \| \| Device ownership \| Device ownership \| Device ownership \| \| \| Smartphone (iPhone or Android) \| 338 (89.2) \| \| \| Laptop or home computer \| 203 (53.6) \| \| \| iPad/tablet \| 135 (35.6) \| \| \| Mobile phone (calls/text only) \| 62 (16.4) \| \| \| I do not own any of these \| 2 (0.5) \| \| Access to the internet \| Access to the internet \| Access to the internet \| \| \| Home \| 353 (93.1) \| \| \| Workplace \| 165 (43.5) \| \| \| Commuting/travel \| 90 (23.7) \| \| \| Community center \| 48 (12.7) \| \| \| Somewhere else \| 17 (4.5) \| \| \| I don’t have access to the internet \| 8 (2.1) \| \| Use of social media in the last 12 months \| Use of social media in the last 12 months \| Use of social media in the last 12 months \| \| \| Everyday \| 337 (89) \| \| \| A few times per week \| 33 (8.7) \| \| \| Not at all \| 5 (1.3) \| \| \| About once a week \| 4 (1.1) \| \| Use of internet for other things in the last 12 months \| Use of internet for other things in the last 12 months \| Use of internet for other things in the last 12 months \| \| \| Everyday \| 285 (75.2) \| \| \| A few times per week \| 70 (18.5) \| \| \| About once a week \| 16 (4.2) \| \| \| Less than once a week \| 7 (1.8) \| \| \| Not at all \| 1 (0.3) \| ## Current mHealth Modalities Used The most common function used on the mobile phone for health was Google ($\frac{232}{379}$, $61.2\%$), followed by social media ($\frac{182}{379}$, $48\%$), health trackers ($\frac{130}{379}$, $34.3\%$), health apps ($\frac{124}{379}$, $32.7\%$), telehealth ($\frac{116}{379}$, $30.6\%$), and text messages ($\frac{69}{379}$, $18.2\%$) (Table 3). Table 3 \| Characteristics \| Characteristics.1 \| Values, n (%) \| \| --- \| --- \| --- \| \| Current mobile health modalities used \| Current mobile health modalities used \| Current mobile health modalities used \| \| \| I use Google to find health information \| 232 (61.2) \| \| \| I read posts or watch videos about health on about health on social media \| 182 (48) \| \| \| I use a health tracker \| 130 (34.3) \| \| \| I use health apps \| 124 (32.7) \| \| \| I use telehealth \| 116 (30.6) \| \| \| I use text messages \| 69 (18.2) \| \| \| Other \| 8 (2.1) \| \| \| No, I do not use my phone for health \| 51 (13.5) \| \| Future mobile health modalities of interest \| Future mobile health modalities of interest \| Future mobile health modalities of interest \| \| \| A text messaging service \| 211 (55.7) \| \| \| Social media \| 195 (51.5) \| \| \| Health apps \| 184 (48.5) \| \| \| Health tracker \| 164 (43.3) \| \| \| Phone calls to talk to a health worker \| 152 (40.1) \| \| \| Videoconferencing to video call with a health worker \| 100 (26.4) \| \| \| Other \| 5 (1.3) \| \| \| No, I would not use my phone for health in the future \| 20 (5.3) \| \| Mobile health topics of interest \| Mobile health topics of interest \| Mobile health topics of interest \| \| \| Help me improve what I eat \| 210 (55.4) \| \| \| Engage with Aboriginal and Torres Strait Islander culture \| 205 (54.1) \| \| \| Show/teach me exercises \| 162 (42.7) \| \| \| Improve my mental health \| 155 (40.9) \| \| \| Help me to stop smoking \| 122 (32.2) \| \| \| Women’s health \| 61 (16.1) \| \| \| Help me limit or quit cannabis or other drugs \| 48 (12.7) \| \| \| Child’s health \| 45 (11.9) \| \| \| Help with family violence \| 28 (7.4) \| \| \| Help me limit drinking \| 23 (6.1) \| \| \| Other \| 9 (2.4) \| \| \| None of these topics interest me \| 5 (1.3) \| ## Future mHealth Modalities of Interest The most preferred mHealth modality for future health care was text messages ($\frac{211}{379}$, $55.7\%$), followed by social media ($\frac{195}{379}$, $51.5\%$), health apps ($\frac{124}{379}$, $48.5\%$), health trackers ($\frac{164}{379}$, $43.3\%$), telehealth ($\frac{152}{379}$, $40.1\%$), and videos ($\frac{100}{379}$, $26.4\%$) (Table 3). ## mHealth Topics of Interest The most preferred topic for future mHealth programs was healthy eating ($\frac{210}{379}$, $55.4\%$), followed by cultural engagement ($\frac{205}{379}$, $54.1\%$), exercise ($\frac{162}{379}$, $42.7\%$), mental health ($\frac{155}{379}$, $40.9\%$), stop smoking ($\frac{122}{379}$, $32.2\%$), women’s health ($\frac{61}{379}$, $16.1\%$), limit or quit cannabis or other drugs ($\frac{48}{379}$, $12.7\%$), child’s health ($\frac{45}{379}$, $11.9\%$), family violence ($\frac{28}{379}$, $7.4\%$), and limit unsafe drinking ($\frac{23}{379}$, $6.1\%$) (Table 3). ## Confidence to Talk With a Health Professional About Different Health Topics Women reported high rates of confidence to discuss all health topics with a health professional (Table 4). Table 4 \| Health topics \| Yes, n (%) \| No, n (%) \| Not relevant, n (%) \| \| --- \| --- \| --- \| --- \| \| Women’s health \| 328 (86.6) \| 36 (9.5) \| 15 (4) \| \| Eating/diet \| 314 (82.8) \| 51 (13.5) \| 14 (3.7) \| \| Exercise \| 307 (81) \| 51 (13.5) \| 21 (5.5) \| \| Child’s health \| 305 (80.5) \| 12 (3.2) \| 62 (16.4) \| \| Mental health \| 285 (75.2) \| 50 (13) \| 44 (12) \| \| Quitting smoking \| 265 (70) \| 49 (13.2) \| 65 (17.2) \| \| Reducing alcohol \| 162 (42.7) \| 47 (12.4) \| 170 (44.9) \| \| Family violence \| 135 (35.6) \| 79 (20.8) \| 165 (43.5) \| \| Cannabis or other drug use \| 111 (29.2) \| 68 (17.9) \| 200 (52.7) \| ## Association Between Participant Characteristics and Device Ownership For every 5-year increase in age, the odds of owning a smartphone decreased by $35\%$ (OR 0.723, $95\%$ CI 0.509-0.834; $P \leq .001$). Of those aged 16 to 21 years, $100\%$ ($\frac{43}{43}$) owned a smartphone; of those aged 21-34 years, ownership was $90.8\%$ ($\frac{178}{196}$); and of those aged 34-49 years, ownership was $83.6\%$ ($\frac{117}{140}$). There was no association between owning a smartphone and level of education attainment or caring for a child younger than 5 years (Tables S1 and S2 in Multimedia Appendix 1). Women who had completed tertiary education were almost twice as likely (OR 1.916, $95\%$ CI 1.095-3.354; $$P \leq .02$$) to own an iPad or tablet compared to those whose highest education was high school completion. No other characteristics (age, remoteness, child younger than 5 years) were associated with ownership of an iPad or tablet (Tables S3 and S4 in Multimedia Appendix 1). There was a statistically significant overall association with the level of education and ownership of a laptop or computer. Individuals who had completed tertiary education were more than twice as likely (OR 2.176, $95\%$ CI 1.180-4.012; $$P \leq .01$$) to own a laptop or home computer compared to those who had completed high school. Those who had not completed high school were $69\%$ less likely to own a laptop or computer compared to those who had completed high school (OR 0.310, $95\%$ CI 0.190-0.506; P≤.001). No other characteristics (age, remoteness, or caring for a child younger than 5 years) were associated with ownership of a laptop or computer (Tables S5 and S6 in Multimedia Appendix 1). The likelihood of owning a mobile phone (text and calls only) increased as age increased (per 5-year increase in age; OR 1.222, $95\%$ CI 1.006-1.484; $$P \leq .04$$). No other characteristics (education, remoteness, or caring for a child younger than 5 years) were associated with ownership of an iPad or tablet (Tables S7 and S8 in Multimedia Appendix 1). ## Association Between Participant Characteristics and Future mHealth Modalities of Interest For every 5-year increase in age, the odds of selecting telehealth as a future mHealth modality of interest increased by $22\%$ (per 5-year increase in age; OR 1.232, $95\%$ CI 1.065-1.425; $$P \leq .005$$). No other characteristics (education, remoteness, or caring for a child younger than 5 years) were associated with selecting telehealth as a preferred mHealth modality (Tables S9 and S10 in Multimedia Appendix 1). Women who had completed high school were more likely than those who did not complete high school to select videoconferencing as a future mHealth modality of interest (OR 0.497, $95\%$ CI 0.284-0.872; $$P \leq .01$$). No other characteristics (tertiary education, remoteness, or caring for a child younger than 5 years) were associated with selecting videoconferencing as a preferred mHealth modality (Tables S11 and S12 in Multimedia Appendix 1). No statistically significant associations between participant characteristics and the selection of text messaging (Tables S13 and S14 in Multimedia Appendix 1), social media (Tables S15 and S16 in Multimedia Appendix 1), health apps (Tables S17 and S18 in Multimedia Appendix 1), or health tracker as preferred mHealth modalities (Tables S19 and S20 in in Multimedia Appendix 1) were found. ## Relationship Between Participant Characteristics and Preferred mHealth Topics Women living in regional and remote areas were $43\%$ less likely than those in urban areas to select cultural engagement as a topic (OR 0.437, $95\%$ CI 0.287-0.666; P≤.001) (Tables S21 and S22 in Multimedia Appendix 1). No statistically significant associations were found between participant characteristics and the selection of healthy eating (Tables S23 and S24 in Multimedia Appendix 1) or exercise (Tables S25 and S26 in Multimedia Appendix 1). ## Relationship Between Confidence to Discuss a Health Topic With a Health Professional and Selecting That Topic for Future mHealth Interventions Overall, women showed a similar likelihood of selecting a topic in mHealth whether they were or were not confident to talk to a health professional about that topic (Table S27 in Multimedia Appendix 1). Topics that showed a similar likelihood of being selected included diet, exercise, family violence, quitting smoking, cannabis and other drug use, mental health, women’s health, and children’s health. Reducing alcohol was the only topic that showed significance ($$P \leq .002$$). Notably, for most women ($\frac{170}{379}$, $44.9\%$), reducing alcohol was not a relevant health topic, and $42.7\%$ ($\frac{162}{379}$) of the women were confident to discuss with a health provider. The number of women not confident to discuss reducing alcohol with a health provider was low ($\frac{47}{379}$, $12.4\%$) as such, and the number of women selecting alcohol reduction as an mHealth topic who were not confident to discuss with a health professional was also low ($\frac{11}{379}$, $2.9\%$). ## Principal Results The findings of our study suggest that Aboriginal and Torres Strait Islander women have high access to smartphones and social media and their interest in using technology for health care is high. SMS text messaging was the most preferred mHealth modality. ## Strengths and Limitations A strength of this study is that it was led and governed by Aboriginal researchers. MK, the senior author and lead investigator of the larger study, is a Wiradjuri woman working with a team of Aboriginal and Torres Strait Islander researchers. Aboriginal Health Services are full partners and co-owners of this research. The full details of the governance are available in the protocol for the larger study [30]. As a non-Indigenous researcher (SJP) leading the mHealth portion of the survey, it was important to be guided by Aboriginal leadership and partnership to ensure cultural safety and best practice of ethical standards of research with Aboriginal and Torres Strait Islander people [36,37]. A key limitation of this study is that all recruitments were conducted online and it is therefore biased toward people who have access to digital devices and the internet. We planned to complete a portion of the surveys in person; however, due to COVID-19 restrictions, this was not possible. Unsurprisingly, due to the recruitment strategy, access to devices and the internet was much higher in this study than in other available data. In this study, $99.5\%$ ($\frac{377}{398}$) of the women owned either a smartphone or a mobile phone. It is unlikely that this proportion reflects all Aboriginal and Torres Strait Islander people, particularly people living in remote locations or in poverty. Two recent studies [38,39] with Aboriginal and Torres Strait Islander people reported lower access to mobile phones. In 1 study, $12.9\%$ ($\frac{39}{301}$) of the women did not have access to a phone [38], and the other study reported frequent sharing of phones (rather than individual ownership), as is common practice in low-resource settings [39]. In this study, $93.1\%$ ($\frac{353}{379}$) of the women had access to the internet at home compared to $72\%$ of the Aboriginal and Torres Strait Islander people reported in the 2016 census [40]. Further, as this survey was embedded in a larger study on nonpharmacological smoking support, only current or former smokers were included, possibly creating further bias. Lastly, the usage of complete case analysis further limits the generalizability of this study to people with similar characteristics. Although these weaknesses may limit generalization, overall, these results offer useful insights into the type of mHealth modalities and topics of interest for the future development of mHealth programs. ## Comparison With Prior Work In this study, SMS text messaging was found to be the most desired modality, with $55.7\%$ ($\frac{211}{379}$) of the women reporting an interest in using SMS text messaging for future health care. Interestingly, SMS text messaging was the least currently utilized mHealth modality for health care at $18.2\%$ ($\frac{69}{379}$). There were no significant associations found between participant characteristics and women selecting SMS text messaging as the desired modality; as such, SMS text messaging appears to be equally desirable by women of different ages (16-49 years), women living in cities and in regional or remote areas, women with and without young children, and women with different levels of educational attainment. Two studies using SMS text messaging with Aboriginal and Torres Strait Islander women in remote settings found high acceptability for SMS text messages but no difference in the clinical outcomes, including attendance for appointments for otitis media [41] and postpartum screening following gestational diabetes [39]. In a randomized controlled trial with Aboriginal and Torres Strait Islander families in urban and remote settings, SMS text messages were used to retain women; over $96.7\%$ ($\frac{180}{186}$) of the children remained in the randomized controlled trial until their clinical end point at day 21 [38]. There is great potential for more effectively using SMS text messaging to reach Aboriginal and Torres Strait Islander women to improve health outcomes. Social media was the second most preferred modality, with $51.5\%$ ($\frac{195}{379}$) of the women selecting social media as an mHealth modality of interest for the future. Daily social media use among the women was high at $88.9\%$ ($\frac{337}{379}$)—much higher than that reported in the rest of the population. A web-based survey in 2021 with 2000 Australians reported that $55\%$ of the population used social media daily [42]—similar to the trend in a 2014 survey with 400 Aboriginal and Torres Strait Islander people, wherein $69\%$ used Facebook compared with $40\%$ of the other Australians [20]. The “Social Media Mob: Being Indigenous Online” report suggests that social media uptake is higher among Aboriginal and Torres Strait Islanders than the rest of the nation, including in remote and very remote areas [21]. Carlson and Frazer [21] outlined that Aboriginal and Torres Strait Islander people use social media for a range of positive reasons—to connect with friends and family, share jokes, seek love, find information, seek help, and political activism—but that ensuring psychological and cultural safety should be a priority. Several Aboriginal and Torres Strait Islander–led social marketing campaigns for health promotion, such as Tackling Indigenous Smoking and Deadly Choices, create posts that appeal to positive emotions, photos and (short) videos, simple content, and real-time support, among other strategies [43]. In an ethnographic study of Deadly Choices, the 5 important principles for the success of the campaign were outlined: [1] create a dialogue, [2] build communities online and offline, [3] incentivize healthy web-based engagement, [4] celebrate Indigenous identity and culture, and [5] prioritize partnerships [44]. Future health initiatives on social media for Aboriginal and Torres Strait Islander women should lean on these findings. The most preferred topics for future mHealth programs were healthy eating ($\frac{210}{379}$, $55.4\%$), followed by cultural engagement ($\frac{205}{379}$, $54.1\%$), exercise ($\frac{162}{379}$, $42.7\%$), and mental health ($\frac{155}{379}$, $40.9\%$). A recent qualitative study examined the types of health content that were shared among Aboriginal and Torres Strait Islander people through social media networks as well as how people engage with and are influenced by it [45]. They found that posts on mental health and nutrition were more commonly shared than posts on health topics where there is concern about stigma and shame, such as smoking and alcohol consumption [45]. The findings in our survey somewhat reflect those findings. Most of the topics associated with shame and stigma, including limiting or quitting cannabis or other drugs, family violence, and reducing alcohol, were in the bottom 4 (out of 9) health topics of interest. Although quitting smoking was the fifth (out of 9) popular choice, the difference may be due to most of the 20 participants in the qualitative study being smokers [45] compared to $37.5\%$ ($\frac{141}{379}$) of the participants in our study being nonsmokers. For topics that have concern for shame and stigma, it is suggested that negative messages that have been successful at a whole population level, such as quitting smoking, may need to be adapted for Aboriginal and Torres strait Islander mHealth initiatives [45]. The importance of centering positive cultural identity and narratives in mHealth initiatives is evident [43-45]. This was highlighted in a recent qualitative study that found Facebook posts celebrating culture and cultural achievements as well as challenging racism were mostly posted by women [45]. The popularity of embedding culture in mHealth programs was shown in the results of that study [45], with “culture” as the second most popular topic. The existing evidence suggests that future mHealth programs must integrate culturally relevant content. Further research to determine its effect on engagement and health outcomes is warranted. Although smartphone ownership was relatively high at $89.2\%$ ($\frac{338}{379}$) (similar to national figures of $92\%$ [42]), ownership of other digital devices, including laptops, was low at $53.6\%$ ($\frac{203}{379}$) (national figure is $78\%$ [42]). Access to devices (as well as the internet and data), alongside affordability and digital ability are combined to provide a digital inclusion score (out of 100) [46]. The digital inclusion gap between Aboriginal and Torres Strait Islander people and the rest of the nation is 7.9 (55.1 compared to 63) [46]. Aboriginal and Torres Strait Islander people are less likely to have access to consistent, fast, and large amounts of data, more likely to be mobile-only users, and more likely to use prepaid data; these factors all decrease digital inclusion [46]. Digital inclusion facilitates efficient delivery and uptake of critical services, including health care, as well as employment and education opportunities [46]. Digital inclusion remains in The National Agreement on Closing the Gap as part of the Access to Information target (Target 17)—by 2026, Aboriginal and Torres Strait Islander people will have equal levels of digital inclusion. It is imperative that we seek to advance mHealth solutions developed for and by Aboriginal and Torres Strait Islander people. ## Conclusions Aboriginal and Torres Strait Islander women are avid users of technology and have a strong interest in mHealth. New mHealth initiatives should consider having strong partnerships with ACCHOs and be designed by and for Aboriginal and Torres Strait Islander women to meet their digital and health needs. Nutrition and culture should be considered as topics of particular interest. 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--- title: 'Projection of Premature Cancer Mortality in Hunan, China, Through 2030: Modeling Study' journal: JMIR Public Health and Surveillance year: 2023 pmcid: PMC10028508 doi: 10.2196/43967 license: CC BY 4.0 --- # Projection of Premature Cancer Mortality in Hunan, China, Through 2030: Modeling Study ## Abstract ### Background The United Nations Sustainable Development Goals for 2030 include reducing premature mortality from noncommunicable diseases by one-third. Although previous modeling studies have predicted premature mortality from noncommunicable diseases, the predictions for cancer and its subcategories are less well understood in China. ### Objective The aim of this study was to project premature cancer mortality of 10 leading cancers in Hunan Province, China, based on various scenarios of risk factor control so as to establish the priority for future interventions. ### Methods We used data collected between 2009 and 2017 from the Hunan cancer registry annual report as empirical data for projections. The population-attributable fraction was used to disaggregate cancer deaths into parts attributable and unattributable to 10 risk factors: smoking, alcohol use, high BMI, diabetes, physical inactivity, low vegetable and fruit intake, high red meat intake, high salt intake, and high ambient fine particulate matter (PM2.5) levels. The unattributable deaths and the risk factors in the baseline scenario were projected using the proportional change model, assuming constant annual change rates through 2030. The comparative risk assessment theory was used in simulated scenarios to reflect how premature mortality would be affected if the targets for risk factor control were achieved by 2030. ### Results The cancer burden in Hunan significantly increased during 2009-2017. If current trends for each risk factor continued to 2030, the total premature deaths from cancers in 2030 would increase to 97,787 in Hunan Province, and the premature mortality ($9.74\%$) would be $44.47\%$ higher than that in 2013 ($6.74\%$). In the combined scenario where all risk factor control targets were achieved, $14.41\%$ of premature cancer mortality among those aged 30-70 years would be avoided compared with the business-as-usual scenario in 2030. Reductions in the prevalence of diabetes, high BMI, ambient PM2.5 levels, and insufficient fruit intake played relatively important roles in decreasing cancer premature mortality. However, the one-third reduction goal would not be achieved for most cancers except gastric cancer. ### Conclusions Existing targets on cancer-related risk factors may have important roles in cancer prevention and control. However, they are not sufficient to achieve the one-third reduction goal in premature cancer mortality in Hunan Province. More aggressive risk control targets should be adopted based on local conditions. ## Introduction Cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020 or nearly one in six deaths [1]. In China, cancer affected more than 4.56 million people and caused 3 million deaths in 2020, accounting for $30.2\%$ of all deaths in the world [2]. Furthermore, cancer is a leading cause of disability-adjusted life years (DALYs), accounting for $17.64\%$ of all DALYs in China in 2019, which was nearly double the global value of $9.88\%$ [3]. The high mortality and disability related to cancer pose a major disease burden worldwide. Cancer not only directly affects the lives of those diagnosed but also has its sequelae impact the family, society, and economy. The socioeconomic burden of cancer has been reported to be substantial, which includes both the direct health care costs and the lost productivity through premature mortality of the working population [4,5]. Cancer is a disease commonly believed to be preventable. In a nationwide study on the risk factors of cancer across 31 provinces of mainland China, Chen et al [6] found that nearly half ($45.2\%$) of all cancer deaths could be attributed to 23 potentially controllable risk factors, which were classified into five categories: behavior, diet, metabolism, environment, and infection. This suggests that millions of deaths could be prevented or delayed each year if appropriate strategies were developed to control these modifiable risk factors, which would undoubtedly greatly reduce the cancer burden in China. In 2016, the United Nations (UN) set a target to achieve a relative one-third reduction from the 2015 level in premature mortality from noncommunicable diseases (NCDs), including cancer, by 2030 in its Sustainable Development Goal (SDG) target 3.4 [7]. In response to UN SDG target 3.4, the Chinese government issued the Healthy China 2030 Plan with disease prevention and longevity improvement as two of its primary goals [8,9]. A series of policies targeting cancer prevention and treatment was introduced by the government to reduce the burden of premature cancer death in an effort to accomplish SDG target 3.4 of a one-third reduction in premature mortality of NCDs overall. However, considering the vast territory in China and substantial regional differences in socioeconomic and hygienic conditions, the ability to meet SDG target 3.4 will vary greatly across the provinces. Therefore, there is an urgent need for China to implement tailored, region-specific cancer control measures based on reliable data and valid evidence from studies focusing on the local populations. Hunan Province, located in the central-south of China, is one of the most populous provinces with over 73 million residents. The gross domestic product of Hunan Province was 4.18 trillion yuan (approximately US $606.03 billion) in 2020, ranking 9th among 32 provincial administrative divisions in mainland China. Cancer incidence and mortality rates in Hunan Province in 2018 were $\frac{248.24}{100}$,000 and $\frac{154.50}{100}$,000, respectively, representing a medium level across the nation [10]. The burdens of years of life lost and DALYs of cancer in Hunan Province were reported to be significantly higher than the national averages [11]. Of note, due to the special local lifestyle habits, Hunan Province has a much higher burden of certain cancer types than other regions, such as oral cavity cancer and nasopharynx cancer, with obviously higher incidences and mortality than national average levels [12,13]. Since publication of the World Health Organization (WHO) Global Monitoring Framework in 2013 [14], several modeling analyses have been conducted to examine the effects of selected risk factor control on reducing NCD mortality under different scenarios worldwide. Kontis et al [15] set six risk factor scenarios based on the Global Monitoring Framework, and estimated their impact on global and regional NCD mortality between 2010 and 2025 using a time-based, population impact fraction formula. They found that achieving all of these targets could efficiently reduce premature mortality from the four main NCDs, including cancers, by nearly $25\%$ globally and in some specific regions [15]. Su et al [16] used an age-period-cohort model for prediction, which suggested that targets of reducing tobacco use could be more ambitious in Taiwan to meet the goal of a $25\%$ reduction in premature cardiovascular disease mortality. Other studies indicated that the risk factor targets recommended by the WHO would be sufficient to help achieve the goal of a one-third reduction in premature mortality for all NCDs combined, but not for certain major subdivisions such as cancers [17,18]. In general, most previous modeling studies focused on overall NCDs at a national level, and there is limited evidence on cancer and its subcategories at a local provincial level. Moreover, the WHO’s voluntary global targets did not include dietary and environmental factors, which are known to be important risk factors for cancer, and it remains unknown whether and how control of these factors may help with cancer prevention. In light of such research gaps, we performed this study to project premature mortality from cancer in Hunan Province through 2030 under different risk factor control scenarios. Specifically, we projected whether SDG target 3.4 can be met for cancer prevention in Hunan Province and how many deaths from cancers can be prevented if all selected risk factors were controlled. The risk factors were selected based on the Global Monitoring Framework [14] while adding dietary and environmental factors. Projecting future premature mortality under various risk factor control scenarios is crucial for informing decision-making on cancer control and allocating the limited clinical and public health resources to curb the increasing deaths caused by cancer. Our results will help policy makers better formulate priorities for interventions that focus on these risk factors for cancer prevention and control. ## Ethics Approval This analysis centered on publicly available data with no identifiable information on the subjects studied. Therefore, research ethics board approval was not required for this study. ## Selection of Cancer Sites and Related Risk Factors Cancer sites were selected based on the rank of cancer deaths in the Hunan cancer registry annual report series over the past 10 years, while also taking into account endemic cancer types associated with special local lifestyle habits in Hunan, such as oral cavity cancer and nasopharynx cancer. Finally, 10 leading subcategories were selected, including cancers of the lung, esophagus, liver, stomach, pancreas, prostate, breast, colorectum, oral cavity, and nasopharynx. Correspondingly, risk factors were selected based on the following criteria: [1] causally associated with cancers as evidenced by the latest Global Burden of Disease (GBD) study [3] or the Continuous Update Project (CUP) Expert Report (low vegetable and fruit intake were included due to consistent evidence showing their causal associations with cancers [19]); [2] available data of exposure levels from officially representative surveys or epidemiological studies; [3] potentially modifiable by available interventions; and [4] recognized as one of the leading global or national causes of disease burden. Ultimately, 10 risk factors were selected, including smoking, alcohol use, physical inactivity, high BMI, low vegetable intake, low fruit intake, high red meat intake, high salt intake, diabetes, and fine particulate matter (PM2.5). Details of cancer sites and related risk factors are shown in Multimedia Appendix 1. ## Data Sources The National Program of Cancer Registries (NPCR), launched in 2008, is responsible for the collection, evaluation, and publication of cancer data in China. In Hunan Province, $70.4\%$ of cancer patients have registered in the NPCR to date [20], and all cancer cases are coded according to the International Classification of Diseases, 10th Revision. The details of data collection, management, and analysis for cancer registration in China have been described elsewhere [21]. In this study, we extracted the cause-specific cancer death rates, along with annual population data, between 2009 and 2017 from Hunan Statistical Yearbook 2020 to estimate the number of cancer deaths throughout the province. Information on risk factors was mainly obtained from the Chinese Chronic Disease and Risk Factor Surveillance (CCDRFS) survey, which is an ongoing, nationally representative survey involving a set of standard questionnaire interviews, physical examinations, and biological sampling [22]. In this analysis, we used exposure data in the years 2010 and 2018 to represent 2009 and 2017, respectively, due to a lack of investigations. Regarding PM2.5, since systematic monitoring in China did not start until 2013, we extracted data for Hunan from China Regional Estimates (V4.CH.03) of the Saint Louis University Atmospheric Composition Analysis Group, which applied a geographically weighted regression to calibrate regional PM2.5 concentrations through ground-based observations. Overall, population exposures to these risk factors were measured using metrics related to their variable types. For instance, smoking status and alcohol use were measured as the prevalence of people exposed, while dietary factors were measured as continuous variables. The specific details of risk factor measurements are shown in Multimedia Appendix 1. To ensure data quality, the relative risk (RR) estimates for risk-cancer pairs were preferentially derived from summary results published by the GBD series and the CUP Expert Report. If these were not available, priority was given to meta-analyses or systematic reviews conducted in China or Asia. Studies that provided RRs on our predefined metrics were preferred and estimates for both genders were assumed to be equal if no separate values were available. ## Constructing Risk Factor Scenarios Based on the Global Monitoring Framework and Healthy China 2030, we constructed 12 separate scenarios of risk factor exposure for the year 2030. Among them, the baseline scenario projected cancer mortality to 2030 assuming that all risk factors continue to follow current trends (see Multimedia Appendix 2 for details on the risk factor exposure estimation). The baseline scenario was simulated using a proportional change model based on the historical trends of risk factors. The other 11 scenarios projected cancer mortality assuming that each of the 10 risk factors achieved the target of domestic or foreign control standards, both separately and in combination. Specifically, the first 10 scenarios were modeled when each risk factor achieved its target, respectively, and the last scenario was modeled when all 10 risk factors achieved their targets. Targets for red meat, vegetable, and fruit intake were set according to the currently available literature. Given that the current annual average PM2.5 concentration in Hunan Province has reached grade II of China’s air quality standards, we used grade I of 15 μg/m3 as the target for PM2.5, which was also consistent with phase III of the WHO’s air quality guidelines [23]. Targets for other risk factors were set according to the WHO’s voluntary global targets [14]. Among the 10 risk factors, categorical exposures were lowered directly to the target levels in 2030, whereas targets for continuous exposures were established by shifting the population distributions left or right, assuming a constant distribution for each age-sex stratum. Exceptionally, metrics for exposures such as BMI and diabetes were held constant. Table 1 provides the details of the risk factors for each of the 12 scenarios. Table 1 \| Scenario \| Scenario specification \| \| --- \| --- \| \| Natural trend \| Age- and sex-specific risk factor exposures were projected assuming the annual change rate remained similar to that between 2009 and 2017. \| \| Harmful alcohol use \| Age- and sex-specific prevalence of harmful alcohol use is reduced relatively by 10% from the 2013 level. All other risk factors follow the natural trends. \| \| Smoking \| Age- and sex-specific prevalence of smoking in 2030 is reduced relatively by 30% from the 2013 level. All other risk factors follow the natural trends. \| \| Physical inactivity \| Age- and sex-specific prevalence of physical inactivity in 2030 is 10% relatively lower than that in 2013. All other risk factors follow the natural trends. \| \| Diabetes \| Age- and sex-specific prevalence of diabetes in 2030 is the same as in 2013. All other risk factors follow the natural trends. \| \| High BMI \| Age- and sex-specific distributions of BMI in 2030 are the same as in 2013. All other risk factors follow the natural trends. \| \| Low vegetable intake \| Age- and sex-specific prevalence of low vegetable intake in 2030 is reduced relatively by 30% from the 2013 level. All other risk factors follow the natural trends. \| \| Low fruit intake \| Age- and sex-specific prevalence of low fruit intake in 2030 is reduced relatively by 30% from the 2013 level. All other risk factors follow the natural trends. \| \| High red meat intake \| Age- and sex-specific prevalence of high red meat intake in 2030 is reduced relatively by 30% from the 2013 level. All other risk factors follow the natural trends. \| \| High salt intake \| Age- and sex-specific mean population salt intake in 2030 is reduced relatively by 30% from the 2013 level. All other risk factors follow the natural trends. \| \| PM2.5a \| The annually averaged PM2.5 concentration in 2030 is reduced to 15 μg/m3, according to grade I of air quality standard GB3095-2012. All other risk factors follow the natural trends. \| \| All targets are achieved in 2030 \| All targets described above are achieved in 2030. \| ## Projection of Cancer Mortality Our analysis was focused on examining premature mortality under 12 different scenarios. Consistent with the global documents, we defined premature cancer mortality as the probability of dying from cancers between the ages of 30 and 70 years [24]. To predict mortality for 2030, we considered the annual total deaths to be a function of two separate projections: a trend of deaths potentially driven by the selected risk factors and a business-as-usual (BAU) trend unattributable to these risk factors. Based on these trends, several steps were involved. First, according to the comparative risk assessment [25], we used the population-attributable fraction (PAF) to divide annual deaths into two parts that were attributable or unattributable to the specified risk factors. The PAF estimates the fraction of health outcomes that would be eliminated if the exposures were altered to ideally counterfactual distributions, and different formulas were applied for discrete and continuous variables in the calculations [25]. Second, we projected the unattributable deaths using the proportional change model, assuming that changes in this part would continue to follow the trends observed between 2009 and 2017. Risk factors in the baseline scenario were projected in the same manner. For the other 11 scenarios, the targeted change of each factor was distributed evenly between 2017 and 2030 to obtain an annual PAF value. Third, the total deaths for different scenarios in 2030 could be estimated using the unattributable deaths and PAFs, and the premature mortality could be calculated using age-specific death rates (in 5-year age groups) with a life table method. The levels in 2013 were considered as the baseline for calculations of relative reductions in this study. Analyses for PAF were performed using MATLAB 7.0, while other data were analyzed in SAS 9.4. Details of the analysis methods are shown in Multimedia Appendix 3. ## Premature Mortality From Cancers 2009-2017 Table 2 shows the estimated number of cancer deaths and mortality by gender in Hunan Province, China, from 2009 to 2017. The number of cancer deaths increased each year for both genders. The age-standardized mortality rates showed an increasing trend, with an annual percentage change of $1.60\%$ for the total population, $2.19\%$ for men, and $1.65\%$ for women. The premature mortality rates from selected cancers are shown in Table 3 and the trend analysis results are shown in Table 4. The average annual percentage change (AAPC) showed a significant increasing trend for all cancers combined ($2.61\%$), with the largest AAPC occurring in oral cavity cancer ($17.26\%$), followed by esophageal cancer ($7.60\%$), colorectal cancer ($7.13\%$), pancreatic cancer ($7.10\%$), and lung cancer ($4.06\%$). For the other cancers, the premature mortality remained stable with nonsignificant AAPC values, although a decreasing trend was observed in stomach cancer (–$1.16\%$) and liver cancer (–$1.92\%$), while an increasing trend was observed in nasopharynx cancer ($2.00\%$), prostate cancer ($11.91\%$), and breast cancer ($4.82\%$). ## Premature Mortality in the Baseline Scenario by 2030 Table 5 shows a comparison of premature deaths and mortality between 2013 and projected for 2030 in the baseline scenario where all risk factors continue their current trends. In 2030, an estimated 97,787 people would die prematurely from all cancers, with a premature mortality rate of $9.74\%$, which was $44.47\%$ higher than that in 2013 (Table 5). For the number of premature deaths, lung cancer was expected to account for the largest proportion of total cancer deaths (29,337 deaths), followed by liver cancer (15,545 deaths), colorectal cancer (12,342 deaths), oral cavity cancer (7470 deaths), and esophageal cancer (5977 deaths). All cancers showed increases in the number of premature deaths except for gastric cancer, which was expected to decrease from 4104 in 2013 to 3053 in 2030. The mortality rate of all cancers was estimated to increase by $81.55\%$ from 143.54 to 260.59 per 100,000 people. All cancers exhibited increasing trends in mortality rate except for gastric cancer, which was expected to decrease by $24.62\%$ from 2013 to 2030. The premature mortality rates for all cancers and each subcategory were consistently higher in men than in women, with differences of more than 5-fold (Table 5). For all cancers, relative increments of $61.47\%$ and $13.46\%$ were observed among men and women, respectively. For men, all cancers showed substantial increases in premature mortality, ranging from $38.25\%$ for lung cancer to $1194.33\%$ for oral cavity cancer, except for gastric cancer, which showed a decreasing trend (–$32.51\%$). For women, most cancers showed increases in premature mortality, ranging from $14.04\%$ for lung cancer to $158.84\%$ for colorectal cancer, except for esophageal cancer (–$64.61\%$), gastric cancer (–$52.16\%$), pancreatic cancer (–$45.08\%$), and nasopharynx cancer (–$21.86\%$). Despite significant gender discrepancies, premature mortality for the whole population generally showed an increasing trend, with the greatest increase occurring in oral cavity cancer ($971.53\%$). Table 5 \| Disease \| Disease.1 \| 2013 \| 2013.1 \| 2013.2 \| 2030 \| 2030.1 \| 2030.2 \| 2030.3 \| Percent change \| Percent change.1 \| Percent change.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| Premature deaths, n \| Mortality rate (1/100,000) \| Premature mortality (%) \| Premature deaths, n \| Mortality rate (1/100,000) \| Premature mortality (%) \| Premature deaths \| Premature deaths \| Mortality rate \| Premature mortality \| \| Men \| Men \| Men \| Men \| Men \| Men \| Men \| Men \| Men \| Men \| Men \| Men \| \| \| Total \| 36886 \| 188.18 \| 8.82 \| 71920 \| 382.42 \| 14.24 \| 94.98 \| 94.98 \| 103.22 \| 61.47 \| \| \| Lung cancer \| 12700 \| 64.79 \| 3.28 \| 22599 \| 120.17 \| 4.54 \| 77.95 \| 77.95 \| 85.47 \| 38.35 \| \| \| Gastric cancer \| 2785 \| 14.21 \| 0.69 \| 2290 \| 12.17 \| 0.46 \| –17.78 \| –17.78 \| –14.30 \| –32.51 \| \| \| Liver cancer \| 7373 \| 37.61 \| 1.71 \| 12360 \| 65.72 \| 2.61 \| 67.64 \| 67.64 \| 74.72 \| 52.34 \| \| \| Colorectal cancer \| 2405 \| 12.27 \| 0.60 \| 7539 \| 40.09 \| 1.60 \| 213.49 \| 213.49 \| 226.74 \| 164.82 \| \| \| Esophageal cancer \| 1624 \| 8.29 \| 0.43 \| 5916 \| 31.46 \| 1.35 \| 264.19 \| 264.19 \| 279.58 \| 210.91 \| \| \| Pancreatic cancer \| 591 \| 3.01 \| 0.15 \| 1414 \| 7.52 \| 0.28 \| 139.34 \| 139.34 \| 149.45 \| 82.95 \| \| \| Nasopharynx cancer \| 1508 \| 7.70 \| 0.34 \| 2852 \| 15.16 \| 0.63 \| 89.07 \| 89.07 \| 97.06 \| 82.95 \| \| \| Oral cavity cancer \| 591 \| 3.01 \| 0.13 \| 7255 \| 38.58 \| 1.71 \| 1128.22 \| 1128.22 \| 1180.13 \| 1194.33 \| \| \| Prostate cancer \| 253 \| 1.29 \| 0.07 \| 2504 \| 13.31 \| 0.52 \| 889.01 \| 889.01 \| 930.81 \| 592.15 \| \| \| Other cancers \| 7046 \| 23.68 \| 1.72 \| 7193 \| 38.25 \| 1.46 \| 2.08 \| 2.08 \| 61.53 \| –14.85 \| \| Women \| Women \| Women \| Women \| Women \| Women \| Women \| Women \| Women \| Women \| Women \| Women \| \| \| Total \| 17687 \| 97.25 \| 4.51 \| 25867 \| 138.19 \| 5.11 \| 46.25 \| 46.25 \| 42.10 \| 13.46 \| \| \| Lung cancer \| 3409 \| 18.75 \| 0.95 \| 6738 \| 36.00 \| 1.38 \| 97.63 \| 97.63 \| 92.02 \| 44.93 \| \| \| Gastric cancer \| 1319 \| 7.25 \| 0.36 \| 763 \| 4.08 \| 0.17 \| –42.13 \| –42.13 \| –43.78 \| –52.16 \| \| \| Liver cancer \| 1928 \| 10.60 \| 0.51 \| 3185 \| 17.01 \| 0.59 \| 65.19 \| 65.19 \| 60.50 \| 14.04 \| \| \| Colorectal cancer \| 1380 \| 7.59 \| 0.36 \| 4803 \| 25.66 \| 0.92 \| 248.04 \| 248.04 \| 238.15 \| 158.84 \| \| \| Esophageal cancer \| 112 \| 0.61 \| 0.04 \| 61 \| 0.33 \| 0.01 \| –45.30 \| –45.30 \| –46.86 \| –64.61 \| \| \| Pancreatic cancer \| 365 \| 2.01 \| 0.10 \| 271 \| 1.45 \| 0.06 \| –25.73 \| –25.73 \| –27.84 \| –45.08 \| \| \| Nasopharynx cancer \| 497 \| 2.73 \| 0.11 \| 400 \| 2.14 \| 0.09 \| –19.52 \| –19.52 \| –21.81 \| –21.86 \| \| \| Oral cavity cancer \| 101 \| 0.56 \| 0.03 \| 215 \| 1.15 \| 0.04 \| 112.05 \| 112.05 \| 106.02 \| 55.81 \| \| \| Breast cancer \| 1827 \| 10.04 \| 0.44 \| 3185 \| 17.01 \| 0.69 \| 74.37 \| 74.37 \| 69.42 \| 57.56 \| \| \| Other cancers \| 6748 \| 29.51 \| 1.69 \| 6245 \| 33.36 \| 1.27 \| –7.45 \| –7.45 \| 13.03 \| –24.88 \| \| Both genders \| Both genders \| Both genders \| Both genders \| Both genders \| Both genders \| Both genders \| Both genders \| Both genders \| Both genders \| Both genders \| Both genders \| \| \| Total \| 54572 \| 143.54 \| 6.74 \| 97787 \| 260.59 \| 9.74 \| 79.19 \| 79.19 \| 81.55 \| 44.47 \| \| \| Lung cancer \| 16109 \| 42.18 \| 2.15 \| 29337 \| 78.18 \| 2.96 \| 82.12 \| 82.12 \| 85.33 \| 37.57 \| \| \| Gastric cancer \| 4104 \| 10.79 \| 0.53 \| 3053 \| 8.14 \| 0.32 \| –25.61 \| –25.61 \| –24.62 \| –39.93 \| \| \| Liver cancer \| 9301 \| 24.35 \| 1.13 \| 15545 \| 41.42 \| 1.59 \| 67.13 \| 67.13 \| 70.11 \| 40.72 \| \| \| Colorectal cancer \| 3785 \| 9.97 \| 0.48 \| 12342 \| 32.89 \| 1.25 \| 226.09 \| 226.09 \| 229.86 \| 158.76 \| \| \| Esophageal cancer \| 1736 \| 4.52 \| 0.24 \| 5977 \| 15.93 \| 0.68 \| 244.29 \| 244.29 \| 252.39 \| 182.06 \| \| \| Pancreatic cancer \| 956 \| 2.52 \| 0.13 \| 1685 \| 4.49 \| 0.17 \| 76.26 \| 76.26 \| 78.18 \| 30.58 \| \| \| Nasopharynx cancer \| 2006 \| 5.26 \| 0.23 \| 3252 \| 8.67 \| 0.35 \| 62.15 \| 62.15 \| 64.77 \| 53.36 \| \| \| Oral cavity cancer \| 692 \| 1.81 \| 0.08 \| 7470 \| 19.91 \| 0.87 \| 979.24 \| 979.24 \| 1001.08 \| 971.53 \| \| \| Prostate cancer \| 253 \| 1.29 \| 0.07 \| 2504 \| 13.31 \| 0.52 \| 889.01 \| 889.01 \| 930.81 \| 592.15 \| \| \| Breast cancer \| 1827 \| 10.04 \| 0.44 \| 3185 \| 17.01 \| 0.69 \| 74.37 \| 74.37 \| 69.42 \| 57.56 \| \| \| Other cancers \| 13794 \| 26.54 \| 1.70 \| 13438 \| 35.81 \| 1.36 \| –2.58 \| –2.58 \| 34.91 \| –19.87 \| ## Premature Deaths Avoided and Premature Mortality in Multiple Scenarios Table 6 shows the projected premature deaths and mortality in 2030 with all risk factors under control, which was further compared with the rates for 2013 and the baseline scenario. Compared with the baseline scenario where all risk factors continue their current trends, $14.90\%$ of premature deaths from cancers would be avoided if all risk factor targets were achieved. Lung cancer had the largest decrease in the number of cancer deaths compared to the baseline scenario (–$27.30\%$), followed by colorectal cancer (–$19.88\%$), esophageal cancer (–$17.73\%$), and breast cancer (–$14.08\%$). A further comparison of avoided cancer deaths by gender showed that men (–$15.53\%$) benefited much more through these combined risk factor control targets than women (–$13.16\%$). The modeling scenarios seek to avert one-third of premature mortality by 2030. However, this goal is hard to accomplish. For all cancers combined, premature mortality among the total population was expected to increase by $23.65\%$ compared to the baseline year of 2013, even if all risk factor control targets were reached by 2030 (Table 6, Figure 1). For subcategories, all cancers showed increases in premature mortality compared with that in 2013 in the combined risk factor control target-achieved scenarios, except for gastric cancer with a decrease of $41.63\%$ in the total population ($34.45\%$ for men and $53.40\%$ for women). However, it should be noted that the premature deaths and mortality of gastric cancer would still decrease substantially even if all risk factors continue their current trends, as shown in the baseline scenario. A decrease in premature mortality was also found for women in esophageal cancer ($72.63\%$), pancreatic cancer ($48.71\%$), and nasopharynx cancer ($29.44\%$) under combined target–achieved scenarios. Although the combined risk factor control targets failed to achieve the one-third reduction of the cancer mortality rate set by the UN, they could still lead to notable decreases in premature mortality compared with the baseline scenario in 2030. Moreover, the impact on cancer premature mortality varied substantially across different risk factors. For all cancers combined, diabetes and low fruit intake were the top two leading risk factors of cancer premature mortality for both genders. For instance, halting the rise in the prevalence of diabetes may contribute to nearly half of the reductions in cancer premature mortality for both genders (Figure 1). The impact of other risk factors on cancer premature mortality depended on gender. A halt in the rise of BMI had the third-largest impact for men, whereas for women (Figure 2), it was the reduction in ambient PM2.5 levels (Figure 3). For risk factors such as smoking, high red meat intake, and physical inactivity, the predefined risk factor control targets seemed to be insufficient to have pronounced benefits on cancer premature mortality. For risk factors such as harmful alcohol use and high salt intake, the predefined control targets set by the WHO showed much smaller reductions in mortality than the baseline BAU scenario, indicating the need for setting more ambitious targets. In all simulated scenarios, men were projected to have greater gains than women for all cancers after risk factor controls. Scenario projections by gender are shown in Multimedia Appendix 4. ## Principal Findings In this study, we projected premature mortality from 10 leading cancers in Hunan Province under 12 different risk factor control scenarios in 2030 and evaluated whether SDG target 3.4 can be met for cancer prevention. The results suggest that the one-third reduction goal in premature cancer mortality would not be achieved in Hunan, even if all related risk factor control targets were reached by 2030. This finding is similar to that of a previous study conducted by Li et al [17], showing that achieving all related risk targets would lead to a one-third reduction for all NCDs combined, but not for cancers, in China. These findings suggest that meeting SDG target 3.4 for cancers would require extra efforts from both national and local governments. Notably, Li et al [17] only included six risk factors, in which merely three (ie, smoking, BMI, and physical inactivity) were modeled in the projection of cancer burdens. It seems difficult to achieve dramatic reductions in premature cancer mortality by only controlling for a limited number of risk factors; thus, further inclusion of more risk factors is needed for simulation in the whole country. Although our study contained a wider range of risk factors, the predicted premature cancer mortality in Hunan Province failed to reduce by one-third in 2030, which may be due to the combined effect of several factors such as the conservativeness of some risk factor targets and population aging. According to Chen et al [26], the proportion of people aged over 55 years is expected to increase to more than $30\%$ in Hunan in 2030 [26]. In addition, epidemiological studies have shown that cancer mortality is significantly higher in people over 55 years old than in other age groups, and approximately $27\%$ of cancer deaths were attributed to population aging in China in 2019 [10,21,26], which makes it more difficult to reverse the status quo. Hence, more stringent targets on key risk factors may be required in Hunan. As for various cancer subcategories, it is interesting that risk factor control targets appeared to generate relatively minor additional benefits for cancers that had already experienced dramatic reductions under the BAU scenario. For other cancers that had not experienced reductions under the BAU scenario, the joint control of all related risk factors may generate larger additional benefits. For instance, the control of all five modifiable risk factors of lung cancer would reduce premature mortality by $28.2\%$ for men and by $23.6\%$ for women compared to the BAU scenario in 2030. These findings suggest that it is more cost-effective to control risk factors for cancers with tendencies toward worse conditions. Through modeling, our estimates also illustrated significant discrepancies in premature mortality reduction across various risk factor control targets. In parallel with the previous study conducted by Li et al [17], improving physical inactivity resulted in minimal reduction in premature cancer mortality, which might be largely because physical inactivity was only causally related to two cancer sites (ie, colorectal cancer and breast cancer). Another explanation may be that the mild reduction of $10\%$ in physical inactivity was not sufficient to produce a significant reduction in premature cancer mortality in 2030. A similar result was also seen for high red meat intake, which was only causally associated with breast and colorectal cancers. In comparison, smoking is causally associated with a wide range of cancers and showed the largest effect in reducing premature cancer mortality by smoking control. As the largest tobacco producer and consumer globally, China has enacted a set of antismoking policies and regulations after signing the WHO’s Framework Convention on Tobacco Control in 2003 [27], which include public smoking bans, a rise in tobacco taxes, and warnings on the cigarette pack. The serial national surveys on smoking showed that the smoking prevalence in China had been declining steadily in recent years, although remaining at a high plateau among men [28]. Hunan Province has a consistently higher smoking prevalence than the national average level [29], possibly due to a later implementation of smoking restrictions. Recently, Hunan Province launched a series of feasible and effective smoking restriction policies, which have greatly reduced the smoking rate. Several rounds of CCDRFS surveys have also indicated a favorably moderate decrease of the smoking prevalence in Hunan, which may partly account for the relatively lower benefit in mortality reduction through the control of smoking than the control of high BMI and diabetes that have been rising steadily in Hunan. In addition, due to the especially low smoking prevalence among women in Hunan, the WHO target of a $30\%$ reduction may produce much less benefit for women than that among men. The past decade has also seen significant decreases in the mean population salt intake and prevalence of harmful alcohol use in Hunan, both of which led to more favorable trends than those of the WHO targets. Of note, the prevalence of current alcohol use was still maintained at a high level and was much higher in men than women, especially for hazardous and harmful alcohol use. The high alcohol use prevalence among men in China may be explained by the traditional Chinese culture that encourages drinking as a socially acceptable way to show their dominant positions in society. Disturbingly, there was a substantial increase in alcohol use following the rapid economic transition in China [30]; yet, the current legislation on alcohol use only focuses on problematic alcohol use such as drunk driving, rather than on alcohol use per se. Evidence has shown that alcohol use is the fourth-leading modifiable risk factor that contributes to cancer mortality among Chinese men [6], and abstinence from alcohol would increase life expectancy by 0.77 years [31]. These findings suggest that more effective measures are warranted to reduce alcohol use. The WHO has recommended some cost-effective alcohol prevention measures named “best buys,” which included increasing taxes, enforcing bans on alcohol advertising, and restricting the availability of alcohol [32]. With respect to high salt intake, studies have shown that the daily salt intake in China had been decreasing slowly, which was largely attributed to the government’s multicomponent strategies, including labeling, media campaigns, and voluntary reformulation of the salt industry, among others [33,34]. However, the mean salt intake in 2019 was approximately 9.3 grams per day [35], which was still much higher than the WHO’s recommended level of 6.0 grams per day. Hence, there is still room for improvement in reducing the salt intake among Chinese residents. Apart from the above risk factors, whether SDG target 3.4 can be realized in 2030 largely depends on the high-impact factors, including diabetes, high BMI, and insufficient intake of fruits and vegetables. China has the largest population of individuals with diabetes in the world, and the prevalence of diabetes has been sharply increasing in recent decades in China, including in Hunan Province [36]. Without proper measures to control blood glucose, China will continue to have the largest population of diabetes in 2030 with a predicted number of 140 million [37]. Previous studies discovered that high concentrations of glucose could provide steady energy for the growth of tumor cells [38,39], and thus diabetes was identified as an independent risk factor for several cancer types [40]. It was estimated that achieving the risk control target for diabetes in 2030 would avoid 57,400 deaths from NCDs in China [17], and our study also predicted notable benefits in premature cancer mortality reduction from the control of diabetes. In China, a range of programs such as primary diabetic health care have been promoted to curb the rapid rise in diabetes [41]. However, due to the imbalanced regional economic development, the capacity of primary medical services is relatively backward in the central and western regions of China, resulting in a lack of standardized diabetic management and low screening rates of diabetes-related complications [42]. Thus, there is still a long way to go to meet the commitments of the UN agenda. Overweight and obesity prevalence among adolescents and adults has been increasing steadily in China, including Hunan Province, for the past two decades [35]. This is largely attributable to high-calorie dietary habits and reduced physical activity. Researchers have projected that the prevalence of overweight and obesity, if not controlled effectively, might reach $65.3\%$ in adults and $31.8\%$ in adolescents by 2030, with more than 800 million people reaching the overweight and obese categories in China [43]. The government has made great efforts to prevent overweight and obesity, including several school-based programs (such as the Healthy Children Action Plan and National Nutrition Campus Program) and community-based programs (such as the National Healthy Lifestyle Action) to promote healthy diet and exercise among the population [43]. However, existing policies on overweight and obesity are still fragmented in China; thus, coordinated and multisectoral strategies are needed to reach the target of halting the rise in overweight and obesity by 2030. Fruit and vegetable intake is an indispensable part of a healthy diet. Studies have shown that the average daily intake of fruit in Hunan Province has been increasing steadily in recent years, while the intake of vegetables has been declining [44,45], which reflects a remarkable transition in dietary structure. Despite these trends, the proportions of adults who reached the recommended levels of Chinese dietary guidelines for both fruit and vegetable intake remained at low levels. Globally, inadequate consumption of fruits and vegetables has been validated to contribute to a large portion of the cancer burden in many countries such as the United Kingdom, Japan, and China [3,6,46]. In China, an unhealthy diet including low fruit and vegetable intake has accounted for more than $10\%$ of DALYs [3]. Dietary guidelines with different recommendations have been developed worldwide to tackle the problem. The WHO recommends a total consumption of at least 400 grams per day of fruits and vegetables for adults, while in China the recommended level is 500 grams per day. Although the Global NCD Action Plan has included the prevalence of inadequate intake of fruits and vegetables as a monitoring indicator, no specific target has been set by the WHO to date. In our study, a $30\%$ reduction for inadequate fruit and vegetable intake displayed relatively high benefits in cancer mortality reduction, further supporting the beneficial role of dietary risk control in achieving SDG target 3.4. Currently, health education with a focus on children is the major approach for promoting a healthy diet in China, which indeed has increased people’s awareness [47]. Nevertheless, given that other factors such as availability and prices may also influence eating behavior, there is still much work to be done. We also validated our projection with other methods or assumptions. In the baseline scenario, where all risk factors continue their current trends, the number of premature deaths from cancers increased from 65,443 in 2017 to 97,787 in 2030. Given that approximately 4 million deaths are expected to occur in 2030 in China [48], an increase of only 32,344 premature deaths in such a populous province may be underestimated. Nevertheless, we further estimated the total number of cancer deaths for all ages and found that the value increased from 111,719 in 2017 to 235,578 in 2030 (see Multimedia Appendix 5), which indicated that people older than 70 years represented the major group of cancer deaths. In addition, assuming that cancer mortality trends continued to 2030 with constant change, we validated the predicted deaths in the BAU scenario using the proportional change model, which showed similar results with only a $3.6\%$ difference in total cancer deaths and a $1.6\%$ difference in overall premature mortality (see Multimedia Appendix 6). Therefore, we believe our projections to be credible. ## Limitations Some limitations of our work sƒhould be considered. First, Hunan launched its cancer registry program in 2009, and the work at the early stages might be imperfect with low population coverage. Even by the end of 2020, the cancer registration in Hunan had only covered approximately $70\%$ of the population, without achieving full coverage, which may to some extent cause a certain bias in the estimation of actual cancer deaths. Nevertheless, with the efforts of local governments, the coverage of cancer registration has rapidly expanded and the data quality has steadily improved in recent years. Some of the data were even cited in monographs of the International Agency for Research on Cancer. In addition, our data for risk factors were drawn from the CCDRFS survey; therefore, all limitations in estimates of levels in the CCDRFS study apply to this analysis. Second, we used RR estimates primarily from the GBD series and the CUP Expert Report due to the lack of high-quality meta-analyses and prospective cohort studies in China, which may make our results statistically unstable. However, with more and more large cohort studies being carried out in China, more reliable RRs for China could be available in future studies. Third, due to a lack of dynamic monitoring data, some region-specific risk factors such as betel quid, which is classified as a class 1 carcinogen and has a high prevalence in Hunan, has not been included in this analysis. Considering that the Chinese government has made great efforts on sales restriction and increasing the public awareness of its harm in recent years, the prevalence of betel quid chewing in Hunan may decline steadily; thus, modeling without consideration of betel quid may lead to overestimation of future cancer mortality. Furthermore, there are potential interactions among the selected risk factors; however, due to the absence of information on most interactions, we simply calculated their combined effects on cancers based on the assumption of independence, which may lead to some uncertainties in our results. Hence, solid evidence–based joint RRs on cancers are warranted for future studies. Fourth, we did not investigate the impact of population aging on premature cancer mortality due to insufficient technological conditions and time. In further studies, we will try to examine the fractions and trends attributable to population aging and its interactions with various risk factors on premature cancer mortality. Fifth, since the current health outcome reflects the cumulative effect of past exposures, risk factors such as smoking and alcohol were subdivided by duration and amount whenever possible. However, no lag effect was considered when data on specific information were unavailable. Nevertheless, calculations of PAFs in our study referred to the comparative risk assessment model from the latest GBD series, and the results were similar to those of previously published literature [6]. ## Conclusions In summary, this modeling study illustrates that the absolute burden of premature deaths due to cancers will continue to increase over the next dozen years in the Hunan province of China. Notable health gains could be achieved by addressing unhealthy risk factors for cancers. However, existing targets on related risk factors are not sufficient, particularly in men, to achieve the one-third reduction goal in premature cancer mortality. More aggressive risk targets based on local conditions are urgently needed. ## Data Availability The data will be available from the corresponding author on request. ## References 1. Cancer. World Health Organization 2. China: Globocan 2020. Global Cancer Observatory 3. 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--- title: 'Rethinking the Difficult Patient: Formative Qualitative Study Using Participatory Theater to Improve Physician-Patient Communication in Rheumatology' journal: JMIR Formative Research year: 2023 pmcid: PMC10028511 doi: 10.2196/40573 license: CC BY 4.0 --- # Rethinking the Difficult Patient: Formative Qualitative Study Using Participatory Theater to Improve Physician-Patient Communication in Rheumatology ## Abstract ### Background Effective physician-patient communication is crucial for positive health outcomes for patients with chronic diseases. However, current methods of physician education in communication are often insufficient to help physicians understand how patients’ actions are influenced by the contexts within which they live. An arts-based participatory theater approach can provide the necessary health equity framing to address this deficiency. ### Objective The aim of this study was to develop, pilot, and conduct a formative evaluation of an interactive arts-based communication skills intervention for graduate-level medical trainees grounded in a narrative representative of the experience of patients with systemic lupus erythematosus. ### Methods We hypothesized that the delivery of interactive communication modules through a participatory theater approach would lead to changes in both attitudes and the capacity to act on those attitudes among participants in 4 conceptual categories related to patient communication (understanding social determinants of health, expressing empathy, shared decision-making, and concordance). We developed a participatory, arts-based intervention to pilot this conceptual framework with the intended audience (rheumatology trainees). The intervention was delivered through routine educational conferences at a single institution. We conducted a formative evaluation by collecting qualitative focus group feedback to evaluate the implementation of the modules. ### Results Our formative data suggest that the participatory theater approach and the design of the modules added value to the participants’ learning experience by facilitating interconnection of the 4 communication concepts (eg, participants were able to gain insight into both what physicians and patients were thinking about on the same topic). Participants also provided several suggestions for improving the intervention such as ensuring that the didactic material had more active engagement and considering additional ways to acknowledge real-world constraints (eg, limited time with patients) in implementing communication strategies. ### Conclusions Our findings from this formative evaluation of communication modules suggest that participatory theater is an effective method for framing physician education with a health equity lens, although considerations in the realms of functional demands of health care providers and use of structural competency as a framing concept are needed. The integration of social and structural contexts into the delivery of this communication skills intervention may be important for the uptake of these skills by intervention participants. Participatory theater provided an opportunity for dynamic interactivity among participants and facilitated greater engagement with the communication module content. ## Background Patient-physician interactions represent a pathway through which health differences manifest [1]. Interactions that make patients feel rushed, disrespected, or unheard reduce patient satisfaction and health outcomes [2], whereas those that demonstrate respect for patients increase their adherence to prescribed medication regimens, lead to more consistent follow-up, and improve their health outcomes [3]. Marginalized people (eg, those who identify with nondominant social identities such as race, gender, sexual orientation, gender expression, or class) are more likely to experience poor interactions [4]. Communication facilitates effective physician-patient interactions, enhances patient empowerment, improves patient understanding of health conditions, and enriches the therapeutic alliance between patients and practitioners. Effective communication has been linked to positive physical and mental health outcomes [2,5], thus making it a component of health care for all patients and is especially critical among those with chronic diseases because of the frequency of interaction between physicians and patients [6]. Patients with the chronic condition systemic lupus erythematosus (SLE) report challenges when communicating with their health care providers [7]. SLE is an autoimmune disease that follows an unpredictable course and disproportionately affects women of color [8]. Nonspecific symptoms and unpredictability of the syndrome create communication barriers with physicians [9]. In addition, medical mistrust among marginalized patients further strains physician-patient communication [10], because the health care professionals may not know how to best address it or how to recognize the social determinants of health (SDOH), which can contribute to what some have deemed contextual medical error (error in medical decision-making derived from overlooking patient context) [11]. Similarly, bias based on socioeconomic status exhibited by providers or other members of the health care system can also present challenges for patient-provider communication [12] by, for instance, relying on culturally produced stereotypes of individuals with low socioeconomic status [13,14]. These factors strain the therapeutic relationships between individuals with SLE and providers, thereby contributing to disparities in SLE outcomes [15]. Concordance reflects the degree to which patients and practitioners agree with the treatment plan [16,17]. A core component of concordance is empathy [18], which can be divided into 3 domains: cognitive, emotional, and behavioral. Although the cognitive and emotional domains are focused on understanding another person’s perspective (cognitive) and internalizing those feelings (emotional), behavioral empathy is the ability to express the (internally) experienced (cognitive and emotive) process [19]. The acquisition of these different components of empathy requires different teaching modes. For instance, instructional sessions focused on cognitive and emotional empathy may use patient narratives to teach recipients how to understand others’ situations or perspectives regarding living with a chronic disease. To improve behavioral empathy, educators might use patients who have been standardized to build physicians’ verbal and nonverbal communication skills to convey their understanding of patient experiences and viewpoints. Interventions that address patient communication among physicians need to be tailored to the needs of specific vulnerable populations, both in the nature of disease and in the multitude of contextual factors that shape people’s abilities to engage with their health care providers [2]. Skills training for physicians often relies on information-sharing modules accompanied with observed practices with actors or patients [20]. The format of these sessions may hamper the incorporation of the patient context, the structural and social factors that influence that context, or how to address them during clinical encounters. To explore these topics, participants need a safe learning environment to ask questions about potentially sensitive topics, as well as opportunities to ask questions about the impact of their approach with the actual or standardized patient. Arts-based approaches to education are particularly well positioned to meet these needs because they facilitate active engagement among participants [21], particularly on social issues [22]. Given the relative novelty of arts-based interventions for physicians and medical trainees, a formative evaluation approach incorporating participant reflections is important for intervention refinement. ## Objective The purpose of this study was to develop, pilot, and conduct a formative evaluation of an interactive arts-based communication skills intervention for graduate-level medical trainees grounded in a narrative representative of the experience of patients with SLE. The results reported in this paper discuss the immediate impact of the intervention on participant perceptions of this mode of communication skills training and ways to improve the curriculum for future implementation. ## Participants We used a convenience sampling strategy for recruitment. All trainees in the rheumatology fellowship program at an academic medical center attended the session as a part of their required conferences ($$n = 7$$). Internal medicine residents rotating through the specialty of rheumatology were also invited to attend ($$n = 1$$). All studies were conducted in accordance with the relevant guidelines and regulations. Verbal informed consent was obtained from all the participants. ## Conceptual Model The intended outcome of the session was to alter physicians’ attitudes toward concordance and provide introductory skills training that might lead to better communication and concordance with patients during routine interactions. Through discussions with patients and health care providers and a review of the communication skills training literature, we identified 3 key concepts: SDOH, expression of empathy (behavioral empathy), and shared decision-making (SDM; Figure 1). Importantly, the SDOH module consisted of how providers may engage with various SDOH with their patients and group discussions on how to best facilitate connection to resources for patients when necessary. Figure 1: Conceptual model. We posited that each of these concepts are independently associated with concordance and also build on each other. Understanding of social determinants is seen as cognitive empathy, and the ability to move that toward behavioral empathy requires additional skill building. The increased understanding, and ability to express it, is then seen as leading to the ability to better engage with patients in shared decision-making processes, which in turn contributes to provider-patient concordance. ## Intervention Development We conducted a literature review to create interactive modules that align with the 3 components of the conceptual model (SDOH, empathy expression, and SDM) [23-27]. For delivery of the intervention content, we worked in partnership with the Metro Theater Company, a nonprofit educational theater organization. The research team collaborated with the members of the organization to develop a script for the initial scenario that depicted a physician-patient conflict and reflected a typical experience of living with SLE (eg, patients who were feeling that they were not being heard and physician were ignoring patient concerns). The actors playing the physician and patient had expertise in improvisation, which allowed for dynamic interaction with audience members. In addition, the research team assisted in preparing the actors for SLE-specific issues by providing relevant background information and feedback from both physicians and patients during rehearsals. ## Ethics Approval This study was approved by the Washington University Institutional Review Board (protocol #202010105). ## Intervention Description The intervention consisted of a 120-minute session with a mixture of didactic and interactive modules. Although originally designed for in-person learning, the intervention was adapted to web-based learning and delivered through videoconferencing software because of the COVID-19 restrictions. The videoconferencing environment functioned as the main “forum” of the forum theater. The primary adaptation made in the videoconferencing environment was that one member of the study team (JL) used the spotlight feature throughout the session so that only those speaking were visible to all (eg, actors during the actual scenes). All the participants were visible during the hot seating portion of the initial scenario. There were 5 phases of intervention, as described in Textbox 1. ## Pedagogical Approach We used an arts-based participatory theater approach to deliver the intervention. Participatory theater is grounded in the empowerment theory by Paulo Freire [28] and the initial Theater of the Oppressed by Augusto Boal [29]. This leverages the knowledge of disenfranchised people to transform oppressive structures by inviting members from the audience to participate in the performance and by asking them to envision ways to transform their current social realities. Audiences first engaged with performers and facilitators to represent the reality of their current experiences and then cocreate ways to modify these conditions using theater techniques as a vision to achieve change [29,30]. Actors play various parts outlined in the script in the actual application of this participatory theater approach. A facilitator engages the audience and allows for stoppage and replaying of scenes and prompts members from the audience to provide suggestions on how to redo interactions for different outcomes. Audience members are then asked to tap into the scene and take on the role of the characters (replacing the actors). This differs from standardized patients in medical education in that the trainees typically only play the role of health care provider and do not necessarily have in-built facilitation or structured observation [31]. ## Measurements Participants shared feedback through 2 focus groups facilitated by members of the research team during the last 20 minutes of the session. Facilitators asked participants about the strengths and weaknesses of the intervention’s design and implementation as well as suggestions for improvements. Focus group questions were provided to the participants by the authors. ## Analysis We conducted a qualitative analysis using a constructivist paradigm. The entire session was audio recorded. Focus groups and discussions for each phase were transcribed verbatim and analyzed using focused coding techniques [32], in which multiple members of the research team independently assigned codes to transcripts based on the interview topics, discussed disagreements, and arrived at a consensus on the best way to address discrepancies. After the initial coding, codes were arranged into clusters with summary paragraphs describing key elements of each cluster, along with supporting quotations from the focus groups. The focus groups were audio recorded and transcribed. To maintain confidentiality, audio files and corresponding transcripts were stored on a password-protected computer. Complete anonymity could not be maintained with the audio recordings because voices could potentially be identified; however, the participants did not state their names during the focus groups. In addition, unique identifiers were generated to label the speakers in the transcript documents. Participants were not compensated for their participation. ## Overview A total of 8 trainees participated in the workshop (Table 1). All the 8 participants also participated in the postsession focus groups (4 participants per group) and the demographic questionnaire. The participants’ ages ranged from 26 to 40 years, with the majority ($\frac{6}{8}$, $75\%$) being aged between 31 and 35 years, women ($\frac{5}{8}$, $63\%$), and non-Hispanic or Latinx ($\frac{7}{8}$, $87\%$). Table 1 \| Demographic variables \| Demographic variables.1 \| Demographic variables.2 \| Value, n (%) \| \| --- \| --- \| --- \| --- \| \| Age group (years) \| Age group (years) \| Age group (years) \| Age group (years) \| \| \| 26-30 \| 1 (13) \| 1 (13) \| \| \| 31-35 \| 6 (75) \| 6 (75) \| \| \| 36-40 \| 1 (13) \| 1 (13) \| \| Gender \| Gender \| Gender \| Gender \| \| \| Woman \| 5 (63) \| 5 (63) \| \| \| Man \| 3 (37) \| 3 (37) \| \| Hispanic or Latinx \| Hispanic or Latinx \| Hispanic or Latinx \| Hispanic or Latinx \| \| \| Yes \| 1 (13) \| 1 (13) \| \| \| No \| 7 (87) \| 7 (87) \| \| Race \| Race \| Race \| Race \| \| \| White \| 2 (25) \| 2 (25) \| \| \| Asian \| 3 (38) \| 3 (38) \| \| \| Other \| 3 (38) \| 3 (38) \| \| Medical training \| Medical training \| Medical training \| Medical training \| \| \| First year fellow \| 4 (50) \| 4 (50) \| \| \| Second- or third-year fellow \| 3 (38) \| 3 (38) \| \| \| Resident \| 1 (12) \| 1 (12) \| We grouped participant feedback into 3 categories as follows: value added by method of content delivery, suggestions for improvements in the method of delivery, and overall reflections. ## Value Added Through Method of Content Delivery Participants generally thought that the opportunity to deeply investigate the interaction between the patient and provider, rather than just observing it, added value to their learning experiences. They found value in being able to ask questions of both the patient and provider after the videotaped interaction, something they had not been able to do either in prior education settings or in actual interactions with patients (Table 2; quotation [Q] 1). The ability to ask questions to both the patient and provider and have each respond while maintaining their role functioned as a type of confessional interview that allowed the respondents to hear the internal narrative of both the patient and provider. This was an important experience for providers who often walked out of a room and asked themselves what the patient took from the interaction but did not have the opportunity to ask the patient (Table 2; Q2). Table 2 \| Categories \| Quotations \| \| --- \| --- \| \| Value added through method of content delivery \| 1. Yeah. I would say I got a lot of value out of being able to ask questions, uh, after that first stage, that initial interaction. I think that was, you know, something that I don’t really ever get to do. Sometimes we get that information [from patients] through a survey results and things like that, but it’s really interesting. 2. ...I thought it would be...nice that—kind of get the answers from those two [physician and patient actors] just for that perspective, because sometimes I think it—or I guess it offered a time to ask questions that maybe sometimes I walk out of the room and I’m like, “Huh, I wonder, uh, did they get that, or did I totally miss it?”...you know, you just keep moving because you have to, but I thought that that was a unique experience... 3. I thought that was great, actually, that they [actors] were willing to give their time to, you know, shoot that video in advance and then, after the fact, let us play role and do those breakout sessions. I felt like that was more of a believable, you know, standardized patient interaction... 4. ...I tend to like how—observing how other people interact with patients because I think in training very often we’re just sort of “go do it” and you never actually get to see someone role modeling those behaviors... 5. Yeah, so with medical school, we had standardized patients. And then residency is the same thing when we were running mock codes or what not...and so they [instructors] would have this little blurb of who they [standardized patients] would be, and you would walk in to the room and just play role that. But there’s not really someone who would show you how that interaction, like may have occurred—right, or should have occurred. It is just “This is what you’re supposed to do,” and then they would correct you after the—or give you feedback after the fact... 6. [Question for physician-actor]...But do you think the interaction would have been different if she [patient-actor] had shown up on time? Like, maybe it would’ve helped to have had a shared agenda at the beginning? 7. (Participant speaking)...when she [patient-actor] came 25 minutes late, you would have 5 minutes before your next patient...So sometimes I have taken the tact of, like, okay, like, I’m still—you made the effort to come here. I’m making the effort to see you, but I can’t, you know, make my other patients have a negative outcome because you were late. So if I had asked you to, maybe, like—I’m gonna try to fit you in as best as I can, but you may have to wait, like, until I have a free slot in my schedule. How would you [patient-actor] have reacted?... 8. (Patient-actor speaking): I’m walkin’ out. And that-that sucks because, like you’re [participant] saying, you’re doing the best that you can to stay on schedule ‘cause you do have other patients. And the truth is, just because I’m late, you can’t let that ripple out to everyone else. You can only be so late to everyone else...but I’m still waking out. Um, but I think that [a physician] putting the [emphasis on], uh, “But I really do want to see you. So if you can stay, we’ll try to get you in today. Otherwise, let’s really look at what day you have avail-available, and let’s plan ahead.” So, then, it’s like, okay, [the physician might say] let’s look at another two weeks ‘cause I really want you to have enough time to plan to get here and really wanna give you, um, the time that you need so that I can, like, focus on you. Um, ‘cause showing that, you really are important, and I really do wanna see you. And...I can glance at your rash now, but I really want you to do a follow-up so that I can go even deeper... 9. I thought it was useful for demonstrating, you know, the impact and how—sort of the trends of...acquiring more and more social determinants of health in a positive or negative direction. 10. ...I think it was a good exercise, especially for, like, visual people. It was, like, really nice to see, like, those little chips and see them taken away. I think it makes you understand that one person can have two different outcomes that are very polar depending on things that we don’t necessarily—maybe may not think about first, like, you know, one—you know, the same person having social support versus no social support. Outcomes are very different... 11. ...just to be able to use a couple of different tools [was helpful], ‘cause I think each tool may or may not work for the particular person who’s using them. 12. I think the verbiage and examples that were used in that chart are excellent, and I—absolutely see myself using them, uh, and practicing them because I admittedly am not smooth in working through those three phases and, uh, I think...that’ll be really helpful for me, so... 13. Um, and I think one thing I’ve taken away from today is, like, the Ask-Tell-Ask session—is, um, to Ask-Tell-Ask every concern as it comes up and not wait ‘til the end—to kind of hit them [with] all this information that they may not be able to absorb all at once... 14. I think it was a good exercise...getting to kind of...walk back into the bad encounter, kind of apply the strategies we’ve learned...I mean, we practiced the NURSE strategy in the breakout room, and I think that was really helpful. I mean, we do a lot of those things [exercises] separately. We put it in—insert it into our conversations [with patients] somewhere but never really have a systematic approach to it. So I think learning about that, too, and practicing was helpful. \| \| Suggestions for improvements in method of delivery \| 15. ...I think continuing the trend of being interactive and things like the rest of the session...somehow would have been nice too 16. Um, I think where I got lost for a little bit is when I read—I tend to do better when I both read and hear or—sort of—the reading I often just glaze through really quickly [laughter], and then how much it-that it sticks or not you just have to find out later [laughter]. So I think for me the reading portion...if there was some other sort of cue like he read it as we read it, or just reading certain portions...I just know I personally don’t do as well with the just read it on your own kind of thing. 17. ...I thought it would have been nice if we sort of had a little bit more discussion after that segment [empathy expression], maybe like what aspects of each tool, like, a particular group is using that they found useful or not so useful, um, especially if you don’t have time to use all three. Then you get to hear a little bit of some other tools. 18. Um, I think for me it was sort of—I-I felt like our group maybe didn’t, uh, wasn’t as interactive for that part [SDOH game], and maybe it is for some of the reasons that [participant] had mentioned. But also, um, I think when we were reading the card it was just a lot of words and information, um, and so, like, trying to respond to too much may have inhibited a little bit of the—or at least not being able to see the card on the screen. That might have made a difference too. Um—in-instead of just being read, so that-that may be another option to sort of, uh, get more interaction. 19. I thought it [role-playing] was helpful. One thing that I think was a little challenging for me was I wasn’t really sure what to say as the patient, when I was role playing as the patient, just ‘cause some of the initial video, kind of outlined all of [the patient’s] thoughts and her actual symptoms, so I didn’t really remember all the details from it [while role-playing]. 20. ...when we did this as residents, we had, like, little actor notes that would tell us how exactly obnoxious we should be or, you know, like, what we shouldn’t do, what we should say...just like little cues... \| \| Overall takeaways—positive \| 21. So given everything else that we were tryin’ to accomplish during this whole session I would have—I-I don’t know that I found that—that it added a ton of knowledge for me. But in different groups I think it could have—be important... 22. So, during residency, we kinda had things like this too. But I think it’s a little bit different looking at it from the [rheumatology] point of view, as opposed to internal medicine as a whole, ‘cause I feel like, with rheumatology—and I guess with every disease but, I feel like, more so with things...like rheumatologic diseases, the social determinants of health are, I think, even more important. I think...rheumatologists...are, like, the full caregiver for, like, most of our patients, right? If they have a fever, they tell us first before they tell their primary care doctor. So, I think, knowing the social factor that play into their healthcare is more so very important for us to kinda be aware of. 23. I like how the workshop gave us some additional insights into both what the patient and the physician is thinking...because I think a lot of the time we maybe try to figure out how a patient might respond, and there’s no real feedback. We just have to guess and go with something... 24. I think all these tools...go into treating the patients as people first who have medical problems and not medical problems that are associated to a person. So I think it’s important always to have the person be the center of the interaction that hap—a person who happens to have medical issues. And I think, if that’s the focus, you’ll end up catching more social issues that the patients bring to you, and it will help you provide better care. 25. Sure, um, I think for me is, as we see patient in the clinic, especially a lot of time with a busy schedule, sometimes—or by the language can—we sort of focus more on the computer screens instead of looking at the patient and actually sit down, just ask some simple questions, like, “How are you doing?” ...we kind a tend to ignore, uh, the social factors. We focus more on the medicine part of it, uh, but we...we don’t really explore like a—find of a cause or the reason why they [patients] are not taking medication... 26. ...you know, we treat the medical issues, and that’s what we’re there for. But, on the other side, to help with the patient as a whole—their social conditions and...how that affects them, I tend to put on the back burner. But this, you know, this session’s nice to help me bring that back and go, “Okay, well, the—what are we going to be doing, um, in terms of their condition flaring? Why is it flaring? Is it because they don’t have the means for it, and what can we do to help with that?”...just being able to say, “Okay, why—what can we do outside of their medical condition that can help it, um, improve that [flaring]?” 27. ...I think being able to sort of get on the same page from the get go just by simply, you know, introducing and taking a second to do that and make sure you both know each other to start the conversation, that was a big difference. And I think it sort of carried the rest of the conversation. But from there, [participant] did a good job of, like, making her [patient-actor]...[feel heard] regarding her concerns around the rash but, also, you know, some of the other things that are going on as far as just getting to her appointment and some of her life stressors. 28. ...I really think that the most important part of this is to make the patient feel that really listen to their concerns, even if it’s not our [physician]—most important, um, part of that discussion that day... 29. ...[participant] did a good job of just...providing attention to her concern. Um, but, also, you know, in—you know, taking a look at it and kinda going through those steps. But, when she delivered information that maybe the patient wasn’t totally agreeable to, she provided kind of like a back-up to say, “Hey, you know, I think we should try this. But, you know, just so you know, if it doesn’t work, like, let’s keep working towards a solution.” 30. [Participant in role-player] didn’t really provide solutions for those [social stressors], but she [patient-actor], at least, felt like, “Okay, [participant role-player] knows that I’ve been having a day or a week,” or whatever. And they can kind of, uh, move on from the conversation. One of the other big things I think I appreciated that [participant role-player] did was emphasize, um, why [participant role-player] felt the-the medication was important and, um—so she [patient-actor] kinda knew why, like, why does he really care. Like, why don’t we just switch medications? 31. I think that is an important thing to emphasize. Like, let’s really make sure that this rash is from the medication or not because I, you know, I think the medication is important. And that helps her feel like, okay, [participant role-player] not just forcing me on this medication against my will too. So I think that was an additional thing that helped her understand where [participant role-player] was coming from after he had already heard her concerns. \| \| Overall takeaways—concerns \| 32. Um, I think it can play out as [participant role-player] did with [patient-actor] today. But, um, the other chance it could be, okay, like...how much can this derail the actual shared agenda that you want to get completed during the visit? Sometimes, once—at least, I’ll just say for myself. You know, you have, like, that self-catastrophizing type of mentality. And so, like, one bad thing comes up, and then you’ll just continue to the next bad thing. 33. And sometimes it can just span the whole visit where you’re like, “Well, there was the 30 minutes of just complaints, and I couldn’t get a word in with the patient...” 34. I think they went through everything, and I think [participant] had pointed out something important at the beginning. I don’t think most of the time at least when, uh, options are presented to the patient we might not go through the three steps in one visit. We might have to follow up with the patient on-over the phone or do something else. 35. It’s hard to be thorough with each one of those steps [shared decision-making tool] and still make it under 30 minutes and have examined the patient and do everything. \| In comparing the forum theater approach to their previous standardized patient experiences, participants indicated that the forum theater was better because they were able to test alternative strategies for changing the interaction and see how a patient might respond. For instance, the format of letting participants play either the provider or patient role while an actor played the other role was seen as more believable than previous experiences with standardized patients (Table 2; Q3). Participants noted that in prior experiences, they would practice certain scenarios but did not get to see someone role modeling the behaviors (Table 2; Q4). They would then be corrected by the instructor, but the standardized patient typically did not provide feedback or insight into their experience of the provider’s behavior (Table 2; Q5). The desire to test strategies was further evidenced by the types of questions asked by the physician and patient actors. Questions for the physician actor centered on wanting to know more about why the physician did not ask about the patient being late and whether they thought the interaction would have gone differently had the patient been on time (Table 2; Q6). Participants went further by asking the patients how they would feel if they were asked to reschedule when they arrived at their appointment late (Table 2; Q7). The patient-actor response suggested that they would likely leave and be unlikely to reschedule unless the provider showed that they had taken the patient seriously (Table 2; Q8). Participants appreciated when visual cues were built into the activities because they helped reinforce the primary message of that activity. For instance, the SDOH board game contains red chips, which are meant to represent an acquisition (or lack of) advantage in society. Participants commented that these chips were helpful in demonstrating the trends and accumulation of both health-promoting and deleterious impacts of SDOH (Table 2; Q9). The red chips provided an opportunity for critical reflection on the forces that led to the accumulation of certain resources that affected patients’ ability to manage their disease (Table 2; Q10). An additional element of activities that participants found helpful in the training was the presentation of a variety of different tools for the same concept, as well as specific phrasing or verbiage suggestions. The use of a variety of tools enabled participants to begin making connections on how these tools could be used in their practice and future interactions with patients. ( Table 2; Q11-12). Participants began to see that there is not just one way to address the issues, but a range of options they can choose from (Table 2; Q13). Participants praised the interactivity of some parts of the session, particularly the opportunity to interact apply some of the strategies they had practiced throughout the session with the actors (Table 2; Q14). ## Suggestion for Improvements in Method of Delivery Although participants were enthusiastic about the training, they also noted that some parts of the session were not quite engaging. For instance, they mentioned that the presentation of some of the didactic components could have used more active ways to engage with written material (Table 2; Q15-17). Participants also suggested ensuring multiple ways of engaging with information for different activities (ie, both visual and auditory elements). Having multiple methods of engagement would help the participants contribute to a deeper level of interaction during the discussion sections of the session (Table 2; Q18). Suggestions were also made to make some of the activities more fluid. For instance, during role-playing exercises, participants were not sure what to say when role-playing as a patient, which could make the role-playing feel forced, especially because participants were not used to acting. Without cues or guidance, it was sometimes difficult for participants to tell whether they were fulfilling the goals of the session (Table 2; Q19). One suggestion was to provide actor notes that would guide how participants should try to act (Table 2; Q20). ## Overall Reflections In their reflections on the session, participants commented on how the 3 different pieces of the session (SDOH, expressing empathy, and SDM) complemented each other in ways that they had not previously considered (Table 2; Q21). Participants noted that they were familiar with some workshop concepts. However, it was evident from their reflections that this session went further by building on the foundation that participants may have already had and provided additional context for these terms, such as SDOH, and how having this lens impacts their empathy with patients in the SDM process. The participants noted that this was particularly important given the impact SDOH has on the ability of patients with rheumatological diseases to manage their diseases (Table 2; Q22). Specifically, participants saw the tools and activities as helpful by providing insight into what the patient and physician were thinking about the same topic. The activities appeared helpful in contextualizing physician-patient interaction and ensuring that physicians see patients as people who have medical problems and not just medical problems associated with the person. For example, physicians tend to focus on getting patients to take their prescribed medications without exploring why patients are not taking their medication or adhering to care recommendations (Table 2; Q23-25). This reframing then encouraged participants to also think about their role as health care providers in taking action to address some of the factors outside of the medical condition, which may affect lupus (Table 2; Q26). This synthesis was further evident in what the participants took away from the session. For instance, they considered framing the beginning of the interaction differently by spending more time with introductions and establishing a rapport with the patient. This underscores that one of the most important parts of the interaction is ensuring that the patient feels that their physician is listening to their concerns and connecting actions directly to those concerns (Table 2; Q27-28). Participants also connected this rapport building to later points in decision-making with patients. Demonstrating that the physician provided early attention to patient concerns was helpful when delivering information that the patient was not completely agreeable to. Participants noted the importance of having a backup option for a treatment decision and that they would continue working toward an agreeable solution if the initial treatment recommendation was not working for the patient (Table 2; Q29). Participants similarly saw rapport building as generally leading to more productive discussions in the context of patient life stressors and other SDOH. Although the participants noted that direct solutions to other life stressors cannot always be provided within the context of a clinical visit, attention to these life stressors by physicians is still meaningful if the patient feels heard and strategies are discussed to help the patient and provider get on the same page early (Table 2; Q30). Participants directly connected this early rapport building and acknowledgment of SDOH to being able to better emphasize important elements of lupus management such as the importance of consistently taking medications (Table 2; Q31). Participants noted some obstacles to implementing the tools presented during the session given the reality of working in a clinical setting. For instance, some participants voiced concerns that asking how patients are doing may open the conversation to issues beyond what they can address. There was a concern that some of these strategies would invite patients to focus on self-catastrophizing behaviors or that some of the strategies would take more time than allotted during a clinical encounter (Table 2; Q32 and 33). Others have noted that suggestions for interacting with patients might not be realistically implemented in a single patient encounter. Often, a provider may not be able to go through all steps in decision-making in one visit, so it can be challenging to cover each of the SDM processes and examine the patient in a time-limited clinical visit (Table 2; Q34 and 35). ## Principal Findings We presented formative evaluation data from an interactive, arts-based communication skills workshop for rheumatology trainees. Our qualitative data suggest that participants responded positively to the level of interactivity of some session components, the overall cohesiveness of the separate phases, and the unifying approach of theater-based performance. The design of the session enabled participants to integrate the different components of the sessions, relative to the previous types of workshop training (often compared with standardized patients). The novelty of this session was the integration of structural and social factors as a context for patient encounters. This was achieved through the intentional sequence of theory-grounded activities built upon each other (Figure 1) and facilitation, which guided participants in linking activities together. This approach allowed participants to move from simply receiving information about communication behaviors to applying that knowledge in a dynamic environment that incorporated patient lived experience. This was evidenced by participants in their generation of strategies: participants discussed the importance of asking how patients were doing at the beginning of the encounter (SDOH activity or empathy expression), which they saw as helping with rapport building and ultimately facilitating the space for cooperative SDM to occur. Although discussions on structural factors and SDOH have permeated medical education and are increasingly large parts of the curricula [33], the integration of these SDOH in communication-specific modules is less common [2]. Our findings suggest that this integration is critical in the uptake of information and skills in both SDOH and communication domains. The incorporation of the structural context of health inequities was greatly facilitated by the participatory theater approach. The unifying scenario, presented at the beginning and developed in conjunction with an educational theater group, was an important grounding for participants, as they interacted with actors throughout the session. This unifying scenario was received positively by participants, who affirmed that the application of arts-based pedagogy was suitable for graduate medical education. Our findings align with the application of forum theater in other medical settings including general medical education [22] and specific discussions around social factors such as race [34]. In line with these studies, we observed the advantage of forum theater in the exploration and generation of ideas among participants rather than prescribing a particular solution. This was evident in our findings, as participants praised the dynamic interactivity of the theater components, which appeared to be more engaging than their previous experiences in medical education settings with standardized patients. ## Limitations This study has several limitations. First, we performed only a single session with 8 rheumatology trainees. Although we would have ideally conducted additional sessions, our intention was to pilot materials that were specifically designed for a rheumatology setting to assess what activities did or did not work well. Moreover, within this narrow population, we were able to include all rheumatology trainees at the study institution. An additional limitation is that we relied on a single script in our study. This may limit the scope of information received by participants; therefore, future iterations of this type of intervention may consider developing multiple scripts for the same topical area. Future iterations of this session may also consider designing multiple sessions to track changes in attitudes and integrate evaluation metrics with participant observations in actual clinical settings. In addition, the intervention was conducted via videoconferencing because of the COVID-19 pandemic restrictions, but the intervention may have been received differently had it been in person. Finally, focus group methods were limited (eg, not able to engage in member checking) because of funding, but future versions of this work may consider additional recruitment strategies for more rheumatology trainees (ie, multisite intervention) to obtain a larger sample to better gauge data saturation. ## Conclusions Our findings from this pilot test of communication modules suggest that participatory theater is an effective method for framing physician education through a health equity lens. Feedback through qualitative focus groups revealed several considerations for the further exploration of this approach and framing. First, a critical insight from participant feedback was the need to acknowledge the workload of the physicians. Participants raised concerns about the reality of working in fast-paced clinical environments and the difficulty of implementing some of the tools. This aligns with the current understanding of the increasing physician loads, more time spent on administrative tasks or electronic medical record [35,36], and pressure to see high volumes of patients while also providing optimal care management [37]. The prospect of adding additional tasks to an encounter, such as spending time discussing social factors with patients, is daunting. This suggests that education sessions such as the one presented here need to balance the realities of clinical practice with the new knowledge from these sessions. These insights also suggest that complementary sessions designed to educate patients about the demands of physicians are necessary, using similar tools and approaches as those described here. This suggestion is in line with work that has pointed to the disproportionate emphasis of interventions on physician behavior, but which misses an important half of the physician-patient dyad [38]. In addition, our intervention incorporates elements of “structurally competency,” which Metzl and Hansen [39] define as the “ability to discern how a host of issues defined clinically as symptoms, attitudes, or diseases also represent downstream implications of a number of upstream decisions about such matters as health care and food delivery systems, zoning laws...” Structurally competent approaches to medical education prioritize training medical professionals to recognize the ways in which structural (upstream) factors shape the medical encounter and patient outcomes (downstream). The evidence of the basis of this work, supported by our formative findings here, suggests that these structurally competent approaches are effective at integrating SDOH education. Moreover, it suggests that this approach can also facilitate concordance between physicians and patients, a bedrock for a productive therapeutic alliance, and improvement of health outcomes. ## Data Availability Qualitative data analyzed are not publicly available because of the limited number of participants in this pilot study and the possibility of reidentification from raw, anonymized transcripts. These data can be provided by the corresponding author upon reasonable request. ## References 1. Saha S, Arbelaez JJ, Cooper LA. Patient-physician relationships and racial disparities in the quality of health care. Am J Public Health (2003.0) 93 1713-9. DOI: 10.2105/ajph.93.10.1713 2. Street Jr RL, Makoul G, Arora NK, Epstein RM. How does communication heal? 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--- title: 'Step Count, Self-reported Physical Activity, and Predicted 5-Year Risk of Atrial Fibrillation: Cross-sectional Analysis' journal: Journal of Medical Internet Research year: 2023 pmcid: PMC10028513 doi: 10.2196/43123 license: CC BY 4.0 --- # Step Count, Self-reported Physical Activity, and Predicted 5-Year Risk of Atrial Fibrillation: Cross-sectional Analysis ## Abstract ### Background Physical inactivity is a known risk factor for atrial fibrillation (AF). Wearable devices, such as smartwatches, present an opportunity to investigate the relation between daily step count and AF risk. ### Objective The objective of this study was to investigate the association between daily step count and the predicted 5-year risk of AF. ### Methods Participants from the electronic Framingham Heart Study used an Apple smartwatch. Individuals with diagnosed AF were excluded. Daily step count, watch wear time (hours and days), and self-reported physical activity data were collected. Individuals’ 5-year risk of AF was estimated, using the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)–AF score. The relation between daily step count and predicted 5-year AF risk was examined via linear regression, adjusting for age, sex, and wear time. Secondary analyses examined effect modification by sex and obesity (BMI≥30 kg/m2), as well as the relation between self-reported physical activity and predicted 5-year AF risk. ### Results We examined 923 electronic Framingham Heart Study participants (age: mean 53, SD 9 years; female: $$n = 563$$, $61\%$) who had a median daily step count of 7227 (IQR 5699-8970). Most participants ($$n = 823$$, $89.2\%$) had a <$2.5\%$ CHARGE-AF risk. Every 1000 steps were associated with a $0.08\%$ lower CHARGE-AF risk ($P \leq .001$). A stronger association was observed in men and individuals with obesity. In contrast, self-reported physical activity was not associated with CHARGE-AF risk. ### Conclusions Higher daily step counts were associated with a lower predicted 5-year risk of AF, and this relation was stronger in men and participants with obesity. The utility of a wearable daily step counter for AF risk reduction merits further investigation. ## Introduction Atrial fibrillation (AF) is the most common cardiac arrhythmia, and it is an important cause of stroke, heart failure, and death [1]. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)–AF score is a tool that has been validated to estimate an individual’s 5-year risk of developing AF, using relevant clinical information and known risk factors, such as height, weight, blood pressure, and a history of heart failure and myocardial infarction [2-4]. In recent years, researchers have investigated AF risk modification via lifestyle changes, such as decreased alcohol consumption, weight loss, smoking cessation, and other factors [5-11]. In particular, physical activity has been examined as a means to decrease the risk of cardiovascular disease (CVD) and AF [12-20]. Studies that examine objectively measured physical activity and AF have mostly used research-grade accelerometers or implantable loop recorders, which limit applicability to daily life [16-20]. Although research-grade accelerometers provide superior validity and precision in their ability to reflect an individual’s physical activity, they are not as widely available to the general public as commercially available wearable devices [21]. Additionally, in studies that measured physical activity with research-grade accelerometers, physical activity data were collected for only a brief period of time (4-7 days) [17,18]. The sharp rise in the prevalence of wearable devices with step counters has resulted in a unique opportunity for health management and optimization, with direct applicability to individuals’ lifestyles. To our knowledge, a direct relation between the long-term tracking of daily step counts from commercially available wearable devices and the risk of AF has yet to be investigated. We hypothesized that a higher daily step count, as measured by wearable devices, is associated with a lower 5-year risk of AF, as predicted by the CHARGE-AF score. ## Study Sample The Framingham Heart Study (FHS), which originated in 1948 to investigate CVD, is a community-based cohort study that spans 3 generations of families [22,23]. In recent years, participants from the Third Generation Cohort, multiethnic Omni Group 2 Cohort, and New Offspring Spouse Cohort were invited to enroll in the electronic FHS (eFHS) at the time of their third research examination [2016-2019] [24]. The use of the Apple Watch (Series 0; Apple Inc)—a smartwatch that allows for the tracking of daily steps and heart rate—was incorporated into the eFHS in 2016. Participants were required to be English speakers and own an iPhone (Apple Inc) to be eligible for the study. ## Ethics Approval This study was approved by the Institutional Review Board of Boston University Medical Center (approval number: H-36586). All participants provided written informed consent. ## Collection of Objective Physical Activity Data In this analysis, we selected eFHS participants, as displayed in Figure S1 in Multimedia Appendix 1. Of the 3521 participants examined in the research center, 1948 had a compatible iPhone, provided consent, and were ultimately enrolled in our eFHS sample. Only 1185 used the Apple Watch and returned step data. We excluded 244 participants who did not wear the watch for >30 days and 18 participants with prevalent AF. Participants were encouraged to wear their Apple Watch daily. The total number of hours of watch wear time per day and the number of days participants wore the watch were recorded. A wear hour was defined as an hour with at least one heart rate measure or the time when at least 30 steps were accumulated. An active day was defined as a day with at least 5 watch wear hours. Average daily step count and watch wear time were defined as participants’ mean daily step counts and their total wear hours, respectively, for active days. ## Estimation of AF Risk The primary dependent measure was participants’ 5-year risk of AF, which was estimated based on the CHARGE-AF risk scores that were calculated by using the clinical risk factors assessed when participants were examined at the FHS research center [24]. This previously validated prediction model is a Cox proportional hazard regression model. The prediction model uses an individual’s age (years), height (cm), weight (kg), self-reported race and ethnicity, systolic and diastolic blood pressure (mm Hg), current smoking status, antihypertensive medication use, history of diabetes mellitus, history of heart failure, and history of myocardial infarction to predict the individual’s 5-year risk of AF. ## Self-reported Physical Activity Participants were asked to complete a questionnaire during their FHS research examinations to determine their physical activity levels. This questionnaire, which has been used in other FHS studies, asked participants to estimate the number of hours in a typical day that they spent performing varying levels of physical activity over the past year [25,26]. We then calculated a physical activity index (PAI) score as a weighted composite of hours per day spent performing activities of varying physical intensity. For example, sleeping was weighted at 1.0; slight activity, such as standing and walking, was weighted at 1.5; and high-intensity activity, such as jogging and swimming, was weighted at 5. As such, PAI scores could hypothetically range from 24 (eg, 24 hours per day of sleeping) to 120 (eg, 24 hours per day of high-intensity exercise), with higher PAI scores indicating a participant’s perception of higher daily physical activity levels. ## Statistical Methods Daily step count, watch wear time, and clinical variables were reported as means with SDs for continuous variables and as n values with percentages for dichotomous variables. When continuous variable distribution was skewed, medians with IQRs were reported. The primary analysis examined the association between daily step count (independent measure) and the CHARGE-AF risk score (dependent measure) via linear regression, adjusting for age, sex, and average wear time per day. Both daily step count and the CHARGE-AF score were treated as continuous variables. Secondary analyses tested for interactions between daily step count and sex and between daily step count and obesity (BMI≥30 kg/m2) in their association with the CHARGE-AF risk score, given that male sex and obesity are established independent risk factors for AF. For graphic purposes, we performed an analysis that examined the CHARGE-AF score, as a dependent variable, in high versus low physical activity groups (<7500 vs ≥7500 daily steps). High and low physical activity groups were determined by using the average step count of the study sample as the cutoff. The analysis was performed by using a Wilcoxon rank-sum test and adjusted for age. Additional analyses examined the association between self-reported physical activity (PAI score) and CHARGE-AF risk via linear regression, adjusting for age and sex. Sensitivity analyses were performed by using different thresholds for watch wear time (5 vs 10 hours/day) and number of active days (30 vs 60 vs 90 days). A 2-sided P value of <.05 was considered statistically significant. All of the analyses were performed by using R software package version 4.0.3 (R Foundation for Statistical Computing). ## Participant Characteristics We included 923 participants in this study. The mean age of participants was 53 (SD 9) years, 563 ($61\%$) participants were female, and 838 ($90.8\%$) participants identified as White. The median daily step count was 7227 (IQR 5699-8970), and the median watch wear time was 13.6 (IQR 12.4-14.7) hours per day for 324 (IQR 137-563) active days. The median CHARGE-AF risk score was $1\%$ for men and $0.5\%$ for women (Figure 1). Participants’ mean PAI score, which we calculated based on their self-reported physical activity levels, was 33.4 (SD 4.7; range 24-120; lower scores indicate less self-reported physical activity). Additional demographic characteristics can be seen in Table 1. Figure 1: Distribution of 5-year AF risk among participants. Most participants ($\frac{823}{923}$, $89.2\%$) had a 5-year AF risk of <$2.5\%$, as determined by the CHARGE-AF score. AF: atrial fibrillation; CHARGE: Cohorts for Heart and Aging Research in Genomic Epidemiology. TABLE_PLACEHOLDER:Table 1 ## Association Between Daily Step Count and 5-Year Risk of AF After adjusting for age, sex, and watch wear time, our primary analysis, in which linear regression was performed, showed that daily step count and the CHARGE-AF score were inversely associated. Every 1000 steps were associated with a $0.08\%$ lower CHARGE-AF risk score (Table 2). Figure 2 shows an age-adjusted analysis of high versus low physical activity levels. Participants who took ≥7500 steps daily had lower estimated CHARGE-AF risk scores than those of participants who took <7500 steps daily (mean $0.90\%$ vs mean $1.3\%$; $P \leq .001$). We observed significant interactions by sex and obesity; the association between the CHARGE-AF score and daily step count was stronger in men and participants with obesity. The CHARGE-AF risk score was $0.14\%$ and $0.05\%$ lower for every 1000 steps in men and women, respectively (interaction: $P \leq .001$). Similarly, the CHARGE-AF score was $0.10\%$ and $0.03\%$ lower for every 1000 steps in individuals with obesity and individuals without obesity, respectively (interaction: $P \leq .001$). Participants’ self-reported physical activity levels, as determined by the PAI score, were not associated with the CHARGE-AF score. Sensitivity analyses were conducted to investigate the association between daily step count and the CHARGE-AF score, using different device wear thresholds. The watch wear cutoff was increased to ≥60 days and ≥90 days from the cutoff of ≥30 days used in primary analysis. In a separate analysis, the wear time threshold was increased to 10 hours from the threshold of 5 hours used in the primary model. The inverse association between daily step count and the CHARGE-AF risk score remained significant in these subgroups (Tables S1 and S2 in Multimedia Appendix 1). ## Main Findings In our observational cross-sectional analysis of eFHS participants, the 5-year AF risk, as predicted by the CHARGE-AF score, was low ($0.5\%$ in women and $1\%$ in men), and the score was $0.08\%$ lower for every 1000 steps in a model adjusted for age, sex, and wear time. Interactions were significant for sex ($P \leq .001$) and obesity ($P \leq .001$); in men and participants with obesity, the association between the CHARGE-AF score and daily step count was stronger. Finally, self-reported physical activity, as determined by the PAI score, was not associated with the CHARGE-AF score. A graphic abstract that presents our main findings is presented in Multimedia Appendix 2. ## Relation Between Physical Activity and AF The overall benefits of increased physical activity have been well documented, including reductions in all-cause mortality, CVD-related mortality, and overall CVD risk [12-15,27,28]. The relation between physical activity and the risk of AF is more complex. Studies that examined incident AF and physical activity suggested an increased AF risk with vigorous physical activity but a protective benefit of low to moderate physical activity against AF [29-33]. These studies however relied on self-reported physical activity [29-33]. The association between future AF risk and objectively measured physical activity has been investigated in both short-term accelerometer use and the long-term use of implantable devices (≥25 months). AF risk was either estimated with the CHARGE-AF score or calculated after monitoring for incident AF [17-20]. All of these methods detected an association between lower physical activity levels and a higher risk of AF, but these studies have limitations. In studies that used research-grade accelerometers, which are not commercially available, the device wear time was ≤7 days, and it may not have reflected an individual’s habitual, long-term physical activity lifestyle [17,18]. Additionally, these studies categorized physical activity into levels, such as “light,” “moderate,” or “vigorous.” This methodology aligns with the recommendations from public health organizations, which currently recommend ≥150 minutes of moderate physical activity per week or ≥75 minutes of vigorous physical activity per week. Although the use of step counts may result in the overestimation or underestimation of intentional physical activity, step count targets may be more understandable to and practical for the general population than the abstract concepts of moderate and vigorous levels of physical activity. Additionally, evidence is evolving to suggest that ≥7000 daily steps in adults may be equivalent to the current public health recommendation of ≥150 minutes of moderate physical activity per week or ≥75 minutes of vigorous physical activity per week [34]. Studies that use invasive devices, such as implantable loop recorders or implantable cardioverter-defibrillators, are less applicable to the general population, and such devices are not regularly used [19,20]. Our study may offer a more accurate window into a participant’s overall physical activity lifestyle and routine. As such, monitoring daily step count for a longer period of time with a commercially available noninvasive device may provide a more real-life reflection of the relation between physical activity and the estimated risk of AF. Additionally, our use of daily step count instead of a physical activity level (eg, moderate to vigorous physical activity) may provide a clearer and more practical target for AF risk reduction in the future. Our study also demonstrated that sex and obesity may modify the association between AF risk and daily step count, as a stronger association between AF risk and daily step count was noted in men and participants with obesity. The independently increased risk of AF in men and individuals with obesity may have contributed to the stronger association that we observed between step count and the CHARGE-AF score [35]. Finally, our study did not find evidence of an association between self-reported physical activity and predicted AF risk. This lack of association may be related to the inconsistent validity of self-reported physical activity [36,37]. Our findings are consistent with the lack of a significant association between self-reported physical activity and the predicted risk of CVD [15]. The biological mechanisms behind the inverse association between step count, as an objective measure of physical activity, and estimated AF risk may be embedded within the shared risk factors between CVD and AF. On a cellular level, increased step counts have been associated with lower chronic inflammation and cardiac biomarkers, such as lipoproteins, white blood cell count, troponin, and N-terminal pro–brain natriuretic peptide [38-40]. As such, physical activity has been associated with lower atherosclerosis burden, as evidenced by decreased carotid artery plaque, thickness, and stiffness, as well as lower coronary artery calcification scores [41-43]. Finally, higher step counts and physical activity levels have also been associated with lower blood pressure and lower rates of obesity, coronary heart disease, and heart failure, of which all are major risk factors for AF [27,44,45]. ## Study Limitations Our study has several limitations. First, this study is limited by its observational and cross-sectional design, precluding the ability to establish causality, establish temporality, or rule out residual confounding. Additionally, the cross-sectional design precludes prospective follow-ups for the occurrence of incident AF. As such, the use of the estimated CHARGE-AF risk (as opposed to incident AF) as the primary outcome limits interpretations of clinical significance. However, CHARGE-AF risk has been extensively validated in large data sets with good predictive performance for incident AF [46-48]. A second limitation is the absence of heart rate as a variable in our regression model; heart rate data were unavailable to be contemporarily correlated with step count at the time of analysis. The lack of concurrent heart rate data may limit the inference of the association between physical activity and AF risk, but it does not affect the association between objectively measured step count and estimated AF risk. A third limitation is that the majority of participants in this study ($$n = 838$$/923 ($90.8\%$)) were White, middle-aged or older (age: mean 52 years) who were presumably living in Massachusetts. Additionally, compared to the FHS participants who did not enroll in the eFHS, the participants in the eFHS were younger, had higher levels of education, and had fewer CVD risk factors [24]. Further, the accuracy of wearable devices among individuals with darker skin tones is unclear [49,50]. Given these limitations, the applicability of this study to the general population is limited. Additionally, we anticipate participation and observation bias due to the language and smartphone eligibility requirements, as well as inaccuracies in measures derived from wearable devices, as previously reported [51]. The inference of physical activity level by using step counts may result in the overestimation and underestimation of vigorous physical activity. ## Conclusions Increasing daily steps can be a practical, lifestyle-modifying method for reducing an individual’s AF risk. Future studies could investigate a dose-dependent relation between step count and AF risk and examine this relation in more ethnically diverse, racially diverse, and age-diverse populations. 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