Exploring the Relationship Between Depressive Symptoms and Cardiovascular-Kidney-Metabolic syndrome: Evidence from the CHARLS Database

Exploring the Relationship Between Depressive Symptoms and Cardiovascular-Kidney-Metabolic syndrome: Evidence from the CHARLS Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring the Relationship Between Depressive Symptoms and Cardiovascular-Kidney-Metabolic syndrome: Evidence from the CHARLS Database Yilin Pan, Jingru Bi, Long Feng, Xiaonan He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6267987/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic condition characterized by the interrelated pathophysiological interactions between metabolic risk factors, chronic kidney disease (CKD), and the cardiovascular system, leading to an increased incidence of adverse cardiovascular outcomes. Depression has been widely recognized as a major mental health disorder with a significant impact on both cardiovascular disease (CVD) and CKD. However, the relationship between depressive symptoms and CKM syndrome has not been thoroughly explored. This study aims to investigate the association between depressive symptoms and CKM syndrome. Methods: We analyzed baseline data from 2011 and 2015 CKM follow-up data from the China Health and Retirement Longitudinal Study (CHARLS) database. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression (CESD-10) scale, and CKM syndrome was classified according to the American Heart Association (AHA) staging system. Three logistic regression models were used to estimate the odds ratio (OR) and 95% confidence interval (CI) for the relationship between CESD-10 scores and CKM syndrome. Subgroup and interaction analyses were also performed to explore potential modifying factors. Results: A total of 1,804 participants were included in the analysis. After adjusting for demographic and health-related factors, depressive symptoms, as measured by CESD-10, were significantly associated with an increased risk of CKM syndrome. The association was stronger after adjusting for additional health-related factors. The quartile analysis showed that individuals in the highest CESD-10 quartile had a significantly increased risk of CKM syndrome, with an OR of 2.76 (95% CI: 1.79–4.26, P < 0.01) in the fully adjusted model. Additionally, restricted cubic spline (RCS) analysis confirmed a linear relationship between CESD-10 scores and CKM syndrome. Conclusions: This study identified a significant positive correlation between depressive symptoms and CKM syndrome, with the association becoming more pronounced after adjusting for demographic and health-related factors. Our findings suggest that depressive symptoms may serve as an independent risk factor for CKM syndrome, highlighting the importance of incorporating mental health interventions in the prevention and management of CKM. Further research is needed to explore the mechanisms through which depressive symptoms contribute to CKM syndrome and to validate the causal relationship between these factors. Biological sciences/Psychology Health sciences/Cardiology Health sciences/Endocrinology Health sciences/Medical research Depressive Symptoms Cardiovascular-Kidney-Metabolic Syndrome CESD-10 Scale Chronic Kidney Disease Cardiovascular Disease Figures Figure 1 Figure 2 Figure 3 1. Introduction Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disorder defined by the intricate interplay of metabolic risk factors, chronic kidney disease (CKD), and cardiovascular dysfunction, culminating in an elevated risk of adverse cardiovascular events [ 1 ] . The American Heart Association (AHA) classifies CKM syndrome into five stages: Stage 0 (no risk factors), Stage 1 (adipose tissue dysfunction, often presenting as overweight or abdominal obesity), Stage 2 (metabolic risk factors or CKD), Stage 3 (subclinical cardiovascular disease), and Stage 4 (clinical cardiovascular disease) [ 2 ] . A cross-sectional analysis of the National Health and Nutrition Examination Survey reveals that nearly 90% of U.S. adults qualify for CKM syndrome at Stage 1 or higher, with approximately 15% exhibiting advanced stages(Stage 3 or 4) [ 3 ] . Emerging evidence links CKM syndrome to diverse complications, including early cognitive decline, obstructive sleep apnea, and heightened cancer risk, highlighting its pervasive impact across metabolic, renal, and cardiovascular systems [ 4 – 6 ] .This complexity and prevalence underscore the urgent need for deeper insights into its pathogenesis and the development of targeted prevention and treatment strategies. Depression, a leading global cause of disability affecting approximately 280 million people, exerts a profound influence on physical health [ 7 ] . The Center for Epidemiologic Studies Depression (CESD) scale, valued for its simplicity, effectiveness, and independence from clinical diagnosis, is widely used in epidemiological studies, particularly among older adults [ 8 ] . Extensive research has established depression as a risk factor for both cardiovascular disease (CVD) and CKD [ 9 , 10 ] .However, its association with CKM syndrome—a condition integrating these interrelated pathologies—remains underexplored. This gap in knowledge presents a critical opportunity to investigate how depressive symptoms intersect with CKM syndrome, potentially unveiling novel pathways for its prevention and management. This study leverages data from the China Health and Retirement Longitudinal Study (CHARLS) to examine this relationship, aiming to provide fresh theoretical perspectives that enhance CKM syndrome intervention strategies. 2. Methods 2.1 Study population The data for this study were derived from the China Health and Retirement Longitudinal Study (CHARLS) database, which includes baseline data from 2011 and follow-up data on CKM syndrome from 2015 (available at: http://charls.pku.edu.cn/ ). CHARLS is a national cohort study focused on the adult population in China, with participants recruited from 450 villages and communities across 28 provinces, autonomous regions, and municipalities. The CHARLS project was approved by the Ethics Review Board of Peking University in 2008 (IRB00001052-11015), and all participants provided written informed consent prior to their involvement in the study. In the CHARLS study, all staff members received systematic professional training and conducted face-to-face interviews using standardized questionnaires [ 11 ] . The researchers collected venous blood samples from participants following a fasting period of more than 12 hours and performed routine blood tests. Whole blood samples were stored at 4°C, while the remaining samples were sent to a central laboratory for further analysis. Glucose, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels were measured using enzymatic colorimetric methods. Hemoglobin A1c (HbA1c) levels were determined via boronate affinity high-performance liquid chromatography. The flowchart (Fig. 1 ) illustrates the inclusion and exclusion criteria for this study. Participants aged 45 years or older were included, while individuals with missing data on age, gender, education level, marital status, residence, smoking history, alcohol consumption, CESD score, and CKM syndrome staging were excluded. Ultimately, a total of 1,804 participants were included in the analysis. 2.2 Measurements 2.2.1. Measurement of depressive symptoms In the CHARLS study, the CESD-10 scale was used to assess the risk of depressive symptoms [ 12 ] . The CESD-10 scale consists of 10 items, categorized into positively and negatively worded statements. Each item offers four response options: "Rarely or none," "Some," "Occasionally," and "Most or all of the time." For negatively worded items, the scores range from 0 (indicating "Rarely or none") to 3 (indicating "Most or all of the time"), while the scoring for positively worded items is reversed. The total score ranges from 0 to 30, with higher scores reflecting more severe depressive symptoms and an increased risk of depression. Based on prior research using the CESD-10 scale, a cutoff score of 10 was established to differentiate participants with depressive symptoms from those without [ 12 , 13 ] . 2.2.2. Definition of CKM syndrome stage According to the American Heart Association (AHA) classification of CKM syndrome and related studies, CKM syndrome is divided into the following stages: Stage 0, no risk factors; Stage 1–2, early stage; and Stage 3–4, late stage [ 2 , 3 ] . In this classification, very high-risk chronic kidney disease (CKD) (G4 or G5 CKD) and a high 10-year cardiovascular disease (CVD) risk, as predicted by the Framingham risk score, are considered equivalent to subclinical cardiovascular disease risk [ 14 ] . The estimated glomerular filtration rate (eGFR) is calculated using the Chinese Modification of Diet in Renal Disease (C-MDRD) equation and categorized into different CKD stages according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines [ 1 , 15 ] . A detailed classification of CKM syndrome, as outlined by the AHA, is presented in Supplementary Table 1. 2.2.3. Data collection This study collected a comprehensive set of data from participants, including demographic information (age, gender, education level, marital status), physical measurements (systolic blood pressure [SBP], diastolic blood pressure [DBP], waist circumference [WC], height, and weight), lifestyle factors (smoking and alcohol consumption), medical history and medication use (including hypertension, diabetes, liver disease, lung disease, cancer, etc.), and laboratory test results (such as HbA1c, total cholesterol [TC], triglycerides [TG], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], fasting blood glucose, platelets, blood urea nitrogen, serum creatinine, C-reactive protein, uric acid, etc.). By definition, individuals with a history of hypertension or those receiving treatment for hypertension, or participants with a baseline SBP ≥ 130 mmHg or DBP ≥ 80 mmHg, were classified as having hypertension. Similarly, individuals with a history of diabetes or those receiving treatment for diabetes, or participants with a baseline fasting blood glucose ≥ 7.0 mmol/L (126 mg/dL) or HbA1c ≥ 6.5%, were classified as having diabetes. Other medical conditions were determined through self-report. 2.2.4 Statistical analysis Continuous variables are presented as mean ± standard deviation (Mean ± SD); if the data do not follow a normal distribution, they are expressed as the median and interquartile range (IQR). Categorical variables are presented as percentages. The comparison of differences between groups was based on the type and distribution characteristics of the variables, using appropriate statistical methods, including one-way analysis of variance (One-way ANOVA), the Kruskal-Wallis H test, or the chi-square test. Three logistic regression models were employed to estimate the odds ratio (OR) and 95% confidence interval (CI) for the relationship between CESD-10 and CKM, with CESD-10 modeled as either a continuous variable (per IQR increment) or a categorical variable (quartiles). The models included: the unadjusted raw model (Model 1); a model adjusted for age, gender, education level, residence, and marital status (Model 2); and a model further adjusted for smoking status, alcohol consumption, body mass index (BMI), WC, SBP, DBP, TG, LDL-C, and fasting blood glucose (Model 3), with results presented as OR and 95% CI. To examine the potential nonlinear relationship between CESD-10 and the incidence of CKM, restricted cubic splines (RCS) were utilized to test possible nonlinear associations and to visualize the dose-response relationship. Additionally, subgroup and interaction analyses were conducted to explore the relationship between CESD-10 and CKM incidence across different demographic characteristics, health behaviors, and physical measurement indicators. The main analysis used product terms [CESD-10 × (interaction term)], and forest plots were generated. To assess the robustness of the results, two sensitivity analyses were conducted: the first involved multiple imputation for the missing CESD data; the second involved removing metabolic covariates (BMI, WC, SBP, DBP, TG, LDL, and fasting blood glucose) and then repeating the regression analysis.All statistical analyses were performed using R version 4.4.2, with two-sided P values < 0.05 considered statistically significant. 3. Results 3.1 Baseline characteristics A total of 1,804 participants were included in this study, of whom 45.9% were female. Participants were categorized based on the severity of CKM, and the detailed baseline characteristics are presented in Table 1. Notably, significant differences were observed across groups in terms of age, gender, education level, residence, smoking habits, alcohol consumption, BMI, WC, SBP, DBP, TG, LDL-C, and blood glucose levels ( P < 0.05). However, the differences in CESD scores between the groups were not statistically significant ( P = 0.784). Table 1: Baseline Characteristics of Participants Stratified by CKM Severity Level CKM:Zone CKM:Mild CKM:Severe P Age (median(IQR)) 52.5 (12.0) 56 (13.0) 59 (11.0) <0.01 Gender (%) Female 41.0 (89.1) 665.0 (92.4) 122.0 (11.8) <0.01 Male 5.0 (10.9) 55.0 (7.6) 916.0 (88.2) Education (%) Less than lower secondary education 41.0 (89.1) 651.0 (90.4) 897.0 (86.4) 0.04 Secondary or above 5.0 (10.9) 69.0 ( 9.6) 141.0 (13.6) Marital (%) Married 45.0 (97.8) 649.0 (90.1) 951.0 (91.6) 0.15 Non-married 1.0 (2.2) 71.0 ( 9.9) 87.0 ( 8.4) Residence (%) Rural 31.0 (67.4) 419.0 (58.2) 694.0 (66.9) <0.01 Urban 15.0 (32.6) 301.0 (41.8) 344.0 (33.1) Smoking (%) No 44.0 (95.7) 676.0 (93.9) 306.0 (29.5) <0.01 Yes 2.0 ( 4.3) 44.0 ( 6.1) 732.0 (70.5) Drinking (%) No 34.0 (73.9) 608.0 (84.4) 405.0 (39.0) <0.01 Yes 12.0 (26.1) 112.0 (15.6) 633.0 (61.0) BMI (median(IQR)) 20.7 (2.3) 24.1 (4.5) 23.2 (4.9) <0.01 WC (median(IQR)) 73.8 (7.3) 87.0 (12.6) 86.0 (14.6) <0.01 SBP(median(IQR)) 110.0 (11.6) 125.0 (27.5) 130.0 (26) <0.01 DBP(median(IQR)) 64.0 (9.4) 74.0 (15.5) 76.0 (16.5) <0.01 TG(median(IQR)) 75.2 (33.6) 118.0 (85.0) 102.0 (76.8) <0.01 LDL(median(IQR)) 111.0 (30.6) 117.0 (44.1) 112 (45.1) <0.01 Glucose(median(IQR)) 92.0 (7.8) 103.0 (16.7) 102.0 (18.9) <0.01 CESD (median(IQR)) 3.5 (9.0) 3.0 (6.0) 3.0 (6.0) 0.78 CESD_Q (%) Q1 14.0 (30.4) 211.0 (29.3) 313.0 (30.2) 0.07 Q2 9.0 (19.6) 174.0 (24.2) 218.0 (21.0) Q3 6.0 (13.0) 190.0 (26.4) 288.0 (27.7) Q4 17.0 (37.0) 145.0 (20.1) 219.0 (21.1) CESD per IQR (median(IQR)) 0.6 (1.5) 0.5 (1.0) 0.5 (1.0) 0.78 Abbreviations:BMI: Body mass index, CESD: Center for Epidemiologic Studies Depression Scale, CKM: Cardiovascular-Kidney-Metabolic, DBP: Diastolic Blood Pressure, IQR: Interquartile Range, LDL: Low-Density Lipoprotein, SBP: Systolic Blood Pressure, TG: Triglycerides, WC: Waist Circumference. 3.2 Relationship between Depressive Symptoms and CKM Syndrome Table 2 summarizes the association between CESD-10 and CKM syndrome, analyzed via three logistic regression models, treating CESD-10 as a continuous (per IQR increment) or quartile variable. In Model 1 (unadjusted), the association between CESD-10 and CKM did not reach statistical significance (OR = 0.97, 95% CI: 0.88–1.06, P = 0.47). In Model 2, after adjusting for basic demographic characteristics, CESD-10 per IQR increment was significantly positively associated with the risk of CKM (OR = 1.30, 95% CI: 1.14–1.47, P < 0.01). In Model 3, after further adjustment for additional health-related factors, the association between CESD-10 per IQR increment and the risk of CKM was strengthened (OR = 1.42, 95% CI: 1.23–1.65, P < 0.01). In the quartile analysis of CESD-10, the risk of CKM was significantly elevated in the Q4 group, with an OR of 2.17 (95% CI: 1.47–3.22, P < 0.01) in Model 2 and an OR of 2.76 (95% CI: 1.79–4.26, P < 0.01) in Model 3. Trend test results indicated a significant trend between CESD-10 quartiles and CKM ( P for trend < 0.01). Table 2: Association between depression symptoms and the incidence of CKM syndrome Model 1 P Model 2 P Model 3 P CESD-10 per IQR 0.97[0.88,1.06] 0.47 1.30[1.14,1.47] <0.01 1.42[1.23,1.65] <0.01 Quartiles of CESD-10 Q1 Ref Ref Ref Q2 0.96[0.74,1.26] 0.79 1.13[0.76,1.68] 0.55 1.32[0.86,2.04] 0.21 Q3 1.03[0.80,1.33] 0.81 1.03[0.70,1.52] 0.86 1.04[0.69,1.58] 0.85 Q4 1.01[0.77,1.32] 0.94 2.17[1.47,3.22] <0.01 2.76[1.79,4.26] <0.01 P for trend 0.88 <0.01 <0.01 Abbreviations: CESD: Center for Epidemiologic Studies Depression Scale, IQR: Interquartile Range. Fig. 2 illustrates the relationship between CESD scores and CKM syndrome, assessed using RCS. The analysis reveals a significant association between CESD and CKM syndrome (Chi-square = 22.84, P < 0.01). However, the nonlinear term for CESD did not reach statistical significance (Chi-square = 0.03, P = 0.87), indicating that the relationship between CESD and CKM syndrome is linear. To further investigate the relationship between depressive symptoms and the incidence of CKM syndrome, we conducted subgroup and interaction analyses based on different age groups, gender, education level, marital status, residence, smoking status, alcohol consumption, and BMI. According to the World Health Organization's recommendations, a BMI ≥ 23 is used as the cutoff for defining obesity in the Asian population [16] . The results (Fig. 3) reveal a significant interaction only between depressive symptoms and smoking status (interaction P = 0.01), with no significant interactions observed in other subgroups (interaction P > 0.05). The results of the two sensitivity analyses are consistent with the main findings in the text. After performing multiple imputation on the missing CESD data, regression analysis showed that each IQR increment, quartile grouping (Q4 vs Q1), and trend analysis of CESD were all significantly associated with CKM progression (all P <0.05). Furthermore, after removing metabolic covariates related to CKM, each IQR increment, quartile grouping, and trend analysis of CESD remained significantly associated with CKM progression (all P <0.05). These results further confirm the independent role of CESD in CKM incidence. The complete results of the sensitivity analyses are presented in Supplementary Table 2. 4. Discussion This study aimed to elucidate the association between depressive symptoms and CKM syndrome, leveraging baseline data from 2011 and follow-up data from 2015 in the CHARLS. While prior studies have extensively documented the associations between depression and individual components of CKM syndrome, such as CVD and CKD, our study is among the first to explore depression’s role in the integrated CKM framework. This holistic approach is crucial, as CKM syndrome represents a synergistic pathology that exceeds the sum of its parts, offering new insights into shared mechanisms and potential intervention targets. Our findings reveal a statistically significant link between depressive symptoms and CKM syndrome after adjusting for demographic and health-related factors, with evidence of a dose-response relationship. Initially, the unadjusted model showed no significant association between CESD-10 scores and CKM syndrome. However, adjusting for demographic variables revealed a significant positive correlation, with each IQR increment in CESD-10 linked to an elevated CKM risk. This association strengthened further upon incorporating health-related covariates, suggesting that depressive symptoms may serve as an independent risk factor for CKM syndrome. The RCS analysis corroborated a linear relationship, indicating that CKM risk rises progressively with worsening depressive symptoms. Biologically, depression may exacerbate cardiovascular and renal pathology through heightened sympathetic activity, elevated blood pressure, and systemic inflammation, while also accelerating CKD progression via tubular injury and fibrosis [9, 10] . Beyond its role as a standalone risk factor, depression may amplify disease burden through associated unhealthy lifestyle choices. These insights emphasize the critical role of mental health in the early detection and holistic management of CKM syndrome. Quartile analysis further reinforced this dose-response pattern, with the highest CESD-10 quartile (Q4) exhibiting a markedly increased CKM risk (OR: 2.17 in Model 2; 2.76 in Model 3). Several mechanisms may underlie this association. Depression disrupts metabolic, cardiovascular, and renal homeostasis by altering endocrine and immune pathways [2] . Chronic stress from depression activates the hypothalamic-pituitary-adrenal (HPA) axis, driving excessive cortisol release, which promotes hypertension, arteriosclerosis, and subsequent CVD and metabolic syndrome [17] . Prolonged cortisol exposure also burdens the kidneys, inducing tubular damage, fibrosis, and dysfunction [17] . Additionally, depression impairs endothelial function, hastening atherosclerosis through reduced vasodilatio, while inflammatory pathways—marked by elevated cytokines such as tumor necrosis factor-α and interleukin-6—further link depression to CVD and CKD progression [18, 19] . These interconnected mechanisms highlight depression’s dual role as both a risk factor and a catalyst for CKM syndrome progression, offering a novel lens on its pathophysiology. Clinically, these findings carry substantial implications. First, identifying depression as an independent CKM risk factor underscores the need to integrate psychological assessments into preventive strategies for cardiovascular, renal, and metabolic diseases. Second, the interplay between depression and health behaviors suggests a multifaceted approach to patient care, accounting for lifestyle and physiological factors. Third, the CESD-10 scale’s simplicity and efficacy make it a practical tool for population-wide screening, enabling early identification of at-risk individuals and timely mental health interventions to mitigate CKM risk. Despite its contributions, this study has limitations. The reliance on a Chinese cohort may limit generalizability, necessitating validation across diverse populations and cultural contexts to account for regional variations in CKM risk factors and depression prevalence. Although we adjusted for numerous confounders, unmeasured variables—such as genetic predisposition, dietary habits, or physical activity—could influence the observed associations, potentially overestimating or underestimating the true effect of depressive symptoms. Additionally, the self-reported nature of the CESD-10 scale introduces potential subjective bias, which may lead to misclassification of depressive symptoms and affect the precision of our estimates. Future research should incorporate objective diagnostic tools, such as clinical interviews, and explore longitudinal designs to better elucidate causal pathways and refine CKM prevention strategies. 5. Conclusion This study identified a significant positive correlation between depressive symptoms and CKM syndrome, with the association becoming more pronounced after adjusting for demographic and health behavior factors. Our findings offer a new perspective on the prevention and treatment strategies for CKM syndrome, highlighting the potential value of mental health interventions in reducing CKM risk. By bridging the gap between mental health and the emerging CKM syndrome framework, our findings provide a pioneering perspective on the prevention and management of this multifaceted condition. Future research should further investigate the mechanisms through which depressive symptoms contribute to CKM syndrome and validate the causal relationship between these factors. Abbreviations AHA American Heart Association BMI Body mass index CESD Center for Epidemiologic Studies Depression CHARLS China Health and Retirement Longitudinal Study CI Confidence interval CKD Chronic kidney disease CKM Cardiovascular-Kidney-Metabolic C-MDRD Chinese Modification of Diet in Renal Disease CVD Cardiovascular disease DBP Diastolic blood pressure eGFR estimated glomerular filtration rate HbA1c Hemoglobin A1c HDL-C High-density lipoprotein cholesterol IQR Interquartile range KDIGO Kidney Disease:Improving Global Outcomes LDL-C Low-density lipoprotein cholesterol OR Odds ratio RCS Restricted cubic splines SBP Systolic blood pressure SD Standard deviation TC Total cholesterol TG Triglycerides WC Waist circumference Declarations Availability of Data and Materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Author Contributions YP analyzed and interpreted the data and was a major contributor in writing the manuscript. JB performed the creation of figures and tables and contributed to the review and editing of the manuscript. LF conducted data validation and verification. XH supervised the project, provided critical revisions to the manuscript, and secured the funding. All authors read and approved the final manuscript. Ethics Approval and Consent to Participate This study was approved by the Ethics Review Board of Peking University (IRB00001052-11015). All participants provided written informed consent prior to their involvement in the study. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Clinical trial number: not applicable. Consent for Publication Not applicable Acknowledgment Not applicable Funding This research was funded by the National Natural Science Foundation of China, grant number 62272327. The grant provider did not participate in the design, execution, or reporting of the study. Conflict of Interest The authors declare that they have no competing interests. References Correction to. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. Circulation 149 (13), e1023 (2024). Ndumele, C. E. et al. A Synopsis of the Evidence for the Science and Clinical Management of Cardiovascular-Kidney-Metabolic (CKM) Syndrome: A Scientific Statement From the American Heart Association. Circulation 148 (20), 1636–1664 (2023). Aggarwal, R., Ostrominski, J. W. & Vaduganathan, M. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011–2020. Jama 331 (21), 1858–1860 (2024). Ben Assayag, E. et al. Type 2 Diabetes Mellitus and Impaired Renal Function Are Associated With Brain Alterations and Poststroke Cognitive Decline. Stroke 48 (9), 2368–2374 (2017). Coughlin, S. R., Mawdsley, L., Mugarza, J. A., Calverley, P. 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Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J. Am. Soc. Nephrol. 17 (10), 2937–2944 (2006). Appropriate body-mass. index for Asian populations and its implications for policy and intervention strategies. Lancet 363 (9403), 157–163 (2004). Wong, M. L. et al. Pronounced and sustained central hypernoradrenergic function in major depression with melancholic features: relation to hypercortisolism and corticotropin-releasing hormone. Proc. Natl. Acad. Sci. U S A . 97 (1), 325–330 (2000). Greaney, J. L., Saunders, E. F. H., Santhanam, L. & Alexander, L. M. Oxidative Stress Contributes to Microvascular Endothelial Dysfunction in Men and Women With Major Depressive Disorder. Circ. Res. 124 (4), 564–574 (2019). Shao, M. et al. Depression and cardiovascular disease: Shared molecular mechanisms and clinical implications. Psychiatry Res. 285 , 112802 (2020). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6267987","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":447820961,"identity":"ad5ac121-9367-4ed0-976b-a25c45ac3249","order_by":0,"name":"Yilin Pan","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yilin","middleName":"","lastName":"Pan","suffix":""},{"id":447820962,"identity":"843b1817-59c6-434d-b2e1-e5dfa221dda3","order_by":1,"name":"Jingru Bi","email":"","orcid":"","institution":"Nankai University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jingru","middleName":"","lastName":"Bi","suffix":""},{"id":447820963,"identity":"fc614ceb-cd71-4515-9c0e-2b8229062647","order_by":2,"name":"Long Feng","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Feng","suffix":""},{"id":447820966,"identity":"a7a7e21f-4d38-4e0e-ba83-0eb5e42eeb8e","order_by":3,"name":"Xiaonan He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACAyCWALPYGxsffCBBC5DiOdxsOIM0LRLpbdIcxGgxl8g9eONDzR8585kPG6QZGOzkdBsIaLHsOZdsOeOYgbHM7cQG4wKGZGOzA4QcdrzHTJq3wSBxhnRiQ/IMhgOJ2whqOcwD1SJ5sOEwD1Fa4LZIMDY2E6flzBljoF+MjSV4EpsZZxgQ45cbOYbAEJOTk2A//vzHhwo7OYJa0E0gTfkoGAWjYBSMAhwAAC+wP820xoQDAAAAAElFTkSuQmCC","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaonan","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-03-20 09:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6267987/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6267987/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82136399,"identity":"4af92292-a3b0-489a-beb6-de2686bf5015","added_by":"auto","created_at":"2025-05-07 06:12:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":161716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInclusion and exclusion criteria flowchart of this study.\u003c/strong\u003e Abbreviations: CESD:Center for Epidemiologic Studies Depression, CKM:Cardiovascular-Kidney-Metabolic.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6267987/v1/7eeb892b9d2110d169f49523.png"},{"id":82137804,"identity":"ceb6e8f9-e35f-412e-88ac-72ce816ff0c0","added_by":"auto","created_at":"2025-05-07 06:20:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123620,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between Depressive Symptoms and CKM Syndrome: Restricted Cubic Spline Analysis\u003c/strong\u003eAbbreviations: CESD:Center for Epidemiologic Studies Depression; CI: Confidence Interval; OR: Odds Ratio.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6267987/v1/d513abcc933ed61a82d3493c.png"},{"id":82136401,"identity":"3e2e5997-1d3b-486d-9aa9-1a4a00dc17c4","added_by":"auto","created_at":"2025-05-07 06:12:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":218850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of stratified analysis of the association of Depressive Symptoms with the risk of CKM Syndrome.\u003c/strong\u003e Abbreviations:BMI: Body Mass Index,CI:confidence intervals, OR:odds ratio.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6267987/v1/c2b5dd692eccb614b158db47.png"},{"id":94469731,"identity":"20dfc738-7fc4-4bbc-95f8-1464e228b273","added_by":"auto","created_at":"2025-10-27 15:30:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1565462,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6267987/v1/bcfa4b9d-58f3-4a1c-ac81-9eea06766aa7.pdf"},{"id":82137803,"identity":"eb5ae523-67db-4f95-9d3d-85474f56ec86","added_by":"auto","created_at":"2025-05-07 06:20:40","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17965,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6267987/v1/2aef142be07cb934896366ef.docx"},{"id":82136404,"identity":"c0c96f61-6e56-4411-b625-023de1b94db5","added_by":"auto","created_at":"2025-05-07 06:12:40","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10071,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6267987/v1/aaa562b0bd70ece9d71e4390.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Relationship Between Depressive Symptoms and Cardiovascular-Kidney-Metabolic syndrome: Evidence from the CHARLS Database","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disorder defined by the intricate interplay of metabolic risk factors, chronic kidney disease (CKD), and cardiovascular dysfunction, culminating in an elevated risk of adverse cardiovascular events\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The American Heart Association (AHA) classifies CKM syndrome into five stages: Stage 0 (no risk factors), Stage 1 (adipose tissue dysfunction, often presenting as overweight or abdominal obesity), Stage 2 (metabolic risk factors or CKD), Stage 3 (subclinical cardiovascular disease), and Stage 4 (clinical cardiovascular disease)\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. A cross-sectional analysis of the National Health and Nutrition Examination Survey reveals that nearly 90% of U.S. adults qualify for CKM syndrome at Stage 1 or higher, with approximately 15% exhibiting advanced stages(Stage 3 or 4)\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Emerging evidence links CKM syndrome to diverse complications, including early cognitive decline, obstructive sleep apnea, and heightened cancer risk, highlighting its pervasive impact across metabolic, renal, and cardiovascular systems\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.This complexity and prevalence underscore the urgent need for deeper insights into its pathogenesis and the development of targeted prevention and treatment strategies.\u003c/p\u003e \u003cp\u003eDepression, a leading global cause of disability affecting approximately 280\u0026nbsp;million people, exerts a profound influence on physical health\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The Center for Epidemiologic Studies Depression (CESD) scale, valued for its simplicity, effectiveness, and independence from clinical diagnosis, is widely used in epidemiological studies, particularly among older adults\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Extensive research has established depression as a risk factor for both cardiovascular disease (CVD) and CKD\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.However, its association with CKM syndrome\u0026mdash;a condition integrating these interrelated pathologies\u0026mdash;remains underexplored. This gap in knowledge presents a critical opportunity to investigate how depressive symptoms intersect with CKM syndrome, potentially unveiling novel pathways for its prevention and management. This study leverages data from the China Health and Retirement Longitudinal Study (CHARLS) to examine this relationship, aiming to provide fresh theoretical perspectives that enhance CKM syndrome intervention strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThe data for this study were derived from the China Health and Retirement Longitudinal Study (CHARLS) database, which includes baseline data from 2011 and follow-up data on CKM syndrome from 2015 (available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://charls.pku.edu.cn/\u003c/span\u003e\u003cspan address=\"http://charls.pku.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). CHARLS is a national cohort study focused on the adult population in China, with participants recruited from 450 villages and communities across 28 provinces, autonomous regions, and municipalities. The CHARLS project was approved by the Ethics Review Board of Peking University in 2008 (IRB00001052-11015), and all participants provided written informed consent prior to their involvement in the study. In the CHARLS study, all staff members received systematic professional training and conducted face-to-face interviews using standardized questionnaires\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe researchers collected venous blood samples from participants following a fasting period of more than 12 hours and performed routine blood tests. Whole blood samples were stored at 4\u0026deg;C, while the remaining samples were sent to a central laboratory for further analysis. Glucose, total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) levels were measured using enzymatic colorimetric methods. Hemoglobin A1c (HbA1c) levels were determined via boronate affinity high-performance liquid chromatography.\u003c/p\u003e \u003cp\u003eThe flowchart (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) illustrates the inclusion and exclusion criteria for this study. Participants aged 45 years or older were included, while individuals with missing data on age, gender, education level, marital status, residence, smoking history, alcohol consumption, CESD score, and CKM syndrome staging were excluded. Ultimately, a total of 1,804 participants were included in the analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurements\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Measurement of depressive symptoms\u003c/h2\u003e \u003cp\u003eIn the CHARLS study, the CESD-10 scale was used to assess the risk of depressive symptoms\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The CESD-10 scale consists of 10 items, categorized into positively and negatively worded statements. Each item offers four response options: \"Rarely or none,\" \"Some,\" \"Occasionally,\" and \"Most or all of the time.\" For negatively worded items, the scores range from 0 (indicating \"Rarely or none\") to 3 (indicating \"Most or all of the time\"), while the scoring for positively worded items is reversed. The total score ranges from 0 to 30, with higher scores reflecting more severe depressive symptoms and an increased risk of depression. Based on prior research using the CESD-10 scale, a cutoff score of 10 was established to differentiate participants with depressive symptoms from those without\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Definition of CKM syndrome stage\u003c/h2\u003e \u003cp\u003eAccording to the American Heart Association (AHA) classification of CKM syndrome and related studies, CKM syndrome is divided into the following stages: Stage 0, no risk factors; Stage 1\u0026ndash;2, early stage; and Stage 3\u0026ndash;4, late stage\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In this classification, very high-risk chronic kidney disease (CKD) (G4 or G5 CKD) and a high 10-year cardiovascular disease (CVD) risk, as predicted by the Framingham risk score, are considered equivalent to subclinical cardiovascular disease risk\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The estimated glomerular filtration rate (eGFR) is calculated using the Chinese Modification of Diet in Renal Disease (C-MDRD) equation and categorized into different CKD stages according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. A detailed classification of CKM syndrome, as outlined by the AHA, is presented in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Data collection\u003c/h2\u003e \u003cp\u003eThis study collected a comprehensive set of data from participants, including demographic information (age, gender, education level, marital status), physical measurements (systolic blood pressure [SBP], diastolic blood pressure [DBP], waist circumference [WC], height, and weight), lifestyle factors (smoking and alcohol consumption), medical history and medication use (including hypertension, diabetes, liver disease, lung disease, cancer, etc.), and laboratory test results (such as HbA1c, total cholesterol [TC], triglycerides [TG], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], fasting blood glucose, platelets, blood urea nitrogen, serum creatinine, C-reactive protein, uric acid, etc.). By definition, individuals with a history of hypertension or those receiving treatment for hypertension, or participants with a baseline SBP\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg or DBP\u0026thinsp;\u0026ge;\u0026thinsp;80 mmHg, were classified as having hypertension. Similarly, individuals with a history of diabetes or those receiving treatment for diabetes, or participants with a baseline fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L (126 mg/dL) or HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, were classified as having diabetes. Other medical conditions were determined through self-report.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD); if the data do not follow a normal distribution, they are expressed as the median and interquartile range (IQR). Categorical variables are presented as percentages. The comparison of differences between groups was based on the type and distribution characteristics of the variables, using appropriate statistical methods, including one-way analysis of variance (One-way ANOVA), the Kruskal-Wallis H test, or the chi-square test. Three logistic regression models were employed to estimate the odds ratio (OR) and 95% confidence interval (CI) for the relationship between CESD-10 and CKM, with CESD-10 modeled as either a continuous variable (per IQR increment) or a categorical variable (quartiles). The models included: the unadjusted raw model (Model 1); a model adjusted for age, gender, education level, residence, and marital status (Model 2); and a model further adjusted for smoking status, alcohol consumption, body mass index (BMI), WC, SBP, DBP, TG, LDL-C, and fasting blood glucose (Model 3), with results presented as OR and 95% CI. To examine the potential nonlinear relationship between CESD-10 and the incidence of CKM, restricted cubic splines (RCS) were utilized to test possible nonlinear associations and to visualize the dose-response relationship. Additionally, subgroup and interaction analyses were conducted to explore the relationship between CESD-10 and CKM incidence across different demographic characteristics, health behaviors, and physical measurement indicators. The main analysis used product terms [CESD-10 \u0026times; (interaction term)], and forest plots were generated. To assess the robustness of the results, two sensitivity analyses were conducted: the first involved multiple imputation for the missing CESD data; the second involved removing metabolic covariates (BMI, WC, SBP, DBP, TG, LDL, and fasting blood glucose) and then repeating the regression analysis.All statistical analyses were performed using R version 4.4.2, with two-sided P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 1,804 participants were included in this study, of whom 45.9% were female. Participants were categorized based on the severity of CKM, and the detailed baseline characteristics are presented in Table\u0026nbsp;1. Notably, significant differences were observed across groups in terms of age, gender, education level, residence, smoking habits, alcohol consumption, BMI, WC, SBP, DBP, TG, LDL-C, and blood glucose levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, the differences in CESD scores between the groups were not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.784).\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 1: Baseline Characteristics of Participants Stratified by CKM Severity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKM:Zone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKM:Mild\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKM:Severe\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (median(IQR))\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e52.5 (12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e56 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e59 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e41.0 (89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e665.0 (92.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e122.0 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.0 \u0026nbsp;(10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e55.0 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e916.0 (88.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eLess than lower secondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e41.0 (89.1)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e651.0 (90.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e897.0 (86.4)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.04\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSecondary or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e5.0 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e69.0 ( 9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e141.0 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e45.0 (97.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e649.0 (90.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e951.0 (91.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNon-married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.0 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e71.0 ( 9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e87.0 ( 8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e31.0 (67.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e419.0 (58.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e694.0 (66.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e15.0 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e301.0 (41.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e344.0 (33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e44.0 (95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e676.0 (93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e306.0 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.0 ( 4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e44.0 ( 6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e732.0 (70.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e34.0 (73.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e608.0 (84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e405.0 (39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e12.0 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e112.0 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e633.0 (61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (median(IQR))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e20.7 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e24.1 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e23.2 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWC (median(IQR))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e73.8 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e87.0 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e86.0 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSBP(median(IQR))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e110.0 (11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e125.0 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e130.0 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBP(median(IQR))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e64.0 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e74.0 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e76.0 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTG(median(IQR))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e75.2 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e118.0 (85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e102.0 (76.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLDL(median(IQR)) \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e111.0 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e117.0 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e112 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlucose(median(IQR)) \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e92.0 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e103.0 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e102.0 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCESD (median(IQR))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.5 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.0 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e3.0 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCESD_Q (%) \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e14.0 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e211.0 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e313.0 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e9.0 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e174.0 (24.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e218.0 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6.0 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e190.0 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e288.0 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e17.0 (37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e145.0 (20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e219.0 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCESD per IQR (median(IQR))\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.6 (1.5)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.5 (1.0)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.5 (1.0)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;0.78\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations:BMI: Body mass index, CESD: Center for Epidemiologic Studies Depression Scale, CKM: Cardiovascular-Kidney-Metabolic, DBP: Diastolic Blood Pressure, IQR: Interquartile Range, LDL: Low-Density Lipoprotein, SBP: Systolic Blood Pressure, TG: Triglycerides, WC: Waist Circumference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Relationship between Depressive Symptoms and CKM Syndrome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 summarizes the association between CESD-10 and CKM syndrome, analyzed via three logistic regression models, treating CESD-10 as a continuous (per IQR increment) or quartile variable. In Model 1 (unadjusted), the association between CESD-10 and CKM did not reach statistical significance (OR = 0.97, 95% CI: 0.88\u0026ndash;1.06, \u003cem\u003eP\u003c/em\u003e = 0.47). In Model 2, after adjusting for basic demographic characteristics, CESD-10 per IQR increment was significantly positively associated with the risk of CKM (OR = 1.30, 95% CI: 1.14\u0026ndash;1.47, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). In Model 3, after further adjustment for additional health-related factors, the association between CESD-10 per IQR increment and the risk of CKM was strengthened (OR = 1.42, 95% CI: 1.23\u0026ndash;1.65, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). In the quartile analysis of CESD-10, the risk of CKM was significantly elevated in the Q4 group, with an OR of 2.17 (95% CI: 1.47\u0026ndash;3.22, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) in Model 2 and an OR of 2.76 (95% CI: 1.79\u0026ndash;4.26, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) in Model 3. Trend test results indicated a significant trend between CESD-10 quartiles and CKM (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Association between depression symptoms and the incidence of CKM syndrome\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCESD-10 per IQR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.97[0.88,1.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.30[1.14,1.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.42[1.23,1.65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuartiles of CESD-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.96[0.74,1.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.13[0.76,1.68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.32[0.86,2.04]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.03[0.80,1.33]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1.03[0.70,1.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1.04[0.69,1.58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.01[0.77,1.32]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2.17[1.47,3.22]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2.76[1.79,4.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: CESD: Center for Epidemiologic Studies Depression Scale, IQR: Interquartile Range.\u003c/p\u003e\n\u003cp\u003eFig. 2 illustrates the relationship between CESD scores and CKM syndrome, assessed using RCS. The analysis reveals a significant association between CESD and CKM syndrome (Chi-square = 22.84, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). However, the nonlinear term for CESD did not reach statistical significance (Chi-square = 0.03, \u003cem\u003eP\u003c/em\u003e = 0.87), indicating that the relationship between CESD and CKM syndrome is linear.\u003c/p\u003e\n\u003cp\u003eTo further investigate the relationship between depressive symptoms and the incidence of CKM syndrome, we conducted subgroup and interaction analyses based on different age groups, gender, education level, marital status, residence, smoking status, alcohol consumption, and BMI. According to the World Health Organization\u0026apos;s recommendations, a BMI \u0026ge; 23 is used as the cutoff for defining obesity in the Asian population\u003csup\u003e[16]\u003c/sup\u003e. The results (Fig. 3) reveal a significant interaction only between depressive symptoms and smoking status (interaction \u003cem\u003eP\u003c/em\u003e = 0.01), with no significant interactions observed in other subgroups (interaction \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eThe results of the two sensitivity analyses are consistent with the main findings in the text. After performing multiple imputation on the missing CESD data, regression analysis showed that each IQR increment, quartile grouping (Q4 vs Q1), and trend analysis of CESD were all significantly associated with CKM progression (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Furthermore, after removing metabolic covariates related to CKM, each IQR increment, quartile grouping, and trend analysis of CESD remained significantly associated with CKM progression (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). These results further confirm the independent role of CESD in CKM incidence. The complete results of the sensitivity analyses are presented in Supplementary Table 2.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study aimed to elucidate the association between depressive symptoms and CKM syndrome, leveraging baseline data from 2011 and follow-up data from 2015 in the CHARLS. While prior studies have extensively documented the associations between depression and individual components of CKM syndrome, such as CVD and CKD, our study is among the first to explore depression\u0026rsquo;s role in the integrated CKM framework. This holistic approach is crucial, as CKM syndrome represents a synergistic pathology that exceeds the sum of its parts, offering new insights into shared mechanisms and potential intervention targets. Our findings reveal a statistically significant link between depressive symptoms and CKM syndrome after adjusting for demographic and health-related factors, with evidence of a dose-response relationship.\u003c/p\u003e\n\u003cp\u003eInitially, the unadjusted model showed no significant association between CESD-10 scores and CKM syndrome. However, adjusting for demographic variables revealed a significant positive correlation, with each IQR increment in CESD-10 linked to an elevated CKM risk. This association strengthened further upon incorporating health-related covariates, suggesting that depressive symptoms may serve as an independent risk factor for CKM syndrome. The RCS analysis corroborated a linear relationship, indicating that CKM risk rises progressively with worsening depressive symptoms. Biologically, depression may exacerbate cardiovascular and renal pathology through heightened sympathetic activity, elevated blood pressure, and systemic inflammation, while also accelerating CKD progression via tubular injury and fibrosis\u003csup\u003e[9, 10]\u003c/sup\u003e. Beyond its role as a standalone risk factor, depression may amplify disease burden through associated unhealthy lifestyle choices. These insights emphasize the critical role of mental health in the early detection and holistic management of CKM syndrome.\u003c/p\u003e\n\u003cp\u003eQuartile analysis further reinforced this dose-response pattern, with the highest CESD-10 quartile (Q4) exhibiting a markedly increased CKM risk (OR: 2.17 in Model 2; 2.76 in Model 3). Several mechanisms may underlie this association. Depression disrupts metabolic, cardiovascular, and renal homeostasis by altering endocrine and immune pathways\u003csup\u003e[2]\u003c/sup\u003e. Chronic stress from depression activates the hypothalamic-pituitary-adrenal (HPA) axis, driving excessive cortisol release, which promotes hypertension, arteriosclerosis, and subsequent CVD and metabolic syndrome\u003csup\u003e[17]\u003c/sup\u003e. Prolonged cortisol exposure also burdens the kidneys, inducing tubular damage, fibrosis, and dysfunction\u003csup\u003e[17]\u003c/sup\u003e. Additionally, depression impairs endothelial function, hastening atherosclerosis through reduced vasodilatio, while inflammatory pathways\u0026mdash;marked by elevated cytokines such as tumor necrosis factor-\u0026alpha; and interleukin-6\u0026mdash;further link depression to CVD and CKD progression\u003csup\u003e[18, 19]\u003c/sup\u003e. These interconnected mechanisms highlight depression\u0026rsquo;s dual role as both a risk factor and a catalyst for CKM syndrome progression, offering a novel lens on its pathophysiology.\u003c/p\u003e\n\u003cp\u003eClinically, these findings carry substantial implications. First, identifying depression as an independent CKM risk factor underscores the need to integrate psychological assessments into preventive strategies for cardiovascular, renal, and metabolic diseases. Second, the interplay between depression and health behaviors suggests a multifaceted approach to patient care, accounting for lifestyle and physiological factors. Third, the CESD-10 scale\u0026rsquo;s simplicity and efficacy make it a practical tool for population-wide screening, enabling early identification of at-risk individuals and timely mental health interventions to mitigate CKM risk.\u003c/p\u003e\n\u003cp\u003eDespite its contributions, this study has limitations. The reliance on a Chinese cohort may limit generalizability, necessitating validation across diverse populations and cultural contexts to account for regional variations in CKM risk factors and depression prevalence. Although we adjusted for numerous confounders, unmeasured variables\u0026mdash;such as genetic predisposition, dietary habits, or physical activity\u0026mdash;could influence the observed associations, potentially overestimating or underestimating the true effect of depressive symptoms. Additionally, the self-reported nature of the CESD-10 scale introduces potential subjective bias, which may lead to misclassification of depressive symptoms and affect the precision of our estimates. Future research should incorporate objective diagnostic tools, such as clinical interviews, and explore longitudinal designs to better elucidate causal pathways and refine CKM prevention strategies.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study identified a significant positive correlation between depressive symptoms and CKM syndrome, with the association becoming more pronounced after adjusting for demographic and health behavior factors. \u0026nbsp;Our findings offer a new perspective on the prevention and treatment strategies for CKM syndrome, highlighting the potential value of mental health interventions in reducing CKM risk. By bridging the gap between mental health and the emerging CKM syndrome framework, our findings provide a pioneering perspective on the prevention and management of this multifaceted condition. \u0026nbsp;Future research should further investigate the mechanisms through which depressive symptoms contribute to CKM syndrome and validate the causal relationship between these factors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAHA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Heart Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCESD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenter for Epidemiologic Studies Depression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHARLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChina Health and Retirement Longitudinal Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCKM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular-Kidney-Metabolic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eC-MDRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChinese Modification of Diet in Renal Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiastolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestimated glomerular filtration rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemoglobin A1c\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKDIGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKidney Disease:Improving Global Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRestricted cubic splines\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSBP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWaist circumference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYP analyzed and interpreted the data and was a major contributor in writing the manuscript. JB performed the creation of figures and tables and contributed to the review and editing of the manuscript. LF conducted data validation and verification. XH supervised the project, provided critical revisions to the manuscript, and secured the funding. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Review Board of Peking University (IRB00001052-11015). All participants provided written informed consent prior to their involvement in the study. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China, grant number 62272327. The grant provider did not participate in the design, execution, or reporting of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCorrection to. Cardiovascular-Kidney-Metabolic Health: A Presidential Advisory From the American Heart Association. \u003cem\u003eCirculation\u003c/em\u003e \u003cb\u003e149\u003c/b\u003e (13), e1023 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNdumele, C. E. et al. A Synopsis of the Evidence for the Science and Clinical Management of Cardiovascular-Kidney-Metabolic (CKM) Syndrome: A Scientific Statement From the American Heart Association. \u003cem\u003eCirculation\u003c/em\u003e \u003cb\u003e148\u003c/b\u003e (20), 1636\u0026ndash;1664 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAggarwal, R., Ostrominski, J. W. \u0026amp; Vaduganathan, M. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011\u0026ndash;2020. \u003cem\u003eJama\u003c/em\u003e \u003cb\u003e331\u003c/b\u003e (21), 1858\u0026ndash;1860 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen Assayag, E. et al. 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Metabolic syndrome and risk of cancer: a systematic review and meta-analysis. \u003cem\u003eDiabetes Care\u003c/em\u003e. \u003cb\u003e35\u003c/b\u003e (11), 2402\u0026ndash;2411 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal burden. of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e396\u003c/b\u003e (10258), 1204\u0026ndash;1222 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark, S. H. \u0026amp; Yu, H. Y. How useful is the center for epidemiologic studies depression scale in screening for depression in adults? An updated systematic review and meta-analysis(). \u003cem\u003ePsychiatry Res.\u003c/em\u003e \u003cb\u003e302\u003c/b\u003e, 114037 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrittanawong, C. et al. Association of Depression and Cardiovascular Disease. \u003cem\u003eAm. J. Med.\u003c/em\u003e \u003cb\u003e136\u003c/b\u003e (9), 881\u0026ndash;895 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShirazian, S. Depression in CKD: Understanding the Mechanisms of Disease. \u003cem\u003eKidney Int. Rep.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e (2), 189\u0026ndash;190 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, Y., Hu, Y., Smith, J. P., Strauss, J. \u0026amp; Yang, G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). \u003cem\u003eInt. J. Epidemiol.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e (1), 61\u0026ndash;68 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndresen, E. M., Malmgren, J. A., Carter, W. B. \u0026amp; Patrick, D. L. Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). \u003cem\u003eAm. J. Prev. Med.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (2), 77\u0026ndash;84 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, S., Wang, S. \u0026amp; Feng, X. L. Corrigendum to Multimorbidity, depressive symptoms and disability in activities of daily living amongst middle-aged and older Chinese: Evidence from the China Health and Retirement Longitudinal Study. [Journal of Affective Disorders 295 703\u0026ndash;710]. J Affect Disord. 2022;301:497. (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'Agostino, R. B. et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. \u003cem\u003eCirculation\u003c/em\u003e \u003cb\u003e117\u003c/b\u003e (6), 743\u0026ndash;753 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa, Y. C. et al. Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. \u003cem\u003eJ. Am. Soc. Nephrol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (10), 2937\u0026ndash;2944 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAppropriate body-mass. index for Asian populations and its implications for policy and intervention strategies. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e363\u003c/b\u003e (9403), 157\u0026ndash;163 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong, M. L. et al. Pronounced and sustained central hypernoradrenergic function in major depression with melancholic features: relation to hypercortisolism and corticotropin-releasing hormone. \u003cem\u003eProc. Natl. Acad. Sci. U S A\u003c/em\u003e. \u003cb\u003e97\u003c/b\u003e (1), 325\u0026ndash;330 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreaney, J. L., Saunders, E. F. H., Santhanam, L. \u0026amp; Alexander, L. M. Oxidative Stress Contributes to Microvascular Endothelial Dysfunction in Men and Women With Major Depressive Disorder. \u003cem\u003eCirc. Res.\u003c/em\u003e \u003cb\u003e124\u003c/b\u003e (4), 564\u0026ndash;574 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao, M. et al. Depression and cardiovascular disease: Shared molecular mechanisms and clinical implications. \u003cem\u003ePsychiatry Res.\u003c/em\u003e \u003cb\u003e285\u003c/b\u003e, 112802 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Depressive Symptoms, Cardiovascular-Kidney-Metabolic Syndrome, CESD-10 Scale, Chronic Kidney Disease, Cardiovascular Disease","lastPublishedDoi":"10.21203/rs.3.rs-6267987/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6267987/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eCardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic condition characterized by the interrelated pathophysiological interactions between metabolic risk factors, chronic kidney disease (CKD), and the cardiovascular system, leading to an increased incidence of adverse cardiovascular outcomes. Depression has been widely recognized as a major mental health disorder with a significant impact on both cardiovascular disease (CVD) and CKD. However, the relationship between depressive symptoms and CKM syndrome has not been thoroughly explored. This study aims to investigate the association between depressive symptoms and CKM syndrome.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe analyzed baseline data from 2011 and 2015 CKM follow-up data from the China Health and Retirement Longitudinal Study (CHARLS) database. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression (CESD-10) scale, and CKM syndrome was classified according to the American Heart Association (AHA) staging system. Three logistic regression models were used to estimate the odds ratio (OR) and 95% confidence interval (CI) for the relationship between CESD-10 scores and CKM syndrome. Subgroup and interaction analyses were also performed to explore potential modifying factors.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eA total of 1,804 participants were included in the analysis. After adjusting for demographic and health-related factors, depressive symptoms, as measured by CESD-10, were significantly associated with an increased risk of CKM syndrome. The association was stronger after adjusting for additional health-related factors. The quartile analysis showed that individuals in the highest CESD-10 quartile had a significantly increased risk of CKM syndrome, with an OR of 2.76 (95% CI: 1.79\u0026ndash;4.26, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in the fully adjusted model. Additionally, restricted cubic spline (RCS) analysis confirmed a linear relationship between CESD-10 scores and CKM syndrome.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThis study identified a significant positive correlation between depressive symptoms and CKM syndrome, with the association becoming more pronounced after adjusting for demographic and health-related factors. Our findings suggest that depressive symptoms may serve as an independent risk factor for CKM syndrome, highlighting the importance of incorporating mental health interventions in the prevention and management of CKM. Further research is needed to explore the mechanisms through which depressive symptoms contribute to CKM syndrome and to validate the causal relationship between these factors.\u003c/p\u003e","manuscriptTitle":"Exploring the Relationship Between Depressive Symptoms and Cardiovascular-Kidney-Metabolic syndrome: Evidence from the CHARLS Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:12:35","doi":"10.21203/rs.3.rs-6267987/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36cb143a-8aeb-415e-a2ea-43725e462c5f","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47665646,"name":"Biological sciences/Psychology"},{"id":47665647,"name":"Health sciences/Cardiology"},{"id":47665648,"name":"Health sciences/Endocrinology"},{"id":47665649,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-10-27T13:57:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 06:12:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6267987","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6267987","identity":"rs-6267987","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}