Circadian syndrome predicts all-cause mortality risks in adults aged ≥40 years: evidence from US and Chinese national population surveys

Circadian syndrome predicts all-cause mortality risks in adults aged ≥40 years: evidence from US and Chinese national population surveys | 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 Research Article Circadian syndrome predicts all-cause mortality risks in adults aged ≥40 years: evidence from US and Chinese national population surveys Wenlong Xu, Yingxuan Li, Yusheng Ma, Yifei Ruan, Xingqiao Chen, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6669087/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 Circadian syndrome (CircS), a condition characterized by circadian rhythm disruption due to modern lifestyle factors, have been identified as a stronger predictor for the development of cardiovascular disease (CVD). However, its association with all-cause mortality in middle-aged and elderly populations remains unclear. This study aimed to investigate the impact of CircS on all-cause mortality risk in middle-aged and elderly adults. Methods Using data from two population-based cohorts (China Health and Retirement Longitudinal Study [CHARLS, n = 12,106] and the National Health and Nutrition Examination Survey [NHANES, n = 17,096]), we defined CircS as MetS components combined with short sleep duration and depression. The primary outcome was all-cause mortality, which was assessed through standardized follow-up questionnaires. Cox proportional hazards models were employed to evaluate all-cause mortality risks. Results Among 29,202 participants, the prevalence of CircS was 27.70% (CHARLS) and 25.90% (NHANES). After full adjustment for covariates, CircS was independently associated with elevated risk of all-cause mortality in both cohorts (CHARLS: HR = 1.170, 95% CI 1.040–1.315; NHANES: HR = 1.151, 95% CI 1.054–1.258). Notably, this association was more significant in US patients with CircS who aged 40 to 59 years, who are drinkers, who have normal waist circumference. Conclusion CircS was independently associated with elevated risk of all-cause mortality in both Chinese and US adults aged ≥ 40 years, particularly in US patients with CircS who aged 40 to 59 years, who are drinkers, who have normal waist circumference. Endocrinology & Metabolism circadian syndrome CHARLS NHANES mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Circadian syndrome (CircS) represents an evolution of the metabolic syndrome (MetS) concept, incorporating additional circadian-related risk factors including chronic sleep deprivation and depressive symptoms 1 . With a prevalence exceeding 30% in middle-aged and older adults, CircS demonstrates age-dependent increases paralleling the rising trends of both MetS and sleep disorders 2 – 4 . Accumulating evidence from cross-sectional and prospective clinical studies has established has established CircS as a robust predictor of incident cardiometabolic diseases, including type 2 diabetes and obesity 1 . Importantly, recent cohort studies from both Chinese and US populations demonstrated that CircS has superior predictive value over metabolic syndrome (MetS) for cardiovascular disease (CVD) outcomes 5 – 7 . Actually, CircS not only increased the risks of obesity, diabetes, mood disorders, heart and blood pressure problems, and cancer, but also worsen existing health condition 1 . In addition, those serious adverse cardiovascular events, including myocardial infarction, sudden cardiac death, pulmonary embolism, limb ischemia, and aortic aneurysm rupture, all have pronounced circadian rhythmicity 8 . Although current evidence supports CircS as a risk factor for multimorbidity, its association with all-cause mortality in middle-aged and older adults remains to be elucidated in large-scale population-based cohorts. To address the critical gaps in understanding CircS-related mortality risks, this study leverages two nationally representative longitudinal datasets: the China Health and Retirement Longitudinal Study (CHARLS) and the National Health and Nutrition Examination Survey (NHANES). We aim to investigate the independent association between CircS and all-cause mortality in middle-aged and elderly populations, which is essential for developing CircS-targeted prevention strategies in global aging societies. Methods Study design and population The China Health and Retirement Longitudinal Study (CHARLS) 9 and National Health and Nutrition Examination Survey (NHANES) 10 were two large, nationally representative data conducted in China and the US, respectively. The details of the CHARLS and NHANES have been described elsewhere. Briefly, the CHARLS is an ongoing nationally representative survey in China which is longitudinally following-up people aged > 45 years. The NHANES is a nationally representative cross-sectional survey of the non-institutionalized US population with data collected in two-year cycles, conducting biennial assessments of health and nutritional status across individuals nationwide. The CHARLS and NHANES both collected information on demographic characteristics, medical history, prescription drug use, and laboratory testing. The CHARLS and NHANCES were approved by the Ethics Review Committees of Peking University and the Ethics Review Committee of the National Center for Health Statistics, respectively. Informed consent was obtained from each participant in these three cohorts. As such, no additional ethical approval was needed for this analysis. In this study, 2011 of CHARLS and 2005–2006 of NHANCES were regarded as the baseline. Ideally, the last survey was 2020 of CHARLS and 2017–2018 of NHANCES, respectively. In this analysis, we excluded participants aged < 40 years, missing values of MetS diagnosis and follow-up information, and self-reported pregnant, leaving 12,106 participants in CHARLS and 17,096 in NHANES. A flowchart of participant selection is provided in Fig. 1 . Exposure measure Depressive symptoms were assessed using the ten-question version of the Center for Epidemiologic Studies-Depression scale (CES-D) and the Patient Health Questionnaire (PHQ-9). Participants with a CES-D score of ≥ 10 and PHQ-9 score of ≥ 5 were defined as having depressive symptoms in CHARLS 11 and NHANCES 5 , respectively. MetS was defined using the harmonized criteria proposed in the joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention, National Heart, Lung, and Blood Institute, American Heart Association, World Heart Federation, International Atherosclerosis Society, and International Association for the Study of Obesity 12 (Table S1). Having ≥ 3 of the following components was defined as having MetS: (1) elevated waist circumference (CHARLS:≥80/85 cm in female/male, NHANCES:≥88/102 cm in female/male); (2) high blood pressure (Systolic ≥ 130 and/or diastolic ≥ 85 mmHg) or drug treatment for hypertension; (3) low high-density lipoprotein cholesterol (HDL-C) (< 40 mg dL − 1 in male and < 50 mg dL − 1 in female) or drug treatment for low HDL-C; (4) triglycerides (TG) (≥ 150 mg dL − 1 ) or drug treatment for high TG; or (5) elevated fasting glucose (≥ 100 mg/dL, drug treatment of elevated glucose is an alternate indicator). Circadian syndrome (CircS) was based on 6 components including short sleep (< 6 hours day − 1 ), depression and four components used to define MetS. A cut-off for CircS was set as ≥ 4 components. Outcome The primary outcome was all-cause mortality. Mortality status for participants were obtained by matching their records to the National Death Index (NDI) up to December 31, 2019 from NHANES 13 . Survival status data were obtained from the CHARLS. Time of death (years and months) came from the Exit and Verbal Autopsy Questionnaire of the CHARLS. Partially missing time-of-death data (CHARLS waves 3 and 4, 2015–2018) were extrapolated from the closest interview date of the last wave and known vital status at this interview and were determined as the mid of the time interval between two consecutive interviews 14 . Covariates At baseline, participants were asked whether they had been diagnosed by a doctor to have kidney disease, stroke or cardiac events (including heart attack, coronary heart disease, angina, congestive heart failure or other heart problems). We included the following covariates in the current study: age, sex, education (low, medium, high), smoking (current smokers, ex-smoker or nonsmoker) and alcohol consumption (nondrinker or drinker), kidney disease, stroke and cardiac events. For consistency among the CHARLS and NHANCES, marital status was divided into five categories: married, partnered, separated or divorced, unmarried and widowed. Education was classified into three levels: below high school, high school, and college or above. Smoking status was categorized as never smokers, ever smokers, and current smokers. Similarly, drinking status was categorized as drinkers and no-drinkers. In NHANCES, smokers were defined as participants who reported smoking at least 100 cigarettes during their lifetime, with former smokers defined as participants who reported smoking at least 100 cigarettes, but not currently smoking. Drinkers were defined as participants who drank at least 12 alcohol drinks in any given year 15 . Sleep duration was assessed by the question, “How much sleep do you usually get at night on weekdays or workdays?” In CHARLS, self-reported smoking status was categorized as never, former, and now. Drinkers were defined as participants who ever drank any alcohol last year. Sleep duration was assessed by the question, “During the past month, how many hours of actual sleep did you get at night (average hours for one night)?” Statistical Analysis Participant characteristics were calculated based on the presence or absence of CircS, continuous variables were expressed as mean [standard deviation (SD)] or median [interquartile range (IQR)], while categorical variables were expressed as number (percentage). The baseline characteristics were compared between the two CircS group, using t test or Kruskal–Wallis test as appropriate, and categorical baseline data were compared using the χ2 test or Fisher's exact test. The missing rates of covariates were summarized in Tables S2. The missing data of covariates were imputed using the multiple imputation with 5 replications. The proportional hazards assumption for each variable in the model was tested using the Schoenfeld residuals test, and no violations were found. In the test for multicollinearity (Table S3), the results showed that the variance inflation factor (VIF) for each covariate was less than 5, indicating that there was no evidence of significant multicollinearity between the covariates before Cox regression analysis. The Kaplan-Meier method was used to plot the survival curves associated with CircS. To analyze the association of baseline CircS status with the risks of all-cause mortality, Cox proportional hazard regression was used to calculate the hazard ratio (HR) and its 95% confidence interval (95% CI). Two models were fitted for the Cox regression using non-CircS participants as the reference. Model 1 was unadjusted. Model 2 was multivariable-adjusted controlling for age, sex, marital status, education, smoking status, drinking status, self-reported heart diseases, stroke, and kidney disease. To explore the association between CircS and all-cause mortality across different demographic characteristics, subgroup and interaction analyses were conducted among various age groups (< 60 vs. ≥60 years), sex, drinking statuses, self-reported heart, stroke, kidney disease. Statistical analyses were performed using IBM SPSS Statistics 27 program (IBM Corp., Armonk, NY, USA) and Stata 17 software (Stata Corp, College Station, TX, US). Statistical significance was set at a two-sided P value < 0.05. Results Baseline characteristics of the study population According to inclusion and exclusion criteria, 12106 participants from CHARLS (female: 52.3%, median age: 57 years), and 17096 from NHANCES (female: 49.4%, median age: 59 years) were included in the CircS status analyses. Baseline characteristics of these participants are presented in Table 1. In the CHARLS and NHANCES, CircS participants were older, more likely to be female, non-drinker, never smoker, self-reported heart, stroke and kidney diseases, less likely to be married or partnered and had lower education level than non-CircS participants. Table 1 The pooled baseline characteristics across circadian syndrome (CircS) status Variables CHARLS (n = 12,106) NHANES (n = 17,906) Total CircS (n = 3,349) Non-CircS (n = 8,757) P Total CircS (n = 4,420) Non-CircS (n = 12,676) P Age, years 57(51–64) 59(53–66) 57(50–64) < 0.001 60(49–69) 61(51–70) 59(48–69) < 0.001 Gender < 0.001 < 0.001 Female 6328(52.30%) 2276(68.00%) 4052(46.30%) 8437(49.40%) 2540(57.50%) 5897(46.50%) Male 5778(47.70%) 1073(32.00%) 4705(53.70%) 8659(50.60%) 1880(42.50%) 6779(53.50%) Education level < 0.001 < 0.001 Less than high school 10710(88.50%) 3065(91.50%) 7645(87.30%) 4561(26.70%) 1557(35.30%) 3004(23.70%) High school or equivalent 930(7.70%) 189(5.60%) 741(8.50%) 3862(22.60%) 1073(24.30%) 2789(22.00%) College or above 466(3.80%) 95(2.80%) 371(4.20%) 8655(50.70%) 1782(40.40%) 6873(54.30%) Marital status < 0.001 < 0.001 Married 10159(83.90%) 2686(80.20%) 7473(85.30%) 10016(58.60%) 2341(53.00%) 7675(60.60%) Partnered 557(4.60%) 146(4.40%) 411(4.70%) 765(4.50%) 179(4.10%) 586(4.60%) Widowed 1149(9.5%) 452(13.50%) 697(8.00%) 1883(11.00%) 612(13.90%) 1271(10.00%) Divorced/Separated 144(1.20%) 44(1.30%) 100(1.10%) 3062(17.90%) 910(20.60%) 2152(17.00%) Never married 97(0.80%) 21(0.60%) 76(0.90%) 1364(8.00%) 375(8.50%) 989(7.80%) Alcohol drinking < 0.001 < 0.001 Non-drinker 8048(66.50%) 2543(76.00%) 5505(62.90%) 4413(26.90%) 1391(32.70%) 3022(24.90%) Drinker 4051(33.50%) 805(24.00%) 3246(37.10%) 11976(73.10%) 2859(67.30%) 9117(75.10%) Smoking status < 0.001 < 0.001 Never smoker 7308(60.70%) 2363(70.70%) 4945(56.80%) 8707(51.00%) 2063(46.70%) 6644(52.50%) Former smoker 1023(8.50%) 296(8.90%) 727(8.40%) 5119(30.40%) 1412(32.00%) 3787(29.90%) Current smoker 3717(30.90%) 683(20.40%) 3034(34.80%) 3176(18.60%) 942(21.30%) 2234(17.60%) Self-reported chronic diseases Heart diseases 1365(11.30%) 628(18.90%) 737(8.50%) < 0.001 2106(12.40%) 839(19.20%) 1267(10.00%) < 0.001 Stroke 283(2.30%) 145(4.30%) 138(1.60%) < 0.001 914(5.40%) 360(8.20%) 554(4.40%) < 0.001 Kidney disease 655(5.50%) 217(6.50%) 438(5.00%) 0.001 703(4.10%) 322(7.30%) 381(3.00%) < 0.001 CHARLS, China Health and Retirement Longitudinal Study; NHANES, National Health and Nutrition Examination Survey; CircS, circadian syndrome. In the CircS status analyses, unweighted prevalence of CircS was 27.70% (CHARLS) and 25.90% (NHANES), the median follow-up periods were 9.0 years in the CHARLS and 7.9 years in the NHANES. A total of 3927 participants (1437 from CHARLS and 2490 from NHANCES) died during follow-up. Association of circadian syndrome status with all-cause mortality Kaplan-Meier survival curves demonstrated significantly higher cumulative mortality among participants with circadian syndrome (CircS) compared to non-CircS individuals in both cohorts (log-rank P < 0.05; Fig. 2). As displayed in Table 2, Cox proportional hazards models revealed consistent mortality risks associated with CircS across both Chinese and US populations. In the unadjusted models, the HRs (95% CIs) were 1.233 (1.104–1.378) and 1.358 (1.248–1.478) for the all-cause mortality in the Chinese and US adults with CircS. Consistent significant associations were observed in the multivariable Cox regression model that adjusted for confounders. After further adjustment for MetS status, CircS was associated with a 23.5% increase in all - cause mortality risk in CHARLS (HR:1.235, 95% CI:1.032–1.478) and a 18.2% increase in all-cause mortality risk in NHANES (HR:1.182, 95% CI:1.028–1.358) (Table 3). Stratification by CircS components revealed no significant mortality gradient (all P > 0.05; Table 4), implying threshold effects rather than dose-response relationships in CircS pathophysiology. Table 2 Association (Hazard Ratios and 95% CIs) between the circadian syndrome (CircS) and all-cause mortality in middle-aged and elderly population Variables Events (No.) Follow-up Duration (Person-Year) Incident Rate (Per 1000 Person-Year) Model 1 HR1 a (95% CI) P1 a Model 2 HR2 b (95% CI) P2 b CHARLS all-cause mortality 1437/12106 100212 14.34(13.62–15.10) Non-CircS 981/8757 72735 13.49(12.67–14.36) 1(ref.) 1(ref.) CircS 456/3349 27477 16.60(15.14–18.19) 1.233(1.104–1.378) < 0.001 1.170(1.040–1.315) 0.009 NHANES all-cause mortality 2490/17906 123307 20.19(19.42-21.00) Non-CircS 1708/12676 92086 18.55(17.69–19.45) 1(ref.) 1(ref.) CircS 782/4420 31221 25.05(23.35–26.87) 1.358(1.248–1.478) < 0.001 1.151(1.054–1.258) 0.002 a HR1 and P1 were unadjusted. b HR2 and P2 were adjusted for age, sex, education level, marital status, smoking status, drinking status, self-reported heart disease, stroke and kidney disease. Abbreviations: CHARLS, China Health and Retirement Longitudinal Study; CircS, circadian syndrome; HR, hazard ratio; CI, confidence interval; NHANES, National Health and Nutrition Examination Survey. Table 3 Association between circadian syndrome (CircS) and all-cause mortality outcome further adjusted for metabolic syndrome (MetS). Variables Events (No.) Follow-up Duration (Person-Year) Incident Rate (Per 1000 Person-Year) Model 2 HR1 a (95% CI) P1 a Model 2 + MetS HR2 b (95% CI) P2 b CHARLS all-cause mortality 1194/10252 85382 13.98(13.21–14.80) Non-CircS 793/7129 59588 13.31(12.41–14.27) 1(ref.) 1(ref.) CircS 401/3123 25794 15.55(14.10-17.15) 1.168(1.029–1.325) 0.017 1.235(1.032–1.478) 0.021 NHANES all-cause mortality 2041/13927 100369 20.34(19.47–21.24) Non-CircS 1295/9755 71103 18.21(17.25–19.23) 1(ref.) 1(ref.) CircS 746/4172 29266 25.49(23.73–27.39) 1.142(1.040–1.254) 0.006 1.182(1.028–1.358) 0.019 a HR1 and P1 were adjusted for age, sex, education level, marital status, smoking status, drinking status, self-reported heart disease, stroke and kidney disease. b HR2 and P2 were adjusted for age, sex, education level, marital status, smoking status, drinking status, self-reported heart disease, stroke, kidney disease and MetS. Abbreviations: CHARLS, China Health and Retirement Longitudinal Study; NHANES, National Health and Nutrition Examination Survey; CircS, circadian syndrome; MetS, metabolic syndrome. Table 4 Association (Hazard Ratios and 95% CIs) between the components of circadian syndrome (CircS) and all-cause mortality in middle-aged and elderly population Components of CircS Events (No.) Follow-up Duration (Person-Year) Incident Rate (Per 1000 Person-Year) Model 1 HR1 a (95% CI) P1 a Model 2 HR2 b (95% CI) P2 b CHARLS all-cause mortality 456/3349 27477 14.34(13.62–15.10) 4 257/1914 15683 16.39(14.50-18.52) 1(ref.) 1(ref.) 5 126/963 8043 15.67(13.16–18.65) 0.953(0.770–1.180) 0.659 0.983(0.793–1.218) 0.872 ≥ 6 73/462 3750 19.47(15.48–24.49) 1.191(0.918–1.544) 0.188 1.080(0.829–1.406) 0.569 NHANES all-cause mortality 782/4420 31221 25.05(23.35–26.87) 4 507/2914 20544 24.68(22.62–26.92) 1(ref.) 1(ref.) 5 218/1212 8493 25.67(22.48–29.31) 1.041(0.888–1.220) 0.619 1.037(0.884–1.217) 0.656 ≥ 6 57/294 2185 26.09(20.13–33.83) 1.046(0.795–1.375) 0.748 0.923(0.700-1.217) 0.570 a HR1 and P1 were unadjusted. b HR2 and P2 were adjusted for age, sex, education level, marital status, smoking status, drinking status, self-reported heart disease, stroke and kidney disease. Abbreviations: CHARLS, China Health and Retirement Longitudinal Study; CircS, circadian syndrome; HR, hazard ratio; CI, confidence interval; NHANES, National Health and Nutrition Examination Survey. Sensitivity analyses In both CHARLS and NHANES cohorts, the association between CircS and all-cause mortality remained consistent (all P-interaction > 0.05) across subgroups stratified by sex, pre-existing cardiovascular disease, stroke, kidney disease, MetS status, blood pressure levels, HDL cholesterol, triglycerides, and fasting glucose (Fig. 3–4). In the NHANES cohort, participants with CircS aged 40–59 years exhibited significantly elevated mortality risk (HR:1.565, 95% CI:1.265–1.936), whereas those ≥ 60 years showed attenuated risk (HR:1.087, 95% CI:0.986–1.198; P for interaction < 0.001) (Fig. 4). Among individuals with CircS, current drinkers exhibited 21% higher all-cause mortality (HR:1.213, 95% CI 1.088–1.353) compared to nondrinkers with CircS (HR:1.037, 95% CI 0.892–1.205) (Fig. 4). Participants with CircS who maintained normal waist circumference showed significantly higher all-cause mortality risk (HR:1.730, 95% CI 1.353–2.213) compared to those with CircS and elevated waist circumference (HR:1.188, 95% CI 1.065–1.325), with a statistically significant interaction (P for interaction = 0.005; Fig. 4). Discussion In this binational prospective analysis encompassing 29,202 adults aged ≥ 40 years (median follow-up: 9.0 years for CHARLS, 7.9 years for NHANES), we established that CircS was positively associated with the risk for all-cause mortality after multivariate adjustment. Notably, this association was more significant in US adults with CircS who aged 40 to 59 years, who are drinkers, who have normal waist circumference. Our finding reinforced CircS as a superior prognostic construct integrating short sleep and depression with metabolic pathophysiology in predicting risk of mortality. An interesting finding is that, as we know, two additional components, short sleep and depression, were added into the components of MetS to construct the CircS. We found that MetS with short sleep or depression was associated with the higher risk of all-cause mortality than MetS alone. If we only use MetS to prevent and predict all-cause mortality, CircS alone group will be missed. Those findings emphasize the rationale and importance of incorporating short sleep and depression into the MetS risk factor cluster and construct the Circs. Another interesting finding was no dose-dependent link between the number of CircS components and mortality risk. The lack of such a relationship indicating that current criteria of Circs might inadequately capture severity rating within individual components or stem from complex interplay among components. Thus, there is a need for the further development of relevant predictive modelling and risk stratification tools to more effectively identify at - risk populations. As the assessment of short sleep and depression is relatively easy and cheap, they should be routinely measured in combination of MetS in clinical settings to prevent mortality. Our stratified analyses revealed that the association between CircS and mortality risk varied significantly by age, waist circumference (WC), metabolic health status, sleep duration, depression, and alcohol use in different cohort. Notably, the strongest association was observed in adults aged 40–59 years with CircS in US populations, likely reflecting this group’s greater exposure to shift work and circadian disruption as the core working population. Intriguingly, CircS participants with normal WC exhibited higher mortality risk, potentially because the participants in this subgroup may have circadian disruption as their primary pathological driver rather than traditional metabolic dysfunction. Furthermore, alcohol consumption amplified mortality risk in CircS participants, likely through multiple pathways including circadian disruption, hepatic impairment, and enhanced oxidative stress 16 – 18 . These findings collectively suggest that mortality prevention in CircS should address sleep quality, depressive symptoms, and alcohol use alongside metabolic parameters. Although the mechanisms underlying the all-cause mortality of CircS and middle-aged and elderly adults have not been fully elucidated, several possible explanations may explain this association. As a core component of CircS, MetS drives multi-organ damage via insulin resistance, oxidative stress, and renin-angiotensin system activation. These processes accelerate the development of life-threatening comorbidities including type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), and stroke through endothelial dysfunction and chronic inflammation 19 , 20 . In addition, sleep duration can induce hypothalamic-pituitary-adrenal (HPA) axis hyperactivity and elevates cortisol secretion while decreased leptin. This neuroendocrine imbalance exacerbates cardiovascular and metabolic pathologies, ultimately increasing all-cause mortality risk in aging populations 21 – 23 . Furthermore, depression exacerbates CircS mortality risk might through the dual mechanisms. One is that depression also increase HPA hyperactivity, neuroimmune activation, and sympathoadrenal dysfunction. The other is that people with depression usually have unhealthy lifestyles, including physical inactivity, smoking, heavy alcohol consumption, and poor diet patterns, leading to an increased risk of developing MetS 24 , 25 . Therefore, these components interact bidirectionally, creating a self-reinforcing cycle, ultimately leading to increase all-cause mortality in middle- aged and elderly adults. Limitations This study has several limitations. Firstly, the observational nature of the study design precludes establishing a causal relationship between CircS and all-cause mortality. Although we adjusted for a wide range of covariates, residual confounding may still exist. Secondly, the data from the two cohorts were collected using different methods, which may introduce some heterogeneity. Although we tried to standardize the analysis as much as possible, differences in data collection procedures may have affected the results. Thirdly, the assessment of sleep duration relied on the participants’ self-reported, which might cause an information bias. Conclusion This binational cohort study demonstrated that CircS was independently associated with elevated risk of all-cause mortality in both Chinese and US adults aged ≥ 40 years. Notably, this association was more significant in US adults with CircS who aged 40 to 59 years, who are drinkers, who have normal waist circumference. Declarations Acknowledgements We thank the China Health and Retirement Longitudinal Study and National Health and Nutrition Examination Survey investigators, study teams and participants for making these data available for secondary analyses. Ethics approval and consent to participate The China Health and Retirement Longitudinal Study was approved by the Ethics Review Committee of Peking University. National Health and Nutrition Examination Survey was approved by the National Center for Health Statistics Research Ethics Review Board. Written informed consent was obtained from all participants. Funding This work was supported by the National Natural Science Foundation of China (grant 82470288 to YC), Guang Dong Basic and Applied Basic Research Foundation (grant 2023A1515010381 and 2022A1515220013 to YC), Natural Science Foundation of Jiangxi Province (grant 20232ACB216003 to YC), Foundation of NanFang Hospital of Southern Medical University (grant 2023CR011 to YC). The funder had no role in the study design, data collection and analysis, interpretation, or writing of the report. Disclosures The authors declare that they have no competing interests. Authors’ contributions Wenlong Xu : Conceptualization (equal); Data curation (lead); Formal analysis (lead); Methodology (equal); Project administration (lead); Resources (equal); Software (equal); Visualization (equal); Writing-original draft (lead); Writing-review & editing (equal). Yusheng Ma: Methodology (equal); Conceptualization (equal); Writing-review & editing (equal). Yingxuan Li: Conceptualization (equal); Writing-review & editing (equal). Yifei Ruan: Writing-review & editing (equal). Xingqiao Chen: Visualization (equal); Writing-review & editing (equal). Ziyang Ye: Writing-review & editing (equal). Huitong Li: Writing-review & editing (equal). Yuxin Yan: Writing-review & editing (equal). Xiaoyan Fang: Resources (equal); Software (equal); Visualization (equal); Jianping Bin: Conceptualization (equal); Investigation (equal); Xiaobo Huang: Project administration (equal); Supervision (equal); Writing-review & editing (equal); Yanmei Chen: Conceptualization (equal); Investigation (equal); Methodology (equal); Project administration (equal); Supervision (lead); Writing-review & editing (lead). All gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy. Data availability statement The data supporting the findings of this study are publicly available in the NHANES database (https://www.cdc.gov/nchs/nhanes/index.htm) and CHARLS database (https://charls.pku.edu.cn). Data analyzed during the study are available from the corresponding author upon reasonable request. References Zimmet P, Alberti KGMM, Stern N et al. The Circadian Syndrome: is the Metabolic Syndrome and much more!. J Intern Med . 2019;286(2):181-191 Li R, Li W, Lun Z et al. Prevalence of metabolic syndrome in Mainland China: a meta-analysis of published studies. BMC Public Health . 2016;16:296 Zhu X, Ding L, Zhang X, Wang H, Chen N. Association between physical frailty, circadian syndrome and cardiovascular disease among middle-aged and older adults: a longitudinal study. BMC Geriatr . 2024;24(1):199 Van Cauter E, Leproult R, Plat L. Age-related changes in slow wave sleep and REM sleep and relationship with growth hormone and cortisol levels in healthy men. 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Cardiovasc Diabetol . 2025;24(1):38 Li C, Wang L, Ding L, Zhou Y. Determinants and inequities in healthy working life expectancy in China. Nat Med . 2024;30(11):3318-3326 Chen F, Du M, Blumberg JB et al. Association Among Dietary Supplement Use, Nutrient Intake, and Mortality Among U.S. Adults: A Cohort Study. Ann Intern Med . 2019;170(9):604-613 Gao H, Jiang Y, Zeng G et al. Cell-to-cell and organ-to-organ crosstalk in the pathogenesis of alcohol-associated liver disease. eGastroenterology . 2024;2(4) Meyrel M, Rolland B, Geoffroy PA. Alterations in circadian rhythms following alcohol use: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry . 2020;99:109831 Aberg F, Byrne CD, Pirola CJ, Mannisto V, Sookoian S. Alcohol consumption and metabolic syndrome: Clinical and epidemiological impact on liver disease. J Hepatol . 2023;78(1):191-206 Mottillo S, Filion KB, Genest J et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol . 2010;56(14):1113-32 Hsu C, Hou C, Hsu W, Tain Y. Early-Life Origins of Metabolic Syndrome: Mechanisms and Preventive Aspects. Int J Mol Sci . 2021;22(21) Shen L, Li B, Gou W et al. Trajectories of Sleep Duration, Sleep Onset Timing, and Continuous Glucose Monitoring in Adults. JAMA Netw Open . 2025;8(3):e250114 Itani O, Jike M, Watanabe N, Kaneita Y. Short sleep duration and health outcomes: a systematic review, meta-analysis, and meta-regression. Sleep Med . 2017;32:246-256 Liang YY, Chen J, Peng M et al. Association between sleep duration and metabolic syndrome: linear and nonlinear Mendelian randomization analyses. J Transl Med . 2023;21(1):90 Zhang M, Chen J, Yin Z, Wang L, Peng L. The association between depression and metabolic syndrome and its components: a bidirectional two-sample Mendelian randomization study. Transl Psychiatry . 2021;11(1):633 Meng R, Yu C, Liu N et al. Association of Depression With All-Cause and Cardiovascular Disease Mortality Among Adults in China. JAMA Netw Open . 2020;3(2):e1921043 Additional Declarations The authors declare no competing interests. Supplementary Files Supplementarymaterialsnew.docx Supplementary materials floatimage1.jpeg Circadian syndrome is independently associated with an increased risk of all-cause mortality in middle-aged and elderly adults in CHARLS and NHANES. Models adjusted for age, sex, education level, marital status, smoking status, drinking status, self-reported heart disease, stroke and kidney disease. Primary outcome: all-cause mortality. CHARLS, China Health and Retirement Longitudinal Study; CircS, circadian syndrome; HR, hazard ratio; CI, confidence interval; NHANES, National Health and Nutrition Examination Survey. 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. 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University","correspondingAuthor":false,"prefix":"","firstName":"Yingxuan","middleName":"","lastName":"Li","suffix":""},{"id":457057521,"identity":"8c5c1e6f-cc4d-4e51-a5ba-3dd3ffa41ef2","order_by":2,"name":"Yusheng Ma","email":"","orcid":"","institution":"Nanfang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yusheng","middleName":"","lastName":"Ma","suffix":""},{"id":457057522,"identity":"486b3471-1104-46d3-8bc9-61530c4c81c8","order_by":3,"name":"Yifei Ruan","email":"","orcid":"","institution":"Nanfang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yifei","middleName":"","lastName":"Ruan","suffix":""},{"id":457057523,"identity":"fc8bc36a-c545-4d63-a57e-5a4dba84cb90","order_by":4,"name":"Xingqiao Chen","email":"","orcid":"","institution":"Nanfang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xingqiao","middleName":"","lastName":"Chen","suffix":""},{"id":457057524,"identity":"9c620c0f-2959-4184-94d0-427b097de11d","order_by":5,"name":"Ziyang Ye","email":"","orcid":"","institution":"Nanfang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ziyang","middleName":"","lastName":"Ye","suffix":""},{"id":457057525,"identity":"afffeb93-18a8-4b62-afb7-b84757154d4e","order_by":6,"name":"Huitong Li","email":"","orcid":"","institution":"Nanfang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huitong","middleName":"","lastName":"Li","suffix":""},{"id":457057526,"identity":"df8c16e4-b373-4e70-92b8-015a72ae58be","order_by":7,"name":"Yuxin Yan","email":"","orcid":"","institution":"Nanfang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Yan","suffix":""},{"id":457057527,"identity":"2bf30ea9-8a4c-491f-9198-388da6260798","order_by":8,"name":"Xiaoyan Fang","email":"","orcid":"","institution":"Nanfang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Fang","suffix":""},{"id":457057528,"identity":"a9351af9-e54e-4de7-a6f1-457a3ff65993","order_by":9,"name":"Jianping Bin","email":"","orcid":"","institution":"Department of Cardiology, the Sixth Affiliated Hospital, School of Medicine, South China University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jianping","middleName":"","lastName":"Bin","suffix":""},{"id":457057529,"identity":"f7bc714c-c8c9-475b-b57a-ff6a75d75e3d","order_by":10,"name":"Xiaobo Huang","email":"","orcid":"","institution":"Nanfang Hospital, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaobo","middleName":"","lastName":"Huang","suffix":""},{"id":457057530,"identity":"716f0b31-b66e-400a-a45f-3615fdb61651","order_by":11,"name":"Yanmei Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3QLQsCMRjA8ec4mGVcnhzc+REmB0t+mB3CpSkmq4JwSbsW/Qom88mhFl/qDpNcNYgrBoMTk2VqM+wPCw/jxzMGYLP9bZQEqDLIXkP2HWlEHl7zXwgk8ZQI+h2hm91qfunkEQKhrkUKgSe5ozomsm0nxZjmAYLdwm+lEFUld/2xiWSCHTHVW5zRwtUknkuOXGwihzM73mkepy4ulSa9z0TqLUCTOEUYng/j9BOpyjMrhvqTEUbMF3tSn2xPA99EvINg8nYnQTgrSyW6jdDbNJfKRGrZ+0z0cfoGABCar202m82mewCzf0650wZPOgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0005-0576-483X","institution":"Nanfang Hospital, Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yanmei","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-05-15 05:42:35","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6669087/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6669087/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82894228,"identity":"969f15d9-d5c9-42b9-9b7b-7718e8355fed","added_by":"auto","created_at":"2025-05-16 12:35:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":229136,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelection process of the study population. \u003c/strong\u003eCHARLS, China Health and Retirement Longitudinal Study; NHANES, National Health and Nutrition Examination Survey.\u003c/p\u003e","description":"","filename":"Figure1new.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6669087/v1/f595da45f48f7bab25410f70.jpg"},{"id":82892549,"identity":"58a8f566-949f-45e4-9ab6-fd37c250e63c","added_by":"auto","created_at":"2025-05-16 12:19:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":525545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier curves for circadian syndrome and all-cause mortality outcome. \u003c/strong\u003eThe Kaplan–Meier survival function curves showed a higher cumulative incidence of all-cause mortality for CHARLS (A) and NHANES (B) in middle-aged and elderly participants with circadian syndrome than in those without circadian syndrome. CHARLS, China Health and Retirement Longitudinal Study; NHANES, National Health and Nutrition Examination Survey.\u003c/p\u003e","description":"","filename":"Figure2new.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6669087/v1/3096533bf685d1ee9167bdda.jpg"},{"id":82893729,"identity":"c3b781d6-67ff-4805-baed-60412f633a47","added_by":"auto","created_at":"2025-05-16 12:27:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":418402,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of\u003c/strong\u003e \u003cstrong\u003ecircadian syndrome and all-cause mortality outcome by demographics, medical history, and components of MetS in CHARLS cohort. \u003c/strong\u003eHazard ratio* (HR) were adjusted for age, sex, education level, marital status, smoking status, drinking status, self-reported heart disease, stroke and kidney disease.\u003c/p\u003e\n\u003cp\u003eCHARLS, China Health and Retirement Longitudinal Study; HR, hazard ratio; CI, confidence interval; MetS, metabolic syndrome.\u003c/p\u003e","description":"","filename":"Figure3new.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6669087/v1/267195977248bfd2c54c7d23.jpg"},{"id":82894229,"identity":"851bfb05-c583-4420-9d95-47d2af7b8e5f","added_by":"auto","created_at":"2025-05-16 12:35:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":423511,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of\u003c/strong\u003e \u003cstrong\u003ecircadian syndrome and all-cause mortality outcome by demographics, medical history, and components of MetS in NHANES cohort. \u003c/strong\u003eHazard ratio* (HR) were adjusted for age, sex, education level, marital status, smoking status, drinking status, self-reported heart disease, stroke and kidney disease.\u003c/p\u003e\n\u003cp\u003eNHANES, National Health and Nutrition Examination Survey; HR, hazard ratio; CI, confidence interval; MetS, metabolic syndrome.\u003c/p\u003e","description":"","filename":"3963ab79729aed1988d6cb00e02c746.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6669087/v1/0bfdec486207c5e84700b1a3.jpg"},{"id":82895389,"identity":"5b903530-d061-4cd2-80fc-1df9287b0475","added_by":"auto","created_at":"2025-05-16 12:43:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2921781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6669087/v1/ac7e9fd3-d15c-4981-80b7-3131d9cb46a3.pdf"},{"id":82892545,"identity":"5b791634-2b6b-43a9-9fcf-cba61e8cd0fe","added_by":"auto","created_at":"2025-05-16 12:19:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29910,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary materials\u003c/p\u003e","description":"","filename":"Supplementarymaterialsnew.docx","url":"https://assets-eu.researchsquare.com/files/rs-6669087/v1/d2bd58d73ad758a58a9fa0cb.docx"},{"id":82892556,"identity":"df920690-b370-4414-a12b-b60f73504188","added_by":"auto","created_at":"2025-05-16 12:19:53","extension":"jpeg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":385718,"visible":true,"origin":"","legend":"\u003cp\u003eCircadian syndrome is independently associated with an increased risk of all-cause mortality in middle-aged and elderly adults in CHARLS and NHANES. Models adjusted for age, sex, education level, marital status, smoking status, drinking status, self-reported heart disease, stroke and kidney disease.\u003c/p\u003e\n\u003cp\u003ePrimary outcome: all-cause mortality.\u003c/p\u003e\n\u003cp\u003eCHARLS, China Health and Retirement Longitudinal Study; CircS, circadian syndrome; HR, hazard ratio; CI, confidence interval; NHANES, National Health and Nutrition Examination Survey.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6669087/v1/ec6fbd91ac233461cd28d1a1.jpeg"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCircadian syndrome predicts all-cause mortality risks in adults aged ≥40 years: evidence from US and Chinese national population surveys\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eCircadian syndrome (CircS) represents an evolution of the metabolic syndrome (MetS) concept, incorporating additional circadian-related risk factors including chronic sleep deprivation and depressive symptoms\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. With a prevalence exceeding 30% in middle-aged and older adults, CircS demonstrates age-dependent increases paralleling the rising trends of both MetS and sleep disorders\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Accumulating evidence from cross-sectional and prospective clinical studies has established has established CircS as a robust predictor of incident cardiometabolic diseases, including type 2 diabetes and obesity\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Importantly, recent cohort studies from both Chinese and US populations demonstrated that CircS has superior predictive value over metabolic syndrome (MetS) for cardiovascular disease (CVD) outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Actually, CircS not only increased the risks of obesity, diabetes, mood disorders, heart and blood pressure problems, and cancer, but also worsen existing health condition\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In addition, those serious adverse cardiovascular events, including myocardial infarction, sudden cardiac death, pulmonary embolism, limb ischemia, and aortic aneurysm rupture, all have pronounced circadian rhythmicity\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Although current evidence supports CircS as a risk factor for multimorbidity, its association with all-cause mortality in middle-aged and older adults remains to be elucidated in large-scale population-based cohorts.\u003c/p\u003e \u003cp\u003eTo address the critical gaps in understanding CircS-related mortality risks, this study leverages two nationally representative longitudinal datasets: the China Health and Retirement Longitudinal Study (CHARLS) and the National Health and Nutrition Examination Survey (NHANES). We aim to investigate the independent association between CircS and all-cause mortality in middle-aged and elderly populations, which is essential for developing CircS-targeted prevention strategies in global aging societies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThe China Health and Retirement Longitudinal Study (CHARLS)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and National Health and Nutrition Examination Survey (NHANES)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e were two large, nationally representative data conducted in China and the US, respectively. The details of the CHARLS and NHANES have been described elsewhere. Briefly, the CHARLS is an ongoing nationally representative survey in China which is longitudinally following-up people aged\u0026thinsp;\u0026gt;\u0026thinsp;45 years. The NHANES is a nationally representative cross-sectional survey of the non-institutionalized US population with data collected in two-year cycles, conducting biennial assessments of health and nutritional status across individuals nationwide. The CHARLS and NHANES both collected information on demographic characteristics, medical history, prescription drug use, and laboratory testing. The CHARLS and NHANCES were approved by the Ethics Review Committees of Peking University and the Ethics Review Committee of the National Center for Health Statistics, respectively. Informed consent was obtained from each participant in these three cohorts. As such, no additional ethical approval was needed for this analysis. In this study, 2011 of CHARLS and 2005\u0026ndash;2006 of NHANCES were regarded as the baseline. Ideally, the last survey was 2020 of CHARLS and 2017\u0026ndash;2018 of NHANCES, respectively.\u003c/p\u003e \u003cp\u003eIn this analysis, we excluded participants aged\u0026thinsp;\u0026lt;\u0026thinsp;40 years, missing values of MetS diagnosis and follow-up information, and self-reported pregnant, leaving 12,106 participants in CHARLS and 17,096 in NHANES. A flowchart of participant selection is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExposure measure\u003c/h3\u003e\n\u003cp\u003eDepressive symptoms were assessed using the ten-question version of the Center for Epidemiologic Studies-Depression scale (CES-D) and the Patient Health Questionnaire (PHQ-9). Participants with a CES-D score of \u0026ge;\u0026thinsp;10 and PHQ-9 score of \u0026ge;\u0026thinsp;5 were defined as having depressive symptoms in CHARLS\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and NHANCES\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, respectively.\u003c/p\u003e \u003cp\u003eMetS was defined using the harmonized criteria proposed in the joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention, National Heart, Lung, and Blood Institute, American Heart Association, World Heart Federation, International Atherosclerosis Society, and International Association for the Study of Obesity\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e (Table S1). Having\u0026thinsp;\u0026ge;\u0026thinsp;3 of the following components was defined as having MetS: (1) elevated waist circumference (CHARLS:\u0026ge;80/85 cm in female/male, NHANCES:\u0026ge;88/102 cm in female/male); (2) high blood pressure (Systolic\u0026thinsp;\u0026ge;\u0026thinsp;130 and/or diastolic\u0026thinsp;\u0026ge;\u0026thinsp;85 mmHg) or drug treatment for hypertension; (3) low high-density lipoprotein cholesterol (HDL-C) (\u0026lt;\u0026thinsp;40 mg dL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in male and \u0026lt;\u0026thinsp;50 mg dL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in female) or drug treatment for low HDL-C; (4) triglycerides (TG) (\u0026ge;\u0026thinsp;150 mg dL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) or drug treatment for high TG; or (5) elevated fasting glucose (\u0026ge;\u0026thinsp;100 mg/dL, drug treatment of elevated glucose is an alternate indicator). Circadian syndrome (CircS) was based on 6 components including short sleep (\u0026lt;\u0026thinsp;6 hours day\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), depression and four components used to define MetS. A cut-off for CircS was set as \u0026ge;\u0026thinsp;4 components.\u003c/p\u003e\n\u003ch3\u003eOutcome\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was all-cause mortality. Mortality status for participants were obtained by matching their records to the National Death Index (NDI) up to December 31, 2019 from NHANES\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Survival status data were obtained from the CHARLS. Time of death (years and months) came from the Exit and Verbal Autopsy Questionnaire of the CHARLS. Partially missing time-of-death data (CHARLS waves 3 and 4, 2015\u0026ndash;2018) were extrapolated from the closest interview date of the last wave and known vital status at this interview and were determined as the mid of the time interval between two consecutive interviews\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eAt baseline, participants were asked whether they had been diagnosed by a doctor to have kidney disease, stroke or cardiac events (including heart attack, coronary heart disease, angina, congestive heart failure or other heart problems). We included the following covariates in the current study: age, sex, education (low, medium, high), smoking (current smokers, ex-smoker or nonsmoker) and alcohol consumption (nondrinker or drinker), kidney disease, stroke and cardiac events.\u003c/p\u003e \u003cp\u003eFor consistency among the CHARLS and NHANCES, marital status was divided into five categories: married, partnered, separated or divorced, unmarried and widowed. Education was classified into three levels: below high school, high school, and college or above. Smoking status was categorized as never smokers, ever smokers, and current smokers. Similarly, drinking status was categorized as drinkers and no-drinkers.\u003c/p\u003e \u003cp\u003eIn NHANCES, smokers were defined as participants who reported smoking at least 100 cigarettes during their lifetime, with former smokers defined as participants who reported smoking at least 100 cigarettes, but not currently smoking. Drinkers were defined as participants who drank at least 12 alcohol drinks in any given year\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Sleep duration was assessed by the question, \u0026ldquo;How much sleep do you usually get at night on weekdays or workdays?\u0026rdquo; In CHARLS, self-reported smoking status was categorized as never, former, and now. Drinkers were defined as participants who ever drank any alcohol last year. Sleep duration was assessed by the question, \u0026ldquo;During the past month, how many hours of actual sleep did you get at night (average hours for one night)?\u0026rdquo;\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eParticipant characteristics were calculated based on the presence or absence of CircS, continuous variables were expressed as mean [standard deviation (SD)] or median [interquartile range (IQR)], while categorical variables were expressed as number (percentage). The baseline characteristics were compared between the two CircS group, using t test or Kruskal\u0026ndash;Wallis test as appropriate, and categorical baseline data were compared using the χ2 test or Fisher's exact test. The missing rates of covariates were summarized in Tables S2. The missing data of covariates were imputed using the multiple imputation with 5 replications. The proportional hazards assumption for each variable in the model was tested using the Schoenfeld residuals test, and no violations were found. In the test for multicollinearity (Table S3), the results showed that the variance inflation factor (VIF) for each covariate was less than 5, indicating that there was no evidence of significant multicollinearity between the covariates before Cox regression analysis.\u003c/p\u003e \u003cp\u003eThe Kaplan-Meier method was used to plot the survival curves associated with CircS. To analyze the association of baseline CircS status with the risks of all-cause mortality, Cox proportional hazard regression was used to calculate the hazard ratio (HR) and its 95% confidence interval (95% CI). Two models were fitted for the Cox regression using non-CircS participants as the reference. Model 1 was unadjusted. Model 2 was multivariable-adjusted controlling for age, sex, marital status, education, smoking status, drinking status, self-reported heart diseases, stroke, and kidney disease. To explore the association between CircS and all-cause mortality across different demographic characteristics, subgroup and interaction analyses were conducted among various age groups (\u0026lt;\u0026thinsp;60 vs. \u0026ge;60 years), sex, drinking statuses, self-reported heart, stroke, kidney disease.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics 27 program (IBM Corp., Armonk, NY, USA) and Stata 17 software (Stata Corp, College Station, TX, US). Statistical significance was set at a two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eBaseline characteristics of the study population\u003c/h2\u003e\n \u003cp\u003eAccording to inclusion and exclusion criteria, 12106 participants from CHARLS (female: 52.3%, median age: 57 years), and 17096 from NHANCES (female: 49.4%, median age: 59 years) were included in the CircS status analyses. Baseline characteristics of these participants are presented in Table\u0026nbsp;1. In the CHARLS and NHANCES, CircS participants were older, more likely to be female, non-drinker, never smoker, self-reported heart, stroke and kidney diseases, less likely to be married or partnered and had lower education level than non-CircS participants.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe pooled baseline characteristics across circadian syndrome (CircS) status\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCHARLS (n = 12,106)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eNHANES (n = 17,906)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCircS (n = 3,349)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-CircS (n = 8,757)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCircS\u003c/p\u003e\n \u003cp\u003e(n = 4,420)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-CircS\u003c/p\u003e\n \u003cp\u003e(n = 12,676)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57(51–64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59(53–66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57(50–64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60(49–69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61(51–70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59(48–69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6328(52.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2276(68.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4052(46.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8437(49.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2540(57.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5897(46.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5778(47.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1073(32.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4705(53.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8659(50.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1880(42.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6779(53.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10710(88.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3065(91.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7645(87.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4561(26.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1557(35.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3004(23.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school or equivalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e930(7.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189(5.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e741(8.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3862(22.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1073(24.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2789(22.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e466(3.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95(2.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e371(4.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8655(50.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1782(40.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6873(54.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10159(83.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2686(80.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7473(85.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10016(58.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2341(53.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7675(60.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePartnered\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e557(4.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146(4.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e411(4.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e765(4.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179(4.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e586(4.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1149(9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e452(13.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e697(8.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1883(11.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e612(13.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1271(10.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced/Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144(1.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(1.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100(1.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3062(17.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e910(20.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2152(17.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97(0.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(0.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76(0.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1364(8.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375(8.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e989(7.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol drinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8048(66.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2543(76.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5505(62.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4413(26.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1391(32.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3022(24.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4051(33.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e805(24.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3246(37.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11976(73.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2859(67.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9117(75.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7308(60.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2363(70.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4945(56.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8707(51.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2063(46.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6644(52.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1023(8.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e296(8.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e727(8.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5119(30.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1412(32.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3787(29.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3717(30.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e683(20.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3034(34.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3176(18.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e942(21.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2234(17.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelf-reported chronic diseases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeart diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1365(11.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e628(18.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e737(8.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2106(12.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e839(19.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1267(10.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e283(2.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145(4.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138(1.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e914(5.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e360(8.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e554(4.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e655(5.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e217(6.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e438(5.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e703(4.10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e322(7.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e381(3.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003eCHARLS, China Health and Retirement Longitudinal Study; NHANES, National Health and Nutrition Examination Survey; CircS, circadian syndrome.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn the CircS status analyses, unweighted prevalence of CircS was 27.70% (CHARLS) and 25.90% (NHANES), the median follow-up periods were 9.0 years in the CHARLS and 7.9 years in the NHANES. A total of 3927 participants (1437 from CHARLS and 2490 from NHANCES) died during follow-up.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssociation of circadian syndrome status with all-cause mortality\u003c/h3\u003e\n\u003cp\u003eKaplan-Meier survival curves demonstrated significantly higher cumulative mortality among participants with circadian syndrome (CircS) compared to non-CircS individuals in both cohorts (log-rank \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Fig.\u0026nbsp;2). As displayed in Table\u0026nbsp;2, Cox proportional hazards models revealed consistent mortality risks associated with CircS across both Chinese and US populations. In the unadjusted models, the HRs (95% CIs) were 1.233 (1.104–1.378) and 1.358 (1.248–1.478) for the all-cause mortality in the Chinese and US adults with CircS. Consistent significant associations were observed in the multivariable Cox regression model that adjusted for confounders. After further adjustment for MetS status, CircS was associated with a 23.5% increase in all - cause mortality risk in CHARLS (HR:1.235, 95% CI:1.032–1.478) and a 18.2% increase in all-cause mortality risk in NHANES (HR:1.182, 95% CI:1.028–1.358) (Table\u0026nbsp;3). Stratification by CircS components revealed no significant mortality gradient (all P \u0026gt; 0.05; Table\u0026nbsp;4), implying threshold effects rather than dose-response relationships in CircS pathophysiology.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAssociation (Hazard Ratios and 95% CIs) between the circadian syndrome (CircS) and all-cause mortality in middle-aged and elderly population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvents (No.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFollow-up Duration\u003c/p\u003e\n \u003cp\u003e(Person-Year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncident Rate\u003c/p\u003e\n \u003cp\u003e(Per 1000 Person-Year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003eHR1\u003csup\u003ea\u003c/sup\u003e (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003eHR2\u003csup\u003eb\u003c/sup\u003e (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHARLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eall-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1437/12106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.34(13.62–15.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-CircS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e981/8757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.49(12.67–14.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCircS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e456/3349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.60(15.14–18.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.233(1.104–1.378)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.170(1.040–1.315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eall-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2490/17906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e123307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.19(19.42-21.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-CircS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1708/12676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.55(17.69–19.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCircS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e782/4420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.05(23.35–26.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.358(1.248–1.478)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.151(1.054–1.258)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003eHR1 and P1 were unadjusted.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003eHR2 and P2 were adjusted for age, sex, education level, marital status, smoking status, drinking status, self-reported heart disease, stroke and kidney disease.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: CHARLS, China Health and Retirement Longitudinal Study; CircS, circadian syndrome; HR, hazard ratio; CI, confidence interval; NHANES,\u0026nbsp;National Health and Nutrition Examination Survey.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAssociation between circadian syndrome (CircS) and all-cause mortality outcome further adjusted for metabolic syndrome (MetS).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvents (No.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFollow-up Duration\u003c/p\u003e\n \u003cp\u003e(Person-Year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncident Rate\u003c/p\u003e\n \u003cp\u003e(Per 1000 Person-Year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003eHR1\u003csup\u003ea\u003c/sup\u003e (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2 + MetS\u003c/p\u003e\n \u003cp\u003eHR2\u003csup\u003eb\u003c/sup\u003e (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHARLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eall-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1194/10252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.98(13.21–14.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-CircS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e793/7129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.31(12.41–14.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCircS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401/3123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.55(14.10-17.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.168(1.029–1.325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.235(1.032–1.478)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eall-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2041/13927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.34(19.47–21.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-CircS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1295/9755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.21(17.25–19.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCircS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e746/4172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.49(23.73–27.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.142(1.040–1.254)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.182(1.028–1.358)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003eHR1 and P1 were adjusted for age, sex, education level, marital status, smoking status, drinking status, self-reported heart disease, stroke and kidney disease.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003eHR2 and P2 were adjusted for age, sex, education level, marital status, smoking status, drinking status, self-reported heart disease, stroke, kidney disease and MetS.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eAbbreviations: CHARLS, China Health and Retirement Longitudinal Study; NHANES, National Health and Nutrition Examination Survey; CircS, circadian syndrome; MetS, metabolic syndrome.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAssociation (Hazard Ratios and 95% CIs) between the components of circadian syndrome (CircS) and all-cause mortality in middle-aged and elderly population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComponents of CircS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvents (No.)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFollow-up Duration\u003c/p\u003e\n \u003cp\u003e(Person-Year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncident Rate\u003c/p\u003e\n \u003cp\u003e(Per 1000 Person-Year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003eHR1\u003csup\u003ea\u003c/sup\u003e (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003eHR2\u003csup\u003eb\u003c/sup\u003e (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCHARLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eall-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e456/3349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.34(13.62–15.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e257/1914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.39(14.50-18.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126/963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.67(13.16–18.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.953(0.770–1.180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.983(0.793–1.218)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≥ 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73/462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.47(15.48–24.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.191(0.918–1.544)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.080(0.829–1.406)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eall-cause mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e782/4420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.05(23.35–26.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e507/2914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.68(22.62–26.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218/1212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.67(22.48–29.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.041(0.888–1.220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.037(0.884–1.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≥ 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57/294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.09(20.13–33.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.046(0.795–1.375)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.923(0.700-1.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003eHR1 and P1 were unadjusted.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003eHR2 and P2 were adjusted for age, sex, education level, marital status, smoking status, drinking status, self-reported heart disease, stroke and kidney disease.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eAbbreviations: CHARLS, China Health and Retirement Longitudinal Study; CircS, circadian syndrome; HR, hazard ratio; CI, confidence interval; NHANES, National Health and Nutrition Examination Survey.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eSensitivity analyses\u003c/h2\u003e\n \u003cp\u003eIn both CHARLS and NHANES cohorts, the association between CircS and all-cause mortality remained consistent (all P-interaction \u0026gt; 0.05) across subgroups stratified by sex, pre-existing cardiovascular disease, stroke, kidney disease, MetS status, blood pressure levels, HDL cholesterol, triglycerides, and fasting glucose (Fig.\u0026nbsp;3–4). In the NHANES cohort, participants with CircS aged 40–59 years exhibited significantly elevated mortality risk (HR:1.565, 95% CI:1.265–1.936), whereas those ≥ 60 years showed attenuated risk (HR:1.087, 95% CI:0.986–1.198; P for interaction \u0026lt; 0.001) (Fig.\u0026nbsp;4). Among individuals with CircS, current drinkers exhibited 21% higher all-cause mortality (HR:1.213, 95% CI 1.088–1.353) compared to nondrinkers with CircS (HR:1.037, 95% CI 0.892–1.205) (Fig.\u0026nbsp;4). Participants with CircS who maintained normal waist circumference showed significantly higher all-cause mortality risk (HR:1.730, 95% CI 1.353–2.213) compared to those with CircS and elevated waist circumference (HR:1.188, 95% CI 1.065–1.325), with a statistically significant interaction (P for interaction = 0.005; Fig.\u0026nbsp;4).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this binational prospective analysis encompassing 29,202 adults aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years (median follow-up: 9.0 years for CHARLS, 7.9 years for NHANES), we established that CircS was positively associated with the risk for all-cause mortality after multivariate adjustment. Notably, this association was more significant in US adults with CircS who aged 40 to 59 years, who are drinkers, who have normal waist circumference. Our finding reinforced CircS as a superior prognostic construct integrating short sleep and depression with metabolic pathophysiology in predicting risk of mortality.\u003c/p\u003e \u003cp\u003eAn interesting finding is that, as we know, two additional components, short sleep and depression, were added into the components of MetS to construct the CircS. We found that MetS with short sleep or depression was associated with the higher risk of all-cause mortality than MetS alone. If we only use MetS to prevent and predict all-cause mortality, CircS alone group will be missed. Those findings emphasize the rationale and importance of incorporating short sleep and depression into the MetS risk factor cluster and construct the Circs. Another interesting finding was no dose-dependent link between the number of CircS components and mortality risk. The lack of such a relationship indicating that current criteria of Circs might inadequately capture severity rating within individual components or stem from complex interplay among components. Thus, there is a need for the further development of relevant predictive modelling and risk stratification tools to more effectively identify at - risk populations. As the assessment of short sleep and depression is relatively easy and cheap, they should be routinely measured in combination of MetS in clinical settings to prevent mortality.\u003c/p\u003e \u003cp\u003eOur stratified analyses revealed that the association between CircS and mortality risk varied significantly by age, waist circumference (WC), metabolic health status, sleep duration, depression, and alcohol use in different cohort. Notably, the strongest association was observed in adults aged 40\u0026ndash;59 years with CircS in US populations, likely reflecting this group\u0026rsquo;s greater exposure to shift work and circadian disruption as the core working population. Intriguingly, CircS participants with normal WC exhibited higher mortality risk, potentially because the participants in this subgroup may have circadian disruption as their primary pathological driver rather than traditional metabolic dysfunction. Furthermore, alcohol consumption amplified mortality risk in CircS participants, likely through multiple pathways including circadian disruption, hepatic impairment, and enhanced oxidative stress\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. These findings collectively suggest that mortality prevention in CircS should address sleep quality, depressive symptoms, and alcohol use alongside metabolic parameters.\u003c/p\u003e \u003cp\u003eAlthough the mechanisms underlying the all-cause mortality of CircS and middle-aged and elderly adults have not been fully elucidated, several possible explanations may explain this association. As a core component of CircS, MetS drives multi-organ damage via insulin resistance, oxidative stress, and renin-angiotensin system activation. These processes accelerate the development of life-threatening comorbidities including type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), and stroke through endothelial dysfunction and chronic inflammation\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In addition, sleep duration can induce hypothalamic-pituitary-adrenal (HPA) axis hyperactivity and elevates cortisol secretion while decreased leptin. This neuroendocrine imbalance exacerbates cardiovascular and metabolic pathologies, ultimately increasing all-cause mortality risk in aging populations\u003csup\u003e\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Furthermore, depression exacerbates CircS mortality risk might through the dual mechanisms. One is that depression also increase HPA hyperactivity, neuroimmune activation, and sympathoadrenal dysfunction. The other is that people with depression usually have unhealthy lifestyles, including physical inactivity, smoking, heavy alcohol consumption, and poor diet patterns, leading to an increased risk of developing MetS\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Therefore, these components interact bidirectionally, creating a self-reinforcing cycle, ultimately leading to increase all-cause mortality in middle- aged and elderly adults.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. Firstly, the observational nature of the study design precludes establishing a causal relationship between CircS and all-cause mortality. Although we adjusted for a wide range of covariates, residual confounding may still exist. Secondly, the data from the two cohorts were collected using different methods, which may introduce some heterogeneity. Although we tried to standardize the analysis as much as possible, differences in data collection procedures may have affected the results. Thirdly, the assessment of sleep duration relied on the participants\u0026rsquo; self-reported, which might cause an information bias.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis binational cohort study demonstrated that CircS was independently associated with elevated risk of all-cause mortality in both Chinese and US adults aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years. Notably, this association was more significant in US adults with CircS who aged 40 to 59 years, who are drinkers, who have normal waist circumference.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the China Health and Retirement Longitudinal Study and National Health and Nutrition Examination Survey investigators, study teams and participants for making these data available for secondary analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe China Health and Retirement Longitudinal Study was approved by the Ethics Review Committee of Peking University. National Health and Nutrition Examination Survey was approved by the National Center for Health Statistics Research Ethics Review Board. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grant 82470288\u0026nbsp;to YC), Guang Dong Basic and Applied Basic Research Foundation (grant 2023A1515010381 and 2022A1515220013 to YC), Natural Science Foundation of Jiangxi Province (grant 20232ACB216003 to YC), Foundation of NanFang Hospital of Southern Medical University (grant 2023CR011 to YC). The funder had no role in the study design, data collection and analysis, interpretation, or writing of the report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWenlong Xu\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e Conceptualization (equal); Data curation (lead); Formal analysis (lead); Methodology (equal); Project administration (lead); Resources (equal); Software (equal); Visualization (equal); Writing-original draft (lead); Writing-review \u0026amp; editing (equal). \u003cstrong\u003eYusheng Ma:\u003c/strong\u003e Methodology (equal); Conceptualization (equal); Writing-review \u0026amp; editing (equal). \u003cstrong\u003eYingxuan Li:\u003c/strong\u003e Conceptualization (equal); Writing-review \u0026amp; editing (equal). \u003cstrong\u003eYifei Ruan:\u003c/strong\u003e Writing-review \u0026amp; editing (equal).\u0026nbsp;\u003cstrong\u003eXingqiao Chen:\u003c/strong\u003e Visualization (equal); Writing-review \u0026amp; editing (equal). \u003cstrong\u003eZiyang Ye:\u003c/strong\u003e Writing-review \u0026amp; editing (equal). \u003cstrong\u003eHuitong Li:\u003c/strong\u003e Writing-review \u0026amp; editing (equal). \u003cstrong\u003eYuxin Yan:\u003c/strong\u003e Writing-review \u0026amp; editing (equal). \u003cstrong\u003eXiaoyan Fang:\u0026nbsp;\u003c/strong\u003eResources (equal); Software (equal); Visualization (equal);\u0026nbsp;\u003cstrong\u003eJianping Bin:\u003c/strong\u003e Conceptualization (equal); Investigation (equal);\u0026nbsp;\u003cstrong\u003eXiaobo Huang:\u003c/strong\u003e Project administration (equal); Supervision (equal); Writing-review \u0026amp; editing (equal); \u003cstrong\u003eYanmei Chen:\u003c/strong\u003e Conceptualization (equal); Investigation (equal); Methodology (equal); Project administration (equal); Supervision (lead); Writing-review \u0026amp; editing (lead). All gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are publicly available in the NHANES database (https://www.cdc.gov/nchs/nhanes/index.htm) and CHARLS database (https://charls.pku.edu.cn). Data analyzed during the study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZimmet P, Alberti KGMM, Stern N et al. The Circadian Syndrome: is the Metabolic Syndrome and much more!. \u003cem\u003eJ Intern Med\u003c/em\u003e. 2019;286(2):181-191\u003c/li\u003e\n\u003cli\u003eLi R, Li W, Lun Z et al. 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The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. \u003cem\u003eJ Am Coll Cardiol\u003c/em\u003e. 2010;56(14):1113-32\u003c/li\u003e\n\u003cli\u003eHsu C, Hou C, Hsu W, Tain Y. Early-Life Origins of Metabolic Syndrome: Mechanisms and Preventive Aspects. \u003cem\u003eInt J Mol Sci\u003c/em\u003e. 2021;22(21)\u003c/li\u003e\n\u003cli\u003eShen L, Li B, Gou W et al. Trajectories of Sleep Duration, Sleep Onset Timing, and Continuous Glucose Monitoring in Adults. \u003cem\u003eJAMA Netw Open\u003c/em\u003e. 2025;8(3):e250114\u003c/li\u003e\n\u003cli\u003eItani O, Jike M, Watanabe N, Kaneita Y. Short sleep duration and health outcomes: a systematic review, meta-analysis, and meta-regression. \u003cem\u003eSleep Med\u003c/em\u003e. 2017;32:246-256\u003c/li\u003e\n\u003cli\u003eLiang YY, Chen J, Peng M et al. Association between sleep duration and metabolic syndrome: linear and nonlinear Mendelian randomization analyses. \u003cem\u003eJ Transl Med\u003c/em\u003e. 2023;21(1):90\u003c/li\u003e\n\u003cli\u003eZhang M, Chen J, Yin Z, Wang L, Peng L. The association between depression and metabolic syndrome and its components: a bidirectional two-sample Mendelian randomization study. \u003cem\u003eTransl Psychiatry\u003c/em\u003e. 2021;11(1):633\u003c/li\u003e\n\u003cli\u003eMeng R, Yu C, Liu N et al. Association of Depression With All-Cause and Cardiovascular Disease Mortality Among Adults in China. \u003cem\u003eJAMA Netw Open\u003c/em\u003e. 2020;3(2):e1921043\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"bc57704f-cebc-4c63-9971-a3ec29725d99","identifier":"10.13039/501100001809","name":"National Natural Science Foundation of China","awardNumber":"82470288","order_by":0},{"identity":"cdc0d53c-acb7-4c09-a495-f9a049412a07","identifier":"10.13039/501100004479","name":"Natural Science Foundation of Jiangxi Province","awardNumber":"20232ACB216003","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Southern Medical University","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":"circadian syndrome, CHARLS, NHANES, mortality","lastPublishedDoi":"10.21203/rs.3.rs-6669087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6669087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCircadian syndrome (CircS), a condition characterized by circadian rhythm disruption due to modern lifestyle factors, have been identified as a stronger predictor for the development of cardiovascular disease (CVD). However, its association with all-cause mortality in middle-aged and elderly populations remains unclear. This study aimed to investigate the impact of CircS on all-cause mortality risk in middle-aged and elderly adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing data from two population-based cohorts (China Health and Retirement Longitudinal Study [CHARLS, n\u0026thinsp;=\u0026thinsp;12,106] and the National Health and Nutrition Examination Survey [NHANES, n\u0026thinsp;=\u0026thinsp;17,096]), we defined CircS as MetS components combined with short sleep duration and depression. The primary outcome was all-cause mortality, which was assessed through standardized follow-up questionnaires. Cox proportional hazards models were employed to evaluate all-cause mortality risks.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 29,202 participants, the prevalence of CircS was 27.70% (CHARLS) and 25.90% (NHANES). After full adjustment for covariates, CircS was independently associated with elevated risk of all-cause mortality in both cohorts (CHARLS: HR\u0026thinsp;=\u0026thinsp;1.170, 95% CI 1.040\u0026ndash;1.315; NHANES: HR\u0026thinsp;=\u0026thinsp;1.151, 95% CI 1.054\u0026ndash;1.258). Notably, this association was more significant in US patients with CircS who aged 40 to 59 years, who are drinkers, who have normal waist circumference.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCircS was independently associated with elevated risk of all-cause mortality in both Chinese and US adults aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years, particularly in US patients with CircS who aged 40 to 59 years, who are drinkers, who have normal waist circumference.\u003c/p\u003e","manuscriptTitle":"Circadian syndrome predicts all-cause mortality risks in adults aged ≥40 years: evidence from US and Chinese national population surveys","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 12:19:48","doi":"10.21203/rs.3.rs-6669087/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":"52d71f69-7d53-40ab-b4b4-37d622ec2158","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48618971,"name":"Endocrinology \u0026 Metabolism"}],"tags":[],"updatedAt":"2025-05-16T12:19:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 12:19:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6669087","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6669087","identity":"rs-6669087","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}