Association between Air Pollutants and Circadian Syndrome in China: A Longitudinal Cohort Study Based on CHARLS

Association between Air Pollutants and Circadian Syndrome in China: A Longitudinal Cohort Study Based on CHARLS | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between Air Pollutants and Circadian Syndrome in China: A Longitudinal Cohort Study Based on CHARLS Yongshun Su, Li Zhang, Xiaoli Ou, Zhongbo Xiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9202516/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Circadian syndrome (CircS) is a multisystem metabolic disorder related to circadian disruption, characterized by sleep disturbance, central obesity, hypertension, and impaired glucose and lipid metabolism. Evidence linking ambient air pollution to CircS in middle-aged and older adults remains limited. Using the nationally representative China Health and Retirement Longitudinal Study (CHARLS), we examined the individual and joint effects of seven pollutants (PM1, PM2.5, PM10, NO2, SO2, CO, and O3) on incident CircS. We followed 2,975 participants aged ≥45 years who were free of CircS at baseline (2011–2012) through 2015–2016. Long-term residential exposures were assigned using CHAP-based estimates. Multivariable logistic regression and subgroup analyses assessed associations, and several complementary mixture models evaluated joint effects. In fully adjusted models, per-SD increases in pollutants were associated with higher odds of incident CircS (ORs 1.10–1.16), whereas the highest versus lowest quartile yielded ORs of 1.26–1.64. Mixture analyses consistently showed a harmful overall joint effect, with O3 and SO2 contributing most and PM10 also showing a notable role. Associations were stronger among older adults and current smokers, and results were robust across multiple modeling strategies and sensitivity analyses. These findings support prioritizing targeted pollution control for circadian-metabolic health in ageing populations. Health sciences/Diseases Earth and environmental sciences/Environmental sciences Health sciences/Risk factors Circadian syndrome Air pollutant CHARLS Chinese adults BKMR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The circadian rhythm system has long been acknowledged as a major regulator of metabolism 1 . It tightly controls the temporal patterns of hormone secretion, enzyme activity, and energy intake by modulating the expression of clock genes 2 . In recent years, accumulating research has shown that this complex regulatory mechanism is not only closely related to individual lifestyle habits but also significantly impacts the risk of developing chronic disorders. Disruption of the circadian rhythm is associated with many modern chronic diseases, including Metabolic Syndrome (MetS) and hypertension 3 – 5 . In 2019, the concept of Circadian Syndrome (CircS) was formally put forward 1 . It includes indicators related to blood pressure, waist circumference, blood lipids, sleep, and mood, and has greater predictive value for cardiovascular events and chronic digestive system diseases than traditional MetS 6 . Currently, the prevalence of CircS is gradually increasing globally 7 . In the face of this substantial and increasingly pressing public health burden, early identification of modifiable environmental triggers to prevent its onset is extremely important. Air pollution, classified by the WHO as the “single largest avoidable health risk,” has only recently been recognized as a “circadian disruptor.” Recent studies suggest that air pollution not only directly raises the risk of chronic diseases such as cardiovascular conditions but may also further disrupt circadian rhythms by interfering with the endocrine system and metabolic cycles, which are key manifestations of CircS 7 – 9 . Animal studies have demonstrated that long - term exposure to particulate matter like PM 2.5 and PM 10 exacerbates oxidative stress and inflammation, directly disrupts the circadian peaks and troughs of glucose tolerance 10 – 13 . Multiple population - based studies have found that long-term exposure to high concentrations of gaseous pollutants such as SO 2 and NO 2 affects sleep quality and emotional stability: for every 10 µg/m³ increase in NO 2 , nocturnal melatonin levels decrease by 15%, accompanied by a 1.2 fold increase in the odds of depressive symptoms 14 – 16 . Sleep and mood disturbances are precisely core indicators of CircS 7 . Therefore, we hypothesize that air pollution, through multiple mechanisms, acts as a profound potential trigger for circadian rhythm disruption and other chronic metabolic diseases, thereby increasing the risk of CircS. However, current research mainly focuses on single pollutants, overlooking real world mixed exposures to “gaseous + particulate” pollutants 7 , 17 . Additionally, outcomes are often limited to single components such as depression or obesity, lacking quantification of the “holistic phenotype” of CircS. Moreover, previous studies have been mostly concentrated in Europe and North America, leaving a research gap regarding mixed air pollution data and CircS among the middle-aged and elderly population in China. This study is a retrospective cohort study based on the China Health and Retirement Longitudinal Study (CHARLS) database. By integrating multi - year environmental exposure data (including PM 1 , PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 ) with nationally representative health indicators, our research can simultaneously assess the cumulative effects of multiple pollutants across different geographical regions. This study will provide new evidence to clarify the potential link between air pollution and CircS. Results Participant characteristics In this study, 2,975 participants were included in the analysis, comprising 1,440 females and 1,535 males. Stratification was performed based on the classification characteristic of whether new‑onset CircS occurred in 2015 (Table 1). The mean baseline age was 58.15 ± 8.52 years. Compared with the non‑CircS group, the new‑onset CircS group exhibited significant differences in multiple characteristics, including anthropometric measurements, laboratory indicators, lifestyle factors, and comorbidities (all P < 0.05). Table 1 presents the mean concentrations (μg/m³) of seven air pollutants during the follow‑up period. Except for PM₁ and PM₂.₅, all other air pollutants were significantly higher in the CircS group(more detailed information is provided in Supplementary Table 2). Figure 2 illustrates the spatial distribution of air pollutants in the cities where the participants resided. Significant regional disparities in air pollution were observed across China: during the follow‑up period, the most severe air pollution was found in the Northwest and North China regions, with the highest levels of PM₂.₅ and PM₁₀ recorded in the Northwest. Association between Seven Air Pollutants and CircS Table 2 presents the multivariate logistic regression estimates for the association between seven air pollutants and new-onset CircS. When analyzed as continuous variables in Model 3 (adjusted for all potential confounders), each 1 μg/m³ increase in air pollutant concentration was associated with a 10–16% elevated risk of new-onset CircS (OR range: 1.10 to 1.16, all P < 0.05). Among them, PM₁₀ exhibited the strongest effect (OR = 1.16; 95% CI: 1.06–1.26). After categorization by quartiles, the exposure-response relationship became more pronounced: in the fully adjusted model, using the lowest quartile (Q0) as the reference, the ORs for the highest quartile (Q3) ranged from 1.26 to 1.64. Specifically, the Q3 OR for CO was 1.51 (95% CI: 1.19–1.92), and for PM₁₀ it reached 1.64 (95% CI: 1.27–2.11). Notably, for NO₂ and PM₁, the highest risk estimates were observed at Q2 (OR for NO₂ = 1.32, 95% CI: 1.04–1.68; OR for PM₁ = 1.39, 95% CI: 1.09–1.78). Subgroup analyses To assess the heterogeneity of air pollution effects across populations, we performed subgroup analyses for pollutant exposure and CircS across nine dimensions: age, sex, marital status, education, physical activity, smoking, alcohol consumption, CKD, and CVD. Figure 3 presents the results of the subgroup analysis for CO pollutants and CircS. The association between CO exposure and CircS was more pronounced in older adults and current smokers, with the strongest dose-response relationship observed in the ≥66-year-old female. An interaction was identified in patients with CKD ( P = 0.024). However, no interactions were found in the subgroup analyses for other pollutants (Supplementary Figures 3-8). Relationships between concurrent exposure to air pollution and CircS Figure 4 displays the correlation coefficients among the seven air pollutants. The correlation coefficients for PM 1 , PM 2.5 , and PM 10 were the highest (r > 0.9), indicating a high degree of spatial consistency in the distribution of these pollutants. The correlation coefficient between O 3 and CO was the lowest (r = 0.35), while the remaining atmospheric pollutants exhibited moderate correlations. The association between CircS and mixed exposure to air pollutants was further explored. Higher concentrations of the pollutant mixture were generally associated with an increased risk of CircS. When the overall air pollution mixture concentration reached the 80th percentile, the likelihood of the outcome increased by 18.9% (Figure 5A, Supplementary Table 3-4). After controlling for other air pollutants at their 25th, 50th, and 75th percentiles, CO, NO 2 , SO 2 , and O 3 were all positively associated with the occurrence of CircS. PM1, PM 2.5 , and PM 10 were grouped together due to their high correlation, with PM10 showing a positive association with the outcome (Figure 5 B, C). The WQS model, which calculated the weights of the seven mixed pollutants in Figure 5D, revealed that O 3 and SO 2 had the highest weights, indicating their predominant influence on CircS. Sensitivity Analysis We investigated the association between air pollutant mixtures and CircS using the Qgcomp model. The results showed that in the mixture, PM 2.5 exhibited a negative weight, while PM 10 , O 3 , and SO 2 demonstrated the highest positive weights. This finding is consistent with the conclusions from previous analyses based on the BKMR and WQS models (see Supplementary Figure 9). Supplementary Table 5 lists the estimated values, standard errors, and P values for the relevant air pollutants. Discussion This study investigated the associations between air pollutants and the incidence of CircS in middle-aged and elderly populations in China. Findings indicate that exposure to air pollutants, individually or in combination, increases the risk of CircS. In real-world mixed exposure scenarios, O₃, SO₂, and PM₁₀ were identified as the core drivers of this effect. This research aims to offer new evidence and references for improving the health of the elderly and for the scientific prevention and management of CircS. Existing evidence suggests multiple pathways: fine particles (e.g., PM₁, PM₂.₅) can enter systemic circulation, induce oxidative stress, and disrupt circadian gene expression 9 , 13 . Gaseous pollutants like NO₂ and SO₂ may interfere with melatonin secretion and hormonal rhythms 18 . In our analysis, O₃ emerged as the most influential disruptor in mixed exposures 19 . The generally poor sleep quality in this demographic likely results from the interplay of age-related physiological decline, lifestyle factors, and environmental exposures 15 , 20 . Given the accelerating aging trend in China—with the elderly population projected to reach 38.6% by 2050 21 —and their heightened susceptibility to pollution-induced circadian disruption, this demographic shift may substantially increase the disease burden of CircS. Proactive establishment of comprehensive air quality monitoring and stricter emission controls is warranted. Subgroup analyses demonstrated significantly elevated risk among women, older adults, individuals with lower educational attainment, and smokers. Plausible mechanisms include age-dependent hypersensitivity of clock neurons to hypoxia, higher prevalence of circadian-disruptive occupations (e.g., night-time sanitation, long-haul driving) in lower-education groups, and prolonged pulmonary deposition and retention of PM₁₀ attributable to airway inflammation and impaired mucociliary clearance in smokers. These findings underscore the need for clean-air policies that prioritize vulnerable populations—for example, targeted reductions in traffic-derived NOₓ and dust-related PM₁₀ within ageing neighbourhoods—to maximize health gains and attenuate environmental health inequalities. CircS was chosen as the outcome because its diagnostic criteria encompass not only all indicators of metabolic syndrome (Mets) but also include sleep duration, sleep disorders, and depression 22 , 23 . This makes CircS a more holistic indicator for predicting disease risk and assessing health. Epidemiological evidence linking outdoor air pollution to CircS remains limited, especially in China, where data accessibility has hindered large-scale studies. Previous research has largely focused on single pollutants and single health outcomes, often using cross-sectional designs 3 , 7 , 24 . The strengths of this study include: 1) utilization of the large CHARLS cohort; 2) application of multiple statistical methods for cross-validation; and 3) inclusion of relatively comprehensive pollutant data, particularly PM₁, which is often missing in prior studies. Previous studies have explored the relationship between PM 2.5 exposure and CircS in middle-aged and elderly Chinese. We implemented stricter inclusion criteria than their studies in the sleep disorder indicator. At the same time, we included more air pollutants and covariates, and calculated the mixing effects of these mixed pollutants 7 . Limitations must be acknowledged. First, findings may not be generalizable beyond China's middle-aged and elderly population. Second, data on medication use and specific lifestyle changes were not collected; their influence on CircS requires future study. Third, biomarker data in CHARLS were only measured at two time points, limiting continuous monitoring. Furthermore, the mixed-exposure model did not show a linear dose-response relationship, suggesting possible threshold effects or adaptive mechanisms. Finally, high collinearity among PM₁, PM₂.₅, and PM₁₀ prevented reliable quantification of their independent effects. Future well-designed prospective and mechanistic studies are needed to clarify these associations and their underlying biological pathways. Conclusion Based on a representative sample of middle-aged and elderly Chinese adults, this study provides novel evidence linking air pollution exposure to circadian syndrome (CircS). Mixture analysis identifies PM 10 , O 3 , and SO 2 as the core pollutants driving these adverse effects. Our findings underscore the need for targeted policy interventions to reduce air pollution and for further mechanistic research to elucidate the underlying biological pathways. Methods Data source and study population The data for this cohort study were derived from the China Health and Retirement Longitudinal Study (CHARLS). This cohort conducted a nationally representative longitudinal survey of individuals aged 45 years and older in China. The database employed a multi-stage, stratified, probability-proportional-to-size (PPS) sampling design. Full details of the study have been published previously 25 , 26 . Baseline data collection in 2011–2012 (Wave 1) included 17,708 individuals from 10,257 households, covering 28 provinces, 150 counties, and 450 communities nationwide. Subsequently, three follow-up waves of surveys were conducted in 2013, 2015, and 2018. As blood test data were only available in 2011–2012 (Wave 1) and 2015–2016 (Wave 3), this study only included participants with relevant data available in both Wave 1 and Wave 3. The study flowchart is presented in Fig. 1 . The exclusion criteria were as follows: (1) age < 45 years; (2) lack of CircS diagnostic data in both Wave 1 and Wave 3; (3) diagnosis of CircS in Wave 1; and (4) lack of local air pollution data. Ultimately, a total of 2,975 participants were included in the final data analysis. Exposure: Assessment of air pollution The environmental pollution data utilized in this study were sourced from the China High Air Pollutants (CHAP) dataset, a high-resolution, high-quality near-surface air pollutant database accessible via the National Earth System Science Data Sharing Platform ( https://nnu.geodata.cn/data/dataresource.html ). The CHAP dataset encompasses various near-surface air pollutants, including PM 1 , PM 2.5 , PM 10 , CO, NO 2 , SO 2 , and O 3 27–29 . It is a long-term, full-coverage, high spatiotemporal resolution, and high-precision remote sensing dataset generated by integrating multi-source satellite remote sensing technology with extensive ground-based observations, atmospheric reanalysis data, emission inventories, and model simulations. For this study, data for PM 1 , PM 2.5 , PM 10 , and O 3 were obtained for the period from 2011 to 2015, while data for CO and SO 2 were acquired for the period from 2013 to 2015. The spatial resolution for PM1, PM 2.5 , PM 10 , and O 3 is 1 kilometer, whereas for NO 2 , SO 2 , and CO it is 10 kilometers. Outcomes: Assessing new-onset circadian syndrome Subjects without CircS at baseline who developed CircS after a 4-year follow-up were recorded as incident CircS. The criteria for assessing CircS in CHARLS consist of the following seven components 30 : (1) central obesity: waist circumference ≥ 85 cm in men and ≥ 80 cm in women; (2) hypertriglyceridemia: triglycerides ≥ 150 mg/dL or current use of lipid-lowering drugs; (3) low HDL-C: HDL-C < 40 mg/dL in men and < 50 mg/dL in women; (4) hypertension: blood pressure ≥ 130/85 mmHg or current use of antihypertensive drugs; (5) hyperglycemia: fasting blood glucose ≥ 100 mg/dL or current use of anti-hyperglycemic medications; (6) total sleep time 9 h/day (sleep disorder assessment was not available in CHARLS and was therefore excluded from the criteria); and (7) depression: a score ≥ 10 on the CES-D-10 scale for depressive symptoms. A diagnosis of circadian syndrome was made if a subject met at least four of the above metabolic, sleep, and mental health indicators. Total sleep time was defined as the sum of self-reported nap time and nighttime sleep duration. Blood pressure was calculated as the average of three measurements. All participants were confirmed to be in a fasting state. Covariates Covariates were selected based on previous research and clinical knowledge 31 , 32 . Categorical variables are presented as n (%), including sex, educational level (below high school/high school and above), marital status (married/other), physical activity (whether participating in moderate-to-vigorous physical activity ≥ 10 minutes (yes/no)), alcohol and smoking status (never/former/current), cancer, cardiovascular disease (CVD), and chronic kidney disease (CKD). All normally distributed continuous variables were reported as mean ± SD, and skewed continuous variables were presented as median and interquartile range, including age, weight, height, body mass index (BMI), waist circumference, triglycerides (TG), serum creatinine, fasting blood glucose, low-density lipoprotein (LDL), high-density lipoprotein (HDL), hemoglobin A1c (HbA1c), C-reactive protein (CRP), Center for Epidemiologic Studies Depression Scale (CESD) score, uric acid, and estimated glomerular filtration rate (eGFR). The eGFR was calculated using the serum creatinine (SCr)-based CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation. CVD was defined as a physician diagnosis or the use of related medications. Diabetes was defined as a fasting blood glucose level ≥ 126 mg/dL, an HbA1c level ≥ 6.5%, the use of anti-diabetic medications, or a physician diagnosis. CKD was defined as an eGFR level < 60 mL/min/1.73 m², the use of medications for kidney problems, or a physician diagnosis. After excluding records with missing values for the primary variables, the remaining covariates had less than 1% missing values (see Supplementary Table 1 for details on missing data). A complete-case analysis was performed without multiple imputation, as bias is expected to be negligible under this condition. Statistical analysis We used Chi-square or Fisher’s exact (categorical variables), a one-way ANOVA test (normal distribution), or a Kruskal-Whallis H-test (skewed distribution) to test for differences among different groups. Multivariable logistic regression was employed to calculate the odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) for the association between each air pollutant and the risk of CircS occurrence. In subgroup analyses, stratification was conducted by sex, age, education level, moderate-to-vigorous physical activity, marital status, smoking status, drinking status, CKD, and CVD. Fully adjusted covariate models were constructed within each stratum. Multi-pollutant Analysis: First, Spearman's rank correlation was used to analyze the correlations among the seven air pollutants. Given the extremely strong correlations (ρ > 0.9) between PM 1 , PM 2.5 , and PM 10 , these three were synthesized into a single "particulate matter" group, which was then included alongside the other pollutants in a Bayesian Kernel Machine Regression (BKMR) model 33 , 34 . The Markov Chain Monte Carlo (MCMC) iterations for this model were set to 10,000. The BKMR model estimates the exposure-response relationships between components of the mixture and the outcome by constructing a kernel function based on the mixture variables and employing a Bayesian sampling approach. To assess the independent effect shape of a single pollutant while controlling for the background levels of other pollutants, exposure-response curves for individual pollutants were visualized when all other pollutants were fixed at their 25th, 50th, or 75th percentiles. To further quantify the relative contribution of each pollutant to the overall mixture effect, Weighted Quantile Sum (WQS) regression was simultaneously performed. In the WQS model, the dataset was first randomly split into a development set (40%) and a validation set (60%). A WQS index was then calculated based on the quartile exposure levels of the air pollutants, which quantifies the relative weight of each pollutant within the mixture. This model helps to overcome the multicollinearity issues commonly encountered in traditional regression methods 33 . Finally, sensitivity analyses were conducted to examine the robustness of the findings. The quantile-based g-computation (qgcomp) model, implemented using the R package "qgcomp", was applied to analyze the combined effect of mixed air pollutant exposure. This approach can overcome the limitation of the direction-consistency assumption inherent in the WQS regression model. All analyses in this study were performed using R software (version 4.5.1), and a P value < 0.05 was considered statistically significant. Declarations Ethics approval and consent to participate CHARLS was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Peking University (IRB00001052-11015 for the main household survey; IRB00001052-11014 for biomarker collection). All participants provided written informed consent prior to participation. Data availability The datasets analyzed during the current study are available from third-party public repositories. CHARLS data are available from the China Health and Retirement Longitudinal Study database upon application at https://charls.charlsdata.com/. Air pollution data were obtained from the Yangtze River Delta Science Data Center, National Earth System Science Data Sharing Infrastructure, National Science & Technology Infrastructure of China, at http://geodata.nnu.edu.cn/. The analytical code and derived data supporting the findings of this study are available from the corresponding author upon reasonable request. Additional Information Author contributions Y.S.S. conceived the study, developed the methodology, curated the data, performed the formal analysis, developed the software, visualized the results, and drafted the manuscript. L.Z. contributed to methodology development, data curation, formal analysis, visualization, and drafting of the manuscript. X.O. contributed to methodology development, formal analysis, visualization, and drafting of the manuscript. Z.X. conceived and supervised the study, administered the project, and critically revised the manuscript. All authors reviewed and approved the final manuscript. Competing interests The authors declare no competing interests. Funding This research received no external funding. Acknowledgements We thank all participants in the CHARLS study and the project team for data support. Acknowledgement for the data support from Yangtze River Delta Science Data Center, National Earth System Science Data Sharing Infrastructure, National Science & Technology Infrastructure of China. (http://geodata.nnu.edu.cn). References Zimmet, P. et al. The Circadian Syndrome: is the Metabolic Syndrome and much more! J Intern Med 286 , 181-191 (2019). https://doi.org/10.1111/joim.12924 Dong, Y., Lam, S. M., Li, Y., Li, M. D. & Shui, G. The circadian clock at the intersection of metabolism and aging - emerging roles of metabolites. 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The circadian syndrome predicts cardiovascular disease better than metabolic syndrome in Chinese adults. J Intern Med 289 , 851-860 (2021). https://doi.org/10.1111/joim.13204 Carrico, C., Gennings, C., Wheeler, D. C. & Factor-Litvak, P. Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting. J Agric Biol Environ Stat 20 , 100-120 (2015). https://doi.org/10.1007/s13253-014-0180-3 Li, H. et al. Health effects of air pollutant mixtures on overall mortality among the elderly population using Bayesian kernel machine regression (BKMR). Chemosphere 286 , 131566 (2022). https://doi.org/10.1016/j.chemosphere.2021.131566 Tables Table 1. Baseline characteristics of CHARLS participants by incident circadian syndrome (CircS) status during 4-year follow-up. Characteristic Overall Non-Circs Circs P value N= 2975 N=2204 N=771 Demographics Age (mean ± SD, years) 58.15±8.52 58.03±8.37 58.47±8.92 0.22 Male, n (%) 1535 (51.60%) 1194 (54.17%) 341 (44.23%) <0.001 Weight (mean ± SD, kg) 56.58±10.43 55.55±10.11 59.52±10.78 <0.001 Height (mean ± SD, m) 1.58±0.09 1.58±0.09 1.58±0.10 0.11 BMI (mean ± SD, kg/m2) 23.70±4.75 23.41±4.13 24.52±4.07 0.57 Waist (mean ± SD, cm) 80.68±11.75 79.31±11.26 84.58±12.25 <0.001 Marital status: Married, n (%) 2598 (87.33%) 1945 (88.25%) 653 (84.70%) 0.01 Education level: Less than high school, n (%) 2688 (90.35%) 1986 (90.11%) 702 (91.05%) 0.45 Biochemical & Clinical Measures LDL (mean ± SD,mg/dl) 115.94±31.97 113.62±30.59 122.57±34.80 <0.001 HDL (mean ± SD, mg/dl) 56.56±14.70 57.93±14.89 52.64±13.41 <0.001 HbA1C (mean ± SD, mg/dl) 5.14±0.61 5.11±0.55 5.23±0.75 <0.001 CRP (mean ± SD, mg/dl) 2.37±7.54 2.29±7.92 2.59±6.34 0.35 Uric acid (mean ± SD, mg/dl) 4.27±1.17 4.26±1.15 4.30±1.22 0.39 eGFR (mean ± SD, mg/dl) 97.80±13.01 98.12±12.55 96.90±14.21 0.02 SBP(mean ± SD, mmHg) 124.91±19.60 123.44±18.93 129.13±20.85 <0.001 DBP (mean ± SD, mmHg) 73.23±11.51 72.45±11.32 75.47±11.75 <0.001 TG (mean ± SD, mg/dl) 96.78±48.28 92.59±45.67 108.74±53.31 <0.001 FBG (mean ± SD, mg/dl) 102.15±25.66 101.23±22.79 104.78±32.38 <0.001 Lifestyle & Psychosocial Factors CESD (mean ± SD) 7.14±5.63 7.09±5.67 7.30±5.52 0.38 Sleeptime (mean ± SD, hours) 6.67±1.62 6.64±1.64 6.74±1.54 0.16 Physicalactivity, n (%) 1023 (34.38%) 759 (34.44%) 264 (34.24%) 0.97 Drinking, n (%) 0.02 Never 1679 (56.44%) 1210 (54.90%) 469 (60.83%) Former 203 (6.82%) 153 (6.94%) 50 (6.49%) Current 1093 (36.74%) 841 (38.16%) 252 (32.68%) Smoking, n (%) 0.005 Never 1703 (57.24%) 1223 (55.49%) 480 (62.26%) Former 232 (7.80%) 181 (8.21%) 51 (6.61%) Current 1040 (34.96%) 800 (36.30%) 240 (31.13%) Comorbidities Cancer, n (%) 18 (0.61%) 10 (0.45%) 8 (1.04%) 0.07 CVD, n (%) 291 (9.78%) 205 (9.30%) 86 (11.15%) 0.14 CKD, n (%) 233 (7.83%) 178 (8.08%) 55 (7.13%) 0.40 DM, n (%) 270 (9.08%) 171 (7.76%) 99 (12.84%) <0.001 Air Pollutants ( 4-year average) CO(mg/m3) 1.13±0.32 1.12±0.32 1.15±0.33 0.02 NO2(µg/m3) 29.07±9.55 28.85±9.57 29.70±9.48 0.03 SO2(µg/m3) 28.60±13.24 28.28±13.18 29.50±13.36 0.03 O3(µg/m3) 85.32±6.29 85.17±6.31 85.75±6.22 0.03 PM1(µg/m3) 30.33±9.67 30.17±9.65 30.78±9.72 0.14 PM2.5(µg/m3) 54.09±18.04 53.78±18.08 54.97±17.92 0.12 PM10(µg/m3) 91.53±32.12 90.78±32.33 93.65±31.45 0.03 Notes: Baseline characteristics were assessed at the 2011–2012 CHARLS wave and compared between participants who did versus did not develop new-onset CircS by 2015 (Non-CircS vs CircS); data are presented as mean ± SD or n (%) (Yes-only for dichotomous variables), P values are from appropriate between-group tests, and air pollutants are 4-year average CHAP-based exposures linked to residential location (full table in the Supplementary Information). CHARLS , China Health and Retirement Longitudinal Study; BMI , body mass index; CES-D , Center for Epidemiologic Studies Depression Scale; CircS , circadian syndrome; CKD , chronic kidney disease; CO , carbon monoxide; CRP , C-reactive protein; CVD , cardiovascular disease; DBP , diastolic blood pressure; DM , diabetes mellitus; eGFR , estimated glomerular filtration rate; FBG , fasting blood glucose; HbA1c , hemoglobin A1c; HDL-C , high-density lipoprotein cholesterol; LDL-C , low-density lipoprotein cholesterol; NO₂ , nitrogen dioxide; O₃ , ozone; PM , particulate matter; SBP , systolic blood pressure; SD , standard deviation; SO₂ , sulfur dioxide; TG , triglycerides. Table 2. Logistic regression analyses for the association between long-term ambient air pollution and incident CircS in CHARLS. Characteristic Model 1 Model 2 Model 3 OR (95%CI) P value OR (95%CI) P value OR (95%CI) P value CO Per SD increase 1.10 (1.01, 1.19) 0.02 1.11 (1.02, 1.20) 0.01 1.14 (1.05, 1.24) 0.002 Q0 Ref Ref Ref Q1 1.18 (0.93, 1.49) 0.18 1.17 (0.93, 1.49) 0.19 1.21 (0.95, 1.54) 0.12 Q2 1.04 (0.82, 1.32) 0.76 1.02 (0.81, 1.30) 0.85 1.07 (0.84, 1.36) 0.60 Q3 1.38 (1.10, 1.74) 0.006 1.41 (1.11, 1.78) 0.00 1.51 (1.19, 1.92) <0.001 P for trend 0.02 0.02 0.004 NO2 Per SD increase 1.09 (1.01, 1.18) 0.03 1.09 (1.01, 1.19) 0.03 1.13 (1.04, 1.23) 0.005 Q0 Ref Ref Ref Q1 0.92 (0.72, 1.17) 0.50 0.92 (0.72, 1.17) 0.49 0.91 (0.71, 1.16) 0.46 Q2 1.24 (0.98, 1.56) 0.08 1.23 (0.97, 1.56) 0.08 1.32 (1.04, 1.68) 0.02 Q3 1.19 (0.94, 1.50) 0.14 1.20 (0.95, 1.51) 0.14 1.27 (1.00, 1.62) 0.05 P for trend 0.03 0.03 0.005 O3 Per SD increase 1.10 (1.01, 1.19) 0.03 1.09 (1.01, 1.19) 0.03 1.12 (1.03, 1.22) 0.008 Q0 Ref Ref Ref Q1 0.86 (0.68, 1.10) 0.23 0.88 (0.69, 1.12) 0.29 0.84 (0.65, 1.07) 0.16 Q2 1.05 (0.83, 1.33) 0.66 1.08 (0.85, 1.37) 0.53 1.13 (0.89, 1.43) 0.33 Q3 1.28 (1.01, 1.60) 0.04 1.27 (1.01, 1.60) 0.04 1.32 (1.04, 1.67) 0.02 P for trend 0.01 0.01 0.003 PM1 Per SD increase 1.06 (0.98, 1.15) 0.14 1.06 (0.98, 1.16) 0.14 1.10 (1.01, 1.20) 0.03 Q0 Ref Ref Ref Q1 1.03 (0.81, 1.31) 0.79 1.02 (0.80, 1.30) 0.88 1.07 (0.84, 1.37) 0.58 Q2 1.27 (1.00, 1.62) 0.05 1.26 (0.99, 1.60) 0.06 1.39 (1.09, 1.78) 0.01 Q3 1.16 (0.91, 1.47) 0.23 1.15 (0.91, 1.46) 0.25 1.26 (0.99, 1.62) 0.06 P for trend 0.09 0.10 0.02 PM2.5 Per SD increase 1.07 (0.98, 1.16) 0.12 1.07 (0.98, 1.16) 0.12 1.11 (1.02, 1.21) 0.02 Q0 Ref Ref Ref Q1 1.27 (1.00, 1.61) 0.05 1.27 (1.00, 1.62) 0.05 1.39 (1.08, 1.77) 0.009 Q2 1.32 (1.04, 1.68) 0.02 1.31 (1.03, 1.67) 0.03 1.49 (1.17, 1.91) 0.002 Q3 1.24 (0.98, 1.58) 0.08 1.24 (0.98, 1.59) 0.08 1.39 (1.08, 1.79) 0.01 P for trend 0.09 0.09 0.01 PM10 Per SD increase 1.09 (1.01, 1.18) 0.03 1.10 (1.01, 1.19) 0.03 1.16 (1.06, 1.26) <0.001 Q0 Ref Ref Ref Q1 1.32 (1.04, 1.67) 0.02 1.33 (1.04, 1.69) 0.02 1.48 (1.15, 1.89) 0.002 Q2 1.33 (1.05, 1.69) 0.02 1.33 (1.05, 1.70) 0.02 1.60 (1.24, 2.06) <0.001 Q3 1.39 (1.10, 1.77) 0.01 1.41 (1.11, 1.79) 0.005 1.64 (1.27, 2.11) <0.001 P for trend 0.01 0.01 <0.001 SO2 Per SD increase 1.09 (1.01, 1.19) 0.03 1.10 (1.01, 1.19) 0.02 1.15 (1.06, 1.25) 0.001 Q0 Ref Ref Ref Q1 0.93 (0.73, 1.19) 0.56 0.92 (0.72, 1.17) 0.48 0.97 (0.76, 1.24) 0.81 Q2 1.17 (0.93, 1.47) 0.19 1.18 (0.93, 1.48) 0.17 1.35 (1.06, 1.72) 0.01 Q3 1.24 (0.99, 1.57) 0.06 1.26 (1.00, 1.59) 0.05 1.44 (1.13, 1.83) 0.004 P for trend 0.02 0.01 <0.001 Notes: ORs (95% CIs) for incident CircS at 4-year follow-up were estimated per 1-SD increase and by quartiles of each pollutant (Q0–Q3; Q0 as reference) using CHAP-based residential exposure estimates; P for trend tests linear trends across quartiles. Model 1 : unadjusted; Model 2 : adjusted for age and sex; Model 3 : additionally adjusted for ethnicity, education, household income, smoking, drinking, physical activity, BMI, hypertension, diabetes, and dyslipidemia. CHARLS , China Health and Retirement Longitudinal Study; CircS , circadian syndrome; CHAP , China High Air Pollutants; OR , odds ratio; CI , confidence interval; SD , standard deviation; BMI , body mass index; CO, carbon monoxide; NO₂ , nitrogen dioxide; SO₂ , sulfur dioxide; O₃ , ozone; PM₁ , particulate matter ≤1 μm; PM₂.₅ , particulate matter ≤2.5 μm; PM₁₀ , particulate matter ≤10 μm. Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Supplementary Figure S1. Markov chain Monte Carlo (MCMC) trace plot for the β parameter in the BKMR model. Notes: The trace plot shows the sampled values of β₁ across Markov chain Monte Carlo (MCMC) iterations for the BKMR mixture model assessing the joint association of seven air pollutants (CO, NO₂, SO₂, O₃, PM₁, PM₂.₅, and PM₁₀) with incident circadian syndrome (CircS). The stable, well-mixed trace without marked drift indicates adequate convergence and supports the reliability of posterior estimates reported in the main and supplementary mixture analyses. Covariate adjustment followed the fully adjusted main model. MCMC , Markov chain Monte Carlo; BKMR , Bayesian kernel machine regression; CircS , circadian syndrome. FigureS2.tif Supplementary Figure S2. MCMC trace plot for the residual variance parameter (σ²ε) in the BKMR model. Notes: The trace plot displays sampled values of the BKMR residual variance parameter (σ²ε) across Markov chain Monte Carlo (MCMC) iterations for the air-pollution mixture model evaluating incident circadian syndrome (CircS). The flat trace at 1.0 indicates that σ²ε was fixed (or effectively constrained) during model fitting, consistent with the BKMR implementation used, and does not indicate lack of convergence. All BKMR mixture analyses included CO, NO₂, SO₂, O₃, PM₁, PM₂.₅, and PM₁₀ and adjusted for covariates as in the fully adjusted main model. MCMC , Markov chain Monte Carlo; BKMR , Bayesian kernel machine regression; CircS , circadian syndrome; σ²ε, residual variance. FigureS3.tif Supplementary Figure S3. Subgroup analysis of long-term NO₂ exposure and incident CircS. Notes: Forest plot of adjusted ORs (95% CIs) for incident CircS across NO₂ exposure quartiles (G0–G3; G0 reference) within predefined subgroups. P for trend tests the linear trend across quartiles, and P for interaction tests effect modification by each subgroup characteristic. Models followed the fully adjusted main model and omitted the stratification variable where applicable. CircS , circadian syndrome; NO₂ , nitrogen dioxide; OR , odds ratio; CI , confidence interval; CKD , chronic kidney disease; CVD , cardiovascular disease. FigureS4.tif Supplementary Figure S4. Subgroup analysis of long-term O₃ exposure and incident CircS. Notes: Forest plot of adjusted ORs (95% CIs) for incident CircS across O₃ exposure quartiles (G0–G3; G0 reference) within predefined subgroups. P for trend tests the linear trend across quartiles, and P for interaction assesses effect modification by each subgroup characteristic. Models followed the fully adjusted main model and omitted the stratification variable where applicable. CircS , circadian syndrome; O₃ , ozone; OR , odds ratio; CI , confidence interval; CKD , chronic kidney disease; CVD , cardiovascular disease. FigureS5.tif Supplementary Figure S5. Subgroup analysis of long-term PM₁ exposure and incident CircS. Notes: Forest plot of adjusted ORs (95% CIs) for incident CircS across PM₁ exposure quartiles (G0–G3; G0 reference) within predefined subgroups. P for trend tests the linear trend across quartiles, and P for interaction assesses effect modification by each subgroup characteristic. Models followed the fully adjusted main model and omitted the stratification variable where applicable. CircS , circadian syndrome; PM₁ , particulate matter ≤1 μm; OR , odds ratio; CI , confidence interval; CKD , chronic kidney disease; CVD , cardiovascular disease. FigureS6.tif Supplementary Figure S6. Subgroup analysis of long-term PM₂.₅ exposure and incident CircS. Notes: Forest plot of adjusted ORs (95% CIs) for incident CircS across PM₂.₅ exposure quartiles (G0–G3; G0 reference) within predefined subgroups. P for trend tests the linear trend across quartiles, and P for interaction assesses effect modification by each subgroup characteristic. Models followed the fully adjusted main model and omitted the stratification variable where applicable. CircS , circadian syndrome; PM₂.₅ , particulate matter ≤2.5 μm; OR , odds ratio; CI , confidence interval; CKD , chronic kidney disease; CVD , cardiovascular disease. FigureS7.tif Supplementary Figure S7. Subgroup analysis of long-term PM₁₀ exposure and incident CircS. Notes: Adjusted ORs (95% CIs) for incident CircS across PM₁₀ quartiles (G0–G3; G0 reference) are shown within subgroups. P for trend tests trends across quartiles; P for interaction tests effect modification. Models used the fully adjusted covariate set and omitted the stratification variable where applicable. CircS , circadian syndrome; PM₁₀ , particulate matter ≤10 μm; OR , odds ratio; CI , confidence interval; CKD , chronic kidney disease; CVD , cardiovascular disease. FigureS8.tif Supplementary Figure S8. Subgroup analysis of long-term SO₂)exposure and incident CircS. Notes: Adjusted ORs (95% CIs) for incident CircS across SO₂ quartiles (G0–G3; G0 reference) are shown within subgroups. P for trend tests trends across quartiles; P for interaction tests effect modification. Models used the fully adjusted covariate set and omitted the stratification variable where applicable. CircS , circadian syndrome; SO₂ , sulfur dioxide; OR , odds ratio; CI , confidence interval; CKD , chronic kidney disease; CVD , cardiovascular disease. SupplementaryTables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 06 Apr, 2026 Editor invited by journal 26 Mar, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 24 Mar, 2026 First submitted to journal 23 Mar, 2026 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9202516","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619955835,"identity":"0250cb44-af75-49eb-8c94-55fbce297f1e","order_by":0,"name":"Yongshun Su","email":"","orcid":"","institution":"Cancer Hospital of Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yongshun","middleName":"","lastName":"Su","suffix":""},{"id":619955840,"identity":"46634c10-0467-4719-bbce-51965d01cb76","order_by":1,"name":"Li Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""},{"id":619955848,"identity":"535a0ecd-bb53-44a5-9f3d-17dd8865acef","order_by":2,"name":"Xiaoli Ou","email":"","orcid":"","institution":"The First Affiliated Hospital of Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Ou","suffix":""},{"id":619955854,"identity":"1756760e-e610-49c8-8fcd-bbfdd11b81b1","order_by":3,"name":"Zhongbo Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACNv7+7x8SeCR45Pn7Hz5IqKghrIVP4oAZwwcZCxnDGWeYDR6cOUZYixxDghnjDJsKG4YDOWySD1uYiXAYw4G0xzw5EjyMDWePVSQ2sDHwt3cn4NfC3HDcmOeMBA87c1/ajcQdMgwSZ85uIGDLwQZp3h6QLQfMbiSeYWMwkMglpCWZQZr3nwQPw4EEs4LENmZitKSxSc7gAWnJMWMgTosEMGw/ALUYzjiWLJFw5hgPQb/I9/cwPkjgqbOX528++PFHRY0cf3svfi0YgIc05aNgFIyCUTAKsAIAlKZGJi6YbCAAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Shantou University Medical College","correspondingAuthor":true,"prefix":"","firstName":"Zhongbo","middleName":"","lastName":"Xiao","suffix":""}],"badges":[],"createdAt":"2026-03-23 15:39:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9202516/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9202516/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106836726,"identity":"33c2995f-b8ff-4e5a-a0f6-4313e966de26","added_by":"auto","created_at":"2026-04-14 02:09:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201518,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of participant selection from the China Health and Retirement Longitudinal Study (CHARLS), 2011–2015.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants aged ≥45 years with available biomarker data in both Wave 1 (2011–2012) and Wave 3 (2015–2016) were eligible. We excluded individuals who were aged \u0026lt;45 years, lacked CircS assessment data at baseline or follow-up, had prevalent CircS at baseline (Wave 1), or had missing residential air pollution exposure data. The final analytic sample included 2,975 participants free of CircS at baseline who were followed for 4 years to ascertain incident CircS at the 2015 follow-up. \u003cem\u003eCHARLS\u003c/em\u003e, China Health and Retirement Longitudinal Study; \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/4f67a6aeaad173aaf34d7e45.png"},{"id":106836744,"identity":"9df75d3f-2741-45e7-b969-87c9bef2c4a1","added_by":"auto","created_at":"2026-04-14 02:09:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1476159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution of long-term ambient air pollutants in the cities where CHARLS participants resided during follow-up.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAverage concentrations derived from the China High Air Pollutants (CHAP) dataset were linked to participants’ residential locations and mapped across China. Panels show the spatial distribution of (A) CO, (B) NO₂, (C) O₃, (D) PM₁, (E) PM₂.₅, (F) PM₁₀, and (G) SO₂; darker colors indicate higher concentrations (units: CO in mg/m³; all other pollutants in μg/m³). Concentrations represent follow-up period averages (2011–2015; CO and SO₂ available from 2013–2015). \u003cem\u003eCHARLS\u003c/em\u003e, China Health and Retirement Longitudinal Study; \u003cem\u003eCHAP\u003c/em\u003e, China High Air Pollutants; \u003cem\u003eCO\u003c/em\u003e, carbon monoxide; \u003cem\u003eNO₂\u003c/em\u003e, nitrogen dioxide; \u003cem\u003eSO₂\u003c/em\u003e, sulfur dioxide; \u003cem\u003eO₃\u003c/em\u003e, ozone; \u003cem\u003ePM₁\u003c/em\u003e, particulate matter ≤1 μm; \u003cem\u003ePM₂.₅\u003c/em\u003e, particulate matter ≤2.5 μm; \u003cem\u003ePM₁₀\u003c/em\u003e, particulate matter ≤10 μm.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/67da050f69e3486215620eee.png"},{"id":106960572,"identity":"61a43a41-c964-41fe-af5d-d12c10437b80","added_by":"auto","created_at":"2026-04-15 09:21:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":173011,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis of CO exposure levels and the risk of circadian syndrome (CircS).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForest plot showing adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for incident CircS across quartiles of long-term CO exposure (G0–G3; G0 as the reference) within subgroups defined by age, sex, marital status, education level, smoking, drinking, physical activity, chronic kidney disease (CKD), and cardiovascular disease (CVD). Estimates were obtained from logistic regression models adjusted as in the fully adjusted main model, with the stratification variable omitted when applicable. “P for trend” tests the linear trend across CO quartiles within each subgroup, and “P for interaction” tests effect modification by the subgroup characteristic. \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome; \u003cem\u003eCO\u003c/em\u003e, carbon monoxide; \u003cem\u003eOR\u003c/em\u003e, odds ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval; \u003cem\u003eCKD\u003c/em\u003e, chronic kidney disease; \u003cem\u003eCVD\u003c/em\u003e, cardiovascular disease; \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/d4143b4187f0c9d7f505eade.png"},{"id":106836686,"identity":"fdf0701c-9ee9-4d11-b2b2-4f678a6d1885","added_by":"auto","created_at":"2026-04-14 02:09:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1408732,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePairwise correlations among seven ambient air pollutants in the study population.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrelation heatmap/correlogram showing correlation coefficients (r) among long-term residential exposures to CO, NO₂, SO₂, O₃, PM₁, PM₂.₅, and PM₁₀ (CHAP-based exposure estimates). Numeric values denote r, with circle size and color intensity proportional to the magnitude of the correlation; blue indicates positive correlation and red indicates negative correlation. \u003cem\u003eCO\u003c/em\u003e, carbon monoxide; \u003cem\u003eNO₂\u003c/em\u003e, nitrogen dioxide; \u003cem\u003eSO₂\u003c/em\u003e, sulfur dioxide;\u003cem\u003e O₃\u003c/em\u003e, ozone; \u003cem\u003ePM₁\u003c/em\u003e, particulate matter ≤1 μm; \u003cem\u003ePM₂.₅\u003c/em\u003e, particulate matter ≤2.5 μm; \u003cem\u003ePM₁₀\u003c/em\u003e, particulate matter ≤10 μm; \u003cem\u003eCHAP\u003c/em\u003e, China High Air Pollutants.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/1b687b44e734a157546997ee.png"},{"id":106836714,"identity":"98ebaf66-a1fd-4802-bfcc-1e4d3bb144a0","added_by":"auto","created_at":"2026-04-14 02:09:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":177311,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eJoint effects of multiple air pollutants on incident CircS assessed using Bayesian kernel machine regression (BKMR) and weighted quantile sum (WQS) models.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes: \u003c/strong\u003e(A) Relationship between mixed air pollution exposure and CircS assessed by the BKMR model. (B) Using the BKMR model, the effect of specific air pollutants (at median concentrations) on incident CircS was estimated while fixing the exposure levels of other air pollutants at specific percentiles (25th, 50th, and 75th percentiles). (C) Dose-response relationship curves between each air pollutant and CircS under conditions controlling for other pollutants. (D) Weights of the seven air pollutants assessed by the WQS model. \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome; \u003cem\u003eBKMR\u003c/em\u003e, Bayesian kernel machine regression; \u003cem\u003eWQS\u003c/em\u003e, weighted quantile sum\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/2bfbd724ad4f7ebb53c1a21e.png"},{"id":106962949,"identity":"b42df52f-3c36-4bb8-bbb2-4b4fdc1aafe9","added_by":"auto","created_at":"2026-04-15 09:41:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4701978,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/69c61517-ec1f-473b-89b1-2f0d97068e41.pdf"},{"id":106836807,"identity":"66b1febe-b341-46ff-bbf7-3eaee5d7dd13","added_by":"auto","created_at":"2026-04-14 02:09:42","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15625744,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1. Markov chain Monte Carlo (MCMC) trace plot for the β parameter in the BKMR model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes: \u003c/strong\u003eThe trace plot shows the sampled values of β₁ across Markov chain Monte Carlo (MCMC) iterations for the BKMR mixture model assessing the joint association of seven air pollutants (CO, NO₂, SO₂, O₃, PM₁, PM₂.₅, and PM₁₀) with incident circadian syndrome (CircS). The stable, well-mixed trace without marked drift indicates adequate convergence and supports the reliability of posterior estimates reported in the main and supplementary mixture analyses. Covariate adjustment followed the fully adjusted main model. \u003cem\u003eMCMC\u003c/em\u003e, Markov chain Monte Carlo; \u003cem\u003eBKMR\u003c/em\u003e, Bayesian kernel machine regression; \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome.\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/1003b37814062c2a7e89617e.tif"},{"id":106836677,"identity":"95333158-730e-458a-9be2-6d00039f6b25","added_by":"auto","created_at":"2026-04-14 02:09:19","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":6383284,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S2. MCMC trace plot for the residual variance parameter (σ²ε) in the BKMR model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes: \u003c/strong\u003eThe trace plot displays sampled values of the BKMR residual variance parameter (σ²ε) across Markov chain Monte Carlo (MCMC) iterations for the air-pollution mixture model evaluating incident circadian syndrome (CircS). The flat trace at 1.0 indicates that σ²ε was fixed (or effectively constrained) during model fitting, consistent with the BKMR implementation used, and does not indicate lack of convergence. All BKMR mixture analyses included CO, NO₂, SO₂, O₃, PM₁, PM₂.₅, and PM₁₀ and adjusted for covariates as in the fully adjusted main model. \u003cem\u003eMCMC\u003c/em\u003e, Markov chain Monte Carlo; \u003cem\u003eBKMR\u003c/em\u003e, Bayesian kernel machine regression; \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome; σ²ε, residual variance.\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/307355dbef835cec9268e3c2.tif"},{"id":106836653,"identity":"590b1fa1-97b5-4de7-ac86-587c04850624","added_by":"auto","created_at":"2026-04-14 02:09:09","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15363212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S3. Subgroup analysis of long-term NO₂ exposure and incident CircS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Forest plot of adjusted ORs (95% CIs) for incident CircS across NO₂ exposure quartiles (G0–G3; G0 reference) within predefined subgroups. \u003cem\u003eP \u003c/em\u003efor trend tests the linear trend across quartiles, and P for interaction tests effect modification by each subgroup characteristic. Models followed the fully adjusted main model and omitted the stratification variable where applicable. \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome; \u003cem\u003eNO₂\u003c/em\u003e, nitrogen dioxide; \u003cem\u003eOR\u003c/em\u003e, odds ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval; \u003cem\u003eCKD\u003c/em\u003e, chronic kidney disease; \u003cem\u003eCVD\u003c/em\u003e, cardiovascular disease.\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/f76de6f0bd30703f09ae0ab4.tif"},{"id":106836804,"identity":"56849d4c-05b4-4e8c-8bf7-4c59bf7a16db","added_by":"auto","created_at":"2026-04-14 02:09:42","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15141414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S4. Subgroup analysis of long-term O₃ exposure and incident CircS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Forest plot of adjusted ORs (95% CIs) for incident CircS across O₃ exposure quartiles (G0–G3; G0 reference) within predefined subgroups. \u003cem\u003eP \u003c/em\u003efor trend tests the linear trend across quartiles, and P for interaction assesses effect modification by each subgroup characteristic. Models followed the fully adjusted main model and omitted the stratification variable where applicable. \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome;\u003cem\u003e O₃\u003c/em\u003e, ozone; \u003cem\u003eOR\u003c/em\u003e, odds ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval; \u003cem\u003eCKD\u003c/em\u003e, chronic kidney disease; \u003cem\u003eCVD\u003c/em\u003e, cardiovascular disease.\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/af310664ef4e7c6508fcf71e.tif"},{"id":106836766,"identity":"7de49389-c66e-4a51-893a-1245913056d6","added_by":"auto","created_at":"2026-04-14 02:09:36","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15236298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S5. Subgroup analysis of long-term PM₁ exposure and incident CircS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes: \u003c/strong\u003eForest plot of adjusted ORs (95% CIs) for incident CircS across PM₁ exposure quartiles (G0–G3; G0 reference) within predefined subgroups. \u003cem\u003eP \u003c/em\u003efor trend tests the linear trend across quartiles, and P for interaction assesses effect modification by each subgroup characteristic. Models followed the fully adjusted main model and omitted the stratification variable where applicable. \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome; \u003cem\u003ePM₁\u003c/em\u003e, particulate matter ≤1 μm; \u003cem\u003eOR\u003c/em\u003e, odds ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval; \u003cem\u003eCKD\u003c/em\u003e, chronic kidney disease; \u003cem\u003eCVD\u003c/em\u003e, cardiovascular disease.\u003c/p\u003e","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/18a6ab40ed29670e705130f2.tif"},{"id":106836715,"identity":"4a8c8e40-4d97-4564-892a-46302c5618fa","added_by":"auto","created_at":"2026-04-14 02:09:25","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":15299676,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S6. Subgroup analysis of long-term PM₂.₅ exposure and incident CircS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes: \u003c/strong\u003eForest plot of adjusted ORs (95% CIs) for incident CircS across PM₂.₅ exposure quartiles (G0–G3; G0 reference) within predefined subgroups. \u003cem\u003eP \u003c/em\u003efor trend tests the linear trend across quartiles, and P for interaction assesses effect modification by each subgroup characteristic. Models followed the fully adjusted main model and omitted the stratification variable where applicable. \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome; \u003cem\u003ePM₂.₅\u003c/em\u003e, particulate matter ≤2.5 μm; \u003cem\u003eOR\u003c/em\u003e, odds ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval; \u003cem\u003eCKD\u003c/em\u003e, chronic kidney disease; \u003cem\u003eCVD\u003c/em\u003e, cardiovascular disease.\u003c/p\u003e","description":"","filename":"FigureS6.tif","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/39cb682a74d0efb60fc126a8.tif"},{"id":106836667,"identity":"3622fa40-ae4a-4200-a0f1-79c85314a068","added_by":"auto","created_at":"2026-04-14 02:09:15","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":15181122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S7. Subgroup analysis of long-term PM₁₀ exposure and incident CircS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes: \u003c/strong\u003eAdjusted ORs (95% CIs) for incident CircS across PM₁₀ quartiles (G0–G3; G0 reference) are shown within subgroups. \u003cem\u003eP \u003c/em\u003efor trend tests trends across quartiles; P for interaction tests effect modification. Models used the fully adjusted covariate set and omitted the stratification variable where applicable. \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome; \u003cem\u003ePM₁₀\u003c/em\u003e, particulate matter ≤10 μm; \u003cem\u003eOR\u003c/em\u003e, odds ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval; \u003cem\u003eCKD\u003c/em\u003e, chronic kidney disease; \u003cem\u003eCVD\u003c/em\u003e, cardiovascular disease.\u003c/p\u003e","description":"","filename":"FigureS7.tif","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/9eeffcd8c3aa03524f4cc4b0.tif"},{"id":106836853,"identity":"48b85b26-d718-4bdc-8522-64c750039109","added_by":"auto","created_at":"2026-04-14 02:09:50","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":15176546,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S8. Subgroup analysis of long-term SO₂)exposure and incident CircS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes: \u003c/strong\u003eAdjusted ORs (95% CIs) for incident CircS across SO₂ quartiles (G0–G3; G0 reference) are shown within subgroups. \u003cem\u003eP \u003c/em\u003efor trend tests trends across quartiles; P for interaction tests effect modification. Models used the fully adjusted covariate set and omitted the stratification variable where applicable. \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome; \u003cem\u003eSO₂\u003c/em\u003e, sulfur dioxide; \u003cem\u003eOR\u003c/em\u003e, odds ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval; \u003cem\u003eCKD\u003c/em\u003e, chronic kidney disease; \u003cem\u003eCVD\u003c/em\u003e, cardiovascular disease.\u003c/p\u003e","description":"","filename":"FigureS8.tif","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/b4a8ca56244134b5495946c3.tif"},{"id":106836680,"identity":"a2d32290-9cc4-46be-92a6-cd8be3f9e3e0","added_by":"auto","created_at":"2026-04-14 02:09:19","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":29048,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9202516/v1/9cb7753376259c6b5effe427.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Air Pollutants and Circadian Syndrome in China: A Longitudinal Cohort Study Based on CHARLS","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe circadian rhythm system has long been acknowledged as a major regulator of metabolism\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It tightly controls the temporal patterns of hormone secretion, enzyme activity, and energy intake by modulating the expression of clock genes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In recent years, accumulating research has shown that this complex regulatory mechanism is not only closely related to individual lifestyle habits but also significantly impacts the risk of developing chronic disorders. Disruption of the circadian rhythm is associated with many modern chronic diseases, including Metabolic Syndrome (MetS) and hypertension\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In 2019, the concept of Circadian Syndrome (CircS) was formally put forward\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It includes indicators related to blood pressure, waist circumference, blood lipids, sleep, and mood, and has greater predictive value for cardiovascular events and chronic digestive system diseases than traditional MetS\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Currently, the prevalence of CircS is gradually increasing globally\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In the face of this substantial and increasingly pressing public health burden, early identification of modifiable environmental triggers to prevent its onset is extremely important.\u003c/p\u003e \u003cp\u003eAir pollution, classified by the WHO as the \u0026ldquo;single largest avoidable health risk,\u0026rdquo; has only recently been recognized as a \u0026ldquo;circadian disruptor.\u0026rdquo; Recent studies suggest that air pollution not only directly raises the risk of chronic diseases such as cardiovascular conditions but may also further disrupt circadian rhythms by interfering with the endocrine system and metabolic cycles, which are key manifestations of CircS\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Animal studies have demonstrated that long - term exposure to particulate matter like PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e exacerbates oxidative stress and inflammation, directly disrupts the circadian peaks and troughs of glucose tolerance\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Multiple population - based studies have found that long-term exposure to high concentrations of gaseous pollutants such as SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e affects sleep quality and emotional stability: for every 10 \u0026micro;g/m\u0026sup3; increase in NO\u003csub\u003e2\u003c/sub\u003e, nocturnal melatonin levels decrease by 15%, accompanied by a 1.2 fold increase in the odds of depressive symptoms\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Sleep and mood disturbances are precisely core indicators of CircS\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Therefore, we hypothesize that air pollution, through multiple mechanisms, acts as a profound potential trigger for circadian rhythm disruption and other chronic metabolic diseases, thereby increasing the risk of CircS. However, current research mainly focuses on single pollutants, overlooking real world mixed exposures to \u0026ldquo;gaseous\u0026thinsp;+\u0026thinsp;particulate\u0026rdquo; pollutants\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Additionally, outcomes are often limited to single components such as depression or obesity, lacking quantification of the \u0026ldquo;holistic phenotype\u0026rdquo; of CircS. Moreover, previous studies have been mostly concentrated in Europe and North America, leaving a research gap regarding mixed air pollution data and CircS among the middle-aged and elderly population in China.\u003c/p\u003e \u003cp\u003eThis study is a retrospective cohort study based on the China Health and Retirement Longitudinal Study (CHARLS) database. By integrating multi - year environmental exposure data (including PM\u003csub\u003e1\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, CO, and O\u003csub\u003e3\u003c/sub\u003e) with nationally representative health indicators, our research can simultaneously assess the cumulative effects of multiple pollutants across different geographical regions. This study will provide new evidence to clarify the potential link between air pollution and CircS.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, 2,975 participants were included in the analysis, comprising 1,440 females and 1,535 males. Stratification was performed based on the classification characteristic of whether new‑onset CircS occurred in 2015 (Table 1). The mean baseline age was 58.15 ± 8.52 years. Compared with the non‑CircS group, the new‑onset CircS group exhibited significant differences in multiple characteristics, including anthropometric measurements, laboratory indicators, lifestyle factors, and comorbidities (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Table 1 presents the mean concentrations (μg/m³) of seven air pollutants during the follow‑up period. Except for PM₁ and PM₂.₅, all other air pollutants were significantly higher in the CircS group(more detailed information is provided in Supplementary Table 2). Figure 2 illustrates the spatial distribution of air pollutants in the cities where the participants resided. Significant regional disparities in air pollution were observed across China: during the follow‑up period, the most severe air pollution was found in the Northwest and North China regions, with the highest levels of PM₂.₅ and PM₁₀ recorded in the Northwest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between Seven Air Pollutants and CircS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the multivariate logistic regression estimates for the association between seven air pollutants and new-onset CircS. When analyzed as continuous variables in Model 3 (adjusted for all potential confounders), each 1 μg/m³ increase in air pollutant concentration was associated with a 10–16% elevated risk of new-onset CircS (OR range: 1.10 to 1.16, all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). Among them, PM₁₀ exhibited the strongest effect (OR = 1.16; 95% CI: 1.06–1.26). After categorization by quartiles, the exposure-response relationship became more pronounced: in the fully adjusted model, using the lowest quartile (Q0) as the reference, the ORs for the highest quartile (Q3) ranged from 1.26 to 1.64. Specifically, the Q3 OR for CO was 1.51 (95% CI: 1.19–1.92), and for PM₁₀ it reached 1.64 (95% CI: 1.27–2.11). Notably, for NO₂ and PM₁, the highest risk estimates were observed at Q2 (OR for NO₂ = 1.32, 95% CI: 1.04–1.68; OR for PM₁ = 1.39, 95% CI: 1.09–1.78).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the heterogeneity of air pollution effects across populations, we performed subgroup analyses for pollutant exposure and CircS across nine dimensions: age, sex, marital status, education, physical activity, smoking, alcohol consumption, CKD, and CVD. Figure 3 presents the results of the subgroup analysis for CO pollutants and CircS. The association between CO exposure and CircS was more pronounced in older adults and current smokers, with the strongest dose-response relationship observed in the ≥66-year-old female. An interaction was identified in patients with CKD (\u003cem\u003eP\u003c/em\u003e = 0.024). However, no interactions were found in the subgroup analyses for other pollutants (Supplementary Figures 3-8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelationships between concurrent exposure to air pollution and CircS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e displays the correlation coefficients among the seven air pollutants. The correlation coefficients for PM\u003csub\u003e1\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and PM\u003csub\u003e10\u003c/sub\u003e were the highest (r \u0026gt; 0.9), indicating a high degree of spatial consistency in the distribution of these pollutants. The correlation coefficient between O\u003csub\u003e3\u003c/sub\u003e and CO was the lowest (r = 0.35), while the remaining atmospheric pollutants exhibited moderate correlations.\u003c/p\u003e\n\u003cp\u003eThe association between CircS and mixed exposure to air pollutants was further explored. Higher concentrations of the pollutant mixture were generally associated with an increased risk of CircS. When the overall air pollution mixture concentration reached the 80th percentile, the likelihood of the outcome increased by 18.9% (Figure 5A, Supplementary Table 3-4). After controlling for other air pollutants at their 25th, 50th, and 75th percentiles, CO, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e were all positively associated with the occurrence of CircS. PM1, PM\u003csub\u003e2.5\u003c/sub\u003e, and PM\u003csub\u003e10\u003c/sub\u003e were grouped together due to their high correlation, with PM10 showing a positive association with the outcome (Figure \u003cstrong\u003e5\u003c/strong\u003eB, C). The WQS model, which calculated the weights of the seven mixed pollutants in Figure 5D, revealed that O\u003csub\u003e3\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e had the highest weights, indicating their predominant influence on CircS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated the association between air pollutant mixtures and CircS using the Qgcomp model. The results showed that in the mixture, PM\u003csub\u003e2.5\u003c/sub\u003e exhibited a negative weight, while PM\u003csub\u003e10\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e demonstrated the highest positive weights. This finding is consistent with the conclusions from previous analyses based on the BKMR and WQS models (see Supplementary Figure 9). Supplementary Table 5 lists the estimated values, standard errors, and \u003cem\u003eP\u003c/em\u003e values for the relevant air pollutants.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the associations between air pollutants and the incidence of CircS in middle-aged and elderly populations in China. Findings indicate that exposure to air pollutants, individually or in combination, increases the risk of CircS. In real-world mixed exposure scenarios, O₃, SO₂, and PM₁₀ were identified as the core drivers of this effect. This research aims to offer new evidence and references for improving the health of the elderly and for the scientific prevention and management of CircS.\u003c/p\u003e \u003cp\u003eExisting evidence suggests multiple pathways: fine particles (e.g., PM₁, PM₂.₅) can enter systemic circulation, induce oxidative stress, and disrupt circadian gene expression\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Gaseous pollutants like NO₂ and SO₂ may interfere with melatonin secretion and hormonal rhythms\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In our analysis, O₃ emerged as the most influential disruptor in mixed exposures\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The generally poor sleep quality in this demographic likely results from the interplay of age-related physiological decline, lifestyle factors, and environmental exposures\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Given the accelerating aging trend in China\u0026mdash;with the elderly population projected to reach 38.6% by 2050\u003csup\u003e21\u003c/sup\u003e\u0026mdash;and their heightened susceptibility to pollution-induced circadian disruption, this demographic shift may substantially increase the disease burden of CircS. Proactive establishment of comprehensive air quality monitoring and stricter emission controls is warranted.\u003c/p\u003e \u003cp\u003eSubgroup analyses demonstrated significantly elevated risk among women, older adults, individuals with lower educational attainment, and smokers. Plausible mechanisms include age-dependent hypersensitivity of clock neurons to hypoxia, higher prevalence of circadian-disruptive occupations (e.g., night-time sanitation, long-haul driving) in lower-education groups, and prolonged pulmonary deposition and retention of PM₁₀ attributable to airway inflammation and impaired mucociliary clearance in smokers. These findings underscore the need for clean-air policies that prioritize vulnerable populations\u0026mdash;for example, targeted reductions in traffic-derived NOₓ and dust-related PM₁₀ within ageing neighbourhoods\u0026mdash;to maximize health gains and attenuate environmental health inequalities.\u003c/p\u003e \u003cp\u003eCircS was chosen as the outcome because its diagnostic criteria encompass not only all indicators of metabolic syndrome (Mets) but also include sleep duration, sleep disorders, and depression\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. This makes CircS a more holistic indicator for predicting disease risk and assessing health. Epidemiological evidence linking outdoor air pollution to CircS remains limited, especially in China, where data accessibility has hindered large-scale studies. Previous research has largely focused on single pollutants and single health outcomes, often using cross-sectional designs\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The strengths of this study include: 1) utilization of the large CHARLS cohort; 2) application of multiple statistical methods for cross-validation; and 3) inclusion of relatively comprehensive pollutant data, particularly PM₁, which is often missing in prior studies. Previous studies have explored the relationship between PM\u003csub\u003e2.5\u003c/sub\u003e exposure and CircS in middle-aged and elderly Chinese. We implemented stricter inclusion criteria than their studies in the sleep disorder indicator. At the same time, we included more air pollutants and covariates, and calculated the mixing effects of these mixed pollutants\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLimitations must be acknowledged. First, findings may not be generalizable beyond China's middle-aged and elderly population. Second, data on medication use and specific lifestyle changes were not collected; their influence on CircS requires future study. Third, biomarker data in CHARLS were only measured at two time points, limiting continuous monitoring. Furthermore, the mixed-exposure model did not show a linear dose-response relationship, suggesting possible threshold effects or adaptive mechanisms. Finally, high collinearity among PM₁, PM₂.₅, and PM₁₀ prevented reliable quantification of their independent effects. Future well-designed prospective and mechanistic studies are needed to clarify these associations and their underlying biological pathways.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on a representative sample of middle-aged and elderly Chinese adults, this study provides novel evidence linking air pollution exposure to circadian syndrome (CircS). Mixture analysis identifies PM\u003csub\u003e10\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e as the core pollutants driving these adverse effects. Our findings underscore the need for targeted policy interventions to reduce air pollution and for further mechanistic research to elucidate the underlying biological pathways.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData source and study population\u003c/h2\u003e \u003cp\u003eThe data for this cohort study were derived from the China Health and Retirement Longitudinal Study (CHARLS). This cohort conducted a nationally representative longitudinal survey of individuals aged 45 years and older in China. The database employed a multi-stage, stratified, probability-proportional-to-size (PPS) sampling design. Full details of the study have been published previously\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Baseline data collection in 2011\u0026ndash;2012 (Wave 1) included 17,708 individuals from 10,257 households, covering 28 provinces, 150 counties, and 450 communities nationwide. Subsequently, three follow-up waves of surveys were conducted in 2013, 2015, and 2018. As blood test data were only available in 2011\u0026ndash;2012 (Wave 1) and 2015\u0026ndash;2016 (Wave 3), this study only included participants with relevant data available in both Wave 1 and Wave 3. The study flowchart is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The exclusion criteria were as follows: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;45 years; (2) lack of CircS diagnostic data in both Wave 1 and Wave 3; (3) diagnosis of CircS in Wave 1; and (4) lack of local air pollution data. Ultimately, a total of 2,975 participants were included in the final data analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExposure: Assessment of air pollution\u003c/h2\u003e \u003cp\u003eThe environmental pollution data utilized in this study were sourced from the China High Air Pollutants (CHAP) dataset, a high-resolution, high-quality near-surface air pollutant database accessible via the National Earth System Science Data Sharing Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nnu.geodata.cn/data/dataresource.html\u003c/span\u003e\u003cspan address=\"https://nnu.geodata.cn/data/dataresource.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The CHAP dataset encompasses various near-surface air pollutants, including PM\u003csub\u003e1\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, CO, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e27\u0026ndash;29\u003c/sup\u003e. It is a long-term, full-coverage, high spatiotemporal resolution, and high-precision remote sensing dataset generated by integrating multi-source satellite remote sensing technology with extensive ground-based observations, atmospheric reanalysis data, emission inventories, and model simulations. For this study, data for PM\u003csub\u003e1\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e were obtained for the period from 2011 to 2015, while data for CO and SO\u003csub\u003e2\u003c/sub\u003e were acquired for the period from 2013 to 2015. The spatial resolution for PM1, PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e is 1 kilometer, whereas for NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and CO it is 10 kilometers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes: Assessing new-onset circadian syndrome\u003c/h2\u003e \u003cp\u003eSubjects without CircS at baseline who developed CircS after a 4-year follow-up were recorded as incident CircS. The criteria for assessing CircS in CHARLS consist of the following seven components\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e: (1) central obesity: waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;85 cm in men and \u0026ge;\u0026thinsp;80 cm in women; (2) hypertriglyceridemia: triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL or current use of lipid-lowering drugs; (3) low HDL-C: HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL in men and \u0026lt;\u0026thinsp;50 mg/dL in women; (4) hypertension: blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130/85 mmHg or current use of antihypertensive drugs; (5) hyperglycemia: fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/dL or current use of anti-hyperglycemic medications; (6) total sleep time\u0026thinsp;\u0026lt;\u0026thinsp;6 h/day or \u0026gt;\u0026thinsp;9 h/day (sleep disorder assessment was not available in CHARLS and was therefore excluded from the criteria); and (7) depression: a score\u0026thinsp;\u0026ge;\u0026thinsp;10 on the CES-D-10 scale for depressive symptoms. A diagnosis of circadian syndrome was made if a subject met at least four of the above metabolic, sleep, and mental health indicators. Total sleep time was defined as the sum of self-reported nap time and nighttime sleep duration. Blood pressure was calculated as the average of three measurements. All participants were confirmed to be in a fasting state.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eCovariates were selected based on previous research and clinical knowledge\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Categorical variables are presented as n (%), including sex, educational level (below high school/high school and above), marital status (married/other), physical activity (whether participating in moderate-to-vigorous physical activity\u0026thinsp;\u0026ge;\u0026thinsp;10 minutes (yes/no)), alcohol and smoking status (never/former/current), cancer, cardiovascular disease (CVD), and chronic kidney disease (CKD). All normally distributed continuous variables were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, and skewed continuous variables were presented as median and interquartile range, including age, weight, height, body mass index (BMI), waist circumference, triglycerides (TG), serum creatinine, fasting blood glucose, low-density lipoprotein (LDL), high-density lipoprotein (HDL), hemoglobin A1c (HbA1c), C-reactive protein (CRP), Center for Epidemiologic Studies Depression Scale (CESD) score, uric acid, and estimated glomerular filtration rate (eGFR). The eGFR was calculated using the serum creatinine (SCr)-based CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation. CVD was defined as a physician diagnosis or the use of related medications. Diabetes was defined as a fasting blood glucose level\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, an HbA1c level\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, the use of anti-diabetic medications, or a physician diagnosis. CKD was defined as an eGFR level\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;, the use of medications for kidney problems, or a physician diagnosis. After excluding records with missing values for the primary variables, the remaining covariates had less than 1% missing values (see Supplementary Table\u0026nbsp;1 for details on missing data). A complete-case analysis was performed without multiple imputation, as bias is expected to be negligible under this condition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe used Chi-square or Fisher\u0026rsquo;s exact (categorical variables), a one-way ANOVA test (normal distribution), or a Kruskal-Whallis H-test (skewed distribution) to test for differences among different groups. Multivariable logistic regression was employed to calculate the odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) for the association between each air pollutant and the risk of CircS occurrence. In subgroup analyses, stratification was conducted by sex, age, education level, moderate-to-vigorous physical activity, marital status, smoking status, drinking status, CKD, and CVD. Fully adjusted covariate models were constructed within each stratum.\u003c/p\u003e \u003cp\u003eMulti-pollutant Analysis: First, Spearman's rank correlation was used to analyze the correlations among the seven air pollutants. Given the extremely strong correlations (ρ\u0026thinsp;\u0026gt;\u0026thinsp;0.9) between PM\u003csub\u003e1\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and PM\u003csub\u003e10\u003c/sub\u003e, these three were synthesized into a single \"particulate matter\" group, which was then included alongside the other pollutants in a Bayesian Kernel Machine Regression (BKMR) model\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The Markov Chain Monte Carlo (MCMC) iterations for this model were set to 10,000. The BKMR model estimates the exposure-response relationships between components of the mixture and the outcome by constructing a kernel function based on the mixture variables and employing a Bayesian sampling approach. To assess the independent effect shape of a single pollutant while controlling for the background levels of other pollutants, exposure-response curves for individual pollutants were visualized when all other pollutants were fixed at their 25th, 50th, or 75th percentiles. To further quantify the relative contribution of each pollutant to the overall mixture effect, Weighted Quantile Sum (WQS) regression was simultaneously performed. In the WQS model, the dataset was first randomly split into a development set (40%) and a validation set (60%). A WQS index was then calculated based on the quartile exposure levels of the air pollutants, which quantifies the relative weight of each pollutant within the mixture. This model helps to overcome the multicollinearity issues commonly encountered in traditional regression methods\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFinally, sensitivity analyses were conducted to examine the robustness of the findings. The quantile-based g-computation (qgcomp) model, implemented using the R package \"qgcomp\", was applied to analyze the combined effect of mixed air pollutant exposure. This approach can overcome the limitation of the direction-consistency assumption inherent in the WQS regression model. All analyses in this study were performed using R software (version 4.5.1), and a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHARLS was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of Peking University (IRB00001052-11015 for the main household survey; IRB00001052-11014 for biomarker collection). All participants provided written informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from third-party public repositories. CHARLS data are available from the China Health and Retirement Longitudinal Study database upon application at https://charls.charlsdata.com/. Air pollution data were obtained from the Yangtze River Delta Science Data Center, National Earth System Science Data Sharing Infrastructure, National Science \u0026amp; Technology Infrastructure of China, at http://geodata.nnu.edu.cn/. The analytical code and derived data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.S.S. conceived the study, developed the methodology, curated the data, performed the formal analysis, developed the software, visualized the results, and drafted the manuscript. L.Z. contributed to methodology development, data curation, formal analysis, visualization, and drafting of the manuscript. X.O. contributed to methodology development, formal analysis, visualization, and drafting of the manuscript. Z.X. conceived and supervised the study, administered the project, and critically revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all participants in the CHARLS study and the project team for data support. Acknowledgement for the data support from Yangtze River Delta Science Data Center, National Earth System Science Data Sharing Infrastructure, National Science \u0026amp; Technology Infrastructure of China. (http://geodata.nnu.edu.cn).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZimmet, P.\u003cem\u003e et al.\u003c/em\u003e The Circadian Syndrome: is the Metabolic Syndrome and much more! \u003cem\u003eJ Intern Med\u003c/em\u003e \u003cstrong\u003e286\u003c/strong\u003e, 181-191 (2019). https://doi.org/10.1111/joim.12924\u003c/li\u003e\n\u003cli\u003eDong, Y., Lam, S. M., Li, Y., Li, M. D. \u0026amp; Shui, G. 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C. \u0026amp; Factor-Litvak, P. Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting. \u003cem\u003eJ Agric Biol Environ Stat\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 100-120 (2015). https://doi.org/10.1007/s13253-014-0180-3\u003c/li\u003e\n\u003cli\u003eLi, H.\u003cem\u003e et al.\u003c/em\u003e Health effects of air pollutant mixtures on overall mortality among the elderly population using Bayesian kernel machine regression (BKMR). \u003cem\u003eChemosphere\u003c/em\u003e \u003cstrong\u003e286\u003c/strong\u003e, 131566 (2022). https://doi.org/10.1016/j.chemosphere.2021.131566\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of CHARLS participants by incident circadian syndrome (CircS) status during 4-year follow-up.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Circs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCircs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=\u003c/strong\u003e2975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=2204\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=771\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e58.15\u0026plusmn;8.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e58.03\u0026plusmn;8.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e58.47\u0026plusmn;8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1535 (51.60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1194 (54.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e341 (44.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eWeight (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e56.58\u0026plusmn;10.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e55.55\u0026plusmn;10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e59.52\u0026plusmn;10.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eHeight (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.58\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.58\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.58\u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eBMI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e23.70\u0026plusmn;4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e23.41\u0026plusmn;4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e24.52\u0026plusmn;4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eWaist (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e80.68\u0026plusmn;11.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e79.31\u0026plusmn;11.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e84.58\u0026plusmn;12.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eMarital status: Married, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2598 (87.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1945 (88.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e653 (84.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLess than high school, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2688 (90.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1986 (90.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e702 (91.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiochemical \u0026amp; Clinical Measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eLDL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD,mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e115.94\u0026plusmn;31.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e113.62\u0026plusmn;30.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e122.57\u0026plusmn;34.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eHDL (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e56.56\u0026plusmn;14.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e57.93\u0026plusmn;14.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e52.64\u0026plusmn;13.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eHbA1C (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.14\u0026plusmn;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e5.11\u0026plusmn;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e5.23\u0026plusmn;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eCRP (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.37\u0026plusmn;7.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e2.29\u0026plusmn;7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e2.59\u0026plusmn;6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eUric acid (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.27\u0026plusmn;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e4.26\u0026plusmn;1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e4.30\u0026plusmn;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eeGFR (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e97.80\u0026plusmn;13.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e98.12\u0026plusmn;12.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e96.90\u0026plusmn;14.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eSBP(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e124.91\u0026plusmn;19.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e123.44\u0026plusmn;18.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e129.13\u0026plusmn;20.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eDBP\u0026nbsp;(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e73.23\u0026plusmn;11.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e72.45\u0026plusmn;11.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e75.47\u0026plusmn;11.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eTG (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e96.78\u0026plusmn;48.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e92.59\u0026plusmn;45.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e108.74\u0026plusmn;53.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eFBG (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e102.15\u0026plusmn;25.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e101.23\u0026plusmn;22.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e104.78\u0026plusmn;32.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLifestyle \u0026amp; Psychosocial Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eCESD (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e7.14\u0026plusmn;5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e7.09\u0026plusmn;5.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e7.30\u0026plusmn;5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eSleeptime (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, hours)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.67\u0026plusmn;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e6.64\u0026plusmn;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e6.74\u0026plusmn;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003ePhysicalactivity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1023 (34.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e759 (34.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e264 (34.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eDrinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1679 (56.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1210 (54.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e469 (60.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e203 (6.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e153 (6.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e50 (6.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1093 (36.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e841 (38.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e252 (32.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1703 (57.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1223 (55.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e480 (62.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e232 (7.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e181 (8.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e51 (6.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1040 (34.96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e800 (36.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e240 (31.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eCancer, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e18 (0.61%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e10 (0.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e8 (1.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eCVD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e291 (9.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e205 (9.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e86 (11.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eCKD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e233 (7.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e178 (8.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e55 (7.13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eDM, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e270 (9.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e171 (7.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e99 (12.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAir Pollutants (\u003c/strong\u003e4-year average)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eCO(mg/m3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.13\u0026plusmn;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.12\u0026plusmn;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e1.15\u0026plusmn;0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eNO2(\u0026micro;g/m3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e29.07\u0026plusmn;9.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e28.85\u0026plusmn;9.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e29.70\u0026plusmn;9.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eSO2(\u0026micro;g/m3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e28.60\u0026plusmn;13.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e28.28\u0026plusmn;13.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e29.50\u0026plusmn;13.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003eO3(\u0026micro;g/m3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e85.32\u0026plusmn;6.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e85.17\u0026plusmn;6.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e85.75\u0026plusmn;6.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003ePM1(\u0026micro;g/m3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e30.33\u0026plusmn;9.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e30.17\u0026plusmn;9.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e30.78\u0026plusmn;9.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003ePM2.5(\u0026micro;g/m3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e54.09\u0026plusmn;18.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e53.78\u0026plusmn;18.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e54.97\u0026plusmn;17.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 205px;\"\u003e\n \u003cp\u003ePM10(\u0026micro;g/m3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e91.53\u0026plusmn;32.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 100px;\"\u003e\n \u003cp\u003e90.78\u0026plusmn;32.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 94px;\"\u003e\n \u003cp\u003e93.65\u0026plusmn;31.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e Baseline characteristics were assessed at the 2011\u0026ndash;2012 CHARLS wave and compared between participants who did versus did not develop new-onset CircS by 2015 (Non-CircS vs CircS); data are presented as mean \u0026plusmn; SD or n (%) (Yes-only for dichotomous variables), \u003cem\u003eP\u003c/em\u003e values are from appropriate between-group tests, and air pollutants are 4-year average CHAP-based exposures linked to residential location (full table in the Supplementary Information). \u003cem\u003eCHARLS\u003c/em\u003e, China Health and Retirement Longitudinal Study; \u003cem\u003eBMI\u003c/em\u003e, body mass index; \u003cem\u003eCES-D\u003c/em\u003e, Center for Epidemiologic Studies Depression Scale; \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome; \u003cem\u003eCKD\u003c/em\u003e, chronic kidney disease; \u003cem\u003eCO\u003c/em\u003e, carbon monoxide; \u003cem\u003eCRP\u003c/em\u003e, C-reactive protein; \u003cem\u003eCVD\u003c/em\u003e, cardiovascular disease; \u003cem\u003eDBP\u003c/em\u003e, diastolic blood pressure; \u003cem\u003eDM\u003c/em\u003e, diabetes mellitus; \u003cem\u003eeGFR\u003c/em\u003e, estimated glomerular filtration rate; \u003cem\u003eFBG\u003c/em\u003e, fasting blood glucose; \u003cem\u003eHbA1c\u003c/em\u003e, hemoglobin A1c; \u003cem\u003eHDL-C\u003c/em\u003e, high-density lipoprotein cholesterol;\u003cem\u003e\u0026nbsp;LDL-C\u003c/em\u003e, low-density lipoprotein cholesterol;\u003cem\u003e\u0026nbsp;NO₂\u003c/em\u003e, nitrogen dioxide; \u003cem\u003eO₃\u003c/em\u003e, ozone; \u003cem\u003ePM\u003c/em\u003e, particulate matter; \u003cem\u003eSBP\u003c/em\u003e, systolic blood pressure; \u003cem\u003eSD\u003c/em\u003e, standard deviation; \u003cem\u003eSO₂\u003c/em\u003e, sulfur dioxide; \u003cem\u003eTG\u003c/em\u003e, triglycerides.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Logistic regression analyses for the association between long-term ambient air pollution and incident CircS in CHARLS.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"636\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePer SD increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.10 (1.01, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.11 (1.02, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.14 (1.05, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.18 (0.93, 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.17 (0.93, 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.21 (0.95, 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.04 (0.82, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.02 (0.81, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.07 (0.84, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.38 (1.10, 1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.41 (1.11, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.51 (1.19, 1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eNO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePer SD increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.09 (1.01, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.09 (1.01, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.13 (1.04, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.92 (0.72, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.92 (0.72, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.91 (0.71, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.24 (0.98, 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.23 (0.97, 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.32 (1.04, 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.19 (0.94, 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.20 (0.95, 1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.27 (1.00, 1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eO3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePer SD increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.10 (1.01, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.09 (1.01, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.12 (1.03, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.86 (0.68, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.88 (0.69, 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.84 (0.65, 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.05 (0.83, 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.08 (0.85, 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.13 (0.89, 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.28 (1.01, 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.27 (1.01, 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.32 (1.04, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePer SD increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.06 (0.98, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.06 (0.98, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.10 (1.01, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.03 (0.81, 1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.02 (0.80, 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.07 (0.84, 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.27 (1.00, 1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.26 (0.99, 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.39 (1.09, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.16 (0.91, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.15 (0.91, 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.26 (0.99, 1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePM2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePer SD increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.07 (0.98, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.07 (0.98, 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.11 (1.02, 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.27 (1.00, 1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.27 (1.00, 1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.39 (1.08, 1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.32 (1.04, 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.31 (1.03, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.49 (1.17, 1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.24 (0.98, 1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.24 (0.98, 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.39 (1.08, 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePM10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePer SD increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.09 (1.01, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.10 (1.01, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.16 (1.06, 1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.32 (1.04, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.33 (1.04, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.48 (1.15, 1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.33 (1.05, 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.33 (1.05, 1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.60 (1.24, 2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.39 (1.10, 1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.41 (1.11, 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.64 (1.27, 2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eSO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePer SD increase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.09 (1.01, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.10 (1.01, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.15 (1.06, 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.93 (0.73, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.92 (0.72, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0.97 (0.76, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.17 (0.93, 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.18 (0.93, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.35 (1.06, 1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.24 (0.99, 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.26 (1.00, 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e1.44 (1.13, 1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 120px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e ORs (95% CIs) for incident CircS at 4-year follow-up were estimated per 1-SD increase and by quartiles of each pollutant (Q0\u0026ndash;Q3; Q0 as reference) using CHAP-based residential exposure estimates; P for trend tests linear trends across quartiles. \u003cem\u003eModel 1\u003c/em\u003e: unadjusted; \u003cem\u003eModel 2\u003c/em\u003e: adjusted for age and sex;\u003cem\u003e\u0026nbsp;Model 3\u003c/em\u003e: additionally adjusted for ethnicity, education, household income, smoking, drinking, physical activity, BMI, hypertension, diabetes, and dyslipidemia. \u003cem\u003eCHARLS\u003c/em\u003e, China Health and Retirement Longitudinal Study; \u003cem\u003eCircS\u003c/em\u003e, circadian syndrome; \u003cem\u003eCHAP\u003c/em\u003e, China High Air Pollutants; \u003cem\u003eOR\u003c/em\u003e, odds ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval; \u003cem\u003eSD\u003c/em\u003e, standard deviation; \u003cem\u003eBMI\u003c/em\u003e, body mass index; CO, carbon monoxide; \u003cem\u003eNO₂\u003c/em\u003e, nitrogen dioxide; \u003cem\u003eSO₂\u003c/em\u003e, sulfur dioxide;\u003cem\u003e\u0026nbsp;O₃\u003c/em\u003e, ozone; \u003cem\u003ePM₁\u003c/em\u003e, particulate matter \u0026le;1 \u0026mu;m; \u003cem\u003ePM₂.₅\u003c/em\u003e, particulate matter \u0026le;2.5 \u0026mu;m; \u003cem\u003ePM₁₀\u003c/em\u003e, particulate matter \u0026le;10 \u0026mu;m.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Circadian syndrome, Air pollutant, CHARLS, Chinese adults, BKMR","lastPublishedDoi":"10.21203/rs.3.rs-9202516/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9202516/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCircadian syndrome (CircS) is a multisystem metabolic disorder related to circadian disruption, characterized by sleep disturbance, central obesity, hypertension, and impaired glucose and lipid metabolism. Evidence linking ambient air pollution to CircS in middle-aged and older adults remains limited. Using the nationally representative China Health and Retirement Longitudinal Study (CHARLS), we examined the individual and joint effects of seven pollutants (PM1, PM2.5, PM10, NO2, SO2, CO, and O3) on incident CircS. We followed 2,975 participants aged ≥45 years who were free of CircS at baseline (2011–2012) through 2015–2016. Long-term residential exposures were assigned using CHAP-based estimates. Multivariable logistic regression and subgroup analyses assessed associations, and several complementary mixture models evaluated joint effects. In fully adjusted models, per-SD increases in pollutants were associated with higher odds of incident CircS (ORs 1.10–1.16), whereas the highest versus lowest quartile yielded ORs of 1.26–1.64. Mixture analyses consistently showed a harmful overall joint effect, with O3 and SO2 contributing most and PM10 also showing a notable role. Associations were stronger among older adults and current smokers, and results were robust across multiple modeling strategies and sensitivity analyses. These findings support prioritizing targeted pollution control for circadian-metabolic health in ageing populations.\u003c/p\u003e","manuscriptTitle":"Association between Air Pollutants and Circadian Syndrome in China: A Longitudinal Cohort Study Based on CHARLS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 02:08:15","doi":"10.21203/rs.3.rs-9202516/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-07T01:13:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-26T12:13:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-24T11:26:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-24T11:25:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-23T15:27:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d69ada6-e117-4a6a-9b06-9b7704928652","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65974884,"name":"Health sciences/Diseases"},{"id":65974885,"name":"Earth and environmental sciences/Environmental sciences"},{"id":65974887,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-14T02:08:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 02:08:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9202516","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9202516","identity":"rs-9202516","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}