Association between subjective well-being and the risk of cardiovascular diseases among older adults: evidence from the Chinese Longitudinal Healthy Longevity Survey

Association between subjective well-being and the risk of cardiovascular diseases among older adults: evidence from the Chinese Longitudinal Healthy Longevity Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between subjective well-being and the risk of cardiovascular diseases among older adults: evidence from the Chinese Longitudinal Healthy Longevity Survey Jing Wang, Wenting Zuo, Yuwei Tan, Li Dou, Tao Guo, Zhenxing Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6821093/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Sep, 2025 Read the published version in BMC Geriatrics → Version 1 posted 12 You are reading this latest preprint version Abstract Background/Objective Evidence regarding the impact of subjective well-being (SWB) on the incidence of cardiovascular diseases (CVD) among Chinese older adults was limited. This study aimed to ascertain the association between SWB and the risk of CVD among Chinese older adults. Methods A prospective cohort study was designed based on the data from the survey in 2011–2012 and 2014 of Chinese Longitudinal Healthy Longevity Survey (CLHLS). Participants aged over 65 years without CVD at baseline were included in this study. SWB was measured by a scale consisting of 8-item question. The outcome was CVD (heart disease or stroke) that occurred during the observation period. Restricted cubic splines were used to determine the linear relationship between SWB and CVD risk. Hierarchical regression based on modified Poisson regressions was performed to estimate the association between SWB and CVD risk. Subgroup analyses were conducted in mutually exclusive and overlapping subgroups based on healthy lifestyles. Moreover, sensitivity analyses were performed to confirm the robustness of the main analysis. Results A total of 5,120 eligible participants were included in this cohort study, and 827 participants suffered from CVD during follow-up period (the incidence of CVD was 16.15%). Per 1-standard deviation (SD) increase in SWB was associated with 10.5% reduction in the risk of CVD (adjusted relative risk [RR] = 0.895, 95% CI: 0.833 to 0.962). The robustness of the association was verified by sensitivity analyses. The heterogeneity of association was observed in subgroups with different number of healthy lifestyles. In subgroups with a number of healthy lifestyles of 2 to 4, 3 to 5, or 4 to 6, per 1-SD increase in SWB was associated with a 9.6% (adjusted RR = 0.904, 95% CI: 0.836 to 0.977), 13.0% (adjusted RR = 0.870, 95% CI: 0.799 to 0.948) and 17.9% (adjusted RR = 0.821, 95% CI: 0.731 to 0.922) reduction in CVD risk. Conclusion An inverse linear association is observed between SWB and CVD risk among Chinese older adults. The strength of the association was greater in subgroups with more modifiable healthy lifestyles than that with less modifiable healthy lifestyles. Enhancing subjective well-being and fostering more healthy lifestyle behaviors among older adults are contributed to the prevention of CVD. subjective well-being cardiovascular disease stroke cohort study healthy lifestyles modified Poisson regression subgroup analyses Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction China has transitioned into an aging society, facing a growing burden of age-related chronic conditions, especially cardiovascular diseases (CVD). CVD is one of leading cause of death and disability worldwide and in the Western Pacific region. 1 , 2 In China, the prevalent cases, new cases of CVD and death contributed to CVD are 120 million, 12.34 million and 4.58 million in 2019, 3 which poses challenges and burden to health care systems, health insurance payments, social development, and family well-being. In order to cope with that caused by CVD, it is the most cost-effective measure to pay attention to its prevention, especially interventions targeting modifiable risk factors of CVD, including physical disease (e.g., hypertension, diabetes), lifestyles (e.g., drinking, sleep, diet), and positive or negative affect (e.g., optimism, loneliness, hopelessness). 1 , 4 – 10 Subjective well-being (SWB) is a comprehensive indicator and referred to the evaluations of individuals’ subjective cognitive and emotion, including life satisfaction, positive affect and negative affect. 11 , 12 As reviewed by Diener E, 13 SWB may enhance or impair human health and longevity through its effects on the cardiovascular, immune, and endocrine systems. Regarding older adults, cohort studies from China have demonstrated greater SWB is associated with lower risk of cognitive impairment and all-cause mortality. 14 – 16 And participants aged more than 50 years with high level SWB live longer and healthier than that with low level SWB, proved by a prospective cohort study with ten years follow-up based on English Longitudinal Study of Ageing. 17 Therefore, it is believed that improving individual SWB has a potential role in promoting healthy aging. Specific to CVD, a cohort study from the UK biobank demonstrated higher SWB was significantly associated with a lower risk of cardiometabolic diseases. 18 And, a cohort study conducted in the America also showed the similar finding. 19 Although the Chinese guidelines for primary prevention of cardiovascular diseases acknowledge that maintaining a favorable psychological status may reduce cardiovascular risk, it is noteworthy that none of the cited evidence is derived from Chinese populations. 20 Namely, evidence specific to the association between SWB and the CVD risk among Chinses older adults is limited. Particularly, the heterogeneous effect of SWB on CVD in subpopulation with different number of healthy behaviors is unclear. Therefore, a cohort study of Chinese older adults based on CLHLS, a nationally representative longitudinal survey, was designed to analyze the association between SWB and CVD risk. Meanwhile, the heterogeneity of the association in subpopulation with different number of healthy lifestyles was explored. Methods Study design and participants This study was characterized as a prospective cohort study established based on the data from the survey in 2011–2012 (baseline) and 2014 (end of observation time) of the CLHLS. The CLHLS is the national community-based longitudinal survey for older Chinese adults randomly selected from 23 to 31 province in China, of which the first survey was conducted in 1998 and subsequent surveys were conducted every 2–3 years. The participants and data of CLHLS were representative due to the project covered 85% of total Chinese people. Participants’ information, including demographic characteristic, lifestyles and health and functional status were recorded in a standardized questionnaire in the form of face-to-face interviews by well-trained interviewers. More detailed information about CLHLS can be browsed in the official website ( https://opendata.pku.edu.cn/dataverse/CHADS ) and previous studies. 21 , 22 All participants surveyed at baseline were enrolled in this study (n = 9765). And, participants aged less than 65 years (n = 85) at baseline, suffered from CVD (n = 1765) or were missing the record (n = 509) at baseline, with missing records of either item on the SWB scale ( Supplementary Table 1 ) at baseline (n = 1289), lost to follow up in 2014 (n = 473), with the time of death recorded in the questionnaire was before the baseline survey time (n = 51) and with missing records of CVD in 2014 (n = 472) were excluded (Fig. 1 ). Measures Subjective well-being A 5-point Likert scale consisting of 8-item was used to assess the SWB, including life satisfaction and affective well-being ( Supplementary Table 1 ). Life satisfaction was measured by the question “How do you rate your life at present?”, with the answer and scored ranging from 1 (very bad) to 5 (very good). The positive affective well-being was assessed by 4 questions: “do you always look on the bright side of things?” “do you like to keep your belongings neat and clean?” “can you make your own decisions?” and “are you happy as younger?”, and the negative affective well-being was assessed by 3 questions: “do you often feel fearful or anxious?” “do you feel lonely and isolated?” and “do you feel useless with age?”. According to existed studies, answers for positive affective items were scored from 1(never) to 5 (always), and that for negative affective items were reversely scored. The total SWB score was the sum of each score from 8 items, with the range from 15 to 40. The higher the SWB score, the more happiness and positive emotions the individual subjectively feels. Cardiovascular disease The endpoint of this study was the onset of new CVD (heart disease, or stroke) during the observation period from 2011 to 2014. Historical CVD was collected using standardized questionnaires. At each survey, alive participants were asked if he had suffered from heart disease or stroke, and the information about whether deceased participants suffered from CVD prior to death was obtained from their proxies. Healthy lifestyles Regarding body mass index (BMI), if the value was in the range from 18.5 to 23.9, it was considered as healthy BMI. On the contrary, it was considered as unhealthy BMI. Regarding smoking and drinking, never smoking and drinking were considered as healthy lifestyles, ever or currently smoking and drinking were unhealthy lifestyles. Regarding duration of sleep, if the rang of that was from 6 to 8 hours, it was considered as healthy sleep. On the contrary, it was considered as unhealthy sleep. Regarding exercise, currently do physical exercise were considered as healthy lifestyles. Regarding diet, if the dietary diversity (DD) score ( Supplementary Table 1 ) was greater than or equal to the median, it was considered as healthy diet, else it was considered as unhealthy diet. Covariates The covariates included in this study were baseline age, sex (male or female), nation (Han or minority nationality), category of residence (rural or urban), current marital status (married or others), education (0 year, 1–6 years, or ≥ 7 years), main occupation before 60 years, pension (yes or no), mildew odor at home (yes or no), cooking fuels (clean fuels, solid fuels, or others), BMI, smoking status (never, ever or currently), drinking status (never, ever or currently), duration of sleep ( 8 hours), activities scores ( Supplementary Table 1 ), regular exercise (never, ever or currently), DD scores, adequate medical care (yes or no), regular physical examination (yes or no), hearing impairment (yes or no), visual impairment (yes or no), teeth loss (yes or no), depressive symptoms (If any answer of the following two questions was yes, participant would be judged to have depressive symptoms: ①have you felt sad, blue, or depressed for two weeks or more in last 12 months? ②have you lost interest in most things like hobbies, work, or similar activities?), the mini-mental state examination (MMSE) score ( Supplementary Table 1 ), basic activities of daily living (BADL) score ( Supplementary Table 1 ), multiple chronic conditions (MCCs, two or more coexisting chronic conditions in an individual, including hypertension, diabetes, bronchitis/emphysema/pneumonia/asthma, tuberculosis, cataract, glaucoma, cancer, gastric or duodenal ulcer, Parkinson's disease, arthritis, dementia, epilepsy, cholecystitis/cholelith disease, dyslipidemia, rheumatism or rheumatoid disease, chronic nephritis and hepatitis). Statistical analyses Regarding data governance, when the answer for question was “unable to answer” “don’t know” or below its 2.5th percentile for weight and height, it was replaced by NA. The proportion of missing variables in the data set was calculated ( Supplementary Table 2 ) after the process for inclusion and exclusion of participants. And, variables with a missing proportion of less than 30% were imputed by the Hot-Deck method. All transformations of variables and statistical analyses were performed on the basis of imputed dataset. The continuous variables were described as mean and standard deviation (SD) or median and interquartile range (IQR) according to whether the variables follow a normal distribution. Categorical variables were described as frequency (n) and percentage (%). The comparison of baseline characteristics and outcomes among four groups was conducted as appropriate by t test, Wilcoxon sum test or Chi-square test. Restricted cubic splines (RCS) were used to determine the linear relationship between SWB and CVD risk. Hierarchical regression based on modified Poisson regressions were performed to estimate the association and its strength between standardized SWB score (z-score) and the risk of CVD, in which crude relative risk (RR) and adjusted RR and 95% confidence interval (CI) were calculated. Confounders based on a priori experience, including demographic characteristic (age, sex, race, residence, marital status, education, occupation, pension), home environment and lifestyles (mildew odor at home, cooking fuels, BMI, smoking status, drinking status, duration of sleep, activities score, regular exercise, DD score), healthcare-related information (adequate medical care, regular physical examination), physical and mental health-related information (hearing impairment, visual impairment, teeth loss, depressive symptoms, the MMSE score, BADL score, MCCs) were gradually entered in the hierarchical regression analysis. Subgroup analyses were conducted in mutually exclusive and overlapping subgroups splitting by individual healthy lifestyle and different numbers of healthy lifestyles, in view of the impact of lifestyles on CVD risk. Additionally, sensitivity analyses were performed to verity the robustness of this association, including (1) multivariable log-binomial regressions were fitted adjusting for all confounders (Model S1); (2) multivariable modified Poisson regressions were fitted on the basis of complete case method that excluded observations containing missing values (Model S2); (3) the covariate MCCs in the main analysis was replaced by risk factors of CVD, including hypertension, diabetes, dyslipidemia, chronic nephritis and cancer (Model S3); (4) SWB in the form of continuous measure was transformed into categorical variables according to the tertile of that, and multivariable modified Poisson regressions were fitted again (Model S4); (5) multivariable modified Poisson regressions were fitted again adjusting for covariates with P < 0.10 in differential comparison between CVD patients and non-CVD patients (Model S5). Data governance, statistical analyses and plots were conducted by using R 4.4.0 (R Core Team) with R packages “haven”, “lubridate”, “VIM”, “rms”, “rqlm”, “ggplot2”, and “forestploter”. A 2-tailed hypothesis test was used, and the significance level for that was set at 0.05. Ethic consideration The CLHLS was conducted according to the guidelines of the Declaration of Helsinki, and approved by the biomedical ethics committee of Peking University (IRB00001052–24713074). All participants or their proxies signed the informed consent before the collection of individual data. Moreover, article 32 of the Ethical Review Measures for Life Science and Medical Research involving Human Beings issued by China in 2023 points out that scientific research based on legally obtained anonymized public data, which does not involve biological samples and does not violate the commercial interests of others, can be exempted from ethical review. Therefore, no second ethical review was conducted for this study, and no informed consent were signed again due to the anonymity of the participants. Results Baseline characteristics of participants The characteristics of eligible participants was listed in Table 1 . A total of 5120 participants were included in the final analyzed dataset, the mean baseline age was 84.52 years (SD 10.88), 50.75% of all were female, 46.23% of all lived in urban areas, and the mean SWB score was 30.30 (SD 4.29). During a median of 935 days of follow-up, 827 (16.15%) participants suffered from CVD. Comparing with the non-CVD patients, CVD patients were more likely to have lower SWB score, be younger, be Han nation, live in urban areas, have pension, use clean fuels, higher BMI, ever or currently smoke, ever or currently drink, ever or currently do physical exercise, receive adequate medical care, have depressive symptoms and MCCs (all P < 0.05). Table 1 Baseline characteristics of eligible participants stratified by outcomes Variables Total ( n = 5120) Cardiovascular disease P-value No ( n = 4293) Yes ( n = 827) SWB score 30.30 ± 4.29 30.37 ± 4.28 29.93 ± 4.36 0.007 Z-score of SWB -0.07 (-0.77, 0.63) -0.07 (-0.77, 0.63) -0.07 (-0.77, 0.63) 0.012 Age, year 84.52 ± 10.88 84.64 ± 11.03 83.87 ± 10.07 0.047 Sex , n (%) 0.077 Male 2419 (47.25) 2005 (46.70) 414 (50.06) Female 2701 (52.75) 2288 (53.30) 413 (49.94) Nation , n (%) 0.010 Han 4816 (94.06) 4022 (93.69) 794 (96.01) Minority 304 (5.94) 271 (6.31) 33 (3.99) Residence , n (%) 0.010 Rural 2753 (53.77) 2342 (54.55) 411 (49.70) Urban 2367 (46.23) 1951 (45.45) 416 (50.30) Marital status , n (%) 0.393 Married 2123 (41.46) 1769 (41.21) 354 (42.81) Others 2997 (58.54) 2524 (58.79) 473 (57.19) Education , n (%) 0.194 0 year 2887 (56.39) 2444 (56.93) 443 (53.57) 1–6 years 1659 (32.40) 1376 (32.05) 283 (34.22) ≥ 7 years 574 (11.21) 473 (11.02) 101 (12.21) *Occupation , n (%) < 0.001 Category 1 3646 (71.21) 3114 (72.54) 532 (64.33) Category 2 949 (18.54) 744 (17.33) 205 (24.79) Category 3 525 (10.25) 435 (10.13) 90 (10.88) Pension , n (%) < 0.001 No 4351 (84.98) 3691 (85.98) 660 (79.81) Yes 769 (15.02) 602 (14.02) 167 (20.19) Mildew odor at home , n (%) 0.733 Yes 969 (18.93) 816 (19.01) 153 (18.50) No 4151 (81.07) 3477 (80.99) 674 (81.50) Cooking fuels , n (%) 0.014 Clean fuels 2397 (46.82) 1981 (46.14) 416 (50.30) Solid fuels 2369 (46.27) 2024 (47.15) 345 (41.72) Other fuels 354 (6.91) 288 (6.71) 66 (7.98) BMI, kg/m 2 21.02 ± 4.12 20.90 ± 4.03 21.64 ± 4.52 < 0.001 Smoking status , n (%) < 0.001 Never 3278 (64.02) 2782 (64.80) 496 (59.98) Ever 798 (15.59) 628 (14.63) 170 (20.56) Currently 1044 (20.39) 883 (20.57) 161 (19.47) Drinking status , n (%) 0.001 Never 3402 (66.45) 2873 (66.92) 529 (63.97) Ever 718 (14.02) 567 (13.21) 151 (18.26) Currently 1000 (19.53) 853 (19.87) 147 (17.78) Duration of sleep , n (%) 0.153 6–8 hours 2701 (52.75) 2255 (52.53) 446 (53.93) 8 hours 1639 (32.01) 1396 (32.52) 243 (29.38) Activities score 11.0 (5.0, 16.0) 11.0 (5.0, 16.0) 11.0 (6.0, 15.0) 0.840 Regular exercise 0.002 Never 2727 (53.26) 2327 (54.20) 400 (48.37) Ever 550 (10.74) 438 (10.20) 112 (13.54) Currently 1843 (36.00) 1528 (35.59) 315 (38.09) DD score 5.0 (3.0, 6.0) 5.0 (3.0, 6.0) 4.0 (3.0, 6.0) 0.997 Adequate medical care , n (%) 0.010 Yes 4861 (94.94) 4061 (94.60) 800 (96.74) No 259 (5.06) 232 (5.40) 27 (3.26) Regular physical examination , n (%) 0.090 Yes 1581 (30.88) 1305 (30.40) 276 (33.37) No 3539 (69.12) 2988 (69.60) 551 (66.63) Hearing impairment , n (%) 0.662 0.416 Yes 2127 (41.54) 1794 (41.79) 333 (40.27) No 2993 (58.46) 2499 (58.21) 494 (59.73) Visual impairment , n (%) 0.225 No 4183 (81.70) 3495 (81.41) 688 (83.19) Yes 937 (18.30) 798 (18.59) 139 (16.81) Teeth loss , n (%) 0.882 No 409 (7.99) 344 (8.01) 65 (7.86) Yes 4711 (92.01) 3949 (91.99) 762 (92.14) Depressive symptoms , n (%) 0.001 No 4302 (84.02) 3639 (84.77) 663 (80.17) Yes 818 (15.98) 654 (15.23) 164 (19.83) MMSE score 24.84 ± 6.39 24.82 ± 6.45 24.91 ± 6.08 0.730 BADL score 17.37 ± 1.77 17.37 ± 1.77 17.38 ± 1.77 0.933 Hypertension , n (%) < 0.001 No 3833 (74.86) 3297 (76.80) 536 (64.81) Yes 1287 (25.14) 996 (23.20) 291 (35.19) Diabetes , n (%) < 0.001 No 4967 (97.01) 4184 (97.46) 783 (94.68) Yes 153 (2.99) 109 (2.54) 44 (5.32) Dyslipidemia 0.246 No 5033 (98.30) 4224 (98.39) 809 (97.82) Yes 87 (1.70) 69 (1.61) 18 (2.18) Chronic nephritis 0.238 No 5096 (99.54) 4275 (99.58) 821 (99.27) Yes 24 (0.46) 18 (0.41) 6 (0.73) Cancer 0.321 No 5084 (99.31) 4265 (99.35) 819 (99.03) Yes 36 (0.69) 28 (0.65) 8 (0.97) MCCs , n (%) < 0.001 No 3990 (77.93) 3391 (78.99) 599 (72.43) Yes 1130 (22.07) 902 (21.01) 228 (27.57) *Category 1: staff of farming/forestry/animal husbandry/side-line production/fishery; Category 2: workers/teachers/doctors/military/government workers/businessmen; Category 3: others SWB: subjective well-being; BMI: body mass index; DD score: diversity of dietary score; MMSE score: the mini-mental state examination score; BADL score: basic activity of daily living score; MCCs: multiple chronic conditions Subjective well-being and the risk of cardiovascular diseases As showed in Supplementary Fig. 1 , the RCS based on univariable and multivariable binary logistic regressions all showed a linear relationship between SWB and the risk of CVD (all P for linear test 0.05). The result of Model 1(crude RR = 0.918, 95% CI: 0.863 to 0.977), Model 2 (adjusted RR = 0.876, 95% CI: 0.821 to 0.934), Model 3 (adjusted RR = 0.886, 95% CI: 0.828 to 0.949), Model 4 (adjusted RR = 0.875, 95% CI: 0.815 to 0.938) and Model 5 (adjusted RR = 0.895, 95% CI: 0.833 to 0.962) all demonstrated that increased SWB (per 1-SD) was associated with reduced risk of CVD (Fig. 2 and Supplementary Table 3 ). Subgroup analyses The result of subgroup analyses was presented in Fig. 3 , Supplementary Table 4-Supplementary Table 20 . For individual healthy lifestyle, the inverse association between SWB and the risk of CVD was significant in subgroups with healthy BMI (per 1-SD, adjusted RR = 0.843, 95% CI: 0.760 to 0.935), never smoking (per 1-SD, adjusted RR = 0.887, 95% CI: 0.808 to 0.974), never drinking (per 1-SD, adjusted RR = 0.875, 95% CI: 0.801 to 0.956), never or ever do physical exercise (per 1-SD, adjusted RR = 0.884, 95% CI: 0.807 to 0.969), currently do physical exercise (per 1-SD, adjusted RR = 0.852, 95% CI: 0.769 to 0.945), healthy sleep (per 1-SD, adjusted RR = 0.869, 95% CI: 0.787 to 0.960) and unhealthy diet (per 1-SD, adjusted RR = 0.879, 95% CI: 0.794 to 0.973). For subgroups with overlapping numbers of healthy lifestyles, the significant association were observed only in subgroups containing 2–4 (per 1-SD, adjusted RR = 0.904, 95% CI: 0.836 to 0.977), 3–5 (per 1-SD, adjusted RR = 0.870, 95% CI: 0.799 to 0.948) and 4–6 (per 1-SD, adjusted RR = 0.821, 95% CI: 0.731 to 0.922) healthy lifestyles. Sensitivity analyses The main finding was verified by several sensitivity analyses. First, the association between SWB score and CVD risk showed in multivariable log-binomial regressions (Model S1, adjusted RR = 0.894, 95% CI: 0.832 to 0.960) similar to that in multivariable modified Poisson regressions (Fig. 4 , Supplementary Table 21 ). Second, the result (Model S2, adjusted RR = 0.916, 95% CI: 0.840 to 0.999) remained similar to main analysis when the complete case method was used to deal with missing value (Fig. 4 , Supplementary Table 22 ). Third, the multivariable modified Poisson regression with MCCs was replaced by hypertension and diabetes (Model S2, adjusted RR = 0.897, 95% CI: 0.835 to 0.964) also verified the robustness of main findings (Fig. 4 , Supplementary Table 23 ). Fourth, comparing with the first tertile of SWB score, the risk of CVD in second and third tertile was reduced by 16.3% (adjusted RR = 0.837, 95% CI: 0.711 to 0.985) and 22.0% (adjusted RR = 0.780, 95% CI: 0.658 to 0.924) suggested by Model S4, respectively (Fig. 4 , Supplementary Table 24 ). Finally, the simplified modified Poisson regression (Model S5) demonstrated increased SWB was associated with reduced risk of CVD (per 1-SD, adjusted RR = 0.875, 95% CI: 0.816 to 0.938), similarly (Fig. 4 , Supplementary Table 25 ). Discussion Main findings This nationwide prospective cohort study based on CLHLS reveals two significant findings: (1) higher SWB score was associated with lower risk of CVD among Chinese older adults, and (2) this protective association is more pronounced in subgroups with normal BMI, never smoking, never drinking, healthy sleep, poor DD score, and a greater number of healthy lifestyles. Interpretation and comparison The American Heart Association (AHA) has summarized the relationship between psychological health and CVD, and issued a scientific statement titled 'Psychological Health, Well-Being, and the Mind-Heart-Body Connection'. 23 In the statement, clear association between the increase in the component of SWB and overall SWB and the decrease in risk of CVD were presented, although several studies have suggested no association between positive affect and the incidence of CVD. The findings of this study are consistent with the majority of previous research, supporting the association between the comprehensive indicator SWB and cardiovascular conditions risk. Reviewing the components of SWB in this study (e.g., life satisfaction, 8 optimism, 24 hopelessnes, 9 loneliness, 7 anxiety 25 ), substantial evidence already supports their association with CVD incidence. While previous studies conducted in America, Europe and countries other than China have reported similar protective associations, this study provides the first robust evidence from a large-scale longitudinal aging study in a Chinese context. Notably, the observed effect modification by health behaviors offers new insights into potential synergistic effects between psychological and lifestyle factors. Regarding the mechanisms that link the SWB and incidence of CVD, promoting adaptive physiological functioning, buffering the detrimental influences of stressful experiences and motivating more healthy lifestyles are the potential pathways for high SWB to mitigate the risk of cardiovascular conditions. For instance, (1) individuals maintaining higher SWB have lower risk of hypertension, dyslipidemia and vascular stiffness those are common age-related changes in physical functioning and important risk factor of CVD. 26 (2) Negative psychological affect could result in dysregulation of the autonomic nervous system and a series of chain reactions that increase the risk of developing CVD. 27 And this suggests alleviating negative psychological affect may suppress these adverse biological processes. (3) individuals with positive affect and high life satisfaction are more likely to adopt healthy lifestyles, whereas those with negative affect may be more susceptible to developing unhealthy health behaviors. Moreover, health-related behaviors may reciprocally influence or modify SWB. 28 – 32 The heterogeneous association between SWB and the risk of CVD was showed by subgroup analyses. And the relationships among healthy lifestyles, SWB, and CVD mentioned above provide substantial support for the majority of findings from subgroup analyses. Especially, the more healthy lifestyles adopted, the greater the prevention effect of SWB on CVD. Furthermore, those findings may suggest a potential interaction between health-related lifestyle behaviors and SWB in preventing CVD. Similar interaction was also observed in studies examining SWB and all-cause mortality, particularly when comparing subgroups of never-smokers versus former/current smokers. 15 In addition, participants with low DD score were more likely to benefit from high SWB. the DD is more influenced by socioeconomic factors than by well-being or self-discipline, 33 which may be the reason for that. Importantly, this finding might extend to the notion that SWB counteracts the risk of CVD associated with low diversity of dietary. Implications These findings carry important implications for China's aging population, including that targeting clinical and public health. First, SWB assessment could help identify high-risk elderly for future CVD, and it was recommended be incorporated into routine geriatric evaluations, who at risk for CVD especially. Second, it is recommended to comprehensively assess SWB and its association with health-related outcomes using multidimensional indicators, rather than evaluating a single dimension or aspect, such as optimism, pessimism, loneliness, or self-control. Third, comprehensive psychology interventions (e.g., enhance positive mood and reduce negative affect) may complement traditional CVD prevention, and combined healthy lifestyle and psychological approaches in the healthcare of general elderly population and primary prevention of CVD may yield maximal benefits. Fourth, in subpopulations with absent or limited adoption of healthy lifestyles, SWB failed to demonstrate significant CVD risk reduction. Consequently, within the precision prevention paradigm, a thorough cost-benefit assessment is warranted when considering SWB-specific interventions. Finally, developing culturally appropriate SWB metrics and carrying out more intervention studies targeting integrated SWB and health-related lifestyles are necessary. Strengths and limitations This study has several notable strengths as following. First, the longitudinal design, a representative sample of Chinese older adults and large sample size strengthen the findings. Second, SWB is a composite measure comprising life satisfaction, positive affect and negative affect, which may occur concurrently or sequentially. This study did not analyze the association between each component and the outcomes, thereby preserving the integrity of this holistic measure. Meanwhile, this may help fill the evidence gap in the CVD prevention guidelines issued by the AHA, 23 European Society of Cardiology, 34 and Chinese Society of Cardiology of Chinese Medical Association, 20 where there is a lack of evaluation of the association between the comprehensive indicator assessing SWB and CVD risk. Third, rigorous statistical scheme and appropriate statistical methods have enhanced the reliability of the findings, including the assessment of linear relationship between SWB score and CVD risk by RCS, the use of modified Poisson regressions to calculate RR with 95%CI, multiple covariates adjustment and sensitivity analyses. Finally, guided by the concept of the subpopulation treatment effect pattern plot (STEPP) approach, 35 this study categorized participants into four overlapping subgroups based on their number of healthy lifestyles, revealing heterogeneous associations between SWB and CVD risk across these subgroups. This study has two main limitations that need to be acknowledged. First, although the SWB scale used in the CLHLS has been widely adopted in previous studies, existing evidence suggests that it may require further psychometric refinement. 36 , 37 Consequently, the assessment of SWB in the present study might be subject to measurement bias. Second, exact onset time of participants' CVD was not recorded in the CLHLS, precluding the application of time-to-event analytical methods (e.g., Cox proportional hazards models) in the data analysis. Thus, although statistical methods were employed to estimate RR, potential time-related biases could not be adequately controlled. Conclusion This study provides compelling evidence that SWB is an independent predictor of cardiovascular health in Chinese older adults, with particularly strong benefits among those maintaining more healthy lifestyles. These findings highlight the importance of integrating psychological well-being into comprehensive approaches to healthy aging and CVD prevention. As China faces unprecedented population aging, these results underscore the need for innovative strategies that address both psychological and physical health dimensions in elderly care. Future research should focus on developing culturally appropriate interventions and elucidating the underlying biological mechanisms of these observed associations. Abbreviations SWB subjective well-being CVD cardiovascular diseases CLHLS the Chinese Longitudinal Healthy Longevity Survey SD standard deviation RR relative risk BMI body mass index DD score dietary diversity score MMSE the mini-mental state examination BADL basic activities of daily living MCCs multiple chronic conditions IQR interquartile RCS restricted cubic splines CI confidence interval Declarations Clinical trial number not applicable Acknowledgments All authors acknowledge the organizations, teams, and individuals who contributed to the implementation of the CLHLS, database establishment, and data sharing. Author’s contributions JW and WZ: conception and design of this study, statistical analysis, original draft writing; YT, LD, QY: Review, editing and methodology; TG: Review, editing and supervision; ZW: conception and design of this study, review, editing, and methodology. Funding This study was supported by Project of Jiangsu Provincial Bureau of Traditional Chinese Medicine (ZD201802), Open Project of Jiangsu Provincial Traditional Chinese Medicine Epidemic Disease Research Center(JSYB2024KF09, JSYB2024KF09), Jiangsu Provincial Traditional Chinese Medicine Science and Technology Development Plan Project (MS202218), Hospital-level project of Jiangsu Province Hospital of Chinese medicine (Y2021CX16,Y24004).All funding organizations had no role in the study design, implementation, analysis, or interpretation of the data. Conflict of Interest Disclosures All authors declare no competing interests. Data availability Upon application and approval, the data of CLHLS is available at the official website: https://opendata.pku.edu.cn/dataverse/CHADS. References Roth GA, Mensah GA, Johnson CO, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. 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Chinese Society of Cardiology of Chinese Medical Association, Cardiovascular Disease Prevention and Rehabilitation Committee of Chinese Association of Rehabilitation Medicine, Cardiovascular Disease Committee of Chinese Association of Gerontology and Geriatrics, et al. Chinese guideline on the primary prevention of cardiovascular diseases in primary health care. Zhonghua Xin Xue Guan Bing Za Zhi et al. 2023;51:1–21. 10.3760/cma.j.cn112148-20230106-00014 Lv Y, Zhang Y, Li X, et al. Body mass index, waist circumference, and mortality in subjects older than 80 years: a Mendelian randomization study. Eur Heart J. 2024;45(24):2145–54. 10.1093/eurheartj/ehae206 . Chen H, Shen J, Xuan J, et al. Plant-based dietary patterns in relation to mortality among older adults in China. Nat Aging. 2022;2(3):224–30. 10.1038/s43587-022-00180-5 . Levine GN, Cohen BE, Commodore-Mensah Y, et al. Psychological Health, Well-Being, and the Mind-Heart-Body Connection: A Scientific Statement From the American Heart Association. Circulation. 2021;143(10):e763–83. 10.1161/CIR.0000000000000947 . Felt JM, Russell MA, Ruiz JM, et al. A multimethod approach examining the relative contributions of optimism and pessimism to cardiovascular disease risk markers. J Behav Med. 2020;43(5):839–49. 10.1007/s10865-020-00133-6 . Batelaan NM, Seldenrijk A, Bot M, et al. Anxiety and new onset of cardiovascular disease: critical review and meta-analysis. Br J Psychiatry. 2016;208(3):223–31. 10.1192/bjp.bp.114.156554 . McLeish AC, Smith T, Riggs DW, et al. Community-Based Evaluation of the Associations Between Well-Being and Cardiovascular Disease Risk. J Am Heart Assoc. 2022;11(22):e027095. 10.1161/JAHA.122.027095 . Cohen BE, Edmondson D, Kronish IM. State of the art review: depression, stress, anxiety, and cardiovascular disease. Am J Hypertens. 2015;28:1295–302. 10.1093/ajh/hpv047 . 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BMC Geriatr. 2021;21(1):206. 10.1186/s12877-021-02121-0 . Morseth MS, Grewal NK, Kaasa IS, et al. Dietary diversity is related to socioeconomic status among adult Saharawi refugees living in Algeria. BMC Public Health. 2017;17(1):621. 10.1186/s12889-017-4527-x . Visseren FLJ, Mach F, Smulders YM, et al. 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. Eur J Prev Cardiol. 2022;29(1):5–115. 10.1093/eurjpc/zwab154 . Lazar AA, Cole BF, Bonetti M, et al. Evaluation of treatment-effect heterogeneity using biomarkers measured on a continuous scale: subpopulation treatment effect pattern plot. J Clin Oncol. 2010;28(29):4539–44. 10.1200/JCO.2009.27.9182 . Das KV, Jones-Harrell C, Fan Y, et al. Understanding subjective well-being: perspectives from psychology and public health. Public Health Rev. 2020;41(1):25. 10.1186/s40985-020-00142-5 . Diener E, Lucas RE, Oishi S. Advances and Open Questions in the Science of Subjective Well-Being. Collabra Psychol. 2018;4(1):15. 10.1525/collabra.115 . Additional Declarations No competing interests reported. Supplementary Files Graphicabstract.png Supplementarymaterials.docx Supplementary Material Supplementary Table 1 Multiple scales, their answers and score assignment rule used in this study Supplementary Table 2 The variable with missing value and the percentage of missing value Supplementary Table 3 The association between SWB and the risk of CVD among Chinese older adults suggested by hierarchical regression based on modified Poisson regressions Supplementary Table 4The association between SWB and the risk of CVD among Chinese older adults with healthy BMI suggested by multivariable modified Poisson regressions Supplementary Table 5The association between SWB and the risk of CVD among Chinese older adults with unhealthy BMI suggested by multivariable modified Poisson regressions Supplementary Table 6The association between SWB and the risk of CVD among Chinese older adults never smoking suggested by multivariable modified Poisson regressions Supplementary Table 7The association between SWB and the risk of CVD among Chinese older adults ever or currently smoking suggested by multivariable modified Poisson regressions Supplementary Table 8The association between SWB and the risk of CVD among Chinese older adults never drinking suggested by multivariable modified Poisson regressions Supplementary Table 9The association between SWB and the risk of CVD among Chinese older adults ever or currently drinking suggested by multivariable modified Poisson regressions Supplementary Table 10The association between SWB and the risk of CVD among Chinese older adults never or ever do physical exercise suggested by multivariable modified Poisson regressions Supplementary Table 11The association between SWB and the risk of CVD among Chinese older adults currently do physical exercise suggested by multivariable modified Poisson regressions Supplementary Table 12The association between SWB and the risk of CVD among Chinese older adults with healthy sleep suggested by multivariable modified Poisson regressions Supplementary Table 13The association between SWB and the risk of CVD among Chinese older adults with unhealthy sleep suggested by multivariable modified Poisson regressions Supplementary Table 14The association between SWB and the risk of CVD among Chinese older adults with healthy diet suggested by multivariable modified Poisson regressions Supplementary Table 15The association between SWB and the risk of CVD among Chinese older adults with unhealthy diet suggested by multivariable modified Poisson regressions Supplementary Table 16The association between SWB and the risk of CVD among Chinese older adults with 0-2 healthy lifestyles suggested by multivariable modified Poisson regressions Supplementary Table 17The association between SWB and the risk of CVD among Chinese older adults with 1-3 healthy lifestyles suggested by multivariable modified Poisson regressions Supplementary Table 18The association between SWB and the risk of CVD among Chinese older adults with 2-4 healthy lifestyles suggested by multivariable modified Poisson regressions Supplementary Table 19The association between SWB and the risk of CVD among Chinese older adults with 3-5 healthy lifestyles suggested by multivariable modified Poisson regressions Supplementary Table 20The association between SWB and the risk of CVD among Chinese older adults with 4-6 healthy lifestyles suggested by multivariable modified Poisson regressions Supplementary Table 21The association between SWB and the risk of CVD among Chinese older adults suggested by multivariable log-binomial regressions (Model S1) Supplementary Table 22The association between SWB and the risk of CVD among Chinese older adults suggested by multivariable modified Poisson regressions based on complete-case method (Model S2) Supplementary Table 23The association between SWB and the risk of CVD among Chinese older adults suggested by multivariable modified Poisson regressions with MCCs was replaced by chronic conditions considered as risk factors of CVD (Model S3) Supplementary Table 24The association between SWB and the risk of CVD among Chinese older adults suggested by multivariable modified Poisson regressions with SWB score was transformed to categorical variable (Model S4) Supplementary Table 25The association between SWB and the risk of CVD among Chinese older adults suggested by multivariable modified Poisson regressions adjusting for covariates with P < 0.10 in differential comparison (Model S5) Supplementary Figure 1The linear relationship between SWB score and the risk of CVD showed by restrict cubic spline on the basis ofbinary logistic regressions. A: univariable binary logistic regressions, B: multivariable binary logistic regressions Cite Share Download PDF Status: Published Journal Publication published 26 Sep, 2025 Read the published version in BMC Geriatrics → Version 1 posted Editorial decision: Revision requested 01 Jul, 2025 Reviews received at journal 28 Jun, 2025 Reviews received at journal 24 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviews received at journal 12 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers invited by journal 12 Jun, 2025 Editor invited by journal 06 Jun, 2025 Editor assigned by journal 06 Jun, 2025 Submission checks completed at journal 06 Jun, 2025 First submitted to journal 04 Jun, 2025 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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09:13:16","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":431126,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e Multiple scales, their answers and score assignment rule used in this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 2 \u003c/strong\u003eThe variable with missing value and the percentage of missing value\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 3 \u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults suggested by hierarchical regression based on modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 4\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults with healthy BMI suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 5\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults with unhealthy BMI suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 6\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults never smoking suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 7\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults ever or currently smoking suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 8\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults never drinking suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 9\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults ever or currently drinking suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 10\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults never or ever do physical exercise suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 11\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults currently do physical exercise suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 12\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults with healthy sleep suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 13\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults with unhealthy sleep suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 14\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults with healthy diet suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 15\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults with unhealthy diet suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 16\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults with 0-2 healthy lifestyles suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 17\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults with 1-3 healthy lifestyles suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 18\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults with 2-4 healthy lifestyles suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 19\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults with 3-5 healthy lifestyles suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 20\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults with 4-6 healthy lifestyles suggested by multivariable modified Poisson regressions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 21\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults suggested by multivariable log-binomial regressions (Model S1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 22\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults suggested by multivariable modified Poisson regressions based on complete-case method (Model S2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 23\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults suggested by multivariable modified Poisson regressions with MCCs was replaced by chronic conditions considered as risk factors of CVD (Model S3)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 24\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults suggested by multivariable modified Poisson regressions with SWB score was transformed to categorical variable (Model S4)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 25\u003c/strong\u003eThe association between SWB and the risk of CVD among Chinese older adults suggested by multivariable modified Poisson regressions adjusting for covariates with P \u0026lt; 0.10 in differential comparison (Model S5)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003eThe linear relationship between SWB score and the risk of CVD showed by restrict cubic spline on the basis ofbinary logistic regressions. A: univariable binary logistic regressions, B: multivariable binary logistic regressions\u003c/p\u003e","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6821093/v1/802a05a727c5460e655bafd4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between subjective well-being and the risk of cardiovascular diseases among older adults: evidence from the Chinese Longitudinal Healthy Longevity Survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChina has transitioned into an aging society, facing a growing burden of age-related chronic conditions, especially cardiovascular diseases (CVD). CVD is one of leading cause of death and disability worldwide and in the Western Pacific region.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e In China, the prevalent cases, new cases of CVD and death contributed to CVD are 120\u0026nbsp;million, 12.34\u0026nbsp;million and 4.58\u0026nbsp;million in 2019,\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e which poses challenges and burden to health care systems, health insurance payments, social development, and family well-being. In order to cope with that caused by CVD, it is the most cost-effective measure to pay attention to its prevention, especially interventions targeting modifiable risk factors of CVD, including physical disease (e.g., hypertension, diabetes), lifestyles (e.g., drinking, sleep, diet), and positive or negative affect (e.g., optimism, loneliness, hopelessness).\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSubjective well-being (SWB) is a comprehensive indicator and referred to the evaluations of individuals\u0026rsquo; subjective cognitive and emotion, including life satisfaction, positive affect and negative affect.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e As reviewed by Diener E,\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e SWB may enhance or impair human health and longevity through its effects on the cardiovascular, immune, and endocrine systems. Regarding older adults, cohort studies from China have demonstrated greater SWB is associated with lower risk of cognitive impairment and all-cause mortality.\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 And participants aged more than 50 years with high level SWB live longer and healthier than that with low level SWB, proved by a prospective cohort study with ten years follow-up based on English Longitudinal Study of Ageing.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Therefore, it is believed that improving individual SWB has a potential role in promoting healthy aging. Specific to CVD, a cohort study from the UK biobank demonstrated higher SWB was significantly associated with a lower risk of cardiometabolic diseases.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e And, a cohort study conducted in the America also showed the similar finding.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlthough the Chinese guidelines for primary prevention of cardiovascular diseases acknowledge that maintaining a favorable psychological status may reduce cardiovascular risk, it is noteworthy that none of the cited evidence is derived from Chinese populations.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Namely, evidence specific to the association between SWB and the CVD risk among Chinses older adults is limited. Particularly, the heterogeneous effect of SWB on CVD in subpopulation with different number of healthy behaviors is unclear. Therefore, a cohort study of Chinese older adults based on CLHLS, a nationally representative longitudinal survey, was designed to analyze the association between SWB and CVD risk. Meanwhile, the heterogeneity of the association in subpopulation with different number of healthy lifestyles was explored.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis study was characterized as a prospective cohort study established based on the data from the survey in 2011\u0026ndash;2012 (baseline) and 2014 (end of observation time) of the CLHLS. The CLHLS is the national community-based longitudinal survey for older Chinese adults randomly selected from 23 to 31 province in China, of which the first survey was conducted in 1998 and subsequent surveys were conducted every 2\u0026ndash;3 years. The participants and data of CLHLS were representative due to the project covered 85% of total Chinese people. Participants\u0026rsquo; information, including demographic characteristic, lifestyles and health and functional status were recorded in a standardized questionnaire in the form of face-to-face interviews by well-trained interviewers. More detailed information about CLHLS can be browsed in the official website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://opendata.pku.edu.cn/dataverse/CHADS\u003c/span\u003e\u003cspan address=\"https://opendata.pku.edu.cn/dataverse/CHADS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and previous studies.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAll participants surveyed at baseline were enrolled in this study (n\u0026thinsp;=\u0026thinsp;9765). And, participants aged less than 65 years (n\u0026thinsp;=\u0026thinsp;85) at baseline, suffered from CVD (n\u0026thinsp;=\u0026thinsp;1765) or were missing the record (n\u0026thinsp;=\u0026thinsp;509) at baseline, with missing records of either item on the SWB scale (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e) at baseline (n\u0026thinsp;=\u0026thinsp;1289), lost to follow up in 2014 (n\u0026thinsp;=\u0026thinsp;473), with the time of death recorded in the questionnaire was before the baseline survey time (n\u0026thinsp;=\u0026thinsp;51) and with missing records of CVD in 2014 (n\u0026thinsp;=\u0026thinsp;472) were excluded (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eSubjective well-being\u003c/p\u003e \u003cp\u003eA 5-point Likert scale consisting of 8-item was used to assess the SWB, including life satisfaction and affective well-being (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Life satisfaction was measured by the question \u0026ldquo;How do you rate your life at present?\u0026rdquo;, with the answer and scored ranging from 1 (very bad) to 5 (very good). The positive affective well-being was assessed by 4 questions: \u0026ldquo;do you always look on the bright side of things?\u0026rdquo; \u0026ldquo;do you like to keep your belongings neat and clean?\u0026rdquo; \u0026ldquo;can you make your own decisions?\u0026rdquo; and \u0026ldquo;are you happy as younger?\u0026rdquo;, and the negative affective well-being was assessed by 3 questions: \u0026ldquo;do you often feel fearful or anxious?\u0026rdquo; \u0026ldquo;do you feel lonely and isolated?\u0026rdquo; and \u0026ldquo;do you feel useless with age?\u0026rdquo;. According to existed studies, answers for positive affective items were scored from 1(never) to 5 (always), and that for negative affective items were reversely scored. The total SWB score was the sum of each score from 8 items, with the range from 15 to 40. The higher the SWB score, the more happiness and positive emotions the individual subjectively feels.\u003c/p\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003cp\u003eThe endpoint of this study was the onset of new CVD (heart disease, or stroke) during the observation period from 2011 to 2014. Historical CVD was collected using standardized questionnaires. At each survey, alive participants were asked if he had suffered from heart disease or stroke, and the information about whether deceased participants suffered from CVD prior to death was obtained from their proxies.\u003c/p\u003e \u003cp\u003eHealthy lifestyles\u003c/p\u003e \u003cp\u003eRegarding body mass index (BMI), if the value was in the range from 18.5 to 23.9, it was considered as healthy BMI. On the contrary, it was considered as unhealthy BMI. Regarding smoking and drinking, never smoking and drinking were considered as healthy lifestyles, ever or currently smoking and drinking were unhealthy lifestyles. Regarding duration of sleep, if the rang of that was from 6 to 8 hours, it was considered as healthy sleep. On the contrary, it was considered as unhealthy sleep. Regarding exercise, currently do physical exercise were considered as healthy lifestyles. Regarding diet, if the dietary diversity (DD) score (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e) was greater than or equal to the median, it was considered as healthy diet, else it was considered as unhealthy diet.\u003c/p\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003cp\u003eThe covariates included in this study were baseline age, sex (male or female), nation (Han or minority nationality), category of residence (rural or urban), current marital status (married or others), education (0 year, 1\u0026ndash;6 years, or \u0026ge;\u0026thinsp;7 years), main occupation before 60 years, pension (yes or no), mildew odor at home (yes or no), cooking fuels (clean fuels, solid fuels, or others), BMI, smoking status (never, ever or currently), drinking status (never, ever or currently), duration of sleep (\u0026lt;\u0026thinsp;6 hours, 6 to 8 hours or \u0026gt;\u0026thinsp;8 hours), activities scores (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e), regular exercise (never, ever or currently), DD scores, adequate medical care (yes or no), regular physical examination (yes or no), hearing impairment (yes or no), visual impairment (yes or no), teeth loss (yes or no), depressive symptoms (If any answer of the following two questions was yes, participant would be judged to have depressive symptoms: ①have you felt sad, blue, or depressed for two weeks or more in last 12 months? ②have you lost interest in most things like hobbies, work, or similar activities?), the mini-mental state examination (MMSE) score (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e), basic activities of daily living (BADL) score (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e), multiple chronic conditions (MCCs, two or more coexisting chronic conditions in an individual, including hypertension, diabetes, bronchitis/emphysema/pneumonia/asthma, tuberculosis, cataract, glaucoma, cancer, gastric or duodenal ulcer, Parkinson's disease, arthritis, dementia, epilepsy, cholecystitis/cholelith disease, dyslipidemia, rheumatism or rheumatoid disease, chronic nephritis and hepatitis).\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eRegarding data governance, when the answer for question was \u0026ldquo;unable to answer\u0026rdquo; \u0026ldquo;don\u0026rsquo;t know\u0026rdquo; or below its 2.5th percentile for weight and height, it was replaced by NA. The proportion of missing variables in the data set was calculated (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e) after the process for inclusion and exclusion of participants. And, variables with a missing proportion of less than 30% were imputed by the Hot-Deck method. All transformations of variables and statistical analyses were performed on the basis of imputed dataset.\u003c/p\u003e \u003cp\u003eThe continuous variables were described as mean and standard deviation (SD) or median and interquartile range (IQR) according to whether the variables follow a normal distribution. Categorical variables were described as frequency (n) and percentage (%). The comparison of baseline characteristics and outcomes among four groups was conducted as appropriate by t test, Wilcoxon sum test or Chi-square test. Restricted cubic splines (RCS) were used to determine the linear relationship between SWB and CVD risk. Hierarchical regression based on modified Poisson regressions were performed to estimate the association and its strength between standardized SWB score (z-score) and the risk of CVD, in which crude relative risk (RR) and adjusted RR and 95% confidence interval (CI) were calculated. Confounders based on a priori experience, including demographic characteristic (age, sex, race, residence, marital status, education, occupation, pension), home environment and lifestyles (mildew odor at home, cooking fuels, BMI, smoking status, drinking status, duration of sleep, activities score, regular exercise, DD score), healthcare-related information (adequate medical care, regular physical examination), physical and mental health-related information (hearing impairment, visual impairment, teeth loss, depressive symptoms, the MMSE score, BADL score, MCCs) were gradually entered in the hierarchical regression analysis. Subgroup analyses were conducted in mutually exclusive and overlapping subgroups splitting by individual healthy lifestyle and different numbers of healthy lifestyles, in view of the impact of lifestyles on CVD risk. Additionally, sensitivity analyses were performed to verity the robustness of this association, including (1) multivariable log-binomial regressions were fitted adjusting for all confounders (Model S1); (2) multivariable modified Poisson regressions were fitted on the basis of complete case method that excluded observations containing missing values (Model S2); (3) the covariate MCCs in the main analysis was replaced by risk factors of CVD, including hypertension, diabetes, dyslipidemia, chronic nephritis and cancer (Model S3); (4) SWB in the form of continuous measure was transformed into categorical variables according to the tertile of that, and multivariable modified Poisson regressions were fitted again (Model S4); (5) multivariable modified Poisson regressions were fitted again adjusting for covariates with P\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in differential comparison between CVD patients and non-CVD patients (Model S5).\u003c/p\u003e \u003cp\u003eData governance, statistical analyses and plots were conducted by using R 4.4.0 (R\u003c/p\u003e \u003cp\u003eCore Team) with R packages \u0026ldquo;haven\u0026rdquo;, \u0026ldquo;lubridate\u0026rdquo;, \u0026ldquo;VIM\u0026rdquo;, \u0026ldquo;rms\u0026rdquo;, \u0026ldquo;rqlm\u0026rdquo;, \u0026ldquo;ggplot2\u0026rdquo;, and \u0026ldquo;forestploter\u0026rdquo;. A 2-tailed hypothesis test was used, and the significance level for that was set at 0.05.\u003c/p\u003e\n\u003ch3\u003eEthic consideration\u003c/h3\u003e\n\u003cp\u003e The CLHLS was conducted according to the guidelines of the Declaration of Helsinki, and approved by the biomedical ethics committee of Peking University (IRB00001052\u0026ndash;24713074). All participants or their proxies signed the informed consent before the collection of individual data. Moreover, article 32 of the \u003cem\u003eEthical Review Measures for Life Science and Medical Research involving Human Beings\u003c/em\u003e issued by China in 2023 points out that scientific research based on legally obtained anonymized public data, which does not involve biological samples and does not violate the commercial interests of others, can be exempted from ethical review. Therefore, no second ethical review was conducted for this study, and no informed consent were signed again due to the anonymity of the participants.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of participants\u003c/h2\u003e \u003cp\u003eThe characteristics of eligible participants was listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 5120 participants were included in the final analyzed dataset, the mean baseline age was 84.52 years (SD 10.88), 50.75% of all were female, 46.23% of all lived in urban areas, and the mean SWB score was 30.30 (SD 4.29). During a median of 935 days of follow-up, 827 (16.15%) participants suffered from CVD. Comparing with the non-CVD patients, CVD patients were more likely to have lower SWB score, be younger, be Han nation, live in urban areas, have pension, use clean fuels, higher BMI, ever or currently smoke, ever or currently drink, ever or currently do physical exercise, receive adequate medical care, have depressive symptoms and MCCs (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of eligible participants stratified by outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4293)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;827)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSWB score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.30\u0026thinsp;\u0026plusmn;\u0026thinsp;4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.37\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZ-score of SWB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.07 (-0.77, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.07 (-0.77, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07 (-0.77, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.52\u0026thinsp;\u0026plusmn;\u0026thinsp;10.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.64\u0026thinsp;\u0026plusmn;\u0026thinsp;11.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.87\u0026thinsp;\u0026plusmn;\u0026thinsp;10.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2419 (47.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2005 (46.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e414 (50.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2701 (52.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2288 (53.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e413 (49.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNation\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHan\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4816 (94.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4022 (93.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e794 (96.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMinority\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e304 (5.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e271 (6.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (3.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2753 (53.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2342 (54.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e411 (49.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUrban\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2367 (46.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1951 (45.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e416 (50.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarried\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2123 (41.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1769 (41.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e354 (42.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOthers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2997 (58.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2524 (58.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e473 (57.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e0 year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2887 (56.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2444 (56.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443 (53.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u0026ndash;6 years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1659 (32.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1376 (32.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283 (34.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge; 7 years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e574 (11.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e473 (11.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (12.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e*Occupation\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCategory 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3646 (71.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3114 (72.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e532 (64.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCategory 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e949 (18.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e744 (17.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (24.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCategory 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e525 (10.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e435 (10.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (10.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePension\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4351 (84.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3691 (85.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e660 (79.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e769 (15.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e602 (14.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167 (20.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMildew odor at home\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e969 (18.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e816 (19.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (18.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4151 (81.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3477 (80.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e674 (81.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCooking fuels\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClean fuels\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2397 (46.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1981 (46.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e416 (50.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSolid fuels\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2369 (46.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2024 (47.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e345 (41.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther fuels\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e354 (6.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e288 (6.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (7.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.02\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.90\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.64\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3278 (64.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2782 (64.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e496 (59.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEver\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e798 (15.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e628 (14.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (20.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrently\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1044 (20.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e883 (20.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161 (19.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking status\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3402 (66.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2873 (66.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e529 (63.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEver\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e718 (14.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e567 (13.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151 (18.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrently\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000 (19.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e853 (19.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147 (17.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration of sleep\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u0026ndash;8 hours\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2701 (52.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2255 (52.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e446 (53.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; 6 hours\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e780 (15.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e642 (14.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (16.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt; 8 hours\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1639 (32.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1396 (32.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243 (29.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eActivities score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0 (5.0, 16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0 (5.0, 16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0 (6.0, 15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegular exercise\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNever\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2727 (53.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2327 (54.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400 (48.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEver\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e550 (10.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e438 (10.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (13.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrently\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1843 (36.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1528 (35.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e315 (38.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDD score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0 (3.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0 (3.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0 (3.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdequate medical care\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4861 (94.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4061 (94.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e800 (96.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e259 (5.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232 (5.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegular physical examination\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1581 (30.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1305 (30.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e276 (33.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3539 (69.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2988 (69.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e551 (66.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHearing impairment\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2127 (41.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1794 (41.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333 (40.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2993 (58.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2499 (58.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e494 (59.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVisual impairment\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4183 (81.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3495 (81.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e688 (83.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e937 (18.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e798 (18.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 (16.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTeeth loss\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e409 (7.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e344 (8.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (7.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4711 (92.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3949 (91.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e762 (92.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepressive symptoms\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4302 (84.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3639 (84.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e663 (80.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e818 (15.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e654 (15.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164 (19.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMMSE score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.84\u0026thinsp;\u0026plusmn;\u0026thinsp;6.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.82\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.91\u0026thinsp;\u0026plusmn;\u0026thinsp;6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBADL score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3833 (74.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3297 (76.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e536 (64.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1287 (25.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e996 (23.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e291 (35.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4967 (97.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4184 (97.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e783 (94.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153 (2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (5.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDyslipidemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5033 (98.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4224 (98.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e809 (97.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChronic nephritis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5096 (99.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4275 (99.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e821 (99.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5084 (99.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4265 (99.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e819 (99.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMCCs\u003c/b\u003e, \u003cb\u003en\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3990 (77.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3391 (78.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e599 (72.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1130 (22.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e902 (21.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e228 (27.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Category 1: staff of farming/forestry/animal husbandry/side-line production/fishery; Category 2: workers/teachers/doctors/military/government workers/businessmen; Category 3: others\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSWB: subjective well-being; BMI: body mass index; DD score: diversity of dietary score; MMSE score: the mini-mental state examination score; BADL score: basic activity of daily living score; MCCs: multiple chronic conditions\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSubjective well-being and the risk of cardiovascular diseases\u003c/h3\u003e\n\u003cp\u003eAs showed in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e, the RCS based on univariable and multivariable binary logistic regressions all showed a linear relationship between SWB and the risk of CVD (all P for linear test\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and that for non-linear test\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The result of Model 1(crude RR\u0026thinsp;=\u0026thinsp;0.918, 95% CI: 0.863 to 0.977), Model 2 (adjusted RR\u0026thinsp;=\u0026thinsp;0.876, 95% CI: 0.821 to 0.934), Model 3 (adjusted RR\u0026thinsp;=\u0026thinsp;0.886, 95% CI: 0.828 to 0.949), Model 4 (adjusted RR\u0026thinsp;=\u0026thinsp;0.875, 95% CI: 0.815 to 0.938) and Model 5 (adjusted RR\u0026thinsp;=\u0026thinsp;0.895, 95% CI: 0.833 to 0.962) all demonstrated that increased SWB (per 1-SD) was associated with reduced risk of CVD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eand Supplementary Table\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSubgroup analyses\u003c/h3\u003e\n\u003cp\u003eThe result of subgroup analyses was presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eSupplementary Table\u0026nbsp;4-Supplementary Table\u0026nbsp;20\u003c/b\u003e. For individual healthy lifestyle, the inverse association between SWB and the risk of CVD was significant in subgroups with healthy BMI (per 1-SD, adjusted RR\u0026thinsp;=\u0026thinsp;0.843, 95% CI: 0.760 to 0.935), never smoking (per 1-SD, adjusted RR\u0026thinsp;=\u0026thinsp;0.887, 95% CI: 0.808 to 0.974), never drinking (per 1-SD, adjusted RR\u0026thinsp;=\u0026thinsp;0.875, 95% CI: 0.801 to 0.956), never or ever do physical exercise (per 1-SD, adjusted RR\u0026thinsp;=\u0026thinsp;0.884, 95% CI: 0.807 to 0.969), currently do physical exercise (per 1-SD, adjusted RR\u0026thinsp;=\u0026thinsp;0.852, 95% CI: 0.769 to 0.945), healthy sleep (per 1-SD, adjusted RR\u0026thinsp;=\u0026thinsp;0.869, 95% CI: 0.787 to 0.960) and unhealthy diet (per 1-SD, adjusted RR\u0026thinsp;=\u0026thinsp;0.879, 95% CI: 0.794 to 0.973). For subgroups with overlapping numbers of healthy lifestyles, the significant association were observed only in subgroups containing 2\u0026ndash;4 (per 1-SD, adjusted RR\u0026thinsp;=\u0026thinsp;0.904, 95% CI: 0.836 to 0.977), 3\u0026ndash;5 (per 1-SD, adjusted RR\u0026thinsp;=\u0026thinsp;0.870, 95% CI: 0.799 to 0.948) and 4\u0026ndash;6 (per 1-SD, adjusted RR\u0026thinsp;=\u0026thinsp;0.821, 95% CI: 0.731 to 0.922) healthy lifestyles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eThe main finding was verified by several sensitivity analyses. First, the association between SWB score and CVD risk showed in multivariable log-binomial regressions (Model S1, adjusted RR\u0026thinsp;=\u0026thinsp;0.894, 95% CI: 0.832 to 0.960) similar to that in multivariable modified Poisson regressions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eSupplementary Table\u0026nbsp;21\u003c/b\u003e). Second, the result (Model S2, adjusted RR\u0026thinsp;=\u0026thinsp;0.916, 95% CI: 0.840 to 0.999) remained similar to main analysis when the complete case method was used to deal with missing value (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eSupplementary Table\u0026nbsp;22\u003c/b\u003e). Third, the multivariable modified Poisson regression with MCCs was replaced by hypertension and diabetes (Model S2, adjusted RR\u0026thinsp;=\u0026thinsp;0.897, 95% CI: 0.835 to 0.964) also verified the robustness of main findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eSupplementary Table\u0026nbsp;23\u003c/b\u003e). Fourth, comparing with the first tertile of SWB score, the risk of CVD in second and third tertile was reduced by 16.3% (adjusted RR\u0026thinsp;=\u0026thinsp;0.837, 95% CI: 0.711 to 0.985) and 22.0% (adjusted RR\u0026thinsp;=\u0026thinsp;0.780, 95% CI: 0.658 to 0.924) suggested by Model S4, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eSupplementary Table\u0026nbsp;24\u003c/b\u003e). Finally, the simplified modified Poisson regression (Model S5) demonstrated increased SWB was associated with reduced risk of CVD (per 1-SD, adjusted RR\u0026thinsp;=\u0026thinsp;0.875, 95% CI: 0.816 to 0.938), similarly (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eSupplementary Table\u0026nbsp;25\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMain findings\u003c/h2\u003e \u003cp\u003eThis nationwide prospective cohort study based on CLHLS reveals two significant findings: (1) higher SWB score was associated with lower risk of CVD among Chinese older adults, and (2) this protective association is more pronounced in subgroups with normal BMI, never smoking, never drinking, healthy sleep, poor DD score, and a greater number of healthy lifestyles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eInterpretation and comparison\u003c/h2\u003e \u003cp\u003eThe American Heart Association (AHA) has summarized the relationship between psychological health and CVD, and issued a scientific statement titled 'Psychological Health, Well-Being, and the Mind-Heart-Body Connection'.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e In the statement, clear association between the increase in the component of SWB and overall SWB and the decrease in risk of CVD were presented, although several studies have suggested no association between positive affect and the incidence of CVD. The findings of this study are consistent with the majority of previous research, supporting the association between the comprehensive indicator SWB and cardiovascular conditions risk. Reviewing the components of SWB in this study (e.g., life satisfaction,\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e optimism,\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e hopelessnes,\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e loneliness,\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e anxiety\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e), substantial evidence already supports their association with CVD incidence. While previous studies conducted in America, Europe and countries other than China have reported similar protective associations, this study provides the first robust evidence from a large-scale longitudinal aging study in a Chinese context. Notably, the observed effect modification by health behaviors offers new insights into potential synergistic effects between psychological and lifestyle factors.\u003c/p\u003e \u003cp\u003eRegarding the mechanisms that link the SWB and incidence of CVD, promoting adaptive physiological functioning, buffering the detrimental influences of stressful experiences and motivating more healthy lifestyles are the potential pathways for high SWB to mitigate the risk of cardiovascular conditions. For instance, (1) individuals maintaining higher SWB have lower risk of hypertension, dyslipidemia and vascular stiffness those are common age-related changes in physical functioning and important risk factor of CVD.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e (2) Negative psychological affect could result in dysregulation of the autonomic nervous system and a series of chain reactions that increase the risk of developing CVD.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e And this suggests alleviating negative psychological affect may suppress these adverse biological processes. (3) individuals with positive affect and high life satisfaction are more likely to adopt healthy lifestyles, whereas those with negative affect may be more susceptible to developing unhealthy health behaviors. Moreover, health-related behaviors may reciprocally influence or modify SWB.\u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe heterogeneous association between SWB and the risk of CVD was showed by subgroup analyses. And the relationships among healthy lifestyles, SWB, and CVD mentioned above provide substantial support for the majority of findings from subgroup analyses. Especially, the more healthy lifestyles adopted, the greater the prevention effect of SWB on CVD. Furthermore, those findings may suggest a potential interaction between health-related lifestyle behaviors and SWB in preventing CVD. Similar interaction was also observed in studies examining SWB and all-cause mortality, particularly when comparing subgroups of never-smokers versus former/current smokers.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e In addition, participants with low DD score were more likely to benefit from high SWB. the DD is more influenced by socioeconomic factors than by well-being or self-discipline,\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e which may be the reason for that. Importantly, this finding might extend to the notion that SWB counteracts the risk of CVD associated with low diversity of dietary.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eThese findings carry important implications for China's aging population, including that targeting clinical and public health. First, SWB assessment could help identify high-risk elderly for future CVD, and it was recommended be incorporated into routine geriatric evaluations, who at risk for CVD especially. Second, it is recommended to comprehensively assess SWB and its association with health-related outcomes using multidimensional indicators, rather than evaluating a single dimension or aspect, such as optimism, pessimism, loneliness, or self-control. Third, comprehensive psychology interventions (e.g., enhance positive mood and reduce negative affect) may complement traditional CVD prevention, and combined healthy lifestyle and psychological approaches in the healthcare of general elderly population and primary prevention of CVD may yield maximal benefits. Fourth, in subpopulations with absent or limited adoption of healthy lifestyles, SWB failed to demonstrate significant CVD risk reduction. Consequently, within the precision prevention paradigm, a thorough cost-benefit assessment is warranted when considering SWB-specific interventions. Finally, developing culturally appropriate SWB metrics and carrying out more intervention studies targeting integrated SWB and health-related lifestyles are necessary.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study has several notable strengths as following. First, the longitudinal design, a representative sample of Chinese older adults and large sample size strengthen the findings. Second, SWB is a composite measure comprising life satisfaction, positive affect and negative affect, which may occur concurrently or sequentially. This study did not analyze the association between each component and the outcomes, thereby preserving the integrity of this holistic measure. Meanwhile, this may help fill the evidence gap in the CVD prevention guidelines issued by the AHA,\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e European Society of Cardiology,\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e and Chinese Society of Cardiology of Chinese Medical Association,\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e where there is a lack of evaluation of the association between the comprehensive indicator assessing SWB and CVD risk. Third, rigorous statistical scheme and appropriate statistical methods have enhanced the reliability of the findings, including the assessment of linear relationship between SWB score and CVD risk by RCS, the use of modified Poisson regressions to calculate RR with 95%CI, multiple covariates adjustment and sensitivity analyses. Finally, guided by the concept of the subpopulation treatment effect pattern plot (STEPP) approach,\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e this study categorized participants into four overlapping subgroups based on their number of healthy lifestyles, revealing heterogeneous associations between SWB and CVD risk across these subgroups.\u003c/p\u003e \u003cp\u003eThis study has two main limitations that need to be acknowledged. First, although the SWB scale used in the CLHLS has been widely adopted in previous studies, existing evidence suggests that it may require further psychometric refinement.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Consequently, the assessment of SWB in the present study might be subject to measurement bias. Second, exact onset time of participants' CVD was not recorded in the CLHLS, precluding the application of time-to-event analytical methods (e.g., Cox proportional hazards models) in the data analysis. Thus, although statistical methods were employed to estimate RR, potential time-related biases could not be adequately controlled.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides compelling evidence that SWB is an independent predictor of cardiovascular health in Chinese older adults, with particularly strong benefits among those maintaining more healthy lifestyles. These findings highlight the importance of integrating psychological well-being into comprehensive approaches to healthy aging and CVD prevention. As China faces unprecedented population aging, these results underscore the need for innovative strategies that address both psychological and physical health dimensions in elderly care. Future research should focus on developing culturally appropriate interventions and elucidating the underlying biological mechanisms of these observed associations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSWB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esubjective well-being\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiovascular diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLHLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Chinese Longitudinal Healthy Longevity Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erelative risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDD score\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edietary diversity score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe mini-mental state examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBADL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebasic activities of daily living\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emultiple chronic conditions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erestricted cubic splines\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e not applicable\u003c/p\u003e\n\u003ch3\u003eAcknowledgments\u003c/h3\u003e\n\u003cp\u003eAll authors acknowledge the organizations, teams, and individuals who contributed to the implementation of the CLHLS, database establishment, and data sharing.\u003c/p\u003e\n\u003ch3\u003eAuthor’s contributions\u003c/h3\u003e\n\u003cp\u003eJW and WZ: conception and design of this study, statistical analysis, original draft writing; YT, LD, QY: Review, editing and methodology; TG: Review, editing and supervision; ZW: conception and design of this study, review, editing, and methodology.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis study was supported by Project of Jiangsu Provincial Bureau of Traditional Chinese Medicine (ZD201802), Open Project of Jiangsu Provincial Traditional Chinese Medicine Epidemic Disease Research Center(JSYB2024KF09, JSYB2024KF09), Jiangsu Provincial Traditional Chinese Medicine Science and Technology Development Plan Project (MS202218), Hospital-level project of Jiangsu Province Hospital of Chinese medicine (Y2021CX16,Y24004).All funding organizations had no role in the study design, implementation, analysis, or interpretation of the data.\u003c/p\u003e\n\u003ch3\u003eConflict of Interest Disclosures\u003c/h3\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e\n\u003ch3\u003eData availability\u003c/h3\u003e\n\u003cp\u003eUpon application and approval, the data of CLHLS is available at the official website:\u0026nbsp;https://opendata.pku.edu.cn/dataverse/CHADS.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoth GA, Mensah GA, Johnson CO, et al. 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Understanding subjective well-being: perspectives from psychology and public health. Public Health Rev. 2020;41(1):25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s40985-020-00142-5\u003c/span\u003e\u003cspan address=\"10.1186/s40985-020-00142-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiener E, Lucas RE, Oishi S. Advances and Open Questions in the Science of Subjective Well-Being. Collabra Psychol. 2018;4(1):15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1525/collabra.115\u003c/span\u003e\u003cspan address=\"10.1525/collabra.115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"subjective well-being, cardiovascular disease, stroke, cohort study, healthy lifestyles, modified Poisson regression, subgroup analyses","lastPublishedDoi":"10.21203/rs.3.rs-6821093/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6821093/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/Objective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvidence regarding the impact of subjective well-being (SWB) on the incidence of cardiovascular diseases (CVD) among Chinese older adults was limited. This study aimed to ascertain the association between SWB and the risk of CVD among Chinese older adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA prospective cohort study was designed based on the data from the survey in 2011–2012 and 2014 of Chinese Longitudinal Healthy Longevity Survey (CLHLS). Participants aged over 65 years without CVD at baseline were included in this study. SWB was measured by a scale consisting of 8-item question. The outcome was CVD (heart disease or stroke) that occurred during the observation period. Restricted cubic splines were used to determine the linear relationship between SWB and CVD risk. Hierarchical regression based on modified Poisson regressions was performed to estimate the association between SWB and CVD risk. Subgroup analyses were conducted in mutually exclusive and overlapping subgroups based on healthy lifestyles. Moreover, sensitivity analyses were performed to confirm the robustness of the main analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 5,120 eligible participants were included in this cohort study, and 827 participants suffered from CVD during follow-up period (the incidence of CVD was 16.15%). Per 1-standard deviation (SD) increase in SWB was associated with 10.5% reduction in the risk of CVD (adjusted relative risk [RR] = 0.895, 95% CI: 0.833 to 0.962). The robustness of the association was verified by sensitivity analyses. The heterogeneity of association was observed in subgroups with different number of healthy lifestyles. In subgroups with a number of healthy lifestyles of 2 to 4, 3 to 5, or 4 to 6, per 1-SD increase in SWB was associated with a 9.6% (adjusted RR = 0.904, 95% CI: 0.836 to 0.977), 13.0% (adjusted RR = 0.870, 95% CI: 0.799 to 0.948) and 17.9% (adjusted RR = 0.821, 95% CI: 0.731 to 0.922) reduction in CVD risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn inverse linear association is observed between SWB and CVD risk among Chinese older adults. The strength of the association was greater in subgroups with more modifiable healthy lifestyles than that with less modifiable healthy lifestyles. Enhancing subjective well-being and fostering more healthy lifestyle behaviors among older adults are contributed to the prevention of CVD.\u003c/p\u003e","manuscriptTitle":"Association between subjective well-being and the risk of cardiovascular diseases among older adults: evidence from the Chinese Longitudinal Healthy Longevity Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 09:13:11","doi":"10.21203/rs.3.rs-6821093/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-01T09:56:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-28T13:41:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-24T08:49:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291148724780978778058991007076259182172","date":"2025-06-16T07:57:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92651513052833227024043146573252110670","date":"2025-06-12T14:01:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-12T07:58:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261992187488980280392241330670912344583","date":"2025-06-12T07:47:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-12T07:35:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-06T13:05:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-06T05:46:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-06T05:42:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-06-04T13:28:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a8d30c1-6c99-48ef-8eec-1d62e455b0a0","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T16:05:01+00:00","versionOfRecord":{"articleIdentity":"rs-6821093","link":"https://doi.org/10.1186/s12877-025-06373-y","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2025-09-26 15:57:31","publishedOnDateReadable":"September 26th, 2025"},"versionCreatedAt":"2025-06-17 09:13:11","video":"","vorDoi":"10.1186/s12877-025-06373-y","vorDoiUrl":"https://doi.org/10.1186/s12877-025-06373-y","workflowStages":[]},"version":"v1","identity":"rs-6821093","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6821093","identity":"rs-6821093","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}