Metabolic Syndrome Severity and All-Cause Mortality in a Nationally Representative Cohort of Older Chinese Adults

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A continuous MetS Score has been developed to quantify MetS severity, but its association with all-cause mortality in older Chinese adults remains unclear. Methods We analyzed 2,443 participants from the Chinese Longitudinal Healthy Longevity Survey (CLHLS, 2008–2018 and 2011–2018). MetS Score was derived by confirmatory factor analysis using triglycerides, HDL-C, fasting glucose, systolic blood pressure, and BMI. Participants were categorized into quartiles (Q1-Q4). Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality. Subgroup analyses examined effect modification by age, sex, residence, and other covariates, and restricted cubic splines explored dose–response relationships. Results During 10,356 person-years of follow-up, 1,412 deaths occurred (136.3/1,000 person-years). Each 1-unit increase in MetS Score was associated with a 10.8% lower mortality risk (HR = 0.892, 95% CI: 0.817–0.973). The protective effect was concentrated in Q4 vs. Q1 (HR = 0.840, 95% CI: 0.713–0.989). Subgroup analyses showed stronger associations in those ≥ 80 years (HR = 0.861, 95% CI: 0.783–0.945) and urban residents (HR = 0.466, 95% CI: 0.293–0.741). Restricted cubic splines confirmed a linear inverse association overall and among the ≥ 80 years group. Conclusions MetS Score, reflecting MetS severity, was inversely associated with all-cause mortality in older Chinese adults, particularly among those aged ≥ 80 years. These findings suggest that higher MetS Score may paradoxically confer survival benefits in the elderly, warranting further mechanistic studies. Metabolic syndrome MetS score All-cause mortality Chinese older population CLHLS Figures Figure 1 Figure 2 Figure 3 Introduction Metabolic syndrome (MetS), defined as a cluster of cardiometabolic abnormalities including central obesity, insulin resistance, hypertension, and dyslipidemia, has become a major global public health concern [1,2] . Substantial evidence indicates that both MetS and its individual components independently increase the risk of cardiovascular diseases (CVDs) and related adverse outcomes [3–5] . Globally, the prevalence of MetS ranges from 12.5% to 31.4%, with marked regional differences [6] . In China, prevalence is estimated at about 24.5%, while in parts of Africa it exceeds 30% [6–8] . These gradients underscore the need for context-specific risk quantification in populations undergoing rapid ageing. Conventional definitions, such as the NCEP ATP III criteria [9] , classify MetS when three or more abnormalities are present. While clinically straightforward, this approach cannot capture the severity of metabolic dysfunction. To address this limitation, researchers have proposed continuous severity scores (“MetS Score”) [10–12] , derived through confirmatory factor analysis of triglycerides, HDL-C, fasting glucose, blood pressure, and adiposity indices. These scores provide a graded measure of risk and outperform categorical definitions in predicting diabetes, CVD events, and related biomarkers [13–15] . Notably, a recent Chinese study developed an age-, sex-, and ethnicity-specific MetS Score, which showed strong associations with CVD risk factors and biomarkers [16] . Despite these advances, the relationship between MetS Score and all-cause mortality remains unclear, especially in older adults. Some studies report linear associations, whereas others suggest paradoxical or J-shaped patterns, possibly reflecting the “obesity paradox” and age-related changes in body composition [17,18] . These uncertainties highlight the importance of evaluating MetS Score in China’s rapidly ageing population. Using data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), we aimed to assess the association between MetS Score and all-cause mortality in older Chinese adults, with further evaluation of subgroup differences, particularly among those aged ≥ 80 years, and the potential linearity of this relationship. Methods Study population The CLHLS is a nationally representative, prospective cohort of adults aged ≥ 60 years across 23 provinces of China [19] . More detailed information of CLHLS study design can be found at the following website: https://www.icpsr.umich.edu/web/NACDA/studies/38899 [20] . All interviewees were informed and signed the consent form willingly before collecting blood samples and conducting physical examinations. Ethical approval was obtained from the Biomedical Ethics Committee of Peking University (IRB00001052–13074), and all participants provided written informed consent. For the present analysis, we used three datasets: the CLHLS biomarker dataset, and two follow-up datasets (2008–2018 and 2011–2018). Participants were excluded if they were < 65 years of age, lacked biomarker measurements, were duplicated across survey waves, had no survival outcome data, or were lost to follow-up before the subsequent wave. After exclusions, 2,443 participants were eligible and included in the analysis. A detailed flowchart of enrollment and exclusions is presented in Fig. 1 . Data collection Biological specimens were collected according to standardized protocols, and blood samples were processed and analyzed in certified central laboratories using calibrated equipment [21] . Assays included triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), fasting blood glucose (FBG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C). Physical examinations were conducted by trained physicians from the local Centers for Disease Control (CDC) [20] . Measurements comprised systolic blood pressure (SBP), diastolic blood pressure (DBP), height, and weight, from which body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Information on sociodemographic characteristics and lifestyle factors was obtained through structured face-to-face interviews [20] . Covariates included age (< 80 vs. ≥80 years), sex, ethnicity (Han vs. minority), residence (urban vs. rural), education (illiterate, primary, middle school, high school or above), marital status (married/cohabiting vs. other), living arrangement (alone, institution, or with family), pension status (yes vs. no), smoking (never, former, current), alcohol consumption (never, former, current), and physical activity (never, former, current). Calculation of the MetS Score A continuous MetS Score was constructed using confirmatory factor analysis (CFA) of five continuous metabolic indicators: TG, HDL-C, FBG, SBP, and BMI [10,16,22] . To address skewed distributions, TG, FBG, and BMI were natural log-transformed, and HDL-C values were inverted so that higher values indicated greater metabolic risk, consistent with the direction of the other variables. All indicators were standardized prior to analysis, and factor loadings were estimated in a single-factor CFA model. Individual MetS Scores were then computed for each participant based on the fitted model equation. Clinical outcomes The primary outcome was all-cause mortality, determined from CLHLS follow-up surveys conducted between 2008–2018 and 2011–2018. Vital status and dates of death were obtained during household interviews with close family members or village doctors and, when available, verified against official registration records. Statistical analysis Participants were categorized into quartiles of the MetS Score (Q1-Q4). Baseline characteristics were summarized as mean ± standard deviation (SD) or median (interquartile range, IQR) for continuous variables and as proportions for categorical variables. Differences across quartiles were assessed using analysis of variance (ANOVA) or Kruskal-Wallis tests for continuous variables and χ² tests for categorical variables. The association between MetS Score and all-cause mortality was evaluated using Cox proportional hazards regression, with hazard ratios (HRs) and 95% confidence intervals (CIs) reported. The proportional hazards assumption was tested using Schoenfeld residuals, and no significant violations were observed. Three nested models were specified: Model 1, crude and unadjusted; Model 2, adjusted for age, sex, and ethnicity; and Model 3, further adjusted for residence, education, marital status, pension status, smoking, alcohol consumption, physical activity, and BMI. Linear trends across quartiles were tested by modeling the median value of each quartile as a continuous variable, as described in previous studies [23] . Prespecified subgroup analyses were conducted by age (< 80 vs. ≥80 years), sex, residence, education, marital status, pension status, and BMI categories. Effect modification was evaluated by including multiplicative interaction terms, with p-values for interaction reported. Restricted cubic spline regression was applied to assess potential non-linear associations, with knots placed at the 5th, 35th, 65th, and 95th percentiles of the ln-transformed MetS Score, and the 10th percentile used as the reference [24] . Departure from linearity was tested using likelihood ratio tests. Missing covariates were addressed using multilevel multiple imputation with chained equations and random effects, accounting for the hierarchical structure of the CLHLS survey data [25] . Twenty imputed datasets were created, and final estimates were pooled according to Rubin’s rules [26,27] . All analyses were conducted with SPSS version 26.0 (IBM Corp., Armonk, NY), and a two-sided P < 0.05 was considered statistically significant. Results Development of the MetS Score Using CFA, a continuous MetS Score was derived from five metabolic indicators (TG, HDL-C, FBG, SBP, and BMI). The fitted formula for participants aged ≥ 60 years was: MetS score = -2.095 + 0.003 * SBP + 0.501 * ln(BMI) + 0.673 * ln(FBG) -0.664 * HDL-C + 0.793 * ln(TG). Factor loadings were highest for TG (0.569), followed by HDL-C (0.394), FBG (0.363), BMI (0.179), and SBP (0.104) ( Table S1 ). Baseline characteristics of participants Baseline characteristics of participants across quartiles of MetS Score are summarized in Table 1 . Among 2,443 participants, 58.7% were female, and 71.9% were aged ≥ 80 years. Most were Han (91.2%), rural residents (82.2%), lived with family or in institutions (80.5%), and had no pension (92.1%). Nearly 17.7% were current smokers, 17.3% current drinkers, and 16.7% engaged in regular exercise. Median (IQR) MetS Scores were -0.70 (0.37) for Q1, -0.24 (0.18) for Q2, 0.14 (0.20) for Q3, and 0.75 (0.63) for Q4. Participants in Q4 were younger, more likely to be female, urban residents, married or cohabiting, overweight/obese, and more likely to receive pensions and engage in physical activity. Table 1 Baseline characteristics of participants from CLHLS according to quartiles of MetS Score Characteristics Total (n = 2443) Q1 (<-0.44) (n = 610) Q2 (-0.44–0.07) (n = 609) Q3 (-0.06-0.35) (n = 612) Q4 (≥ 0.36) (n = 612) P -value Age (years), median (IQR) 88 (22) 89 (21) 91 (20) 91 (20) 83 (21) < 0.001 Age (years) < 0.001 < 80 686 (28.08) 157 (25.74) 142 (23.32) 152 (24.84) 235 (38.40) ≥ 80 1757 (71.92) 453 (74.26) 467 (76.68) 460 (75.16) 377 (61.60) Sex < 0.001 Male 1010 (41.34) 290 (47.54) 218 (35.80) 232 (37.91) 270 (44.12) Female 1433 (58.66) 320 (52.46) 391 (64.20) 380 (62.09) 342 (55.88) Race/ethnic 0.004 Han nationality 2227 (91.16) 576 (94.43) 558 (91.63) 546 (89.22) 547 (89.38) Minority nationality 216 ( 8.84) 34 ( 5.57) 51 ( 8.37) 66 (10.78) 65 (10.62) Marital status < 0.001 Married/cohabiting 807 (33.03) 221 (36.23) 170 (27.91) 170 (27.78) 246 (40.20) Divorced/never married/separated/widowed 1636 (66.97) 389 (63.77) 439 (72.09) 442 (72.22) 366 (59.80) Residence place < 0.001 Urban 435 (17.81) 93 (15.25) 87 (14.29) 87 (14.21) 168 (27.45) Rural 2008 (82.19) 517 (84.75) 522 (85.71) 525 (85.78) 444 (72.55) Coresidence type 0.828 Alone 477 (19.53) 127 (20.82) 116 (19.05) 118 (19.28) 116 (18.95) Not alone (lived with family members or in an institute) 1966 (80.47) 483 (79.18) 493 (80.95) 494 (80.72) 496 (81.05) Education levels 0.009 Illiterate 1649 (67.50) 399 (65.41) 439 (72.09) 434 (70.92) 377 (61.60) Primary school 623 (25.50) 168 (27.54) 133 (21.84) 142 (23.20) 180 (29.41) Middle school 112 ( 4.58) 30 ( 4.92) 22 ( 3.61) 24 ( 3.92) 36 ( 5.88) High school and above 59 ( 2.42) 13 ( 2.13) 15 ( 2.46) 12 ( 1.96) 19 ( 3.10) Smoking status 0.806 Never 1800 (73.68) 441 (72.30) 464 (76.19) 445 (72.71) 450 (73.53) Former 211 ( 8.64) 57 ( 9.34) 46 ( 7.55) 55 ( 8.99) 53 ( 8.66) Current 432 (17.68) 112 (18.36) 99 (16.26) 112 (18.30) 109 (17.81) Drinking status 0.158 Never 1872 (76.63) 453 (74.26) 464 (76.19) 480 (78.43) 475 (77.61) Former 149 ( 6.10) 35 ( 5.74) 32 ( 5.25) 37 ( 6.05) 45 ( 7.35) Current 422 (17.27) 122 (20.00) 113 (18.56) 95 (15.52) 92 (15.03) Exercise < 0.001 Never 1919 (78.55) 509 (83.44) 492 (80.79) 478 (78.10) 440 (71.90) Former 115 ( 4.71) 18 ( 2.95) 30 ( 4.93) 36 ( 5.88) 31 ( 5.07) Current 409 (16.74) 83 (13.61) 87 (14.29) 98 (16.01) 141 (23.04) BMI < 0.001 Underweight 742 (30.37) 245 (40.16) 197 (32.35) 180 (29.41) 120 (19.61) Normal 1313 (53.75) 327 (53.61) 334 (54.84) 329 (53.76) 323 (52.78) Overweight and obesity 388 (15.88) 38 ( 6.23) 78 (12.81) 103 (16.83) 169 (27.61) Pension subsidies 0.001 No 2251 (92.14) 559 (91.64) 572 (93.92) 577 (94.28) 543 (88.73) Yes 192 ( 7.86) 51 ( 8.36) 37 ( 6.08) 35 ( 5.72) 69 (11.27) Abbreviations: MetS, metabolic syndrome; Q1–Q4, quartile 1-quartile 4; IQR, interquartile range; BMI, body mass index. Quartile 1: MetS<-0.44, Quartile 2: -0.44 < MetS<-0.07, Quartile 3: -0.06 < MetS < 0.05, Quartile 4: MetS ≥ 0.36. Table 2 The hazard ratios for the association between MetS Score and all-cause mortality(N = 2443) HR (95%CI) P -value HR (95%CI) P for trend as continuous variable Q1 Q2 Q3 Q4 Model 1 0.804 (0.743–0.869) < 0.001 ref 1.023 (0.883–1.186) 1.056 (0.913–1.222) 0.707 (0.605–0.828) < 0.001 Model 2 0.864 (0.796–0.938) < 0.001 ref 0.968 (0.835–1.123) 1.026 (0.886–1.187) 0.805 (0.687–0.942) 0.012 Model 3 0.892 (0.817–0.973) 0.010 ref 0.958 (0.825–1.112) 1.010 (0.870–1.171) 0.840 (0.713–0.989) 0.061 Abbreviations: MetS, metabolic syndrome; HR, hazard ratios; 95% CI, 95% confidence interval; ref, reference. Model 1: crude model, no co-variables was adjusted; Model 2: adjusted for age, sex and race/ethnic; Model 3: further adjusted for residence area, educational levels, current marital status, pension subsidies, smoking status, drinking status, exercising status and BMI. When analyzed by quartiles, only the highest quartile (Q4) demonstrated a consistent protective effect compared with Q1. The hazard ratios for Q4 were 0.707 (95% CI: 0.605-0.828, P <0.001) in Model 1, 0.805 (95% CI: 0.687-0.942, P =0.012) in Model 2, and 0.840 (95% CI: 0.713-0.989, P =0.061) in Model 3 (Table 2). Kaplan-Meier survival curves ( Figure 2 ) further illustrated the lower cumulative mortality risk in Q4 compared with the other quartiles. Restricted cubic spline analysis ( Figure 3 ) confirmed a linear inverse dose-response association between MetS Score and mortality ( P -overall=0.012; P -nonlinear=0.067). Subgroup analysis Tables 3 and 4 summarize the subgroup analyses of the association between MetS Score and all-cause mortality. As a continuous variable, MetS Score was significantly protective in those aged ≥80 years (HR=0.861, 95% CI: 0.783-0.945, P =0.002), in women (HR=0.847, 95% CI: 0.754-0.951, P =0.005), and in urban residents (HR=0.779, 95% CI: 0.649-0.935, P =0.007). Protective effects were also evident among illiterate participants (HR=0.898, 95% CI: 0.811-0.995, P =0.040) and those with primary school education (HR=0.816, 95% CI: 0.677-0.983, P =0.032). When analyzed categorically, Q4 remained protective in participants aged ≥80 years (HR=0.811, 95% CI: 0.687-0.959, P -for-trend=0.012) and in urban residents (HR=0.466, 95% CI: 0.293-0.741, P -for-trend=0.003), whereas no significant associations were observed in younger (<80 years) or rural populations (Table 4) . Table 3 Subgroup analysis of the association between MetS score (as continuous variable) and the risk of all-cause mortality Characteristics Event/No. as continuous variable P -value P -interaction HR (95%CI) Age (years) 0.126 < 80 131/686 1.056 (0.830–1.342) 0.659 ≥ 80 1281/1757 0.861 (0.783–0.945) 0.002 Sex 0.136 Male 499/1010 0.953 (0.832–1.092) 0.492 Female 913/1433 0.847 (0.754–0.951) 0.005 Marital status 0.278 Married/cohabiting 259/807 1.010 (0.846–1.204) 0.914 Divorced/never married/separated/widowed 1153/1636 0.860 (0.778–0.952) 0.004 Race/ethnic 0.828 Han nationality 1281/2227 0.888 (0.810–0.973) 0.011 Minority nationality 131/216 1.024 (0.742–1.415) 0.884 Residence place 0.085 Rural 1172/2008 0.943 (0.851–1.044) 0.258 Urban 240/435 0.779 (0.649–0.935) 0.007 Pension subsidies 0.183 No 1341/2251 0.883 (0.807–0.967) 0.007 Yes 71/192 1.195 (0.825–1.731) 0.345 BMI 0.822 Underweight 535/742 0.870 (0.746–1.014) 0.074 Normal 719/1313 0.909 (0.808–1.024) 0.115 Overweight and obesity 158/388 0.842 (0.648–1.093) 0.197 Education levels 0.024 Illiterate 1105/1649 0.898 (0.811–0.995) 0.040 Primary school 261/623 0.816 (0.677–0.983) 0.032 Middle school 29/112 1.540 (0.815–2.912) 0.184 High school and above 17/59 5.027 (1.250-20.218) 0.023 Abbreviations: MetS, metabolic syndrome; HR, hazard ratios; 95% CI, 95% confidence interval; BMI, body mass index. Model was adjusted for age, sex, race/ethnic, residence area, educational levels, current marital status, pension subsidies, smoking status, drinking status, exercising status and BMI. Table 4 Subgroup analysis of the association between quartiles of MetS score and the risk of all-cause mortality Characteristics Event/No. Q1 Q2 Q3 Q4 P for trend P -interaction HR (95%CI) Age(years) 0.061 < 80 131/686 ref 1.734 (0.997–3.016) 1.564 (0.886–2.759) 1.673 (0.929–3.014) 0.182 ≥ 80 1281/1757 ref 0.941 (0.804–1.103) 0.906 (0.773–1.062) 0.811 (0.687–0.959) 0.012 Sex 0.629 Male 499/1010 ref 1.005 (0.778–1.296) 1.058 (0.820–1.365) 0.953 (0.724–1.253) 0.786 Female 913/1433 ref 0.938 (0.780–1.127) 0.950 (0.790–1.143) 0.812 (0.663–0.994) 0.054 Marital status 0.436 Married/cohabiting 259/807 ref 1.189 (0.835–1.694) 1.193 (0.836–1.704) 1.179 (0.801–1.736) 0.440 Divorced/never married/separated/ widowed 1153/1636 ref 0.973 (0.824–1.148) 0.922 (0.781–1.089) 0.856 (0.718–1.020) 0.063 Race/ethnic 0.077 Han nationality 1281/2227 ref 1.003 (0.858–1.172) 0.951 (0.812–1.113) 0.868 (0.731–1.031) 0.076 Minority nationality 131/216 ref 0.833 (0.478–1.449) 1.611 (0.961–2.702) 0.789 (0.440–1.416) 0.915 Residence place 0.012 Rural 1172/2008 ref 0.980 (0.831–1.156) 0.893 (0.756–1.055) 0.958 (0.804–1.142) 0.454 Urban 240/435 ref 1.028 (0.716–1.474) 1.153 (0.797–1.667) 0.466 (0.293–0.741) 0.003 Pension subsidies 0.171 No 1341/2251 ref 0.994 (0.853–1.159) 1.000 (0.858–1.165) 0.859 (0.727–1.014) 0.078 Yes 71/192 ref 1.846 (0.846–4.027) 3.107 (1.258–7.674) 2.012 (0.797–5.082) 0.227 BMI 0.880 Underweight 535/742 ref 0.820 (0.638–1.054) 0.883 (0.688–1.134) 0.843 (0.651–1.090) 0.313 Normal 719/1313 ref 0.981 (0.791–1.216) 0.985 (0.796–1.220) 0.952 (0.761–1.190) 0.678 Overweight and obesity 158/388 ref 0.933 (0.599–1.453) 0.762 (0.488–1.191) 0.832 (0.519–1.334) 0.334 Education levels 0.106 Illiterate 1105/1649 ref 1.034 (0.873–1.226) 0.998 (0.841–1.184) 0.941 (0.786–1.126) 0.423 Primary school 261/623 ref 0.877 (0.619–1.244) 0.968 (0.686–1.365) 0.643 (0.430–0.964) 0.051 Middle school 29/112 ref 2.072 (0.598–7.176) 2.132 (0.608–7.473) 2.679 (0.598–11.997) 0.218 High school and above 17/59 ref 0.115 (0.006–2.123) 0.701 (0.046–10.774) 11.516 (0.953-139.136) 0.003 Abbreviations: MetS, metabolic syndrome; HR, hazard ratios; 95% CI, 95% confidence interval; BMI, body mass index, ref, reference. Model was adjusted for age, sex, race/ethnic, residence area, educational levels, current marital status, pension subsidies, smoking status, drinking status, exercising status and BMI. The associations between MetS Score and all-cause mortality in elderly aged ≥ 80 years Table 5 presents the results of Cox models restricted to the 1,757 participants aged ≥80 years. In this subgroup, each one-unit increase in MetS Score was associated with a 13.9% lower mortality risk in the fully adjusted model (HR=0.861, 95% CI: 0.783-0.945, P =0.002). Compared with Q1, those in Q4 consistently had lower mortality risk across all models: HR=0.752 (95% CI: 0.637-0.888, P -for-trend=0.003) in Model 1, HR=0.752 (95% CI: 0.636-0.889, P -for-trend=0.003) in Model 2, and HR=0.778 (95% CI: 0.653-0.925, P -for-trend=0.012) in Model 3. In addition, restricted cubic spline analysis ( Supplementary Figure 1B ) confirmed a linear inverse association between MetS Score and mortality among the oldest-old ( P -overall=0.004; P -nonlinear=0.216). Table 5 Associations between MetS Score and the risk of all-cause mortality in Adults Aged 80 and above (N = 1757) as continuous variable P -value Q1 Q2 Q3 Q4 P for trend HR (95%CI) HR (95%CI) Model 1 0.841 (0.770–0.918) < 0.001 ref 0.951 (0.817–1.107) 0.971 (0.834–1.129) 0.752 (0.637–0.888) 0.003 Model 2 0.840 (0.769–0.918) < 0.001 ref 0.946 (0.812–1.103) 0.968 (0.831–1.127) 0.752 (0.636–0.889) 0.003 Model 3 0.861 (0.783–0.945) 0.002 ref 0.930 (0.797–1.085) 0.948 (0.812–1.107) 0.778 (0.653–0.925) 0.012 Abbreviations: MetS, metabolic syndrome; HR, hazard ratios; 95% CI, 95% confidence interval; ref, reference. Model 1: crude model; Model 2: adjusted for age, sex and race/ethnic; Model 3: further adjusted for residence area, educational levels, current marital status, pension subsidies, smoking status, drinking status, exercising status and BMI. Discussion In this large, nationally representative cohort of older Chinese adults, we found that a higher MetS Score was inversely associated with all-cause mortality. This protective association was particularly evident among the oldest-old (≥80 years), women, and urban residents, and persisted after multivariable adjustment. When analyzed by quartiles, only participants in the highest quartile (Q4) consistently demonstrated lower mortality compared with Q1. Restricted cubic spline analyses confirmed a linear inverse relationship in the overall population and in the oldest-old subgroup. Our findings provide new insights into the relationship between MetS severity and mortality in older adults. First, we observed that higher continuous MetS Scores were associated with lower all-cause mortality. This is in contrast to many studies conducted in middle-aged populations, where both traditional MetS definitions and continuous severity scores have been positively associated with incident CVD and mortality [13,28] . For example, analyses from the U.S. NHANES cohort and the Iranian Lipid and Glucose Study demonstrated that each unit increase in MetS severity score predicted higher risk of death and cardiovascular events, often with linear or J-shaped patterns [13,29] . Only participants in the highest quartile (Q4) showed a consistent survival benefit. In younger populations, mortality generally rises across the full gradient of MetS severity [30] , whereas in our older sample the protective effect was limited to those with the greatest metabolic load. This pattern may reflect the “obesity paradox,” where greater adiposity and metabolic reserves become advantageous in late life [31] .Subgroup analyses showed stronger inverse associations among the oldest-old (≥80 years), women, and urban residents. Prior studies suggest that survival benefits of higher BMI and certain metabolic traits are more evident in women and the very old [17,32] , while urban–rural differences may reflect disparities in healthcare access and socioeconomic conditions. Our results extend recent work in China that developed an age-, sex-, and ethnicity-specific MetS Score and showed strong associations with CVD-related biomarkers [16] . By linking this score to mortality outcomes, we provide novel evidence that the prognostic meaning of MetS severity differs by age, with paradoxical survival benefits emerging in the oldest-old. Several mechanisms may underlie the paradoxical finding that higher MetS Scores were associated with lower mortality among older adults. One explanation is the well‐described “obesity paradox,” whereby overweight or mildly obese individuals, especially in late life, demonstrate better survival than those with normal weight. Meta-analyses of patients with heart failure and other chronic conditions consistently show that higher BMI confers a survival advantage in older populations [33] . A second explanation relates to nutritional and body‐composition reserves: higher MetS Scores may partly capture greater fat and lean mass, which can provide essential energy and immunometabolic reserves during illness or stress. Epidemiologic studies indicate that sarcopenia is a strong predictor of mortality in older adults [34] and that inflammation‐related muscle loss contributes to frailty and adverse outcomes [35] . In this context, individuals with higher metabolic load but preserved muscle may have enhanced resilience compared with leaner counterparts. A third consideration is survivorship bias: those who reach advanced ages despite long‐standing metabolic abnormalities may constitute a biologically robust subgroup with protective genetic, immunologic, or behavioral factors. Such selection effects have been widely discussed in epidemiologic methodology [36] . Collectively, these mechanisms suggest that while high MetS severity increases cardiometabolic risk in younger populations, its prognostic meaning may shift in late life, where greater metabolic and nutritional reserves, combined with selective survival, could paradoxically indicate improved longevity. Our findings have direct implications for geriatric risk assessment and management. In younger and middle-aged populations, continuous MetS Scores are valuable tools for identifying individuals at elevated cardiometabolic risk. However, in the oldest-old, their interpretation requires caution, as a higher score may not uniformly indicate vulnerability and may in some contexts reflect protective metabolic or nutritional reserves. These results underscore the importance of moving away from a “one-size-fits-all” threshold and adopting age-specific approaches to metabolic risk stratification. Clinicians should consider the broader physiological and social context of older adults when applying MetS-based tools, and policymakers should incorporate these nuances into guidelines for chronic disease prevention and healthy ageing. This study has several notable strengths, including the use of a large, nationally representative cohort of older Chinese adults, long-term follow-up with validated mortality outcomes, and application of a CFA-derived MetS Score that reflects metabolic severity on a continuous scale. Nonetheless, limitations should be acknowledged. First, residual confounding cannot be fully excluded despite extensive adjustment for demographic, socioeconomic, and lifestyle factors. Second, BMI was used as the adiposity measure; although practical, it does not distinguish between fat and lean mass, and future studies should incorporate waist circumference or imaging-based assessments of visceral fat. Third, survival bias is possible, as individuals surviving to ≥80 years may systematically differ from those who died earlier. Finally, cause-specific mortality could not be assessed, limiting our ability to determine whether the protective association was driven primarily by cardiovascular or non-cardiovascular deaths. Conclusions In conclusion, higher MetS Scores were paradoxically associated with lower all-cause mortality among older Chinese adults, particularly in the oldest-old subgroup. These findings highlight that the prognostic meaning of MetS severity differs across the life course and emphasize the need for age-tailored strategies in metabolic risk assessment and prevention. Abbreviations MetS: Metabolic syndrome IQR: Interquartile range BMI: Body mass index HR: Hazard ratio ref: Reference Declarations Clinical trial number: not applicable. Ethics approval and consent to participate declarations: The CLHLS study was approved by the research ethics committees of Peking University (IRB00001052–13074). The procedures used in this study adhere to the tenets of the Declaration of Helsinki. All participants provided written informed consent. No experimental interventions were performed. Consent for publication: Not applicable. Availability of data and materials: The dataset that supports the conclusions of this article can be found in the domain of the CLHLS and is accessible at http://opendata.pku.edu.cn/. Conflict of interest: The authors declare that they have no relevant financial interests. Funding: This work was supported by grants from the National Natural Science Foundation of China (NO. 82574160, 82301768, and 82200312) and the startup fund for Principal Investigators from the School of Medicine, Shanghai Jiao Tong University (KJ2-0112-23-0002). Authors' Contributions Conception and design: J.H. Huo, C.Y. Ding, X.W. Li Development of methodology: J.H. Huo, C.Y. Ding, X.W. Li Acquisition of data: J.H. Huo, C.Y. Ding, H. Zhang, Y.Q. Xu, X.W. Li Analysis and interpretation of data: Y.L. Gong, D.R. Hao, X.W. Li Writing of the manuscript: J.H. Huo, C.Y. Ding, X.W. Li Critical review and revision of manuscript: all authors Study supervision: X.W. Li Acknowledgments: The data and samples utilized in this research were obtained from the CLHLS. We extend our gratitude to the Peking University for their support. References M.G. 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Salonen, The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men, JAMA 288 (2002) 2709-2716. 10.1001/jama.288.21.2709. Z. Asadi, H. Ghazizadeh, A. Timar, M.A. Khodadadegan, H. Ghazavi, M. Nasrabadi, T. Sahranavard, M.R. Fazl Mashhadi, M.A. Mohammadi, A. Rastegar-Moghadam, Association between Metabolic Syndrome and Its Components with Cardiovascular Disease Risk in the MASHAD Cohort Study Population, Journal of Cardio-Thoracic Medicine 13 (2025). X. Tang, M. Wu, S. Wu, Y. Tian, Continuous metabolic syndrome severity score and the risk of CVD and all-cause mortality, Eur. J. Clin. Invest. 52 (2022) e13817. 10.1111/eci.13817. C.J. Lavie, R.V. Milani, H.O. Ventura, Obesity and Cardiovascular Disease: Risk Factor, Paradox, and Impact of Weight Loss, Journal of the American College of Cardiology 53 (2009) 1925-1932. https://doi.org/10.1016/j.jacc.2008.12.068. K. Bhaskaran, I. Dos-Santos-Silva, D.A. Leon, I.J. Douglas, L. Smeeth, Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK, Lancet Diabetes Endocrinol 6 (2018) 944-953. 10.1016/s2213-8587(18)30288-2. A. Oreopoulos, R. Padwal, K. Kalantar-Zadeh, G.C. Fonarow, C.M. Norris, F.A. McAlister, Body mass index and mortality in heart failure: a meta-analysis, Am. Heart J. 156 (2008) 13-22. 10.1016/j.ahj.2008.02.014. R.N. Baumgartner, K.M. Koehler, D. Gallagher, L. Romero, S.B. Heymsfield, R.R. Ross, P.J. Garry, R.D. Lindeman, Epidemiology of sarcopenia among the elderly in New Mexico, Am J Epidemiol 147 (1998) 755-763. 10.1093/oxfordjournals.aje.a009520. L.A. Schaap, S.M. Pluijm, D.J. Deeg, M. Visser, Inflammatory markers and loss of muscle mass (sarcopenia) and strength, Am. J. Med. 119 (2006) 526.e529-517. 10.1016/j.amjmed.2005.10.049. J.V. Lindbohm, J. Kaprio, M. Korja, Survival bias explains improved survival in smokers and hypertensive individuals after aSAH, Neurology 93 (2019) e2105-e2109. 10.1212/wnl.0000000000008537. Additional Declarations No competing interests reported. Supplementary Files supplementarytables.docx Supplementary Table 1. Model fitting results for Confirmatory Factor Analysis. CMIN, Chi-square minimum; SRMR, Standardized Root Mean Square Residual; GFI, Goodness of Fit Index; CFI, Comparative Fit Index; NFI, Bentler-Bonett Normed Fit; RMSEA, Root Mean Square Error of Approximation. supplementaryFigure1.tiff Supplementary Figure 1 A-S. Restricted cubic spline models of the association between MetS score and all-cause mortality in different predefined subgroups. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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10:32:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47044,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan-Meier survival analysis curve for all-cause mortality according to quartiles of MetS score.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8644075/v1/e1c517779103dae18979e116.png"},{"id":102239305,"identity":"74ffa721-63ad-477c-8ee8-1c0bf00f11fe","added_by":"auto","created_at":"2026-02-09 16:44:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21710,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline model of the association between MetS score and all-cause mortality in overall population.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8644075/v1/4bdfb7b1ccb6bbc44beb9bfc.png"},{"id":103517985,"identity":"8e5a3023-c4ae-4be4-9238-d8ad20788d6b","added_by":"auto","created_at":"2026-02-26 14:34:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1799777,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8644075/v1/5f4b7819-897c-4165-992f-9e0ea19449f8.pdf"},{"id":102239301,"identity":"d2fb9d92-27b1-4174-941d-d7592f9bd38b","added_by":"auto","created_at":"2026-02-09 16:44:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15961,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1. \u003c/strong\u003eModel fitting results for Confirmatory Factor Analysis.\u003c/p\u003e\n\u003cp\u003eCMIN, Chi-square minimum; SRMR, Standardized Root Mean Square Residual; GFI, Goodness of Fit Index; CFI, Comparative Fit Index; NFI, Bentler-Bonett Normed Fit; RMSEA, Root Mean Square Error of Approximation.\u003c/p\u003e","description":"","filename":"supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8644075/v1/05051341bc0aa1540fcaec9e.docx"},{"id":102239304,"identity":"47ac4924-1819-4328-9d5d-b0e0231236a6","added_by":"auto","created_at":"2026-02-09 16:44:54","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":633576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1 A-S. \u003c/strong\u003eRestricted cubic spline models of the association between MetS score and all-cause mortality in different predefined subgroups.\u003c/p\u003e","description":"","filename":"supplementaryFigure1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-8644075/v1/129d15ff8dbff5307bb14ce4.tiff"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolic Syndrome Severity and All-Cause Mortality in a Nationally Representative Cohort of Older Chinese Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic syndrome (MetS), defined as a cluster of cardiometabolic abnormalities including central obesity, insulin resistance, hypertension, and dyslipidemia, has become a major global public health concern \u003csup\u003e[1,2]\u003c/sup\u003e. Substantial evidence indicates that both MetS and its individual components independently increase the risk of cardiovascular diseases (CVDs) and related adverse outcomes \u003csup\u003e[3\u0026ndash;5]\u003c/sup\u003e. Globally, the prevalence of MetS ranges from 12.5% to 31.4%, with marked regional differences\u003csup\u003e[6]\u003c/sup\u003e. In China, prevalence is estimated at about 24.5%, while in parts of Africa it exceeds 30% \u003csup\u003e[6\u0026ndash;8]\u003c/sup\u003e. These gradients underscore the need for context-specific risk quantification in populations undergoing rapid ageing.\u003c/p\u003e \u003cp\u003eConventional definitions, such as the NCEP ATP III criteria \u003csup\u003e[9]\u003c/sup\u003e, classify MetS when three or more abnormalities are present. While clinically straightforward, this approach cannot capture the severity of metabolic dysfunction. To address this limitation, researchers have proposed continuous severity scores (\u0026ldquo;MetS Score\u0026rdquo;) \u003csup\u003e[10\u0026ndash;12]\u003c/sup\u003e, derived through confirmatory factor analysis of triglycerides, HDL-C, fasting glucose, blood pressure, and adiposity indices. These scores provide a graded measure of risk and outperform categorical definitions in predicting diabetes, CVD events, and related biomarkers \u003csup\u003e[13\u0026ndash;15]\u003c/sup\u003e. Notably, a recent Chinese study developed an age-, sex-, and ethnicity-specific MetS Score, which showed strong associations with CVD risk factors and biomarkers \u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite these advances, the relationship between MetS Score and all-cause mortality remains unclear, especially in older adults. Some studies report linear associations, whereas others suggest paradoxical or J-shaped patterns, possibly reflecting the \u0026ldquo;obesity paradox\u0026rdquo; and age-related changes in body composition\u003csup\u003e[17,18]\u003c/sup\u003e. These uncertainties highlight the importance of evaluating MetS Score in China\u0026rsquo;s rapidly ageing population.\u003c/p\u003e \u003cp\u003eUsing data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), we aimed to assess the association between MetS Score and all-cause mortality in older Chinese adults, with further evaluation of subgroup differences, particularly among those aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years, and the potential linearity of this relationship.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe CLHLS is a nationally representative, prospective cohort of adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years across 23 provinces of China \u003csup\u003e[19]\u003c/sup\u003e. More detailed information of CLHLS study design can be found at the following website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.icpsr.umich.edu/web/NACDA/studies/38899\u003c/span\u003e\u003cspan address=\"https://www.icpsr.umich.edu/web/NACDA/studies/38899\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003csup\u003e[20]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e All interviewees were informed and signed the consent form willingly before collecting blood samples and conducting physical examinations. Ethical approval was obtained from the Biomedical Ethics Committee of Peking University (IRB00001052\u0026ndash;13074), and all participants provided written informed consent.\u003c/p\u003e \u003cp\u003eFor the present analysis, we used three datasets: the CLHLS biomarker dataset, and two follow-up datasets (2008\u0026ndash;2018 and 2011\u0026ndash;2018). Participants were excluded if they were \u0026lt;\u0026thinsp;65 years of age, lacked biomarker measurements, were duplicated across survey waves, had no survival outcome data, or were lost to follow-up before the subsequent wave. After exclusions, 2,443 participants were eligible and included in the analysis. A detailed flowchart of enrollment and exclusions is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eBiological specimens were collected according to standardized protocols, and blood samples were processed and analyzed in certified central laboratories using calibrated equipment \u003csup\u003e[21]\u003c/sup\u003e. Assays included triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), fasting blood glucose (FBG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C).\u003c/p\u003e \u003cp\u003ePhysical examinations were conducted by trained physicians from the local Centers for Disease Control (CDC) \u003csup\u003e[20]\u003c/sup\u003e. Measurements comprised systolic blood pressure (SBP), diastolic blood pressure (DBP), height, and weight, from which body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared.\u003c/p\u003e \u003cp\u003eInformation on sociodemographic characteristics and lifestyle factors was obtained through structured face-to-face interviews \u003csup\u003e[20]\u003c/sup\u003e. Covariates included age (\u0026lt;\u0026thinsp;80 vs. \u0026ge;80 years), sex, ethnicity (Han vs. minority), residence (urban vs. rural), education (illiterate, primary, middle school, high school or above), marital status (married/cohabiting vs. other), living arrangement (alone, institution, or with family), pension status (yes vs. no), smoking (never, former, current), alcohol consumption (never, former, current), and physical activity (never, former, current).\u003c/p\u003e\n\u003ch3\u003eCalculation of the MetS Score\u003c/h3\u003e\n\u003cp\u003eA continuous MetS Score was constructed using confirmatory factor analysis (CFA) of five continuous metabolic indicators: TG, HDL-C, FBG, SBP, and BMI \u003csup\u003e[10,16,22]\u003c/sup\u003e. To address skewed distributions, TG, FBG, and BMI were natural log-transformed, and HDL-C values were inverted so that higher values indicated greater metabolic risk, consistent with the direction of the other variables. All indicators were standardized prior to analysis, and factor loadings were estimated in a single-factor CFA model. Individual MetS Scores were then computed for each participant based on the fitted model equation.\u003c/p\u003e\n\u003ch3\u003eClinical outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was all-cause mortality, determined from CLHLS follow-up surveys conducted between 2008\u0026ndash;2018 and 2011\u0026ndash;2018. Vital status and dates of death were obtained during household interviews with close family members or village doctors and, when available, verified against official registration records.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eParticipants were categorized into quartiles of the MetS Score (Q1-Q4). Baseline characteristics were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range, IQR) for continuous variables and as proportions for categorical variables. Differences across quartiles were assessed using analysis of variance (ANOVA) or Kruskal-Wallis tests for continuous variables and χ\u0026sup2; tests for categorical variables.\u003c/p\u003e \u003cp\u003eThe association between MetS Score and all-cause mortality was evaluated using Cox proportional hazards regression, with hazard ratios (HRs) and 95% confidence intervals (CIs) reported. The proportional hazards assumption was tested using Schoenfeld residuals, and no significant violations were observed. Three nested models were specified: Model 1, crude and unadjusted; Model 2, adjusted for age, sex, and ethnicity; and Model 3, further adjusted for residence, education, marital status, pension status, smoking, alcohol consumption, physical activity, and BMI. Linear trends across quartiles were tested by modeling the median value of each quartile as a continuous variable, as described in previous studies \u003csup\u003e[23]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrespecified subgroup analyses were conducted by age (\u0026lt;\u0026thinsp;80 vs. \u0026ge;80 years), sex, residence, education, marital status, pension status, and BMI categories. Effect modification was evaluated by including multiplicative interaction terms, with p-values for interaction reported. Restricted cubic spline regression was applied to assess potential non-linear associations, with knots placed at the 5th, 35th, 65th, and 95th percentiles of the ln-transformed MetS Score, and the 10th percentile used as the reference\u003csup\u003e[24]\u003c/sup\u003e. Departure from linearity was tested using likelihood ratio tests.\u003c/p\u003e \u003cp\u003eMissing covariates were addressed using multilevel multiple imputation with chained equations and random effects, accounting for the hierarchical structure of the CLHLS survey data\u003csup\u003e[25]\u003c/sup\u003e. Twenty imputed datasets were created, and final estimates were pooled according to Rubin\u0026rsquo;s rules\u003csup\u003e[26,27]\u003c/sup\u003e. All analyses were conducted with SPSS version 26.0 (IBM Corp., Armonk, NY), and a two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eDevelopment of the MetS Score\u003c/h2\u003e\n \u003cp\u003eUsing CFA, a continuous MetS Score was derived from five metabolic indicators (TG, HDL-C, FBG, SBP, and BMI). The fitted formula for participants aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years was:\u003c/p\u003e\n \u003cp\u003eMetS score = -2.095\u0026thinsp;+\u0026thinsp;0.003 * SBP\u0026thinsp;+\u0026thinsp;0.501 * ln(BMI)\u0026thinsp;+\u0026thinsp;0.673 * ln(FBG) -0.664 * HDL-C\u0026thinsp;+\u0026thinsp;0.793 * ln(TG).\u003c/p\u003e\n \u003cp\u003eFactor loadings were highest for TG (0.569), followed by HDL-C (0.394), FBG (0.363), BMI (0.179), and SBP (0.104) (\u003cstrong\u003eTable \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eBaseline characteristics of participants\u003c/h3\u003e\n\u003cp\u003eBaseline characteristics of participants across quartiles of MetS Score are summarized in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Among 2,443 participants, 58.7% were female, and 71.9% were aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years. Most were Han (91.2%), rural residents (82.2%), lived with family or in institutions (80.5%), and had no pension (92.1%). Nearly 17.7% were current smokers, 17.3% current drinkers, and 16.7% engaged in regular exercise.\u003c/p\u003e\n\u003cp\u003eMedian (IQR) MetS Scores were -0.70 (0.37) for Q1, -0.24 (0.18) for Q2, 0.14 (0.20) for Q3, and 0.75 (0.63) for Q4. Participants in Q4 were younger, more likely to be female, urban residents, married or cohabiting, overweight/obese, and more likely to receive pensions and engage in physical activity.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of participants from CLHLS according to quartiles of MetS Score\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2443)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1 (\u0026lt;-0.44)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;610)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2 (-0.44\u0026ndash;0.07)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;609)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3 (-0.06-0.35)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;612)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4 (\u0026ge;\u0026thinsp;0.36)\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;612)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e686 (28.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157 (25.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142 (23.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152 (24.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235 (38.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1757 (71.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e453 (74.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e467 (76.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e460 (75.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e377 (61.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1010 (41.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290 (47.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218 (35.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232 (37.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270 (44.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1433 (58.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320 (52.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e391 (64.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e380 (62.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e342 (55.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/ethnic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHan nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2227 (91.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e576 (94.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e558 (91.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e546 (89.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e547 (89.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinority nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e216 ( 8.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 ( 5.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 ( 8.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (10.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (10.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried/cohabiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e807 (33.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221 (36.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170 (27.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170 (27.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246 (40.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced/never married/separated/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1636 (66.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e389 (63.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e439 (72.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e442 (72.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e366 (59.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence place\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e435 (17.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93 (15.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (14.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168 (27.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2008 (82.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e517 (84.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e522 (85.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525 (85.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e444 (72.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoresidence type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e477 (19.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 (20.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116 (19.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118 (19.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116 (18.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot alone (lived with family members or in an institute)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1966 (80.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e483 (79.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e493 (80.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e494 (80.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e496 (81.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1649 (67.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e399 (65.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e439 (72.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e434 (70.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e377 (61.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e623 (25.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168 (27.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (21.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142 (23.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 (29.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 ( 4.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 ( 4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 ( 3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 ( 3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 ( 5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 ( 2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 ( 2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 ( 2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 ( 1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 ( 3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1800 (73.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441 (72.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e464 (76.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e445 (72.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e450 (73.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211 ( 8.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 ( 9.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 ( 7.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55 ( 8.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 ( 8.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e432 (17.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (18.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (16.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112 (18.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109 (17.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1872 (76.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e453 (74.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e464 (76.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e480 (78.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e475 (77.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149 ( 6.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 ( 5.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 ( 5.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 ( 6.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 ( 7.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422 (17.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113 (18.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (15.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92 (15.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExercise\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1919 (78.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e509 (83.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e492 (80.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e478 (78.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440 (71.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115 ( 4.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 ( 2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 ( 4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 ( 5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 ( 5.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e409 (16.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (13.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (14.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98 (16.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141 (23.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e742 (30.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245 (40.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e197 (32.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 (29.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 (19.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1313 (53.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327 (53.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e334 (54.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e329 (53.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323 (52.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight and obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e388 (15.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 ( 6.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (12.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103 (16.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e169 (27.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePension subsidies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2251 (92.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e559 (91.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e572 (93.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e577 (94.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e543 (88.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192 ( 7.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 ( 8.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 ( 6.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 ( 5.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (11.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eAbbreviations: MetS, metabolic syndrome; Q1\u0026ndash;Q4, quartile 1-quartile 4; IQR, interquartile range; BMI, body mass index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eQuartile 1: MetS\u0026lt;-0.44, Quartile 2: -0.44\u0026thinsp;\u0026lt;\u0026thinsp;MetS\u0026lt;-0.07, Quartile 3: -0.06\u0026thinsp;\u0026lt;\u0026thinsp;MetS\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Quartile 4: MetS\u0026thinsp;\u0026ge;\u0026thinsp;0.36.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" style=\"width: 1046px;\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe hazard ratios for the association between MetS Score and all-cause mortality(N\u0026thinsp;=\u0026thinsp;2443)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 76.2593px;\" rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"width: 209.741px;\" align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 80px;\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 533px;\" colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 102px;\" rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth style=\"width: 209.741px;\" align=\"left\"\u003e\n \u003cp\u003eas continuous variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 35px;\" align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 166px;\" align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 166px;\" align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"width: 166px;\" align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76.2593px;\" align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 209.741px;\" align=\"char\"\u003e\n \u003cp\u003e0.804 (0.743\u0026ndash;0.869)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\" align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\" align=\"char\"\u003e\n \u003cp\u003e1.023 (0.883\u0026ndash;1.186)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\" align=\"char\"\u003e\n \u003cp\u003e1.056 (0.913\u0026ndash;1.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\" align=\"char\"\u003e\n \u003cp\u003e0.707 (0.605\u0026ndash;0.828)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\" align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76.2593px;\" align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 209.741px;\" align=\"char\"\u003e\n \u003cp\u003e0.864 (0.796\u0026ndash;0.938)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\" align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\" align=\"char\"\u003e\n \u003cp\u003e0.968 (0.835\u0026ndash;1.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\" align=\"char\"\u003e\n \u003cp\u003e1.026 (0.886\u0026ndash;1.187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\" align=\"char\"\u003e\n \u003cp\u003e0.805 (0.687\u0026ndash;0.942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\" align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76.2593px;\" align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 209.741px;\" align=\"char\"\u003e\n \u003cp\u003e0.892 (0.817\u0026ndash;0.973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\" align=\"char\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\" align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\" align=\"char\"\u003e\n \u003cp\u003e0.958 (0.825\u0026ndash;1.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\" align=\"char\"\u003e\n \u003cp\u003e1.010 (0.870\u0026ndash;1.171)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 166px;\" align=\"char\"\u003e\n \u003cp\u003e0.840 (0.713\u0026ndash;0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\" align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1001px;\" colspan=\"8\"\u003eAbbreviations: MetS, metabolic syndrome; HR, hazard ratios; 95% CI, 95% confidence interval; ref, reference.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1001px;\" colspan=\"8\"\u003eModel 1: crude model, no co-variables was adjusted;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1001px;\" colspan=\"8\"\u003eModel 2: adjusted for age, sex and race/ethnic;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 1001px;\" colspan=\"8\"\u003eModel 3: further adjusted for residence area, educational levels, current marital status, pension subsidies, smoking status, drinking status, exercising status and BMI.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003eWhen analyzed by quartiles, only the highest quartile (Q4) demonstrated a consistent protective effect compared with Q1. The hazard ratios for Q4 were 0.707 (95% CI: 0.605-0.828, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) in Model 1, 0.805 (95% CI: 0.687-0.942, \u003cem\u003eP\u003c/em\u003e=0.012) in Model 2, and 0.840 (95% CI: 0.713-0.989, \u003cem\u003eP\u003c/em\u003e=0.061) in Model 3 (Table 2). Kaplan-Meier survival curves (\u003cstrong\u003eFigure 2\u003c/strong\u003e) further illustrated the lower cumulative mortality risk in Q4 compared with the other quartiles. Restricted cubic spline analysis (\u003cstrong\u003eFigure 3\u003c/strong\u003e) confirmed a linear inverse dose-response association between MetS Score and mortality (\u003cem\u003eP\u003c/em\u003e-overall=0.012; \u003cem\u003eP\u003c/em\u003e-nonlinear=0.067).\u0026nbsp;\u003cp\u003e\u003cstrong\u003eSubgroup analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTables 3 and 4\u003c/strong\u003e summarize the subgroup analyses of the association between MetS Score and all-cause mortality. As a continuous variable, MetS Score was significantly protective in those aged \u0026ge;80 years (HR=0.861, 95% CI: 0.783-0.945, \u003cem\u003eP\u003c/em\u003e=0.002), in women (HR=0.847, 95% CI: 0.754-0.951, \u003cem\u003eP\u003c/em\u003e=0.005), and in urban residents (HR=0.779, 95% CI: 0.649-0.935, \u003cem\u003eP\u003c/em\u003e=0.007). Protective effects were also evident among illiterate participants (HR=0.898, 95% CI: 0.811-0.995, \u003cem\u003eP\u003c/em\u003e=0.040) and those with primary school education (HR=0.816, 95% CI: 0.677-0.983, \u003cem\u003eP\u003c/em\u003e=0.032).\u003c/p\u003e\n \u003cp\u003eWhen analyzed categorically, Q4 remained protective in participants aged \u0026ge;80 years (HR=0.811, 95% CI: 0.687-0.959, \u003cem\u003eP\u003c/em\u003e-for-trend=0.012) and in urban residents (HR=0.466, 95% CI: 0.293-0.741, \u003cem\u003eP\u003c/em\u003e-for-trend=0.003), whereas no significant associations were observed in younger (\u0026lt;80 years) or rural populations\u003cstrong\u003e\u0026nbsp;(Table 4)\u003c/strong\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSubgroup analysis of the association between MetS score (as continuous variable) and the risk of all-cause mortality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEvent/No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eas continuous variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-interaction\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131/686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.056 (0.830\u0026ndash;1.342)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1281/1757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.861 (0.783\u0026ndash;0.945)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e499/1010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.953 (0.832\u0026ndash;1.092)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e913/1433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.847 (0.754\u0026ndash;0.951)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried/cohabiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e259/807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.010 (0.846\u0026ndash;1.204)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced/never married/separated/widowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1153/1636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.860 (0.778\u0026ndash;0.952)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/ethnic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHan nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1281/2227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.888 (0.810\u0026ndash;0.973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinority nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131/216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.024 (0.742\u0026ndash;1.415)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence place\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1172/2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.943 (0.851\u0026ndash;1.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240/435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.779 (0.649\u0026ndash;0.935)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePension subsidies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1341/2251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.883 (0.807\u0026ndash;0.967)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71/192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.195 (0.825\u0026ndash;1.731)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e535/742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.870 (0.746\u0026ndash;1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e719/1313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.909 (0.808\u0026ndash;1.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight and obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158/388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.842 (0.648\u0026ndash;1.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1105/1649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.898 (0.811\u0026ndash;0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261/623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.816 (0.677\u0026ndash;0.983)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29/112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.540 (0.815\u0026ndash;2.912)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17/59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.027 (1.250-20.218)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eAbbreviations: MetS, metabolic syndrome; HR, hazard ratios; 95% CI, 95% confidence interval; BMI, body mass index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eModel was adjusted for age, sex, race/ethnic, residence area, educational levels, current\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003emarital status, pension subsidies, smoking status, drinking status, exercising status and BMI.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSubgroup analysis of the association between quartiles of MetS score and the risk of all-cause mortality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eEvent/No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-interaction\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131/686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.734 (0.997\u0026ndash;3.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.564 (0.886\u0026ndash;2.759)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.673 (0.929\u0026ndash;3.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1281/1757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.941 (0.804\u0026ndash;1.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.906 (0.773\u0026ndash;1.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.811 (0.687\u0026ndash;0.959)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e499/1010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.005 (0.778\u0026ndash;1.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.058 (0.820\u0026ndash;1.365)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.953 (0.724\u0026ndash;1.253)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e913/1433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.938 (0.780\u0026ndash;1.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.950 (0.790\u0026ndash;1.143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.812 (0.663\u0026ndash;0.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried/cohabiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e259/807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.189 (0.835\u0026ndash;1.694)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.193 (0.836\u0026ndash;1.704)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.179 (0.801\u0026ndash;1.736)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced/never married/separated/\u003c/p\u003e\n \u003cp\u003ewidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1153/1636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.973 (0.824\u0026ndash;1.148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.922 (0.781\u0026ndash;1.089)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.856 (0.718\u0026ndash;1.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/ethnic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHan nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1281/2227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.003 (0.858\u0026ndash;1.172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.951 (0.812\u0026ndash;1.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.868 (0.731\u0026ndash;1.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinority nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e131/216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.833 (0.478\u0026ndash;1.449)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.611 (0.961\u0026ndash;2.702)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.789 (0.440\u0026ndash;1.416)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence place\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1172/2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.980 (0.831\u0026ndash;1.156)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.893 (0.756\u0026ndash;1.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.958 (0.804\u0026ndash;1.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240/435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.028 (0.716\u0026ndash;1.474)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.153 (0.797\u0026ndash;1.667)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.466 (0.293\u0026ndash;0.741)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePension subsidies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1341/2251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.994 (0.853\u0026ndash;1.159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000 (0.858\u0026ndash;1.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.859 (0.727\u0026ndash;1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71/192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.846 (0.846\u0026ndash;4.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.107 (1.258\u0026ndash;7.674)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.012 (0.797\u0026ndash;5.082)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e535/742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.820 (0.638\u0026ndash;1.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.883 (0.688\u0026ndash;1.134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.843 (0.651\u0026ndash;1.090)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e719/1313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.981 (0.791\u0026ndash;1.216)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.985 (0.796\u0026ndash;1.220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.952 (0.761\u0026ndash;1.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight and obesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158/388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.933 (0.599\u0026ndash;1.453)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.762 (0.488\u0026ndash;1.191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.832 (0.519\u0026ndash;1.334)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1105/1649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.034 (0.873\u0026ndash;1.226)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.998 (0.841\u0026ndash;1.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.941 (0.786\u0026ndash;1.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e261/623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.877 (0.619\u0026ndash;1.244)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.968 (0.686\u0026ndash;1.365)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.643 (0.430\u0026ndash;0.964)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29/112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.072 (0.598\u0026ndash;7.176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.132 (0.608\u0026ndash;7.473)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.679 (0.598\u0026ndash;11.997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17/59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.115 (0.006\u0026ndash;2.123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.701 (0.046\u0026ndash;10.774)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.516 (0.953-139.136)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eAbbreviations: MetS, metabolic syndrome; HR, hazard ratios; 95% CI, 95% confidence interval; BMI, body mass index, ref, reference.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eModel was adjusted for age, sex, race/ethnic, residence area, educational levels, current marital status, pension subsidies, smoking status, drinking status, exercising status and BMI.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003eThe associations between MetS Score and all-cause mortality in elderly aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e presents the results of Cox models restricted to the 1,757 participants aged \u0026ge;80 years. In this subgroup, each one-unit increase in MetS Score was associated with a 13.9% lower mortality risk in the fully adjusted model (HR=0.861, 95% CI: 0.783-0.945, \u003cem\u003eP\u003c/em\u003e=0.002). Compared with Q1, those in Q4 consistently had lower mortality risk across all models: HR=0.752 (95% CI: 0.637-0.888, \u003cem\u003eP\u003c/em\u003e-for-trend=0.003) in Model 1, HR=0.752 (95% CI: 0.636-0.889, \u003cem\u003eP\u003c/em\u003e-for-trend=0.003) in Model 2, and HR=0.778 (95% CI: 0.653-0.925,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e-for-trend=0.012) in Model 3.\u003cbr\u003eIn addition, restricted cubic spline analysis (\u003cstrong\u003eSupplementary Figure 1B\u003c/strong\u003e) confirmed a linear inverse association between MetS Score and mortality among the oldest-old (\u003cem\u003eP\u003c/em\u003e-overall=0.004; \u003cem\u003eP\u003c/em\u003e-nonlinear=0.216). \u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociations between MetS Score and the risk of all-cause mortality in Adults Aged 80 and above (N\u0026thinsp;=\u0026thinsp;1757)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eas continuous variable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"4\" align=\"left\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.841 (0.770\u0026ndash;0.918)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.951 (0.817\u0026ndash;1.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.971 (0.834\u0026ndash;1.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.752 (0.637\u0026ndash;0.888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.840 (0.769\u0026ndash;0.918)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.946 (0.812\u0026ndash;1.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.968 (0.831\u0026ndash;1.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.752 (0.636\u0026ndash;0.889)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.861 (0.783\u0026ndash;0.945)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eref\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.930 (0.797\u0026ndash;1.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.948 (0.812\u0026ndash;1.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.778 (0.653\u0026ndash;0.925)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eAbbreviations: MetS, metabolic syndrome; HR, hazard ratios; 95% CI, 95% confidence interval; ref, reference.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eModel 1: crude model;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eModel 2: adjusted for age, sex and race/ethnic;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eModel 3: further adjusted for residence area, educational levels, current marital status, pension subsidies, smoking status, drinking status, exercising status and BMI.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, nationally representative cohort of older Chinese adults, we found that a higher MetS Score was inversely associated with all-cause mortality. This protective association was particularly evident among the oldest-old (≥80 years), women, and urban residents, and persisted after multivariable adjustment. When analyzed by quartiles, only participants in the highest quartile (Q4) consistently demonstrated lower mortality compared with Q1. Restricted cubic spline analyses confirmed a linear inverse relationship in the overall population and in the oldest-old subgroup.\u003c/p\u003e\n\u003cp\u003eOur findings provide new insights into the relationship between MetS severity and mortality in older adults. First, we observed that higher continuous MetS Scores were associated with lower all-cause mortality. This is in contrast to many studies conducted in middle-aged populations, where both traditional MetS definitions and continuous severity scores have been positively associated with incident CVD and mortality\u003csup\u003e[13,28]\u003c/sup\u003e. For example, analyses from the U.S. NHANES cohort and the Iranian Lipid and Glucose Study demonstrated that each unit increase in MetS severity score predicted higher risk of death and cardiovascular events, often with linear or J-shaped patterns\u003csup\u003e[13,29]\u003c/sup\u003e. Only participants in the highest quartile (Q4) showed a consistent survival benefit. In younger populations, mortality generally rises across the full gradient of MetS severity\u003csup\u003e[30]\u003c/sup\u003e, whereas in our older sample the protective effect was limited to those with the greatest metabolic load. This pattern may reflect the “obesity paradox,” where greater adiposity and metabolic reserves become advantageous in late life\u003csup\u003e[31]\u003c/sup\u003e.Subgroup analyses showed stronger inverse associations among the oldest-old (≥80 years), women, and urban residents. Prior studies suggest that survival benefits of higher BMI and certain metabolic traits are more evident in women and the very old\u003csup\u003e[17,32]\u003c/sup\u003e, while urban–rural differences may reflect disparities in healthcare access and socioeconomic conditions. Our results extend recent work in China that developed an age-, sex-, and ethnicity-specific MetS Score and showed strong associations with CVD-related biomarkers\u003csup\u003e[16]\u003c/sup\u003e. By linking this score to mortality outcomes, we provide novel evidence that the prognostic meaning of MetS severity differs by age, with paradoxical survival benefits emerging in the oldest-old.\u003c/p\u003e\n\u003cp\u003eSeveral mechanisms may underlie the paradoxical finding that higher MetS Scores were associated with lower mortality among older adults. One explanation is the well‐described “obesity paradox,” whereby overweight or mildly obese individuals, especially in late life, demonstrate better survival than those with normal weight. Meta-analyses of patients with heart failure and other chronic conditions consistently show that higher BMI confers a survival advantage in older populations\u003csup\u003e[33]\u003c/sup\u003e. A second explanation relates to nutritional and body‐composition reserves: higher MetS Scores may partly capture greater fat and lean mass, which can provide essential energy and immunometabolic reserves during illness or stress. Epidemiologic studies indicate that sarcopenia is a strong predictor of mortality in older adults\u003csup\u003e[34]\u003c/sup\u003e and that inflammation‐related muscle loss contributes to frailty and adverse outcomes\u003csup\u003e[35]\u003c/sup\u003e. In this context, individuals with higher metabolic load but preserved muscle may have enhanced resilience compared with leaner counterparts. A third consideration is survivorship bias: those who reach advanced ages despite long‐standing metabolic abnormalities may constitute a biologically robust subgroup with protective genetic, immunologic, or behavioral factors. Such selection effects have been widely discussed in epidemiologic methodology\u003csup\u003e[36]\u003c/sup\u003e. Collectively, these mechanisms suggest that while high MetS severity increases cardiometabolic risk in younger populations, its prognostic meaning may shift in late life, where greater metabolic and nutritional reserves, combined with selective survival, could paradoxically indicate improved longevity.\u003c/p\u003e\n\u003cp\u003eOur findings have direct implications for geriatric risk assessment and management. In younger and middle-aged populations, continuous MetS Scores are valuable tools for identifying individuals at elevated cardiometabolic risk. However, in the oldest-old, their interpretation requires caution, as a higher score may not uniformly indicate vulnerability and may in some contexts reflect protective metabolic or nutritional reserves. These results underscore the importance of moving away from a “one-size-fits-all” threshold and adopting age-specific approaches to metabolic risk stratification. Clinicians should consider the broader physiological and social context of older adults when applying MetS-based tools, and policymakers should incorporate these nuances into guidelines for chronic disease prevention and healthy ageing.\u003c/p\u003e\n\u003cp\u003eThis study has several notable strengths, including the use of a large, nationally representative cohort of older Chinese adults, long-term follow-up with validated mortality outcomes, and application of a CFA-derived MetS Score that reflects metabolic severity on a continuous scale. Nonetheless, limitations should be acknowledged. First, residual confounding cannot be fully excluded despite extensive adjustment for demographic, socioeconomic, and lifestyle factors. Second, BMI was used as the adiposity measure; although practical, it does not distinguish between fat and lean mass, and future studies should incorporate waist circumference or imaging-based assessments of visceral fat. Third, survival bias is possible, as individuals surviving to ≥80 years may systematically differ from those who died earlier. Finally, cause-specific mortality could not be assessed, limiting our ability to determine whether the protective association was driven primarily by cardiovascular or non-cardiovascular deaths.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, higher MetS Scores were paradoxically associated with lower all-cause mortality among older Chinese adults, particularly in the oldest-old subgroup. These findings highlight that the prognostic meaning of MetS severity differs across the life course and emphasize the need for age-tailored strategies in metabolic risk assessment and prevention.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eMetS:\u003c/strong\u003e Metabolic syndrome\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIQR:\u003c/strong\u003e Interquartile range\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBMI:\u003c/strong\u003e Body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHR:\u0026nbsp;\u003c/strong\u003eHazard ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eref:\u0026nbsp;\u003c/strong\u003eReference\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate declarations:\u0026nbsp;\u003c/strong\u003eThe CLHLS study was approved by the research ethics committees of Peking University (IRB00001052\u0026ndash;13074). The procedures used in this study adhere to the tenets of the Declaration of Helsinki. All participants provided written informed consent. No experimental interventions were performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The dataset that supports the conclusions of this article can be found in the domain of the CLHLS and is accessible at http://opendata.pku.edu.cn/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no relevant financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by grants from the National Natural Science Foundation of China (NO. 82574160,\u0026nbsp;82301768, and 82200312) and the startup fund for Principal Investigators from the School of Medicine, Shanghai Jiao Tong University (KJ2-0112-23-0002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: J.H. Huo, C.Y. Ding, X.W. Li\u003c/p\u003e\n\u003cp\u003eDevelopment of methodology: J.H. Huo, C.Y. Ding, X.W. Li\u003c/p\u003e\n\u003cp\u003eAcquisition of data: J.H. Huo, C.Y. Ding, H. Zhang, Y.Q. Xu, X.W. Li\u003c/p\u003e\n\u003cp\u003eAnalysis and interpretation of data: Y.L. Gong, D.R. Hao, X.W. Li\u003c/p\u003e\n\u003cp\u003eWriting of the manuscript: J.H. Huo, C.Y. Ding, X.W. Li\u003c/p\u003e\n\u003cp\u003eCritical review and revision of manuscript: all authors\u003c/p\u003e\n\u003cp\u003eStudy supervision: X.W. Li\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe data and samples utilized in this research were obtained from the CLHLS. We extend our gratitude to the Peking University for their support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM.G. Saklayen, The Global Epidemic of the Metabolic Syndrome, Curr. Hypertens. Rep. 20 (2018) 12. 10.1007/s11906-018-0812-z.\u003c/li\u003e\n\u003cli\u003eI.J. 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Lindbohm, J. Kaprio, M. Korja, Survival bias explains improved survival in smokers and hypertensive individuals after aSAH, Neurology 93 (2019) e2105-e2109. 10.1212/wnl.0000000000008537.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metabolic syndrome, MetS score, All-cause mortality, Chinese older population, CLHLS","lastPublishedDoi":"10.21203/rs.3.rs-8644075/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8644075/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetabolic syndrome (MetS) is a cluster of risk factors that increases cardiometabolic disease and mortality. A continuous MetS Score has been developed to quantify MetS severity, but its association with all-cause mortality in older Chinese adults remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed 2,443 participants from the Chinese Longitudinal Healthy Longevity Survey (CLHLS, 2008\u0026ndash;2018 and 2011\u0026ndash;2018). MetS Score was derived by confirmatory factor analysis using triglycerides, HDL-C, fasting glucose, systolic blood pressure, and BMI. Participants were categorized into quartiles (Q1-Q4). Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality. Subgroup analyses examined effect modification by age, sex, residence, and other covariates, and restricted cubic splines explored dose\u0026ndash;response relationships.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDuring 10,356 person-years of follow-up, 1,412 deaths occurred (136.3/1,000 person-years). Each 1-unit increase in MetS Score was associated with a 10.8% lower mortality risk (HR\u0026thinsp;=\u0026thinsp;0.892, 95% CI: 0.817\u0026ndash;0.973). The protective effect was concentrated in Q4 vs. Q1 (HR\u0026thinsp;=\u0026thinsp;0.840, 95% CI: 0.713\u0026ndash;0.989). Subgroup analyses showed stronger associations in those\u0026thinsp;\u0026ge;\u0026thinsp;80 years (HR\u0026thinsp;=\u0026thinsp;0.861, 95% CI: 0.783\u0026ndash;0.945) and urban residents (HR\u0026thinsp;=\u0026thinsp;0.466, 95% CI: 0.293\u0026ndash;0.741). Restricted cubic splines confirmed a linear inverse association overall and among the \u0026ge;\u0026thinsp;80 years group.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMetS Score, reflecting MetS severity, was inversely associated with all-cause mortality in older Chinese adults, particularly among those aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years. These findings suggest that higher MetS Score may paradoxically confer survival benefits in the elderly, warranting further mechanistic studies.\u003c/p\u003e","manuscriptTitle":"Metabolic Syndrome Severity and All-Cause Mortality in a Nationally Representative Cohort of Older Chinese Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 16:44:49","doi":"10.21203/rs.3.rs-8644075/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a65caa4-7cb3-4de4-8398-625ce7840ee2","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T14:29:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 16:44:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8644075","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8644075","identity":"rs-8644075","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}