Joint Association of Modifiable Lifestyle and Metabolic Health Status with Incidence of Cardiovascular Disease and All-Cause Mortality: A Perspective Cohort Study

Joint Association of Modifiable Lifestyle and Metabolic Health Status with Incidence of Cardiovascular Disease and All-Cause Mortality: A Perspective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Joint Association of Modifiable Lifestyle and Metabolic Health Status with Incidence of Cardiovascular Disease and All-Cause Mortality: A Perspective Cohort Study Yingting Zuo, Haibin Li, Shuohua Chen, Xue Tian, Dapeng Mo, Shouling Wu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-542402/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Aug, 2021 Read the published version in Endocrine → Version 1 posted 4 You are reading this latest preprint version Abstract Purpose: We investigated the joint associations of modifiable lifestyle and metabolic factors with incident cardiovascular disease and all-cause mortality. Methods: This study included 94,831 participants (men, 79.76%; median age, 51.60 [43.47-58.87]) without a history of cardiovascular disease at baseline from Kailuan study during 2006 to 2007 and followed them until new-onset cardiovascular disease event, death or December 31, 2017. Baseline metabolic health status was assessed by Adult Treatment Panel-III criteria and five lifestyle factors was collected using a self-reported questionnaire. We performed Cox proportional hazards models to evaluate the joint associations. Results: During a median follow-up of 11.03 years, we observed 6,590 cardiovascular disease events and 9,218 all-cause mortality. Participants within more metabolic risk components and least healthy lifestyle had the highest cardiovascular disease risk (hazard ratio 2.06 [95% CI 1.77-2.39]) and mortality risk (hazard ratio 1.53 [95% CI 1.31-1.78]), as compared with the less metabolic risk components and most healthy lifestyle group. Compared with the most healthy lifestyle, the hazard ratio of cardiovascular disease for participants with least healthy lifestyle was 1.26 (95% CI 1.17–1.37) in the category with low metabolic risk, 1.16 (95% CI 1.03–1.31) and 1.07 (95% CI 0.90–1.27) for those with medium and high metabolic risk, respectively. Conclusions: We showed that healthy lifestyle was associated with a lower risk of cardiovascular disease and there was no significant interaction between metabolic risk and healthy lifestyle. Our results indicated that healthy lifestyle should be promoted even for people with high metabolic risk. Endocrinology & Metabolism Lifestyle metabolic health status mortality cardiovascular disease Figures Figure 1 Figure 2 Introdution Cardiovascular disease (CVD) is one of the leading causes of death worldwide and remains the great threat to public global health( 1 ). Clinical therapy has been proven to be beneficial, but may have adverse effects, and often making functional recovery incomplete( 2 ). Therefore, primary prevention is considered the most effective strategy in controlling CVD and its consequences( 3 ). Some previous studies have shown that both healthy lifestyle and metabolic health status could reduce the risk of CVD and all-cause mortality( 4 – 8 ). In most previous studies, lifestyle or metabolic factors have been consider individually, although those factors are typically correlated with one another. Recent studies and meta-analyses have consistently reported that combined lifestyle factors were associated with a markedly lower incidence of cardiometabolic abnormalities( 9 – 12 ). While few studies described the relative relationship of lifestyle factors with risk of CVD, subtypes of CVD and all-cause mortality across a population at different degrees of metabolic risk, and whether different degrees of metabolic status affect the efficacy of lifestyle was inconclusive( 7 ). Whereas some studies have reported that the lifestyle modification is effective in reducing CVD risk factors and CVD, especially stroke, others indicate that the effectiveness of lifestyle interventions for reductions in long-term CVD has yet to be determined( 13 ). The relationship between lifestyle and metabolic health status with incident CVD has become an important public concern, which could improve our understanding of the composition of modifiable risk factors with different level of modifiable risks to prevent the occurrent of CVD. Therefore, the purpose of this study was to use data from a large-scale population-based prospective cohort to examine the jointed associations of lifestyle and metabolic health status with the risk of CVD and all-cause mortality. Materials And Methods Study Design and Participants The Kailuan study is a prospective cohort study designed to identify the risk factors for common noncommunicable disease, especially CVD( 14 , 15 ). The study protocol and informed consent were approved by Ethics Committees of both the Kailuan General Hospital and Beijing Tiantan Hospital. All participants signed the written informed consent. The details of the Kailuan study design have been described previously( 16 ). At baseline, active and retired employees aged ≥ 18 years of the Kailuan Group, Tangshan, China, were invited to participate in this study. Generally, 101,510 participants (81,110 men and 20,400 women) with an age ranging between 18 years and 98 years, were enrolled and completed survey at baseline between June 2006 and October 2007. All participants underwent face-to-face questionnaire measurements, physical examinations, and laboratory assessments in the 11 local health care hospitals. We performed re-examinations biennially to the end of the follow-up on December 31,2017. In the current study, we excluded 3,238 participants without data for any metabolic component at baseline, 3,358 participants with missing data on lifestyle risk factors, 83 participants with a history of myocardial infarction (MI) or stroke at baseline, finally, a total of 94,831 participants was selected for the current analysis (Online Fig. 1 in Supplemental material). Metabolic Health Status Metabolic health status at baseline was determined based on the physical examinations and laboratory assessment by trained nurses and physicians. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured 3 times with the participants in the seated position at least 5 minutes using a mercury sphygmomanometer, and the average of 3 readings was used for further analysis( 17 ). Blood samples were collected after an overnight fast (8-h to 12-h) and measured the fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol levels by an automatic analyzer (Hitachi 747; Hitachi, Tokyo, Japan) at local hospitals. We used Adult Treatment Panel-III (ATP-III) criteria to define metabolic health status in the current study, which has been widely used to determine metabolic syndrome in adults worldwide. In the ATP-III criteria is based on the five CVD risk factors( 18 ): 1) central obesity: waist circumference ≥ 90 cm in men and ≥ 80 cm in women; 2) elevated TG: TG level ≥ 1.69 mmol/L; 3) low HDL cholesterol: HDL cholesterol level < 1.03 mmol/L in men and < 1.29 mmol/L in women; 4) elevated BP: SBP/DBP ≥ 130/85 mmHg or taking antihypertensive drugs or self-reported history of hypertension; 5) elevated FPG: FPG level ≥ 5.6 mmol/L or taking hypoglycemic medications or self-reported history of diabetes. The metabolic health status ranged from 0 to 5, with lower scores indicating normal healthy metabolic, and were subsequently classified into three categories based on the distribution in this population: low risk (0–2 components), medium risk (3 components), and high risk (4–5 components) (Online Table 1 in Supplemental material). Lifestyle Health Status Lifestyle health status at baseline was collected by trained nurses and physicians using a standardized questionnaire interview. Current smoking was defined as smoking at least the previous year. Current alcohol consumption was defined as the average daily strong spirit (alcohol content > 50%) consumption of 100 ml or more than 100 ml for at least the previous year. Physical activity level was categorized as 1) ideally active: ≥80 minutes/week moderate and vigorous intensity; 2) moderately active: <80 minutes/week; 3) inactive: none. Sedentary behavior was classified into three categories: 1) < 4 hour/day; 2) 4–8 hour/day; 3) ≥ 8 hour/day. Considering salt intake plays an important role in the prevention of CVD in previous reports( 19 , 20 ), salt intake was used as a surrogate of health diet. The healthy diet was categorized as 1) ideal: < 6 g/day; 2) intermediate: 6–10 g/day; 3) poor: ≥10 g/day. We estimated lifestyle health status in the current study according to five lifestyle risk factors: 1) current smoking; 2) current alcohol consumption; 3) physical inactivity: <80 minutes/week or none; 4) sedentary behavior: sedentary time ≥ 4 hour/day; 5) unhealth diet: salt intake ≥ 6 g/day. The lifestyle health status ranged from 0 to 5, with higher scores indicating unhealthy lifestyle, and were recorded as three categories: most healthy lifestyle (0–1 risk factor), moderately healthy lifestyle (2 risk factors), and least healthy lifestyle (3–5 risk factors) (Online Table 2 in Supplemental material). Outcome Ascertainment The present study participants were followed-up from the baseline examination at 2006 or 2007 up to December 31, 2017 as the end of the follow-up period, or to the date of a CVD event, or death, whichever came first. CVD events were defined as a composite of nonfatal MI and stroke during follow-up( 21 , 22 ). To retrieve potential CVD events, the subjects were linked to the Municipal Social Insurance and Hospital Discharge Register. All medical records including emergency department or hospitalized in local hospital were collected and adjudicated centrally. Stroke was defined according to the World Health Organization criteria on the basis of clinical symptoms, images obtained by computed tomography or magnetic resonance imaging, and other diagnostic reports( 23 ). MI was defined based on cardiac enzymes levels, symptoms, electrocardiogram (ECG) signs and necropsy( 24 ). Additionally, information on mortality was collected from vital statistics offices, with the death certificate reviewed by the study clinicians( 21 ). Statistical Analyses The baseline characteristics were presented as mean ± standard deviation (SD) or median with inter-quartile range (IQR), or frequencies with percentages. Baseline characteristics across metabolic and lifestyle health status were compared using the ANOVA or Kruskal-Wallis tests for continuous variables and chi-square test for categorical variables. The incidence rate of CVD, stroke, MI and all-cause mortality were reported as per 1,000 person-years (PY) with 95% confidence intervals (CIs). The Kaplan-Meier curves and the log-rank test was used to visual and test the significance of differences in the cumulative-incidence of clinical outcomes by metabolic and lifestyle health status. The multivariable adjusted hazard ratios (HRs) and 95% CIs for CVD, stroke, MI and all-cause mortality were calculated using Cox proportional hazards regression analysis after adjustments for covariates. These included age (continuous, years), sex (categorical, male or female), the family average monthly income (categorical, <¥800” or “≥ ¥800”), body mass index (BMI, calculated as continuous) and education (categorical, literacy/primary or middle school, high school or college/university). We first separately explored the association between lifestyle and metabolic risk and each clinical outcome. Moreover, an interaction between lifestyle and metabolic risk was tested by the likelihood-ratio test, and analyses were stratified by different metabolic risk category. Lastly, we assessed the joint association by creating a product-term between lifestyle and metabolic health status, with most healthy lifestyle and low metabolic risk group as reference. All analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). All reported P values were based on two-sided test of significance, and P < 0.05 was consider statistically significant in the current study. Results Baseline Characteristics A total of 94,831 participants (men, 79.76%; median age, 51.60 [43.47–58.87]) were eventually analyzed in our study. Baseline characteristics of the participants in different baseline metabolic health status are presented in Table 1 . Compared with participants with low metabolic risk, the other two groups with more metabolic risk components were older, were more likely to be women, had a lower self-reported education, and had a higher level of BMI, waist circumference, SBP, DBP, FBG and adverse lipid profile. The proportion of participants with unhealthy salt intake habits or sedentary behavior increased markedly with the more metabolic risk components, whereas the proportion of current smokers and those with physical inactivity decreased (Table 1 ). Table 1 Baseline characteristics of the study population according to the baseline metabolic health status. Overall Metabolic health status P value Low metabolic risk Medium metabolic risk High metabolic risk Participants (n) 94831 69612 (73.41) 17810 (18.78) 7409 (7.81) Sociodemographic Age, years 51.60 (43.47–58.87) 50.72 (42.40–57.80) 53.83 (47.24–61.05) 54.78 (48.84–61.41) < 0.001 Male, sex 75640 (79.76) 55853 (80.23) 14242 (79.97) 5545 (74.84) < 0.001 Education, high school or above 18984 (20.03) 14775 (21.24) 2961 (16.65) 1248 (16.86) < 0.001 Income, yuan/month, ≥¥800 13622 (14.38) 10015 (14.40) 2446 (13.75) 1161 (15.69) < 0.001 Metabolic risk factors BMI, kg/m 2 24.86 (22.64–27.24) 24.09 (22.04–26.26) 26.78 (24.79–28.80) 27.70 (25.86–29.76) < 0.001 Waist circumference, cm 87.00 (80.00–93.00) 84.00 (79.00–90.00) 93.00 (88.00–98.00) 95.00 (91.00-100.00) < 0.001 Systolic blood pressure, mmHg 130.00 (119.30-141.30) 121.00 (111.30–140.00) 140.00 (130.00-151.30) 142.00 (130.70–160.00) < 0.001 Diastolic blood pressure, mmHg 80.00 (78.70–90.00) 80.00 (73.30–89.30) 90.00 (80.00-97.30) 90.00 (81.30–100.00) < 0.001 Fasting plasma glucose, mmol/L 5.11 (4.67–5.71) 5.00 (4.59–5.40) 5.61 (4.92–6.34) 6.27 (5.77–7.63) < 0.001 Triglycerides, mmol/L 1.27 (0.90–1.93) 1.11 (0.81–1.50) 1.94 (1.36–2.81) 2.49 (1.95–3.63) < 0.001 LDL cholesterol, mmol/L 2.34 (1.84–2.83) 2.32 (1.84–2.80) 2.40 (1.86–2.90) 2.40 (1.80–2.96) < 0.001 HDL cholesterol, mmol/L 1.51 (1.28–1.77) 1.53 (1.31–1.78) 1.46 (1.23–1.73) 1.35 (1.10–1.64) < 0.001 Total cholesterol, mmol/L 4.93 (4.28–5.60) 4.86 (4.24–5.49) 5.11 (4.40–5.83) 5.22 (4.49–5.98) < 0.001 Lifestyle risk factors Current smoking 29428 (31.03) 22007 (31.61) 5305 (29.79) 2116 (28.56) < 0.001 Current alcohol 17034 (17.96) 12320 (17.70) 3402 (19.10) 1312 (17.71) < 0.001 Physical inactivity 79947 (84.30) 59303 (85.19) 14796 (83.08) 5848 (78.93) < 0.001 Sedentary time, h/week, ≥ 30 10290 (10.85) 7242 (10.40) 2110 (11.85) 938 (12.66) < 0.001 Salt intake, g/day, ≥6 24037 (25.35) 17597 (25.28) 4434 (24.90) 2006 (27.08) 0.001 Abbreviations: BMI, body mass index; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; N, number. Values are the number (proportion), mean (SD), or median (interquartile range). * P values were for the ANOVA or analyses across the three categories of metabolic health status. Individual Associations of Lifestyle and Metabolic Health Status with Clinical Outcomes During a median follow-up of 11.03 years (IQR: 10.74–11.22 years), we observed 6,590 CVD events (rate 6.74 per 1000 person-year, [95% CI 6.58–6.91]), including 5,233 non-fatal strokes and 1,519 non-fatal MIs, and 9,218 participants died (9.17 per 1000 person-year, [95% CI 8.99–9.36]). The risk of incident CVD increased significantly as the number of metabolic components increased ( P for trend < 0.001, Fig. 2 A). The same pattern of results was observed when categories were used instead of the number of metabolic risk components. CVD risk increased monotonically across metabolic health status categories (Fig. 2 B). In the multivariable model, the HRs for CVD were 1.47 (95% CI 1.39–1.56) for participants with medium metabolic risk, and 1.85 (95% CI 1.72-2.00) for those with high metabolic risk, compared with low metabolic risk ( P for trend < 0.001, Table 2 ). With regards to all-cause mortality, the adjusted HR for the participants with medium metabolic risk was 1.34 (95% CI 1.28–1.41), and 1.55 (95% CI 1.44–1.66) for those with high metabolic risk, compared with low metabolic risk. Similar results were observed for stroke and MI (Table 2 and Online Fig. 2 in Supplemental material). Table 2 Risk of incident cardiovascular disease, stroke, myocardial infarction and all-cause mortality according to metabolic health categories. Metabolic health status Low metabolic risk Medium metabolic risk High metabolic risk Cardiovascular disease Case, n (%) 3925 (5.64) 1749 (9.82) 916 (12.36) Incidence rate, per 1000-person, y (95% CI) 5.42 (5.25–5.59) 9.75 (9.30-10.21) 12.48 (11.70-13.32) HR (95% CI) Reference 1.47 (1.39–1.56) 1.85 (1.72-2.00) P value for trend < 0.001 Stroke Case, n (%) 3141 (4.51) 1386 (7.78) 706 (9.53) Incidence rate, per 1000-person, y (95% CI) 4.31 (4.17–4.47) 7.65 (7.26–8.07) 9.51 (8.83–10.23) HR (95% CI) Reference 1.45 (1.36–1.55) 1.77 (1.62–1.93) P value for trend < 0.001 Myocardial infarction Case, n (%) 868 (1.25) 413 (2.32) 238 (3.21) Incidence rate, per 1000-person, y (95% CI) 1.18 (1.10–1.26) 2.23 (2.03–2.46) 3.12 (2.75–3.54) HR (95% CI) Reference 1.56 (1.38–1.76) 2.13 (1.82–2.48) P value for trend < 0.001 All-cause mortality Case, n (%) 5938 (8.53) 2249 (12.63) 1031 (13.92) Incidence rate, per 1000-person, y (95% CI) 8.02 (7.81–8.22) 12.03 (11.54–12.54) 13.34 (12.55–14.18) HR (95% CI) Reference 1.34 (1.28–1.41) 1.55 (1.44–1.66) P value for trend < 0.001 Abbreviation: HR, hazard ratio. * Adjusted for age, sex, body mass index, education and family income at baseline. There was a significant association with CVD risk as the number of unhealthy lifestyle factors adopted increased ( P for trend < 0.001, Fig. 2 C). The same pattern of results was observed when lifestyle categories was used instead of the number of unhealthy lifestyle factors. CVD risk also increased monotonically across unhealthy lifestyle categories (Fig. 2 D). In the multivariable model, the HRs for CVD were 1.10 (95% CI 1.03–1.17) for participants with moderately healthy lifestyle, and 1.23 (95% CI 1.15–2.30) for those with least healthy lifestyle, compared with most healthy lifestyle ( P for trend < 0.001, Table 3 ). With regards to all-cause mortality, the adjusted HR for the participants with moderately healthy lifestyle was 1.07 (95% CI 1.02–1.13), and 1.08 (95% CI 1.02–1.14) for those with least healthy lifestyle, compared with most healthy lifestyle. Similar results were observed for stroke. However, there was no significant between lifestyle and MI (Table 3 and Online Fig. 3 in Supplemental material). Table 3 Risk of incident cardiovascular disease, stroke, myocardial infarction and all-cause mortality according to lifestyle health categories. Lifestyle health status Most healthy lifestyle Moderately healthy lifestyle Least healthy lifestyle Cardiovascular disease Case, n (%) 3492 (6.76) 1536 (6.76) 1562 (7.65) Incidence rate, per 1000-person, y (95% CI) 6.57 (6.36–6.79) 6.55 (6.23–6.88) 7.40 (7.04–7.78) HR (95% CI) Reference 1.10 (1.03–1.17) 1.23 (1.15–1.30) P value for trend < 0.001 Stroke Case, n (%) 2758 (5.34) 1207 (5.31) 1268 (6.21) Incidence rate, per 1000-person, y (95% CI) 5.16 (4.97–5.35) 5.11 (4.83–5.41) 5.97 (5.65–6.31) HR (95% CI) Reference 1.10 (1.03–1.18) 1.27 (1.18–1.36) P value for trend < 0.001 Myocardial infarction Case, n (%) 826 (1.60) 358 (1.58) 335 (1.64) Incidence rate, per 1000-person, y (95% CI) 1.52 (1.42–1.63) 1.49 (1.35–1.66) 1.55 (1.39–1.72) HR (95% CI) Reference 1.04 (0.92–1.18) 1.08 (0.95–1.23) P value for trend 0.226 All-cause mortality Case, n (%) 5235 (10.13) 2161 (9.51) 1822 (8.92) Incidence rate, per 1000-person, y (95% CI) 9.59 (9.33–9.85) 8.96 (8.59–9.35) 8.37 (7.99–8.76) HR (95% CI) Reference 1.07 (1.02–1.13) 1.08 (1.02–1.14) P value for trend 0.003 Abbreviation: HR, hazard ratio. * Adjusted for age, sex, body mass index, education and family income at baseline. Joint Effects of Lifestyle and Metabolic Health Status with Clinical Outcomes The joint associations of lifestyle and metabolic health status with CVD, stroke, MI and all-cause mortality are shown in Table 4 . Our analyses indicate that the participants within the less metabolic risk components and most healthy lifestyle had the lowest risk of CVD, whereas those within the more metabolic risk components and least healthy lifestyle group had the highest risk (2.06 [95% CI 1.77–2.39]) of CVD. The association persisted for stroke but not presence for MI and all-cause mortality. The risk of CVD was associated with metabolic health in each lifestyle status. No significant interaction between metabolic risk and lifestyle factors was observed (all P = 0.15). Table 4 Risk of cardiovascular disease, stroke, myocardial infarction and all-cause mortality in participants according to the combinations of baseline lifestyle and metabolic health status. Lifestyle health status Most healthy lifestyle Moderately healthy lifestyle Least healthy lifestyle Cardiovascular disease Metabolic health status Low metabolic risk Reference 1.05 (0.97–1.14) 1.25 (1.16–1.35) Medium metabolic risk 1.42 (1.31–1.54) 1.78 (1.60–1.98) 1.72 (1.53–1.92) High metabolic risk 1.93 (1.75–2.14) 1.92 (1.66–2.22) 2.06 (1.77–2.39) Stroke Metabolic health status Low metabolic risk Reference 1.06 (0.96–1.15) 1.28 (1.17–1.39) Medium metabolic risk 1.39 (1.27–1.52) 1.74 (1.54–1.96) 1.77 (1.56-2.00) High metabolic risk 1.83 (1.63–2.05) 1.85 (1.57–2.18) 2.07 (1.75–2.44) Myocardial infarction Metabolic health status Low metabolic risk Reference 1.03 (0.87–1.22) 1.18 (1.00-1.40) Medium metabolic risk 1.58 (1.34–1.86) 1.83 (1.47–2.27) 1.53 (1.20–1.95) High metabolic risk 2.37 (1.95–2.89) 2.05 (1.53–2.74) 2.01 (1.46–2.75) All-cause mortality Metabolic health status Low metabolic risk Reference 1.07 (1.01–1.14) 1.13 (1.06–1.21) Medium metabolic risk 1.39 (1.30–1.48) 1.47 (1.34–1.61) 1.33 (1.20–1.48) High metabolic risk 1.61 (1.47–1.76) 1.68 (1.48–1.91) 1.53 (1.31–1.78) The Cox proportional hazards model was used to detect adjusted HRs (95% CIs). * Adjusted for age, sex, body mass index, education and family income at baseline. Associations of lifestyle health status with CVD, stroke, MI and all-cause mortality were stratified according to the metabolic health status (Fig. 2 ). Overall, participants who had the most healthy lifestyle were associated with a lower risk of CVD across all metabolic health groups. Compared with those with the most healthy lifestyle, the HRs of CVD for participants with least healthy lifestyle was 1.26 (95% CI 1.17–1.37) in the category with low metabolic risk and was 1.16 (95% CI 1.03–1.31) and 1.07 (95% CI 0.90–1.27) for those with medium and high metabolic risk, respectively. Moreover, even a moderately healthy lifestyle conferred an obvious risk of CVD in those with medium metabolic risk (1.24 [95% CI 1.11–1.39]). Similar results were observed for stroke. There was no significant association between healthy lifestyle and MI among participants with different metabolic health status. For all-cause mortality, only in the category with low metabolic risk, the healthy lifestyle was significant association with all-cause mortality (Online Table 3 in Supplemental material). Discussion In this perspective cohort study, we described the joint associations of the lifestyle and metabolic risk factors with the incident of CVD and all-cause mortality. Our results indicate that participants with high metabolic risk and unfavorable lifestyle had a significantly higher risk of incident CVD and all-cause mortality compared with participants with low metabolic risk and a most healthy lifestyle. We found that the association between healthy lifestyle and the risk of CVD remained stable in different metabolic risk. The association between metabolic risk and the risk of CVD was not modified by healthy lifestyle. Previous studies have reported similar but not identical associations of lifestyle factors and cardiometabolic outcomes( 8 , 9 , 13 , 25 ). A recent meta-analysis has shown that the combination of multiple healthy lifestyle factors was associated with a substantially lower risk of incident diabetes and risk of mortality and incident CVD( 11 ). A previous study included over 40,000 Chinese participants aged 30–79, demonstrated that adherence to a healthy lifestyle may substantially lower the burden to diabetes. This study also indicates the population attributable risk percentage of diabetes appeared to be higher among old and obese participants( 26 ). In our study, lifestyle was not significantly associated with risk of MI, inconsistent with results from the INTERHEART Study analysis( 27 ). Individually, these lifestyle factors were more strongly associated with risk of stroke than MI, although power was limited by the few MI cases. Future studies should focus on differences in risk factors between CVD subtypes. Data from the China Cardiometabolic Disease and Cancer Cohort (4C) study have presented robust effects of lifestyle status on new-onset diabetes and major cardiovascular events regardless of metabolic status( 7 ), which is inconsistent with our results. However, our study indicates that the associations between metabolic risk and the risk of CVD, stroke and all-cause mortality was not modified by healthy lifestyle. This discrepancy may be caused by the differences in population characteristics and follow-up duration. We used a long-term follow-up cohort study to analysis a risk evaluation strategy based on the combination of lifestyle and metabolic health status to prevent CVD and all-cause mortality risk. The current study has several strengths. The Kailuan study enrolled a large population-based cohort of Chinese adults. Standardized protocols were used for data collection, including lifestyle health factors, metabolic health components and potential confounders such as income and education. Additionally, long-term follow-up was available during which CVD events were identified and adjudicated by trained staff. Despite these strengths, several limitations should be taken into consideration. First, lifestyle factors were self-reported, which might be susceptible to self-report bias in estimation the associations. Second, females were underrepresented in this cohort so that the generalizability of the results are limited. Third, lifestyle and metabolic health status is artificial, indicating that considerable caution should be taken in quantifying the precise effect of risk factors. Finally, the population of this study came from China, may cannot directly promote the results on other ethnicities. Further studies, including other geographic regions, ethnicities, and races, are needed to confirm the generalizability of the current results. Conclusions We found that healthy lifestyle and metabolic health were associated with a lower risk of CVD, whereas unhealthy lifestyle and high metabolic risk were associated with a higher risk of CVD. The association between healthy lifestyle and the risk of CVD remained stable in different metabolic risk. Our findings highlight the importance of both lifestyle and metabolic health status in the prevention of CVD and suggest the healthy lifestyle should be promoted even for people with high metabolic risk. Declarations Funding This study was funded by Beijing Municipal Administration of Hospitals Incubating Program (PX2020021), Beijing Excellent Talents Training Program (2018000021469G234), Young Elite Scientists Sponsorship Program by CAST (2018QNRC001), National Key R&D Program of China (2017YFC1310902) and National Key Research and Development Program of China (2018YFC1312800 and 2018YFC1312801). Conflicts of Interest The authors have stated explicitly that there are no conflicts of interest in connection with this article. Availability of Data and Code Data are available to researchers on request for purposes of reproducing the results or replicating the procedure by directly contacting the corresponding author. Authors’ contributions YZ and HL wrote the manuscript. YZ, AW, SC, XT and HL collected the data. SC, XT researched data and contributed to discussion. SW and DM reviewed and edited the manuscript. AX contributed to the discussion and reviewed/edited the manuscript. All authors read and approved the final manuscript. Ethical Statement The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was performed according to the guidelines of the Helsinki Declaration and was approved by the Ethics Committee of Kailuan General Hospital (approval number: 2006-05) and Beijing Tiantan Hospital (approval number: 2010-014-01). Written consents were obtained from all participants or their legal representatives. Consent for publication Not applicable. Acknowledgements We thank all study participants, their relatives, the members of the survey teams at the 11 regional hospitals of the Kailuan Medical Group; and the project development and management teams at the Beijing Tiantan Hospital and the Kailuan Group. 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Association of Change in Cardiovascular Risk Factors With Incident Cardiovascular Events. Jama . 2018;320(17):1793-1804. Li M, Xu Y, Wan Q, Shen F, Xu M. Individual and Combined Associations of Modifiable Lifestyle and Metabolic Health Status With New-Onset Diabetes and Major Cardiovascular Events: The China Cardiometabolic Disease and Cancer Cohort (4C) Study. 2020;43(8):1929-1936. Gami AS, Witt BJ, Howard DE, Erwin PJ, Gami LA, Somers VK, Montori VM. Metabolic syndrome and risk of incident cardiovascular events and death: a systematic review and meta-analysis of longitudinal studies. J Am Coll Cardiol . 2007;49(4):403-414. Hu FB, Manson JE, Stampfer MJ, Colditz G, Liu S, Solomon CG, Willett WC. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. The New England journal of medicine . 2001;345(11):790-797. Mozaffarian D, Kamineni A, Carnethon M, Djoussé L, Mukamal KJ, Siscovick D. Lifestyle risk factors and new-onset diabetes mellitus in older adults: the cardiovascular health study. Archives of internal medicine . 2009;169(8):798-807. Zhang Y, Pan XF, Chen J, Xia L, Cao A, Zhang Y, Wang J, Li H, Yang K, Guo K, He M, Pan A. Combined lifestyle factors and risk of incident type 2 diabetes and prognosis among individuals with type 2 diabetes: a systematic review and meta-analysis of prospective cohort studies. Diabetologia . 2020;63(1):21-33. Pan XF, Li Y, Franco OH, Yuan JM, Pan A, Koh WP. Impact of combined lifestyle factors on all-cause and cause-specific mortality and life expectancy in Chinese: the Singapore Chinese Health Study. The journals of gerontology Series A, Biological sciences and medical sciences . 2019. Horton ES. Effects of lifestyle changes to reduce risks of diabetes and associated cardiovascular risks: results from large scale efficacy trials. Obesity (Silver Spring, Md) . 2009;17 Suppl 3:S43-48. Wu S, Huang Z, Yang X, Zhou Y, Wang A, Chen L, Zhao H, Ruan C, Wu Y, Xin A, Li K, Jin C, Cai J. Prevalence of ideal cardiovascular health and its relationship with the 4-year cardiovascular events in a northern Chinese industrial city. Circulation Cardiovascular quality and outcomes . 2012;5(4):487-493. Wang A, Sun Y, Liu X, Su Z, Li J, Luo Y, Chen S, Wang J, Li X, Zhao Z, Zhu H, Wu S, Guo X. Changes in proteinuria and the risk of myocardial infarction in people with diabetes or pre-diabetes: a prospective cohort study. 2017;16(1):104. Wang C, Yuan Y, Zheng M, Pan A, Wang M, Zhao M, Li Y, Yao S, Chen S, Wu S, Xue H. Association of Age of Onset of Hypertension With Cardiovascular Diseases and Mortality. J Am Coll Cardiol . 2020;75(23):2921-2930. Wu S, Song Y, Chen S, Zheng M, Ma Y, Cui L, Jonas JB. Blood Pressure Classification of 2017 Associated With Cardiovascular Disease and Mortality in Young Chinese Adults. Hypertension (Dallas, Tex : 1979) . 2020;76(1):251-258. Tan CE, Ma S, Wai D, Chew SK, Tai ES. Can we apply the National Cholesterol Education Program Adult Treatment Panel definition of the metabolic syndrome to Asians? Diabetes care . 2004;27(5):1182-1186. Li XY, Cai XL, Bian PD, Hu LR. High salt intake and stroke: meta-analysis of the epidemiologic evidence. CNS neuroscience & therapeutics . 2012;18(8):691-701. Mozaffarian D, Fahimi S, Singh GM, Micha R, Khatibzadeh S, Engell RE, Lim S, Danaei G, Ezzati M, Powles J. Global sodium consumption and death from cardiovascular causes. The New England journal of medicine . 2014;371(7):624-634. Wu S, An S, Li W, Lichtenstein AH, Gao J, Kris-Etherton PM, Wu Y, Jin C, Huang S, Hu FB, Gao X. Association of Trajectory of Cardiovascular Health Score and Incident Cardiovascular Disease. JAMA network open . 2019;2(5):e194758. Wang A, Liu X, Su Z, Chen S, Zhang N, Wang Y, Wang Y, Wu S. Two-year changes in proteinuria and risk for myocardial infarction in patients with hypertension: a prospective cohort study. Journal of hypertension . 2017;35(11):2295-2302. Stroke--1989. Recommendations on stroke prevention, diagnosis, and therapy. Report of the WHO Task Force on Stroke and other Cerebrovascular Disorders. Stroke . 1989;20(10):1407-1431. Tunstall-Pedoe H, Kuulasmaa K, Amouyel P, Arveiler D, Rajakangas AM, Pajak A. Myocardial infarction and coronary deaths in the World Health Organization MONICA Project. Registration procedures, event rates, and case-fatality rates in 38 populations from 21 countries in four continents. Circulation . 1994;90(1):583-612. Towfighi A, Cheng EM, Hill VA, Barry F, Lee M, Valle NP, Mittman B, Ayala-Rivera M, Moreno L, Espinosa A, Dombish H, Wang D, Ochoa D, Chu A, Atkins M, Vickrey BG. Results of a Pilot Trial of a Lifestyle Intervention for Stroke Survivors: Healthy Eating and Lifestyle after Stroke. Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association . 2020;29(12):105323. Lv J, Yu C, Guo Y, Bian Z, Yang L, Chen Y, Hu X, Hou W, Chen J, Chen Z, Qi L, Li L. Adherence to a healthy lifestyle and the risk of type 2 diabetes in Chinese adults. International journal of epidemiology . 2017;46(5):1410-1420. Teo KK, Liu L, Chow CK, Wang X, Islam S, Jiang L, Sanderson JE, Rangarajan S, Yusuf S. Potentially modifiable risk factors associated with myocardial infarction in China: the INTERHEART China study. Heart . 2009;95(22):1857-1864. Supplementary Files SupplementalMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 03 Aug, 2021 Read the published version in Endocrine → Version 1 posted Reviews received at journal 23 May, 2021 Reviewers invited by journal 22 May, 2021 Editor assigned by journal 21 May, 2021 First submitted to journal 19 May, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-542402","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":28687111,"identity":"888638cc-e679-4cfb-814b-a5f95b9c302a","order_by":0,"name":"Yingting Zuo","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yingting","middleName":"","lastName":"Zuo","suffix":""},{"id":28687112,"identity":"69477a46-3fab-4a2a-ba5e-c0c768ed0e42","order_by":1,"name":"Haibin Li","email":"","orcid":"","institution":"Beijing Chaoyang Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haibin","middleName":"","lastName":"Li","suffix":""},{"id":28687113,"identity":"016e14fb-ca07-4e75-ba01-f379545a25d1","order_by":2,"name":"Shuohua Chen","email":"","orcid":"","institution":"Kailuan General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuohua","middleName":"","lastName":"Chen","suffix":""},{"id":28687114,"identity":"4d732e33-ed14-4ce7-9e59-173466f5ef42","order_by":3,"name":"Xue Tian","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Tian","suffix":""},{"id":28687115,"identity":"43b01083-a1ed-4675-90ad-b05b1a6c9d57","order_by":4,"name":"Dapeng Mo","email":"","orcid":"","institution":"Beijing Tiantan Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dapeng","middleName":"","lastName":"Mo","suffix":""},{"id":28687116,"identity":"8e6796e8-8da2-4f4a-b024-7c3632c7d2fd","order_by":5,"name":"Shouling Wu","email":"","orcid":"","institution":"Kailuan General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shouling","middleName":"","lastName":"Wu","suffix":""},{"id":28687117,"identity":"21cf4dc3-abf2-43db-aa8f-3a29ab4d55f5","order_by":6,"name":"Anxin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3RuwrCMBSA4QMFuxxxbfHyDEcKFaHQV+nUKRTBxSfo5OUBfBHHQECXiI4VHQRBHBR000EwiF1Tuwnmn7J8ObkAmEw/HII9BALgZQhKIB6VIAAOUzO+IbRYitNjJhod98L7t/sOavXFWk9kEndHMsbuNInUwY7gjllPS3zOfKqmAdKWkSJCbYKRnqzOfvuZOkgb+S3JmHd4T8kwJzbXkjA7+1YzjVFdKiIZC3SHqBXgTph3u6QiVE83p0EgWjW093oDUHHyhfp99aeAVESsa7747F48xWQymf6rF1rwTClKsvcOAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Tiantan Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Anxin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2021-05-20 09:43:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-542402/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-542402/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12020-021-02832-9","type":"published","date":"2021-08-03T15:03:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":9669013,"identity":"816135eb-05d0-41a9-8706-1b1a3d032f13","added_by":"auto","created_at":"2021-05-27 15:15:04","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":619578,"visible":true,"origin":"","legend":"Effect of metabolic and lifestyle factors on the risk of incident of CVD. \nRisk of incident of stroke (A), MI (C) and all-cause mortality (E) according metabolic risk components. The cumulative incidence of stroke (B), MI (D) and all-cause mortality (E) in low, medium and high metabolic risk groups.\n(A) Participants were divided into five groups according to their metabolic risk components, and the HRs for each group were compared with\nthose in 0 of the metabolic risk components. (B) The cumulative incidence of CVD in low, medium and high metabolic risk groups. (C) Participants were divided into five groups according to the number of unhealthy lifestyle factors, and the HRs for each group were compared with those who adopted no unhealthy lifestyle factors. (D) The cumulative incidence of CVD in participants who had a most, moderately and least lifestyle healthy status.\nAbbreviation: CVD, cardiovascular disease; HRs, hazard ratios.","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-542402/v1/f6931b49c76ff8456bacd07d.jpg"},{"id":9669332,"identity":"166f7277-f52d-4027-a5ae-6fe957b2292c","added_by":"auto","created_at":"2021-05-27 15:18:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":534105,"visible":true,"origin":"","legend":"Combined associations of lifestyle and metabolic health status with CVD, stroke, MI and all-cause mortality.\nAssociation of lifestyle health status with CVD (A), stroke (B), MI (C) and all-cause mortality (D) across the metabolic health groups. Cox proportional hazards models were used to generate HRs and corresponding 95% CIs, adjusted for age, sex, body mass index, education and family income at baseline. Abbreviation: CVD, cardiovascular disease; MI, myocardial infarction; HRs, hazard ratios.","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-542402/v1/1fa630a7e30f9f7df8f80b98.jpg"},{"id":13695633,"identity":"1ca990c9-b12c-4f43-af22-2894d5cdfb6f","added_by":"auto","created_at":"2021-09-17 12:58:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":544405,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-542402/v1/08ab3a53-ec86-4ac7-bb35-1f3198889959.pdf"},{"id":9669014,"identity":"12d8b547-5ef0-4ded-955f-464a9868a4c7","added_by":"auto","created_at":"2021-05-27 15:15:04","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":662159,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-542402/v1/ac698b56c9219c38c80b378e.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eJoint Association of Modifiable Lifestyle and Metabolic Health Status with Incidence of Cardiovascular Disease and All-Cause Mortality: A Perspective Cohort Study\u003c/p\u003e","fulltext":[{"header":"Introdution","content":" \u003cp\u003eCardiovascular disease (CVD) is one of the leading causes of death worldwide and remains the great threat to public global health(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Clinical therapy has been proven to be beneficial, but may have adverse effects, and often making functional recovery incomplete(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Therefore, primary prevention is considered the most effective strategy in controlling CVD and its consequences(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Some previous studies have shown that both healthy lifestyle and metabolic health status could reduce the risk of CVD and all-cause mortality(\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn most previous studies, lifestyle or metabolic factors have been consider individually, although those factors are typically correlated with one another. Recent studies and meta-analyses have consistently reported that combined lifestyle factors were associated with a markedly lower incidence of cardiometabolic abnormalities(\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). While few studies described the relative relationship of lifestyle factors with risk of CVD, subtypes of CVD and all-cause mortality across a population at different degrees of metabolic risk, and whether different degrees of metabolic status affect the efficacy of lifestyle was inconclusive(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Whereas some studies have reported that the lifestyle modification is effective in reducing CVD risk factors and CVD, especially stroke, others indicate that the effectiveness of lifestyle interventions for reductions in long-term CVD has yet to be determined(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relationship between lifestyle and metabolic health status with incident CVD has become an important public concern, which could improve our understanding of the composition of modifiable risk factors with different level of modifiable risks to prevent the occurrent of CVD. Therefore, the purpose of this study was to use data from a large-scale population-based prospective cohort to examine the jointed associations of lifestyle and metabolic health status with the risk of CVD and all-cause mortality.\u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eThe Kailuan study is a prospective cohort study designed to identify the risk factors for common noncommunicable disease, especially CVD(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The study protocol and informed consent were approved by Ethics Committees of both the Kailuan General Hospital and Beijing Tiantan Hospital. All participants signed the written informed consent.\u003c/p\u003e \u003cp\u003eThe details of the Kailuan study design have been described previously(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). At baseline, active and retired employees aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years of the Kailuan Group, Tangshan, China, were invited to participate in this study. Generally, 101,510 participants (81,110 men and 20,400 women) with an age ranging between 18 years and 98 years, were enrolled and completed survey at baseline between June 2006 and October 2007. All participants underwent face-to-face questionnaire measurements, physical examinations, and laboratory assessments in the 11 local health care hospitals. We performed re-examinations biennially to the end of the follow-up on December 31,2017.\u003c/p\u003e \u003cp\u003eIn the current study, we excluded 3,238 participants without data for any metabolic component at baseline, 3,358 participants with missing data on lifestyle risk factors, 83 participants with a history of myocardial infarction (MI) or stroke at baseline, finally, a total of 94,831 participants was selected for the current analysis (Online Fig.\u0026nbsp;1 in Supplemental material).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic Health Status\u003c/h2\u003e \u003cp\u003eMetabolic health status at baseline was determined based on the physical examinations and laboratory assessment by trained nurses and physicians. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured 3 times with the participants in the seated position at least 5 minutes using a mercury sphygmomanometer, and the average of 3 readings was used for further analysis(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Blood samples were collected after an overnight fast (8-h to 12-h) and measured the fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol levels by an automatic analyzer (Hitachi 747; Hitachi, Tokyo, Japan) at local hospitals.\u003c/p\u003e \u003cp\u003eWe used Adult Treatment Panel-III (ATP-III) criteria to define metabolic health status in the current study, which has been widely used to determine metabolic syndrome in adults worldwide. In the ATP-III criteria is based on the five CVD risk factors(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e): 1) central obesity: waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;90 cm in men and \u0026ge;\u0026thinsp;80 cm in women; 2) elevated TG: TG level\u0026thinsp;\u0026ge;\u0026thinsp;1.69 mmol/L; 3) low HDL cholesterol: HDL cholesterol level\u0026thinsp;\u0026lt;\u0026thinsp;1.03 mmol/L in men and \u0026lt;\u0026thinsp;1.29 mmol/L in women; 4) elevated BP: SBP/DBP\u0026thinsp;\u0026ge;\u0026thinsp;130/85 mmHg or taking antihypertensive drugs or self-reported history of hypertension; 5) elevated FPG: FPG level\u0026thinsp;\u0026ge;\u0026thinsp;5.6 mmol/L or taking hypoglycemic medications or self-reported history of diabetes. The metabolic health status ranged from 0 to 5, with lower scores indicating normal healthy metabolic, and were subsequently classified into three categories based on the distribution in this population: low risk (0\u0026ndash;2 components), medium risk (3 components), and high risk (4\u0026ndash;5 components) (Online Table\u0026nbsp;1 in Supplemental material).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eLifestyle Health Status\u003c/h2\u003e \u003cp\u003eLifestyle health status at baseline was collected by trained nurses and physicians using a standardized questionnaire interview. Current smoking was defined as smoking at least the previous year. Current alcohol consumption was defined as the average daily strong spirit (alcohol content\u0026thinsp;\u0026gt;\u0026thinsp;50%) consumption of 100 ml or more than 100 ml for at least the previous year. Physical activity level was categorized as 1) ideally active: \u0026ge;80 minutes/week moderate and vigorous intensity; 2) moderately active: \u0026lt;80 minutes/week; 3) inactive: none. Sedentary behavior was classified into three categories: 1)\u0026thinsp;\u0026lt;\u0026thinsp;4 hour/day; 2) 4\u0026ndash;8 hour/day; 3)\u0026thinsp;\u0026ge;\u0026thinsp;8 hour/day. Considering salt intake plays an important role in the prevention of CVD in previous reports(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), salt intake was used as a surrogate of health diet. The healthy diet was categorized as 1) ideal: \u0026lt; 6 g/day; 2) intermediate: 6\u0026ndash;10 g/day; 3) poor: \u0026ge;10 g/day.\u003c/p\u003e \u003cp\u003eWe estimated lifestyle health status in the current study according to five lifestyle risk factors: 1) current smoking; 2) current alcohol consumption; 3) physical inactivity: \u0026lt;80 minutes/week or none; 4) sedentary behavior: sedentary time\u0026thinsp;\u0026ge;\u0026thinsp;4 hour/day; 5) unhealth diet: salt intake\u0026thinsp;\u0026ge;\u0026thinsp;6 g/day. The lifestyle health status ranged from 0 to 5, with higher scores indicating unhealthy lifestyle, and were recorded as three categories: most healthy lifestyle (0\u0026ndash;1 risk factor), moderately healthy lifestyle (2 risk factors), and least healthy lifestyle (3\u0026ndash;5 risk factors) (Online Table\u0026nbsp;2 in Supplemental material).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Ascertainment\u003c/h2\u003e \u003cp\u003eThe present study participants were followed-up from the baseline examination at 2006 or 2007 up to December 31, 2017 as the end of the follow-up period, or to the date of a CVD event, or death, whichever came first. CVD events were defined as a composite of nonfatal MI and stroke during follow-up(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). To retrieve potential CVD events, the subjects were linked to the Municipal Social Insurance and Hospital Discharge Register. All medical records including emergency department or hospitalized in local hospital were collected and adjudicated centrally. Stroke was defined according to the World Health Organization criteria on the basis of clinical symptoms, images obtained by computed tomography or magnetic resonance imaging, and other diagnostic reports(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). MI was defined based on cardiac enzymes levels, symptoms, electrocardiogram (ECG) signs and necropsy(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Additionally, information on mortality was collected from vital statistics offices, with the death certificate reviewed by the study clinicians(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eThe baseline characteristics were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median with inter-quartile range (IQR), or frequencies with percentages. Baseline characteristics across metabolic and lifestyle health status were compared using the ANOVA or Kruskal-Wallis tests for continuous variables and chi-square test for categorical variables.\u003c/p\u003e \u003cp\u003eThe incidence rate of CVD, stroke, MI and all-cause mortality were reported as per 1,000 person-years (PY) with 95% confidence intervals (CIs). The Kaplan-Meier curves and the log-rank test was used to visual and test the significance of differences in the cumulative-incidence of clinical outcomes by metabolic and lifestyle health status. The multivariable adjusted hazard ratios (HRs) and 95% CIs for CVD, stroke, MI and all-cause mortality were calculated using Cox proportional hazards regression analysis after adjustments for covariates. These included age (continuous, years), sex (categorical, male or female), the family average monthly income (categorical, \u0026lt;\u0026yen;800\u0026rdquo; or \u0026ldquo;\u0026ge; \u0026yen;800\u0026rdquo;), body mass index (BMI, calculated as continuous) and education (categorical, literacy/primary or middle school, high school or college/university). We first separately explored the association between lifestyle and metabolic risk and each clinical outcome. Moreover, an interaction between lifestyle and metabolic risk was tested by the likelihood-ratio test, and analyses were stratified by different metabolic risk category. Lastly, we assessed the joint association by creating a product-term between lifestyle and metabolic health status, with most healthy lifestyle and low metabolic risk group as reference.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). All reported \u003cem\u003eP\u003c/em\u003e values were based on two-sided test of significance, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was consider statistically significant in the current study.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\n\u003cp\u003eA total of 94,831 participants (men, 79.76%; median age, 51.60 [43.47\u0026ndash;58.87]) were eventually analyzed in our study. Baseline characteristics of the participants in different baseline metabolic health status are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Compared with participants with low metabolic risk, the other two groups with more metabolic risk components were older, were more likely to be women, had a lower self-reported education, and had a higher level of BMI, waist circumference, SBP, DBP, FBG and adverse lipid profile. The proportion of participants with unhealthy salt intake habits or sedentary behavior increased markedly with the more metabolic risk components, whereas the proportion of current smokers and those with physical inactivity decreased (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline characteristics of the study population according to the baseline metabolic health status.\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 rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOverall\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eMetabolic health status\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\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLow metabolic risk\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMedium metabolic risk\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eHigh metabolic risk\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eParticipants (n)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94831\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e69612 (73.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17810 (18.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7409 (7.81)\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\u003eSociodemographic\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge, years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.60 (43.47\u0026ndash;58.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e50.72 (42.40\u0026ndash;57.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e53.83 (47.24\u0026ndash;61.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e54.78 (48.84\u0026ndash;61.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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, sex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75640 (79.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e55853 (80.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14242 (79.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5545 (74.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eEducation, high school or above\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18984 (20.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14775 (21.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2961 (16.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1248 (16.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eIncome, yuan/month, \u0026ge;\u0026yen;800\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13622 (14.38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10015 (14.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2446 (13.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1161 (15.69)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eMetabolic risk factors\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.86 (22.64\u0026ndash;27.24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24.09 (22.04\u0026ndash;26.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e26.78 (24.79\u0026ndash;28.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.70 (25.86\u0026ndash;29.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eWaist circumference, cm\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.00 (80.00\u0026ndash;93.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e84.00 (79.00\u0026ndash;90.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e93.00 (88.00\u0026ndash;98.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e95.00 (91.00-100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eSystolic blood pressure, mmHg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130.00 (119.30-141.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e121.00 (111.30\u0026ndash;140.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e140.00 (130.00-151.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e142.00 (130.70\u0026ndash;160.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eDiastolic blood pressure, mmHg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80.00 (78.70\u0026ndash;90.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.00 (73.30\u0026ndash;89.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90.00 (80.00-97.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90.00 (81.30\u0026ndash;100.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eFasting plasma glucose, mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.11 (4.67\u0026ndash;5.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.00 (4.59\u0026ndash;5.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.61 (4.92\u0026ndash;6.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.27 (5.77\u0026ndash;7.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eTriglycerides, mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.27 (0.90\u0026ndash;1.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.11 (0.81\u0026ndash;1.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.94 (1.36\u0026ndash;2.81)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.49 (1.95\u0026ndash;3.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eLDL cholesterol, mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.34 (1.84\u0026ndash;2.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.32 (1.84\u0026ndash;2.80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.40 (1.86\u0026ndash;2.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.40 (1.80\u0026ndash;2.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eHDL cholesterol, mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.51 (1.28\u0026ndash;1.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.53 (1.31\u0026ndash;1.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.46 (1.23\u0026ndash;1.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.35 (1.10\u0026ndash;1.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eTotal cholesterol, mmol/L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.93 (4.28\u0026ndash;5.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.86 (4.24\u0026ndash;5.49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.11 (4.40\u0026ndash;5.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.22 (4.49\u0026ndash;5.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eLifestyle risk factors\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCurrent smoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29428 (31.03)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22007 (31.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5305 (29.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2116 (28.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eCurrent alcohol\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17034 (17.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12320 (17.70)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3402 (19.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1312 (17.71)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003ePhysical inactivity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79947 (84.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e59303 (85.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14796 (83.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5848 (78.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eSedentary time, h/week, \u0026ge;\u0026thinsp;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10290 (10.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7242 (10.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2110 (11.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e938 (12.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" 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\u003eSalt intake, g/day, \u0026ge;6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24037 (25.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17597 (25.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4434 (24.90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2006 (27.08)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eAbbreviations: BMI, body mass index; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; N, number.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eValues are the number (proportion), mean (SD), or median (interquartile range).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e values were for the ANOVA or analyses across the three categories of metabolic health status.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eIndividual Associations of Lifestyle and Metabolic Health Status with Clinical Outcomes\u003c/h2\u003e\n\u003cp\u003eDuring a median follow-up of 11.03 years (IQR: 10.74\u0026ndash;11.22 years), we observed 6,590 CVD events (rate 6.74 per 1000 person-year, [95% CI 6.58\u0026ndash;6.91]), including 5,233 non-fatal strokes and 1,519 non-fatal MIs, and 9,218 participants died (9.17 per 1000 person-year, [95% CI 8.99\u0026ndash;9.36]).\u003c/p\u003e\n\u003cp\u003eThe risk of incident CVD increased significantly as the number of metabolic components increased (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). The same pattern of results was observed when categories were used instead of the number of metabolic risk components. CVD risk increased monotonically across metabolic health status categories (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). In the multivariable model, the HRs for CVD were 1.47 (95% CI 1.39\u0026ndash;1.56) for participants with medium metabolic risk, and 1.85 (95% CI 1.72-2.00) for those with high metabolic risk, compared with low metabolic risk (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). With regards to all-cause mortality, the adjusted HR for the participants with medium metabolic risk was 1.34 (95% CI 1.28\u0026ndash;1.41), and 1.55 (95% CI 1.44\u0026ndash;1.66) for those with high metabolic risk, compared with low metabolic risk. Similar results were observed for stroke and MI (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Online Fig.\u0026nbsp;2 in Supplemental material).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRisk of incident cardiovascular disease, stroke, myocardial infarction and all-cause mortality according to metabolic health categories.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eMetabolic health status\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedium metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCardiovascular disease\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCase, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3925 (5.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1749 (9.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e916 (12.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence rate,\u003c/p\u003e\n\u003cp\u003eper 1000-person, y (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.42 (5.25\u0026ndash;5.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.75 (9.30-10.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.48 (11.70-13.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.47 (1.39\u0026ndash;1.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.85 (1.72-2.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\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\u003eStroke\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCase, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3141 (4.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1386 (7.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e706 (9.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence rate,\u003c/p\u003e\n\u003cp\u003eper 1000-person, y (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.31 (4.17\u0026ndash;4.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.65 (7.26\u0026ndash;8.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.51 (8.83\u0026ndash;10.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.45 (1.36\u0026ndash;1.55)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.77 (1.62\u0026ndash;1.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\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\u003eMyocardial infarction\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCase, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e868 (1.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e413 (2.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e238 (3.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence rate,\u003c/p\u003e\n\u003cp\u003eper 1000-person, y (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18 (1.10\u0026ndash;1.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.23 (2.03\u0026ndash;2.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.12 (2.75\u0026ndash;3.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.56 (1.38\u0026ndash;1.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.13 (1.82\u0026ndash;2.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\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\u003eAll-cause mortality\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCase, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5938 (8.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2249 (12.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1031 (13.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence rate,\u003c/p\u003e\n\u003cp\u003eper 1000-person, y (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.02 (7.81\u0026ndash;8.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.03 (11.54\u0026ndash;12.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.34 (12.55\u0026ndash;14.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.34 (1.28\u0026ndash;1.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.55 (1.44\u0026ndash;1.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eAbbreviation: HR, hazard ratio.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\u003csup\u003e*\u003c/sup\u003eAdjusted for age, sex, body mass index, education and family income at baseline.\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\n\u003cp\u003eThere was a significant association with CVD risk as the number of unhealthy lifestyle factors adopted increased (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). The same pattern of results was observed when lifestyle categories was used instead of the number of unhealthy lifestyle factors. CVD risk also increased monotonically across unhealthy lifestyle categories (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). In the multivariable model, the HRs for CVD were 1.10 (95% CI 1.03\u0026ndash;1.17) for participants with moderately healthy lifestyle, and 1.23 (95% CI 1.15\u0026ndash;2.30) for those with least healthy lifestyle, compared with most healthy lifestyle (\u003cem\u003eP\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). With regards to all-cause mortality, the adjusted HR for the participants with moderately healthy lifestyle was 1.07 (95% CI 1.02\u0026ndash;1.13), and 1.08 (95% CI 1.02\u0026ndash;1.14) for those with least healthy lifestyle, compared with most healthy lifestyle. Similar results were observed for stroke. However, there was no significant between lifestyle and MI (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and Online Fig.\u0026nbsp;3 in Supplemental material).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRisk of incident cardiovascular disease, stroke, myocardial infarction and all-cause mortality according to lifestyle health categories.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eLifestyle health status\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMost healthy lifestyle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerately healthy lifestyle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeast healthy lifestyle\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCardiovascular disease\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCase, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3492 (6.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1536 (6.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1562 (7.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence rate,\u003c/p\u003e\n\u003cp\u003eper 1000-person, y (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.57 (6.36\u0026ndash;6.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.55 (6.23\u0026ndash;6.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.40 (7.04\u0026ndash;7.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10 (1.03\u0026ndash;1.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.23 (1.15\u0026ndash;1.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\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\u003eStroke\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCase, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2758 (5.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1207 (5.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1268 (6.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence rate,\u003c/p\u003e\n\u003cp\u003eper 1000-person, y (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.16 (4.97\u0026ndash;5.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.11 (4.83\u0026ndash;5.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.97 (5.65\u0026ndash;6.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10 (1.03\u0026ndash;1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.27 (1.18\u0026ndash;1.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\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\u003eMyocardial infarction\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCase, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e826 (1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e358 (1.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e335 (1.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence rate,\u003c/p\u003e\n\u003cp\u003eper 1000-person, y (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.52 (1.42\u0026ndash;1.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.49 (1.35\u0026ndash;1.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.55 (1.39\u0026ndash;1.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04 (0.92\u0026ndash;1.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.08 (0.95\u0026ndash;1.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.226\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAll-cause mortality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCase, n (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5235 (10.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2161 (9.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1822 (8.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncidence rate,\u003c/p\u003e\n\u003cp\u003eper 1000-person, y (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.59 (9.33\u0026ndash;9.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.96 (8.59\u0026ndash;9.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.37 (7.99\u0026ndash;8.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07 (1.02\u0026ndash;1.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.08 (1.02\u0026ndash;1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eAbbreviation: HR, hazard ratio.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\u003csup\u003e*\u003c/sup\u003eAdjusted for age, sex, body mass index, education and family income at baseline.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eJoint Effects of Lifestyle and Metabolic Health Status with Clinical Outcomes\u003c/h2\u003e\n\u003cp\u003eThe joint associations of lifestyle and metabolic health status with CVD, stroke, MI and all-cause mortality are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Our analyses indicate that the participants within the less metabolic risk components and most healthy lifestyle had the lowest risk of CVD, whereas those within the more metabolic risk components and least healthy lifestyle group had the highest risk (2.06 [95% CI 1.77\u0026ndash;2.39]) of CVD. The association persisted for stroke but not presence for MI and all-cause mortality. The risk of CVD was associated with metabolic health in each lifestyle status. No significant interaction between metabolic risk and lifestyle factors was observed (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRisk of cardiovascular disease, stroke, myocardial infarction and all-cause mortality in participants according to the combinations of baseline lifestyle and metabolic health status.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eLifestyle health status\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\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMost healthy lifestyle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModerately healthy lifestyle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLeast healthy lifestyle\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCardiovascular disease\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMetabolic health status\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.05 (0.97\u0026ndash;1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.25 (1.16\u0026ndash;1.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedium metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.42 (1.31\u0026ndash;1.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.78 (1.60\u0026ndash;1.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.72 (1.53\u0026ndash;1.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.93 (1.75\u0026ndash;2.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.92 (1.66\u0026ndash;2.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.06 (1.77\u0026ndash;2.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStroke\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMetabolic health status\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06 (0.96\u0026ndash;1.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.28 (1.17\u0026ndash;1.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedium metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.39 (1.27\u0026ndash;1.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.74 (1.54\u0026ndash;1.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.77 (1.56-2.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.83 (1.63\u0026ndash;2.05)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.85 (1.57\u0026ndash;2.18)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.07 (1.75\u0026ndash;2.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMyocardial infarction\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMetabolic health status\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03 (0.87\u0026ndash;1.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18 (1.00-1.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedium metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.58 (1.34\u0026ndash;1.86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.83 (1.47\u0026ndash;2.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.53 (1.20\u0026ndash;1.95)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.37 (1.95\u0026ndash;2.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.05 (1.53\u0026ndash;2.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.01 (1.46\u0026ndash;2.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAll-cause mortality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMetabolic health status\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLow metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07 (1.01\u0026ndash;1.14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.13 (1.06\u0026ndash;1.21)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedium metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.39 (1.30\u0026ndash;1.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.47 (1.34\u0026ndash;1.61)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.33 (1.20\u0026ndash;1.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh metabolic risk\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.61 (1.47\u0026ndash;1.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.68 (1.48\u0026ndash;1.91)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.53 (1.31\u0026ndash;1.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eThe Cox proportional hazards model was used to detect adjusted HRs (95% CIs).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\u003csup\u003e*\u003c/sup\u003eAdjusted for age, sex, body mass index, education and family income at baseline.\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\n\u003cp\u003eAssociations of lifestyle health status with CVD, stroke, MI and all-cause mortality were stratified according to the metabolic health status (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Overall, participants who had the most healthy lifestyle were associated with a lower risk of CVD across all metabolic health groups. Compared with those with the most healthy lifestyle, the HRs of CVD for participants with least healthy lifestyle was 1.26 (95% CI 1.17\u0026ndash;1.37) in the category with low metabolic risk and was 1.16 (95% CI 1.03\u0026ndash;1.31) and 1.07 (95% CI 0.90\u0026ndash;1.27) for those with medium and high metabolic risk, respectively. Moreover, even a moderately healthy lifestyle conferred an obvious risk of CVD in those with medium metabolic risk (1.24 [95% CI 1.11\u0026ndash;1.39]). Similar results were observed for stroke. There was no significant association between healthy lifestyle and MI among participants with different metabolic health status. For all-cause mortality, only in the category with low metabolic risk, the healthy lifestyle was significant association with all-cause mortality (Online Table\u0026nbsp;3 in Supplemental material).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this perspective cohort study, we described the joint associations of the lifestyle and metabolic risk factors with the incident of CVD and all-cause mortality. Our results indicate that participants with high metabolic risk and unfavorable lifestyle had a significantly higher risk of incident CVD and all-cause mortality compared with participants with low metabolic risk and a most healthy lifestyle. We found that the association between healthy lifestyle and the risk of CVD remained stable in different metabolic risk. The association between metabolic risk and the risk of CVD was not modified by healthy lifestyle.\u003c/p\u003e\n\u003cp\u003ePrevious studies have reported similar but not identical associations of lifestyle factors and cardiometabolic outcomes(\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e). A recent meta-analysis has shown that the combination of multiple healthy lifestyle factors was associated with a substantially lower risk of incident diabetes and risk of mortality and incident CVD(\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e). A previous study included over 40,000 Chinese participants aged 30\u0026ndash;79, demonstrated that adherence to a healthy lifestyle may substantially lower the burden to diabetes. This study also indicates the population attributable risk percentage of diabetes appeared to be higher among old and obese participants(\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e). In our study, lifestyle was not significantly associated with risk of MI, inconsistent with results from the INTERHEART Study analysis(\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e). Individually, these lifestyle factors were more strongly associated with risk of stroke than MI, although power was limited by the few MI cases. Future studies should focus on differences in risk factors between CVD subtypes.\u003c/p\u003e\n\u003cp\u003eData from the China Cardiometabolic Disease and Cancer Cohort (4C) study have presented robust effects of lifestyle status on new-onset diabetes and major cardiovascular events regardless of metabolic status(\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e), which is inconsistent with our results. However, our study indicates that the associations between metabolic risk and the risk of CVD, stroke and all-cause mortality was not modified by healthy lifestyle.\u003c/p\u003e\n\u003cp\u003eThis discrepancy may be caused by the differences in population characteristics and follow-up duration. We used a long-term follow-up cohort study to analysis a risk evaluation strategy based on the combination of lifestyle and metabolic health status to prevent CVD and all-cause mortality risk.\u003c/p\u003e\n\u003cp\u003eThe current study has several strengths. The Kailuan study enrolled a large population-based cohort of Chinese adults. Standardized protocols were used for data collection, including lifestyle health factors, metabolic health components and potential confounders such as income and education. Additionally, long-term follow-up was available during which CVD events were identified and adjudicated by trained staff. Despite these strengths, several limitations should be taken into consideration. First, lifestyle factors were self-reported, which might be susceptible to self-report bias in estimation the associations. Second, females were underrepresented in this cohort so that the generalizability of the results are limited. Third, lifestyle and metabolic health status is artificial, indicating that considerable caution should be taken in quantifying the precise effect of risk factors. Finally, the population of this study came from China, may cannot directly promote the results on other ethnicities. Further studies, including other geographic regions, ethnicities, and races, are needed to confirm the generalizability of the current results.\u003c/p\u003e"},{"header":"Conclusions","content":" \u003cp\u003eWe found that healthy lifestyle and metabolic health were associated with a lower risk of CVD, whereas unhealthy lifestyle and high metabolic risk were associated with a higher risk of CVD. The association between healthy lifestyle and the risk of CVD remained stable in different metabolic risk. Our findings highlight the importance of both lifestyle and metabolic health status in the prevention of CVD and suggest the healthy lifestyle should be promoted even for people with high metabolic risk.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Beijing Municipal Administration of Hospitals Incubating Program (PX2020021), Beijing Excellent Talents Training Program (2018000021469G234), Young Elite Scientists Sponsorship Program by CAST (2018QNRC001), National Key R\u0026amp;D Program of China (2017YFC1310902) and National Key Research and Development Program of China (2018YFC1312800 and 2018YFC1312801).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have stated explicitly that there are no conflicts of interest in connection with this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of\u0026nbsp;Data and Code \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available to researchers on request for purposes of reproducing the results or replicating the procedure by directly contacting the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYZ and HL wrote the manuscript. YZ, AW, SC, XT and HL collected the data. SC, XT researched data and contributed to discussion. SW and DM reviewed and edited the manuscript. AX contributed to the discussion and reviewed/edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was performed according to the guidelines of the Helsinki Declaration and was approved by the Ethics Committee of Kailuan General Hospital (approval number: 2006-05) and Beijing Tiantan Hospital (approval number: 2010-014-01). Written consents were obtained from all participants or their legal representatives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all study participants, their relatives, the members of the survey teams at the 11 regional hospitals of the Kailuan Medical Group; and the project development and management teams at the Beijing Tiantan Hospital and the Kailuan Group.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. \u003cem\u003eLancet\u003c/em\u003e. 2018;392(10159):1736-1788.\u003c/li\u003e\n\u003cli\u003eCramer SC, Koroshetz WJ, Finklestein SP. 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Potentially modifiable risk factors associated with myocardial infarction in China: the INTERHEART China study. \u003cem\u003eHeart\u003c/em\u003e. 2009;95(22):1857-1864.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lifestyle, metabolic health status, mortality, cardiovascular disease","lastPublishedDoi":"10.21203/rs.3.rs-542402/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-542402/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003ePurpose:\u003c/em\u003e We investigated the joint associations of modifiable lifestyle and metabolic factors with incident cardiovascular disease and all-cause mortality.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMethods:\u003c/em\u003e This study included 94,831 participants (men, 79.76%; median age, 51.60 [43.47-58.87]) without a history of cardiovascular disease at baseline from Kailuan study during 2006 to 2007 and followed them until new-onset cardiovascular disease event, death or December 31, 2017. Baseline metabolic health status was assessed by Adult Treatment Panel-III criteria and five lifestyle factors was collected using a self-reported questionnaire. We performed Cox proportional hazards models to evaluate the joint associations. \u003c/p\u003e\u003cp\u003e\u003cem\u003eResults:\u003c/em\u003e During a median follow-up of 11.03 years, we observed 6,590 cardiovascular disease events and 9,218 all-cause mortality. Participants within more metabolic risk components and least healthy lifestyle had the highest cardiovascular disease risk (hazard ratio 2.06 [95% CI 1.77-2.39]) and mortality risk (hazard ratio 1.53 [95% CI 1.31-1.78]), as compared with the less metabolic risk components and most healthy lifestyle group. Compared with the most healthy lifestyle, the hazard ratio of cardiovascular disease for participants with least healthy lifestyle was 1.26 (95% CI 1.17–1.37) in the category with low metabolic risk, 1.16 (95% CI 1.03–1.31) and 1.07 (95% CI 0.90–1.27) for those with medium and high metabolic risk, respectively.\u003c/p\u003e\u003cp\u003e\u003cem\u003eConclusions: \u003c/em\u003eWe showed that healthy lifestyle was associated with a lower risk of cardiovascular disease and there was no significant interaction between metabolic risk and healthy lifestyle. Our results indicated that healthy lifestyle should be promoted even for people with high metabolic risk.\u003c/p\u003e","manuscriptTitle":"Joint Association of Modifiable Lifestyle and Metabolic Health Status with Incidence of Cardiovascular Disease and All-Cause Mortality: A Perspective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-27 15:15:02","doi":"10.21203/rs.3.rs-542402/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-05-23T12:09:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-05-22T08:45:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-05-21T07:15:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Endocrine","date":"2021-05-19T05:13:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d274bf6a-240a-437e-ac35-b6a90b60b224","owner":[],"postedDate":"May 27th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":4578884,"name":"Endocrinology \u0026 Metabolism"}],"tags":[],"updatedAt":"2021-08-22T15:28:30+00:00","versionOfRecord":{"articleIdentity":"rs-542402","link":"https://doi.org/10.1007/s12020-021-02832-9","journal":{"identity":"endocrine","isVorOnly":false,"title":"Endocrine"},"publishedOn":"2021-08-03 15:03:05","publishedOnDateReadable":"August 3rd, 2021"},"versionCreatedAt":"2021-05-27 15:15:02","video":"","vorDoi":"10.1007/s12020-021-02832-9","vorDoiUrl":"https://doi.org/10.1007/s12020-021-02832-9","workflowStages":[]},"version":"v1","identity":"rs-542402","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-542402","identity":"rs-542402","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}