Metabolic Obesity Phenotypes and Their Transitions as Determinants of Multimorbidity Trajectories: Evidence from Global Aging Cohorts

Metabolic Obesity Phenotypes and Their Transitions as Determinants of Multimorbidity Trajectories: Evidence from Global Aging Cohorts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metabolic Obesity Phenotypes and Their Transitions as Determinants of Multimorbidity Trajectories: Evidence from Global Aging Cohorts Lei Liu, Heng Wang, Jun Li, Shunming Liu, Ching-Yu Cheng, Moluan Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6138718/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Objective : The high prevalence of multimorbidity poses significant challenges to the health burden of the elderly population and healthcare systems, understanding its trajectories is critical for intervention strategies. Metabolic obesity phenotypes are considered key predictors of multimorbidity. This study aimed to analyze the associations between metabolic obesity phenotypes and their transitions with multimorbidity trajectories. Methods : Longitudinal data from three cohort studies (CHARLS, ELSA, and HRS) were used, and trajectories of multimorbidity were identified through trajectory analysis. Baseline metabolic obesity phenotypes were classified into Metabolically Healthy Normal Weight (MHNW), Metabolically Healthy Obesity/Overweight (MHOO), Metabolically Unhealthy Normal Weight (MUNW), and Metabolically Unhealthy Obesity/Overweight (MUOO). The Group-Based Trajectory Modeling method was used to construct multimorbidity trajectories, perform logistic regression analysis on trajectory groups. Findings : In baseline analysis, compared with the MH (both MHNW and MHOO) group, the likelihood of MU group individuals in the low-risk trajectory significantly decreased, and the risk in the high-risk trajectory significantly increased, especially in CHARLS (OR=4.03), ELSA (OR=4.73), HRS (OR=3.01). The analysis of changes in metabolic obesity phenotypes showed that individuals with stable metabolic unhealth had the lowest risk in the low-risk trajectory, and the risk of developing high-risk trajectories significantly increased for phenotypes that had been exposed to metabolic unhealth. In particular, in CHARLS, ELSA, and HRS, individuals continuously exposed to metabolic unhealth significantly increased the risk of developing high-risk trajectories. Interpretations : Metabolic obesity phenotypes and their changes have significant impacts on multimorbidity trajectories, especially the strong association between metabolic unhealthy status and high-risk multimorbidity trajectories. Fundings: This study was funded by GDPH Supporting Fund for Talent Program (KY0120220263), LiaoNing Revitalization Talents Program (XLYC2203192), and Guangzhou School (hospital) Enterprise Joint Funding Project (2025A03J3901). Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity Figures Figure 1 Figure 2 Research in Context Evidence before this study We searched PubMed and Web of Science for articles published in English using the keywords “metabolic obesity phenotypes,” “multimorbidity trajectories,” “obesity” “metabolic heterogeneity” “BMI,” and “longitudinal cohorts” from inception up to October 15, 2024. Previous studies have established that metabolic obesity phenotypes are important predictors of multimorbidity. However, limited research has explored the dynamic relationships between metabolic obesity phenotypes and multimorbidity trajectories, particularly incorporating transitions between phenotypes over time. While a few studies examined associations with single metabolic components, the nonlinear effects of these components on multimorbidity risks remain underexplored. Furthermore, the modifying roles of subgroups based on age, gender, and SES in the association between metabolic health and multimorbidity trajectories are not well understood. Added value of this study This study leverages longitudinal data from three national aging cohorts (CHARLS, ELSA, HRS) to provide robust evidence of the associations between metabolic obesity phenotypes, their transitions, and multimorbidity trajectories. It demonstrates that metabolically unhealthy phenotypes are strongly linked to high-risk multimorbidity trajectories, with stable metabolically unhealthy obesity (MUOO) showing the greatest risk. Subgroup analyses reveal differential risks by age, gender, and SES, and nonlinear analyses identify critical thresholds for components such as HbA1C and BMI. This study emphasizes that persistent metabolic unhealth significantly elevates the risk of unfavorable multimorbidity outcomes. By validating findings using metabolic syndrome criteria, the robustness of the results is enhanced. Implications of all the available evidence The findings underscore the critical need for proactive metabolic health management, especially targeting individuals with metabolically unhealthy phenotypes to prevent high-risk multimorbidity trajectories. These results also highlight the importance of tailored interventions addressing subgroup-specific risks, such as for younger individuals, males, and those with varying SES levels. Furthermore, the nonlinear relationships observed for specific metabolic components suggest that intervention strategies might benefit from targeting specific thresholds to optimize health outcomes. This research supports integrating metabolic health interventions into broader multimorbidity management strategies and advocates for future studies to refine risk prediction and explore causal pathways in diverse populations. Introduction Multimorbidity (i.e. a person suffering from at least two chronic diseases simultaneously) has become a significant public health challenge in aging societies, affecting approximately one-third of the global adult population, with the situation particularly pronounced in low- and middle-income countries (LMIC) 1 . Although existing cross-sectional studies provide information on the prevalence and clustering of multimorbidity 2 , there is a lack of exploration of its dynamic evolution. By repeatedly measuring individual multimorbidity statuses through longitudinal cohort studies, a more comprehensive construction of its progression trajectories can be made, revealing the patterns of diseases changing over time. Studies have shown that multimorbidity trajectory patterns are influenced by various factors, including race, education level, and regional poverty levels 2 , 3 . Longitudinal analysis can not only clarify the roles of these influencing factors but also help to identify high-risk groups early, providing a scientific basis for the development of precise prevention strategies. This approach offers a critical foundation for reducing public health challenges, enhancing the efficient allocation of healthcare resources, and guiding the development of evidence-based policies. Obesity is a global epidemic. In the 21st century, many LMIC have also begun to see a trend of rising obesity rates, in addition to high-income countries 4 . The pathogenic role of adipocyte hypertrophy (a response to excess calorie intake) on the metabolic system has been well established 5 , and these metabolic disorders are closely related to the occurrence of multimorbidity 6 . However, epidemiological surveys show that about one-third of overweight and obese individuals are metabolically healthy, and these individuals are classified as MHOO. Others are classified as Metabolically Unhealthy Overweight/Obesity (MUOO). This heterogeneity is also observed in individuals with normal weight, who are classified as Metabolically Healthy Normal Weight (MHNW) and Metabolically Unhealthy Normal Weight (MUNW) 7 . The impact of obesity and metabolism on chronic diseases has been well described 6 , 8 – 10 . At present, the association between the heterogeneity of metabolic obesity phenotypes and the progression of multimorbidity has not been fully studied. Considering the current trends of aging and increasing obesity prevalence, this research gap is particularly concerning. In this study, we use global aging data from the China Health and Retirement Longitudinal Study (CHARLS) 11 , the Health and Retirement Study (HRS) 12 , and the English Longitudinal Study of Ageing (ELSA) 13 to investigate the relationship between metabolic heterogeneity of obesity and multimorbidity trajectories. In addition, we propose that transitioning from a healthy metabolic state to an unhealthy metabolic state, compared to maintaining a stable metabolic state, will further accelerate the progression of multimorbidity. Our study aims to help better understand the association between metabolic heterogeneity of obesity and the development of multimorbidity trajectories, and provide insights and clinical evidence for public health systems and governments to control the increasingly severe development of multimorbidity in the elderly population. Methods Study design and population This study uses data from three similarly designed large-scale prospective cohort studies (CHARLS, ELSA, and HRS). Detailed design of the cohorts has been published in other journals and is briefly described in the supplementary material. To make the cohorts comparable, we chose data from the same time period. For detailed follow-up time points, see the Supplementary Methods . The screening process of the study can be found in the supplementary material. Individuals who participated in blood tests and questionnaires at baseline were included in the study. Those under the age of 45 or unable to obtain sufficient disease diagnostic data were excluded. Further data missing that led to inability to determine the baseline and progression of metabolic obesity status and BMI less than 18.5 kg/m 2 were also excluded, as well as individuals who lacked any covariates. According to the inclusion and exclusion criteria, 4,064 individuals from CHARLS, 3,468 from ELSA, and 3,745 from HRS were included in the study. Definition for metabolic heterogeneity of obesity In CHARLS and ELSA, metabolic status was assessed through four metabolic criteria 7 , with individuals meeting two or more criteria defined as metabolically unhealthy: (1) elevated blood pressure: systolic blood pressure (SBP) ≥ 130 mmHg or diastolic blood pressure (DBP) ≥ 85 mmHg or use of anti-hypertensive drugs; (2) poor glucose control: fasting blood glucose (FBG) ≥ 5.6 mmol/L or hemoglobin A1c (HbA 1 c) ≥ 6.0% or use of hypoglycemic drugs; (3) elevated triglycerides (TG): TG ≥ 1.7 mmol/L or use of lipid-lowering drugs; (4) reduced high-density lipoprotein cholesterol (HDL-C): HDL-C < 1.03 mmol/L in men or < 1.29 mmol/L in women or use of lipid-lowering drugs. In HRS, the reliance on dried blood spot collection instead of venous blood sampling results in the unavailability of certain biomarkers, including fasting blood glucose and triglycerides, which limits the comprehensiveness of metabolic assessments. We defined individuals meeting two or more of the three criteria as metabolically unhealthy: (1) elevated blood pressure: SBP ≥ 130 mmHg or DBP ≥ 85 mmHg or use of antihypertensive drugs; (2) poor glucose control: hemoglobin A1c (HbA 1 c) ≥ 6.0% or use of hypoglycemic drugs; (3) reduced HDL-C: HDL-C < 1.03 mmol/L in men or < 1.29 mmol/L in women or use of lipid-lowering drugs. According to the definitions of obesity in different countries, in CHARLS, BMI greater than 24 kg/m 2 was defined as obesity, while in ELSA and HRS, BMI greater than 25 kg/m 2 was defined as obesity 14 . Combining individuals' metabolic and obesity statuses, individuals were divided into four metabolic obesity phenotypes: MHNW, MUNW, MHOO, and MUOO 7 . Further, we determined the progression of individuals' metabolic obesity phenotypes, thereby determining changes in individuals' metabolic obesity phenotypes, such as MHNW-MUNW, MHOO-MUOO, and so on. Definition for multimorbidity Information related to multimorbidity was extracted from questionnaires completed by individuals during the follow-up, including whether they were diagnosed by a doctor with hypertension, dyslipidemia, diabetes, cancer, chronic lung disease, liver disease, heart disease, stroke, chronic kidney disease, digestive system disease, asthma, arthritis or rheumatism, Parkinson's, mental and emotional problems, memory-related diseases, etc. The questionnaires of CHARLS, ELSA, and HRS varied in multiple follow-ups, and the specific questionnaires can be found in the supplementary material (Supplementary Table S1 ). We calculated the number of multimorbidity at each follow-up as the individual's current multimorbidity status. multimorbidity trajectories were identified using Group-Based Trajectory Modeling (GBTM), For detailed information on the introduction and analysis of the GBTM model, see the Supplementary Methods 15 , 16 . The results showed that the trajectory grouping results of all cohorts support the assumption of 5 trajectories. Furthermore, we assigned labels and grouped them by the starting number of multimorbidity and growth patterns of different trajectories. At the beginning of the follow-up, the number of multimorbidity less than 1 was deemed "low", between 1 and 2 was defined as "middl e ", and above 2 was defined as "high". The growth of the number of multimorbidity within the complete follow-up cycle is less than 1 defined as "stable", and greater than 1 defined as "growth". Covariates Demographic statistics include individuals' age and gender at baseline and education level. To coordinate the differences brought by different countries' education systems, education level is divided into below high school, high school, college and others. Other covariates include alcohol consumption (never/ever), smoking (never/ever), hsCRP level (mg/L), and socioeconomic status (SES). Considering the influence of SES on various chronic diseases and mortality, we used the total family wealth after three divisions (Q1: lower, Q2: medium, Q3: higher) to represent individual SES, and the hsCRP value extracted from the baseline blood test to represent the individual's chronic inflammation level. All covariates were obtained at baseline. The description of the covariates can be found in the supplementary material (Supplementary Table S2 ). Statistical Analysis To compare the results of the three cohorts, each stage of the analysis process was conducted separately on each dataset based on the same standards and procedures. Continuous variables are expressed as mean (standard deviation [SD]) or median (interquartile range [IQR]). Categorical variables are expressed as numbers (percentages). Missing data in CHARLS, ELSA, HRS Cohort was shown in Supplementary Table S3 . Baseline features were compared using one-way ANOVA or Kruskal-Wallis Rank Sum Test for continuous variables, and chi-square test for categorical variables. We used a logistic regression model to analyze the association between baseline metabolic obesity phenotype and multimorbidity progression trajectory, calculating OR values and confidence intervals with MHNW as a reference. Model 1 adjusted for age and gender, while Model 2 adjusted for all covariates (age, gender, education level, smoking, drinking, SES, hsCRP level). Further, we used a similar method to analyze the association between transitions in metabolic obesity status between two metabolic obesity data collections and multimorbidity progression trajectory. Restricted Cubic Splines (RCS) curves were used to analyze the non-linear relationship between the continuous components of metabolism and obesity and the progression trajectory of multimorbidity. In addition, we conducted subgroup analyses to detect possible differences among different age, gender, and SES subgroups. To verify the robustness of our research results, we conducted sensitivity analyses. We adopted the International Diabetes Federation's definition of metabolic syndrome as metabolic unhealth and conducted the main analysis 17 . All analyses were performed using R software (version 4.4.1) and Stata (version 18.0). As multiple tests were conducted, we used the Bonferroni method to adjust the p-values within the cohort. Role of funding The study sponsor has no role in study design, data analysis and interpretation of data, the writing of manuscript, or the decision to submit the paper for publication. Ethical statement Since this study relies on secondary analysis of publicly available datasets, ethical approvals for the original surveys were obtained as follows: The HRS was authorized by the National Institute on Aging and the Social Security Administration (NIA U01AG009740). The CHARLS was approved by the Ethical Review Committee of Peking University (IRB00001052-11015). The ELSA was sanctioned by the National Research and Ethics Service Committee South Central-Berkshire. All participants in these studies provided written informed consent, as specified in the original survey documentation. Results Baseline characteristics of the study population Table-1 shows the descriptive statistics of each cohort divided according to trajectory grouping. At baseline, 11,277 eligible individuals were included, of which 4,064 were from CHARLS, 3,468 from ELSA, and 3,745 from HRS. In CHARLS, individuals with a high-risk multimorbidity trajectory are more likely to be female, older, and have lower economic status. The baseline characteristics of ELSA and HRS are relatively similar, with older individuals, females, those with lower education level, non-drinkers, smokers, and those with lower economic status more likely to have a high-risk multimorbidity trajectory. In all cohorts, individuals with higher BMI, hsCRP, SBP, HbA 1 C as well as TG at baseline, but lower HDL-C levels at baseline are more likely to have a high-risk multimorbidity trajectory. In the three cohorts, individuals with baseline statuses of MHNW and MHOO generally decrease in proportion as the multimorbidity trajectory level increases, while it is just the opposite for MUOO. Trajectory analysis The trajectory analysis results are shown in Figure-1. We observed different multimorbidity progression patterns in different cohorts. Based on their progression trends and the number of multimorbidity at baseline, we named each trajectory. At the same time, we observed similar progression patterns in different categories between cohorts. Based on their current multimorbidity burden and future expectations on individuals, we divided the trajectories into "low risk", "medium risk", "high risk" groups. In CHARLS, we observed the low-risk group: "Very low-stable", "Low-stable"; medium-risk group: "Low-growth", "Middle-stable"; high-risk group: "High-growth". In ELSA, we observed the low-risk group: "Low-stable"; medium-risk group: "Middle-growth", "High-stable"; high-risk group: "High-growth", "Very high-stable". In HRS, we observed: low-risk group: "Very low-stable", "Low-growth"; medium-risk group: "Middle-growth"; high-risk group: " High-growth", "Very high-growth". The detailed fitting parameters of the trajectory model can be found in the supplementary material (Supplementary Table S4). The association between the metabolic obesity phenotype at baseline and the multimorbidity trajectory Table-2 shows the logistic regression results between baseline metabolic obesity phenotype and multimorbidity trajectory level. Similar results were found in all cohorts, and the results remained stable after adjusting for all covariates. Compared with individuals with MHNW metabolic phenotype, other phenotypes have a lower risk of low-risk multimorbidity trajectory and a higher risk of high-risk trajectory. In all cohorts, the risk of individuals with MU group (MUOO+MUNW) showing a low-risk multimorbidity trajectory is significantly lower than those with the MH group (MHOO+MHNW), including CHARLS (MUNW, OR: 0.65 (0.53 ~ 0.80); MUOO, OR: 0.30 (0.24 ~ 0.38)); ELSA (MUNW, OR: 0.28 (0.20 ~ 0.39); MUOO, OR: 0.26 (0.20 ~ 0.32)); HRS (MUNW, OR: 0.33 (0.20 ~ 0.54); MUOO, OR: 0.14 (0.10 ~ 0.19)). No consistent results were found in the analysis of metabolic obesity phenotype and medium-risk multimorbidity trajectory. The risk of progression to high-risk trajectory in each cohort is basically consistent. The relative risk of MU is significantly higher than that of MH. The highest risk is mostly in the MUOO group. In the outcomes of the worst trajectory in each cohort, CHARLS (MUOO, OR: 4.03 (3.18 ~ 5.11)); ELSA (MUOO, OR: 4.73 (2.64 ~ 8.50)); HRS (MUOO, OR: 3.01 (1.88 ~ 4.80)). It should be noted that we have not detected any phenotype that has a protective effect relative to MHNW individuals. Subgroup Analysis In subgroup analysis (Supplementary Table S8-10), we analyzed different subgroups stratified by age, sex, and SES. After correction by the Bonferroni method, we found that the risk of the subgroup with baseline age ≤ 60 and males progressing to high-risk subgroup was higher. In the CHARLS cohort, the risk of progressing to a high-risk subgroup was higher in high SES status (Q3), and in ELSA and HRS, the risk of progressing to a high-risk subgroup was highest in medium SES status, followed by high SES status. We used total family wealth as a measure of SES, acknowledging that this approach might reflect differing economic and financial frameworks across countries. Associations of metabolic obesity phenotype transitions with multimorbidity trajectories The metabolic obesity phenotype of individuals progresses over time, and we have detected all 16 metabolic obesity model progression patterns in all cohorts, with individuals with a stable MUOO phenotype accounting for the highest proportion in all cohorts (Supplementary Figure S1-S3). We used logistic regression to detect the association between metabolic obesity progression phenotype and all trajectories, with stable MHNW as a reference, as shown in Figure-2. For individuals with stable metabolic unhealthiness (MU-MU), the likelihood of progressing to a low-risk trajectory is significantly lower than other progression phenotypes (CHARLS (MUOO-MUOO, OR: 0.24 (0.18 ~ 0.32)), ELSA (MUOO-MUOO, OR: 0.15 (0.11 ~ 0.20)), HRS (MUOO-MUOO, OR: 0.05 (0.03 ~ 0.07))). For CHARLS progressing to "very low-stable", ELSA progressing to "low-stable", and HRS progressing to "very low-stable" outcomes, individuals with stable obesity who have experienced metabolic unhealthiness have a significantly reduced likelihood. For outcomes progressing to medium-risk trajectories, we found no consistent and meaningful progression phenotype risk differences. For outcomes progressing to high-risk trajectories in all cohorts, any phenotype that has experienced metabolic unhealthy exposure significantly increases the risk, (CHARLS (MHOO-MUNW, OR: 6.82 (2.48 ~ 18.73)), ELSA (MUOO-MUNW, OR: 9.18 (3.54 ~ 23.80)), HRS (MUNW-MUOO, OR: 9.65 (3.30 ~ 28.24))). Among them, individuals who are continuously metabolically unhealthy have a higher risk of developing a high-risk trajectory than those who are temporarily unhealthy. RCS and sensitivity analysis We analyzed the nonlinear association between various components constituting the metabolic obesity phenotype and high-risk trajectories, and the results after adjusting for all covariates are shown mainly in Supplementary Figure S4. The increase in HbA 1 C is nonlinearly related to the high-risk trajectory in all three cohorts, and the increase in BMI and the decrease in HDL‐C are significant in the entire cohort, but no consistent nonlinear trend is observed. The increase in triglycerides is nonlinearly related to the high-risk trajectory in CHARLS and ELSA. Interestingly, we observed that in HRS, the risk of HbA 1 C starts to decline when it is close to 8%, and the risk of triglycerides in ELSA starts to decline when it is close to 2 mmol/L, which may mean that their contributions to the overall risk decrease after reaching the inflection point. After using the standards of metabolic syndrome as the definition, we conducted the main analyses (Supplementary Table S5), and the results remained basically consistent. Discussion In this study, we used follow-up data from large-scale, prospective cohorts from three continents to systematically analyze the association between metabolic obesity phenotype and multimorbidity progression patterns. At baseline, compared with MHNW, all other phenotypes have a higher risk of rapid multimorbidity progression and a lower risk of slow multimorbidity progression. In the analysis of progression phenotypes, exposure to metabolic unhealthiness significantly increases the risk of rapid progression of individual multimorbidity, even if the metabolic unhealthiness is corrected; the risk of continuous exposure is even higher. Further subgroup analysis revealed that individuals with a younger baseline age and males exhibited a higher risk of progressing to high-risk multimorbidity trajectories. Additionally, the RCS curve analysis did not identify a consistent nonlinear relationship across all cohorts, except for HbA 1 C. Our study aims to help better understand the association between obesity metabolic heterogeneity and the progression of multimorbidity, and provide insights and clinical evidence for the public health system and government to control the increasingly severe development of multimorbidity in the elderly. The analysis at baseline shows that compared with metabolically healthy normal weight (MHNW), all other phenotypes have a higher risk of rapid multimorbidity progression. Among them, individuals with metabolic unhealthiness (MUOO+MUNW) have a significantly higher risk of rapid multimorbidity progression compared to metabolically healthy individuals (MHOO+MHNW). Of which, MUOO is the worst phenotype in metabolic obesity, and its risk of rapid multimorbidity progression is several times that of other phenotypes. For the risk of low-risk trajectories, the MHOO phenotype is basically no different from MHNW, suggesting that metabolic factors play a decisive role in it. Metabolic and obesity states change over time, and we found that about 35% of individuals will undergo such transitions. A single assessment of metabolic obesity phenotype is not enough to summarize the individual's situation in the entire follow-up cohort. Therefore, we further determined the phenotype change of individuals after four to five years of follow-up. In CHARLS, individuals who have experienced temporary metabolic unhealthiness have increased the risk of rapid multimorbidity progression by three to four times, and continuously unhealthy individuals can reach four to six times. In ELSA and HRS, this risk can be as high as nine times. This suggests that even short-term exposure to metabolic unhealthiness can significantly increase the risk of rapid progression of multimorbidity, and continuous exposure is the most dangerous group characteristic. It should be noted that the risk of rapid multimorbidity progression will increase for all phenotypes except MHNW, indicating that there is no "Healthy Obesity Phenotype" 18 . For people who can maintain metabolic health, keeping a normal weight is also very important. BMI is unable to provide information on fat distribution and the percentage of muscle mass, which is even more evident in the elderly population with sarcopenia, necessitating a reevaluation of the value of BMI as a measure of obesity in the elderly 19 . Existing studies have found that metabolic unhealthiness and obesity increase the risk of multiple chronic diseases; we found that metabolic unhealthiness and obesity are associated with a faster progression of chronic diseases 20-26 . Considering the heterogeneity of patients with multimorbidity, the mechanisms and pathophysiology behind each disease that constitutes multimorbidity are very complex. Despite this, pathophysiological studies of multimorbidity have proposed possible "common" mechanisms, that is, there may be the same mechanisms behind different diseases in multimorbidity 1 . Obesity is associated with more than 250 genetic variants and multiple clinical diseases. Cohort studies have shown that obesity is closely related to 21 non-overlapping diseases of multiple organ systems 6 . A meta-analysis of prospective studies found that although MHOO faces a higher risk of cardiovascular events compared to MHNW, their risk is lower than that of MUOO and MUNW participants, suggesting the potential importance of metabolic and obesity dual factors in the development of multimorbidity 6 . Treating obesity and correcting metabolic unhealthiness may be a feasible measure to reduce multimorbidity at the population level. Global free trade and urbanization-driven economic growth are major drivers of obesity trends 27 . Studies have shown that adjusting dietary structure to improve obesity and metabolic status is safe and effective for most people 5 . Countries such as China, UK, and US are reducing the incidence of obesity through their own efforts. Public propaganda, taxation of sugary drinks, and removal of trans fatty acids from processed foods have all been effective 28-30 . Using plant-based meat substitutes and insects as alternative protein sources can benefit both human and planetary health. Further research is needed to assess the impact of these foods on obesity compared to traditional diets 27,31 . For individuals who find it difficult to achieve weight loss goals through healthy lifestyles, we believe that restoring metabolic health should be a priority short-term goal. Transitioning from MUOO to MHOO can significantly reduce the risk of rapid multimorbidity progression, and both can be achieved through lifestyle interventions and drug treatments, which are obvious medical benefits for obese patients 5 . A unified metabolic health standard should be established as soon as possible to quickly identify metabolically unhealthy individuals among adults, especially MUNW, as they are exposed to a multimorbidity progression risk similar to MUOO individuals but lack attention. Through population-level metabolic health screening, we can identify individuals who may have rapid multimorbidity progression in the future and implement early interventions to delay multimorbidity progression. The present study has several strengths. First, we used large cohort studies from different continents and races to provide a global perspective for the study. Second, metabolic obesity progresses in different patterns in different cohorts, suggesting that different countries and races need specific health policies to prevent multimorbidity. Our study of phenotype transition during follow-up reveals the important role of metabolism in multimorbidity, and early identification and timely intervention of metabolically unhealthy individuals are very important. However, our study also has some limitations. First, the current definition of metabolic syndrome/unhealthiness is still not unified. We have chosen a highly recognized definition, which may cause bias when comparing our study with other similar studies. Second, we used logistic regression instead of the Cox proportional hazard model in our study, which may not reflect the advantages of cohort studies. Considering that the follow-up time is mainly in years, a large number of individuals will have exactly the same follow-up data time points, which may weaken the statistical power of the Cox model 32 , so we prefer to use logistic regression. Third, because metabolic damage requires multiple blood test results to confirm, only cohorts from China, the UK, and the US were included. The lack of cohorts from the southern hemisphere and low-income countries may affect the representativeness of our conclusions. Fourth, our study did not use imputed data for analysis. We believe that missing data for individuals in the included studies may not occur randomly (for example, individuals with multimorbidities are more likely to be absent from blood tests), which violates the assumptions of commonly used multiple imputation. This may weaken the representativeness of our study. Conclusion This study demonstrates that metabolically unhealthy individuals (MUOO, MUNW) are significantly more likely to follow high-risk multimorbidity trajectories across multiple cohorts, whereas metabolically healthy individuals exhibit a comparatively lower risk. Notably, prolonged exposure to metabolic unhealthiness markedly increases the likelihood of transitioning into a high-risk trajectory. These findings underscore the critical role of metabolic health in mitigating multimorbidity among middle-aged and older adults. They also highlight the need for public health strategies aimed at promoting and sustaining metabolic health. Future research should investigate the underlying mechanisms linking metabolic dysfunction to multimorbidity and evaluate effective interventions to reduce multimorbidity risk through metabolic health improvements. Declarations Contributors HZ and LL conceptualised and designed the study. HW, JL and JHJ managed, analysed and verified the data. HW, SML and LL prepared the first draft. HW, CYC, JL and LL interpreted the data, and HW, JHJ, HZ, and LL were responsible for editing and proofreading the manuscript. All authors contributed to the critical revision of the manuscript and read and approved the final version of the manuscript. All authors had full access to all the data in the study and accepted responsibility for the decision to submit for publication. Data sharing statement Data for this study were obtained from several major cohort studies, through the CHARLS website (https://charls.pku.edu.cn/), ELSA website (https://www.elsa-project.ac.uk/), and HRS website (https://hrs.isr.umich.edu/data-products). Declaration of interests The authors declare no conflict of interest regarding this manuscript. References Skou ST, Mair FS, Fortin M, et al. Multimorbidity. Nat Rev Dis Primers 2022; 8 (1): 48. Cezard G, McHale CT, Sullivan F, Bowles JKF, Keenan K. Studying trajectories of multimorbidity: a systematic scoping review of longitudinal approaches and evidence. BMJ Open 2021; 11 (11): e048485. Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet 2012; 380 (9836): 37-43. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet 2016; 387 (10026): 1377-96. Stefan N, Häring HU, Hu FB, Schulze MB. Metabolically healthy obesity: epidemiology, mechanisms, and clinical implications. Lancet Diabetes Endocrinol 2013; 1 (2): 152-62. Kivimäki M, Strandberg T, Pentti J, et al. Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study. Lancet Diabetes Endocrinol 2022; 10 (4): 253-63. He D, Qiu Y, Yan M, et al. Associations of metabolic heterogeneity of obesity with frailty progression: Results from two prospective cohorts. J Cachexia Sarcopenia Muscle 2023; 14 (1): 632-41. Kivimäki M, Kuosma E, Ferrie JE, et al. Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe. Lancet Public Health 2017; 2 (6): e277-e85. Pietzner M, Stewart ID, Raffler J, et al. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med 2021; 27 (3): 471-9. Cheong CY, Yap P, Yap KB, Ng TP. Associations of Inflammatory, Metabolic, Malnutrition, and Frailty Indexes with Multimorbidity Incidence and Progression, and Mortality Impact: Singapore Longitudinal Aging Study. Gerontology 2023; 69 (4): 416-27. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol 2014; 43 (1): 61-8. Sonnega A, Faul JD, Ofstedal MB, Langa KM, Phillips JW, Weir DR. Cohort Profile: the Health and Retirement Study (HRS). Int J Epidemiol 2014; 43 (2): 576-85. Steptoe A, Breeze E, Banks J, Nazroo J. Cohort profile: the English longitudinal study of ageing. Int J Epidemiol 2013; 42 (6): 1640-8. Gao M, Lv J, Yu C, et al. Metabolically healthy obesity, transition to unhealthy metabolic status, and vascular disease in Chinese adults: A cohort study. PLoS Med 2020; 17 (10): e1003351. Chen H, Zhou Y, Huang L, Xu X, Yuan C. Multimorbidity burden and developmental trajectory in relation to later-life dementia: A prospective study. Alzheimers Dement 2023; 19 (5): 2024-33. Nagin DS, Jones BL, Passos VL, Tremblay RE. Group-based multi-trajectory modeling. Stat Methods Med Res 2018; 27 (7): 2015-23. Alberti KG, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009; 120 (16): 1640-5. Smith GI, Mittendorfer B, Klein S. Metabolically healthy obesity: facts and fantasies. J Clin Invest 2019; 129 (10): 3978-89. Nevill AM, Duncan MJ, Myers T. BMI is dead; long live waist-circumference indices: But which index should we choose to predict cardio-metabolic risk? Nutr Metab Cardiovasc Dis 2022; 32 (7): 1642-50. Ashworth M, Durbaba S, Whitney D, Crompton J, Wright M, Dodhia H. Journey to multimorbidity: longitudinal analysis exploring cardiovascular risk factors and sociodemographic determinants in an urban setting. BMJ Open 2019; 9 (12): e031649. Canizares M, Hogg-Johnson S, Gignac MAM, Glazier RH, Badley EM. Increasing Trajectories of Multimorbidity Over Time: Birth Cohort Differences and the Role of Changes in Obesity and Income. J Gerontol B Psychol Sci Soc Sci 2018; 73 (7): 1303-14. Freisling H, Viallon V, Lennon H, et al. Lifestyle factors and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study. BMC Med 2020; 18 (1): 5. Jackson CA, Dobson A, Tooth L, Mishra GD. Body mass index and socioeconomic position are associated with 9-year trajectories of multimorbidity: A population-based study. Prev Med 2015; 81 : 92-8. Lappenschaar M, Hommersom A, Lucas PJ, et al. Multilevel temporal Bayesian networks can model longitudinal change in multimorbidity. J Clin Epidemiol 2013; 66 (12): 1405-16. Bhaskaran K, Dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK. Lancet Diabetes Endocrinol 2018; 6 (12): 944-53. Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. Jama 2013; 309 (1): 71-82. Malik VS, Willet WC, Hu FB. Nearly a decade on - trends, risk factors and policy implications in global obesity. Nat Rev Endocrinol 2020; 16 (11): 615-6. Wang Y, Zhao L, Gao L, Pan A, Xue H. Health policy and public health implications of obesity in China. Lancet Diabetes Endocrinol 2021; 9 (7): 446-61. Silver LD, Ng SW, Ryan-Ibarra S, et al. Changes in prices, sales, consumer spending, and beverage consumption one year after a tax on sugar-sweetened beverages in Berkeley, California, US: A before-and-after study. PLoS Med 2017; 14 (4): e1002283. Ghebreyesus TA, Frieden T. Trans fat: everyone must join the fight to eliminate this invisible killer from the world's food supply forever. Bmj 2024; 386 : q1525. Willett W, Rockström J, Loken B, et al. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet 2019; 393 (10170): 447-92. Fisher LD, Lin DY. Time-dependent covariates in the Cox proportional-hazards regression model. Annu Rev Public Health 1999; 20 : 145-57. Tables Table-1 CHARLS (n=4,064) Variables Total (n = 4064) Very low-stable (n = 797) Low-stable (n = 851) Low-growth (n = 823) Middle-stable (n = 935) High-growth (n = 658) P Age, Mean ± SD 58.30 ± 8.43 56.18 ± 8.44 57.14 ± 8.34 58.43 ± 8.27 59.64 ± 8.56 60.27 ± 7.75 <.001 Gender, n(%) 0.005 Female 2178 (53.59) 385 (48.31) 476 (55.93) 435 (52.86) 504 (53.90) 378 (57.45) Male 1886 (46.41) 412 (51.69) 375 (44.07) 388 (47.14) 431 (46.10) 280 (42.55) Education, n(%) <.001 Below highschool 3732 (91.83) 700 (87.83) 803 (94.36) 769 (93.44) 858 (91.76) 602 (91.49) Highschool 304 (7.48) 91 (11.42) 41 (4.82) 50 (6.08) 70 (7.49) 52 (7.90) College 28 (0.69) 6 (0.75) 7 (0.82) 4 (0.49) 7 (0.75) 4 (0.61) Drink, n(%) 0.139 No 2461 (60.56) 475 (59.60) 543 (63.81) 478 (58.08) 558 (59.68) 407 (61.85) Yes 1603 (39.44) 322 (40.40) 308 (36.19) 345 (41.92) 377 (40.32) 251 (38.15) Smoke, n(%) 0.357 No 2493 (61.34) 471 (59.10) 541 (63.57) 495 (60.15) 583 (62.35) 403 (61.25) Yes 1571 (38.66) 326 (40.90) 310 (36.43) 328 (39.85) 352 (37.65) 255 (38.75) SES, n(%) <.001 Q1 1338 (32.92) 208 (26.10) 253 (29.73) 262 (31.83) 356 (38.07) 259 (39.36) Q2 1404 (34.55) 286 (35.88) 292 (34.31) 290 (35.24) 325 (34.76) 211 (32.07) Q3 1322 (32.53) 303 (38.02) 306 (35.96) 271 (32.93) 254 (27.17) 188 (28.57) BMI, Mean ± SD, kg/ m2 24.12 ± 3.49 23.23 ± 3.12 23.55 ± 3.28 23.96 ± 3.46 24.50 ± 3.43 25.57 ± 3.78 <.001 hsCRP, Mean ± SD, mmol/L 2.59 ± 7.47 2.33 ± 9.89 2.11 ± 5.39 2.22 ± 4.82 3.30 ± 9.21 2.95 ± 6.15 0.002 SBP, Mean ± SD, mmHg 130.86 ± 21.26 123.46 ± 16.58 126.22 ± 19.94 131.17 ± 20.45 135.78 ± 22.93 138.42 ± 22.17 <.001 DBP, Mean ± SD, mmHg 76.28 ± 12.13 73.29 ± 10.36 74.23 ± 11.69 76.60 ± 12.16 78.19 ± 12.97 79.41 ± 12.13 <.001 Height, Mean ± SD, m 1.58 ± 0.08 1.59 ± 0.08 1.58 ± 0.09 1.58 ± 0.08 1.58 ± 0.08 1.58 ± 0.08 0.001 Weight, Mean ± SD, kg 60.42 ± 10.63 58.99 ± 9.61 58.75 ± 10.28 60.00 ± 10.58 61.19 ± 10.69 63.74 ± 11.37 <.001 Glucose, Mean ± SD, mmol/L 6.20 ± 1.97 5.79 ± 1.14 5.89 ± 1.28 6.12 ± 1.69 6.44 ± 2.17 6.86 ± 3.05 <.001 HbA1C, Mean ± SD, % 5.29 ± 0.79 5.10 ± 0.45 5.17 ± 0.55 5.29 ± 0.78 5.34 ± 0.86 5.59 ± 1.15 <.001 HDL-C, Mean ± SD, mmol/L 1.30 ± 0.39 1.36 ± 0.39 1.34 ± 0.38 1.31 ± 0.42 1.27 ± 0.38 1.24 ± 0.38 <.001 TG, Mean ± SD, mmol/L 1.53 ± 1.32 1.29 ± 0.95 1.42 ± 1.18 1.53 ± 1.32 1.67 ± 1.54 1.74 ± 1.45 <.001 Status 1, n(%) <.001 MHNW 1278 (31.45) 355 (44.54) 330 (38.78) 260 (31.59) 223 (23.85) 110 (16.72) MHOO 579 (14.25) 128 (16.06) 135 (15.86) 119 (14.46) 115 (12.30) 82 (12.46) MUNW 942 (23.18) 179 (22.46) 193 (22.68) 205 (24.91) 235 (25.13) 130 (19.76) MUOO 1265 (31.13) 135 (16.94) 193 (22.68) 239 (29.04) 362 (38.72) 336 (51.06) Status 2, n(%) <.001 MHNW 1316 (32.38) 356 (44.67) 360 (42.30) 262 (31.83) 238 (25.45) 100 (15.20) MHOO 751 (18.48) 165 (20.70) 171 (20.09) 159 (19.32) 161 (17.22) 95 (14.44) MUNW 763 (18.77) 126 (15.81) 137 (16.10) 170 (20.66) 191 (20.43) 139 (21.12) MUOO 1234 (30.36) 150 (18.82) 183 (21.50) 232 (28.19) 345 (36.90) 324 (49.24) ELSA (n=3,468) Variables Total (n = 3468) Low-stable (n = 1112) Middle-growth (n = 1203) High-stable (n = 176) High-growth (n = 720) Very high-stable (n = 257) P Age, Mean ± SD 64.07 ± 8.02 60.43 ± 6.96 64.29 ± 7.59 66.34 ± 8.42 67.08 ± 7.69 68.73 ± 8.25 <.001 Gender, n(%) <.001 Female 1901 (54.82) 564 (50.72) 631 (52.45) 98 (55.68) 441 (61.25) 167 (64.98) Male 1567 (45.18) 548 (49.28) 572 (47.55) 78 (44.32) 279 (38.75) 90 (35.02) Education, n(%) <.001 Below highschool 882 (25.43) 204 (18.35) 294 (24.44) 53 (30.11) 228 (31.67) 103 (40.08) Highschool 1685 (48.59) 589 (52.97) 594 (49.38) 80 (45.45) 320 (44.44) 102 (39.69) College 636 (18.34) 263 (23.65) 222 (18.45) 26 (14.77) 95 (13.19) 30 (11.67) Other 265 (7.64) 56 (5.04) 93 (7.73) 17 (9.66) 77 (10.69) 22 (8.56) Drink, n(%) <.001 No 290 (8.36) 55 (4.95) 83 (6.90) 20 (11.36) 88 (12.22) 44 (17.12) Yes 3178 (91.64) 1057 (95.05) 1120 (93.10) 156 (88.64) 632 (87.78) 213 (82.88) Smoke, n(%) <.001 No 1441 (41.55) 503 (45.23) 501 (41.65) 80 (45.45) 288 (40.00) 69 (26.85) Yes 2027 (58.45) 609 (54.77) 702 (58.35) 96 (54.55) 432 (60.00) 188 (73.15) SES, n(%) <.001 Q1 1020 (29.41) 238 (21.40) 344 (28.60) 45 (25.57) 254 (35.28) 139 (54.09) Q2 1143 (32.96) 378 (33.99) 392 (32.59) 72 (40.91) 228 (31.67) 73 (28.40) Q3 1248 (35.99) 470 (42.27) 447 (37.16) 57 (32.39) 230 (31.94) 44 (17.12) BMI, Mean ± SD, kg/ m2 27.99 ± 4.67 27.06 ± 4.08 27.84 ± 4.61 28.56 ± 5.11 28.91 ± 5.08 29.76 ± 4.85 hsCRP, Mean ± SD, mmol/L 3.35 ± 6.20 2.64 ± 5.22 3.33 ± 6.23 3.50 ± 6.75 3.63 ± 5.52 5.56 ± 9.76 <.001 SBP, Mean ± SD, mmHg 132.20 ± 16.59 128.26 ± 15.26 133.17 ± 16.67 133.77 ± 17.43 134.68 ± 16.63 136.74 ± 17.72 <.001 DBP, Mean ± SD, mmHg 75.09 ± 10.19 75.40 ± 9.69 75.82 ± 10.25 74.17 ± 10.58 74.29 ± 10.32 73.22 ± 11.01 <.001 Height, Mean ± SD, m 1.66 ± 0.09 1.68 ± 0.09 1.67 ± 0.09 1.66 ± 0.09 1.65 ± 0.09 1.64 ± 0.08 <.001 Weight, Mean ± SD, kg 77.69 ± 14.71 76.37 ± 13.67 77.52 ± 14.63 79.06 ± 17.25 78.98 ± 15.48 79.72 ± 14.82 <.001 Glucose, Mean ± SD, mmol/L 4.88 ± 0.81 4.76 ± 0.50 4.86 ± 0.73 4.95 ± 1.01 5.02 ± 1.08 5.20 ± 1.26 <.001 HbA1C, Mean ± SD, % 5.83 ± 0.62 5.67 ± 0.42 5.77 ± 0.55 5.97 ± 0.77 5.98 ± 0.71 6.25 ± 0.93 <.001 HDL-C, Mean ± SD, mmol/L 1.57 ± 0.41 1.60 ± 0.40 1.59 ± 0.42 1.54 ± 0.41 1.53 ± 0.41 1.49 ± 0.40 <.001 TG, Mean ± SD, mmol/L 1.70 ± 0.97 1.58 ± 0.91 1.69 ± 0.99 1.92 ± 1.20 1.80 ± 0.95 1.82 ± 1.03 <.001 Status 1, n(%) MHNW 586 (16.90) 302 (27.16) 198 (16.46) 17 (9.66) 56 (7.78) 13 (5.06) <.001 MHOO 946 (27.28) 448 (40.29) 343 (28.51) 20 (11.36) 115 (15.97) 20 (7.78) MUNW 349 (10.06) 66 (5.94) 130 (10.81) 28 (15.91) 101 (14.03) 24 (9.34) MUOO 1587 (45.76) 296 (26.62) 532 (44.22) 111 (63.07) 448 (62.22) 200 (77.82) Status 2, n(%) <.001 MHNW 637 (18.37) 311 (27.97) 208 (17.29) 21 (11.93) 76 (10.56) 21 (8.17) MHOO 1088 (31.37) 488 (43.88) 393 (32.67) 36 (20.45) 139 (19.31) 32 (12.45) MUNW 320 (9.23) 60 (5.40) 121 (10.06) 22 (12.50) 91 (12.64) 26 (10.12) MUOO 1423 (41.03) 253 (22.75) 481 (39.98) 97 (55.11) 414 (57.50) 178 (69.26) HRS (n=3,745) Variables Total (n = 3745) Very low-stable (n = 635) Low-growth (n = 892) Middle-growth (n = 974) High-growth (n = 902) Very high-growth (n = 342) P Age, Mean ± SD 63.96 ± 9.49 59.87 ± 8.07 61.76 ± 9.01 64.64 ± 9.38 67.07 ± 9.57 67.12 ± 9.06 <.001 Gender, n(%) <.001 Female 2258 (60.29) 326 (51.34) 491 (55.04) 605 (62.11) 591 (65.52) 245 (71.64) Male 1487 (39.71) 309 (48.66) 401 (44.96) 369 (37.89) 311 (34.48) 97 (28.36) Education, n(%) <.001 Below highschool 566 (15.11) 69 (10.87) 130 (14.57) 130 (13.35) 147 (16.30) 90 (26.32) Highschool 2190 (58.48) 339 (53.39) 476 (53.36) 597 (61.29) 578 (64.08) 200 (58.48) College 989 (26.41) 227 (35.75) 286 (32.06) 247 (25.36) 177 (19.62) 52 (15.20) Drink, n(%) <.001 No 1391 (37.14) 180 (28.35) 277 (31.05) 350 (35.93) 412 (45.68) 172 (50.29) Yes 2354 (62.86) 455 (71.65) 615 (68.95) 624 (64.07) 490 (54.32) 170 (49.71) Smoke, n(%) <.001 No 1757 (46.92) 341 (53.70) 435 (48.77) 446 (45.79) 401 (44.46) 134 (39.18) Yes 1988 (53.08) 294 (46.30) 457 (51.23) 528 (54.21) 501 (55.54) 208 (60.82) SES, n(%) <.001 Q1 1188 (31.72) 167 (26.30) 264 (29.60) 279 (28.64) 311 (34.48) 167 (48.83) Q2 1254 (33.48) 208 (32.76) 295 (33.07) 322 (33.06) 331 (36.70) 98 (28.65) Q3 1303 (34.79) 260 (40.94) 333 (37.33) 373 (38.30) 260 (28.82) 77 (22.51) BMI, Mean ± SD, kg/ m2 29.96 ± 5.75 28.14 ± 5.17 29.31 ± 5.26 30.28 ± 5.45 30.89 ± 6.14 31.63 ± 6.62 <.001 hsCRP, Mean ± SD, mmol/L 3.13 ± 5.83 2.13 ± 3.67 2.66 ± 3.80 3.13 ± 4.26 3.81 ± 8.04 4.47 ± 9.10 <.001 SBP, Mean ± SD, mmHg 129.13 ± 20.06 123.96 ± 18.62 128.18 ± 20.40 130.99 ± 19.76 131.06 ± 20.36 130.82 ± 20.01 <.001 DBP, Mean ± SD, mmHg 80.19 ± 12.00 79.19 ± 11.40 80.51 ± 12.50 81.36 ± 11.75 79.85 ± 12.09 78.74 ± 11.94 <.001 Height, Mean ± SD, m 1.66 ± 0.10 1.68 ± 0.10 1.67 ± 0.10 1.65 ± 0.10 1.64 ± 0.10 1.63 ± 0.10 <.001 Weight, Mean ± SD, kg 82.22 ± 17.43 79.33 ± 16.31 81.78 ± 16.96 82.72 ± 17.06 83.54 ± 18.32 83.86 ± 18.63 <.001 HbA1C, Mean ± SD, % 6.05 ± 1.02 5.67 ± 0.47 5.87 ± 0.92 6.08 ± 1.16 6.29 ± 1.07 6.47 ± 1.12 <.001 HDL-C, Mean ± SD, mmol/L 1.50 ± 0.39 1.52 ± 0.40 1.53 ± 0.41 1.50 ± 0.39 1.46 ± 0.37 1.46 ± 0.36 <.001 Status 1, n(%) <.001 MHNW 485 (12.95) 157 (24.72) 144 (16.14) 109 (11.19) 54 (5.99) 21 (6.14) MHOO 1271 (33.94) 359 (56.54) 413 (46.30) 316 (32.44) 152 (16.85) 31 (9.06) MUNW 216 (5.77) 21 (3.31) 32 (3.59) 47 (4.83) 84 (9.31) 32 (9.36) MUOO 1773 (47.34) 98 (15.43) 303 (33.97) 502 (51.54) 612 (67.85) 258 (75.44) Status 2, n(%) <.001 MHNW 503 (13.43) 162 (25.51) 147 (16.48) 105 (10.78) 72 (7.98) 17 (4.97) MHOO 1457 (38.91) 391 (61.57) 487 (54.60) 342 (35.11) 190 (21.06) 47 (13.74) MUNW 216 (5.77) 18 (2.83) 25 (2.80) 63 (6.47) 73 (8.09) 37 (10.82) MUOO 1569 (41.90) 64 (10.08) 233 (26.12) 464 (47.64) 567 (62.86) 241 (70.47) Table-2 CHARLS Variables Model 1 Model 2 OR (95%CI) P OR (95%CI) P Low-Risk Very low-stable MHNW 1.00 (Reference) 1.00 (Reference) MHOO 0.69 (0.54 ~ 0.87) 0.002 0.66 (0.52 ~ 0.84) <.001 MUNW 0.67 (0.54 ~ 0.82) <.001 0.65 (0.53 ~ 0.80) <.001 MUOO 0.31 (0.25 ~ 0.39) <.001 0.30 (0.24 ~ 0.38) <.001 Low-stable MHNW 1.00 (Reference) 1.00 (Reference) MHOO 0.81 (0.64 ~ 1.03) 0.081 0.82 (0.65 ~ 1.04) 0.101 MUNW 0.76 (0.62 ~ 0.93) 0.008 0.75 (0.61 ~ 0.93) 0.007 MUOO 0.50 (0.41 ~ 0.61) <.001 0.49 (0.40 ~ 0.60) <.001 Medium-Risk Low-growth MHNW 1.00 (Reference) 1.00 (Reference) MHOO 1.02 (0.80 ~ 1.30) 0.872 1.03 (0.81 ~ 1.32) 0.814 MUNW 1.09 (0.88 ~ 1.34) 0.428 1.09 (0.89 ~ 1.35) 0.399 MUOO 0.92 (0.75 ~ 1.12) 0.389 0.93 (0.76 ~ 1.13) 0.459 Middle-stable MHNW 1.00 (Reference) 1.00 (Reference) MHOO 1.25 (0.97 ~ 1.60) 0.088 1.26 (0.98 ~ 1.62) 0.075 MUNW 1.51 (1.22 ~ 1.85) <.001 1.52 (1.24 ~ 1.88) <.001 MUOO 1.93 (1.59 ~ 2.34) <.001 1.97 (1.62 ~ 2.38) <.001 High-Risk High-growth MHNW 1.00 (Reference) 1.00 (Reference) MHOO 1.90 (1.40 ~ 2.59) <.001 1.93 (1.42 ~ 2.64) <.001 MUNW 1.57 (1.20 ~ 2.06) 0.001 1.59 (1.21 ~ 2.09) <.001 MUOO 3.90 (3.08 ~ 4.93) <.001 4.03 (3.18 ~ 5.11) <.001 OR: Odds Ratio, CI: Confidence Interval Model1: Adjust: age, gender Model2: Adjust: age, gender, education, drink, smoke, SES，hsCRP ELSA Variables Model 1 Model 2 OR (95%CI) P OR (95%CI) P Low-Risk Low-stable MHNW 1.00 (Reference) 1.00 (Reference) MHOO 0.83 (0.66 ~ 1.03) 0.084 0.86 (0.69 ~ 1.07) 0.176 MUNW 0.26 (0.19 ~ 0.36) <.001 0.28 (0.20 ~ 0.39) <.001 MUOO 0.23 (0.19 ~ 0.29) <.001 0.26 (0.20 ~ 0.32) <.001 Medium-Risk Middle-growth MHNW 1.00 (Reference) 1.00 (Reference) MHOO 1.09 (0.88 ~ 1.35) 0.441 1.09 (0.88 ~ 1.36) 0.428 MUNW 1.11 (0.84 ~ 1.47) 0.458 1.13 (0.85 ~ 1.49) 0.407 MUOO 0.94 (0.77 ~ 1.15) 0.552 0.96 (0.78 ~ 1.18) 0.682 High-stable MHNW 1.00 (Reference) 1.00 (Reference) MHOO 0.72 (0.38 ~ 1.39) 0.333 0.71 (0.37 ~ 1.37) 0.308 MUNW 2.66 (1.42 ~ 4.96) 0.002 2.72 (1.45 ~ 5.10) 0.002 MUOO 2.35 (1.39 ~ 3.98) 0.002 2.37 (1.39 ~ 4.05) 0.002 High-Risk High-growth MHNW 1.00 (Reference) 1.00 (Reference) MHOO 1.36 (0.97 ~ 1.91) 0.078 1.35 (0.96 ~ 1.90) 0.087 MUNW 3.37 (2.34 ~ 4.87) <.001 3.29 (2.27 ~ 4.76) <.001 MUOO 3.48 (2.57 ~ 4.70) <.001 3.39 (2.49 ~ 4.61) <.001 Very high-stable MHNW 1.00 (Reference) 1.00 (Reference) MHOO 1.00 (0.49 ~ 2.02) 0.992 0.95 (0.46 ~ 1.93) 0.878 MUNW 2.61 (1.30 ~ 5.25) 0.007 2.19 (1.08 ~ 4.45) 0.03 MUOO 5.74 (3.23 ~ 10.21) <.001 4.73 (2.64 ~ 8.50) <.001 OR: Odds Ratio, CI: Confidence Interval Model1: Adjust: age, gender Model2: Adjust: age, gender, education, drink, smoke, SES，hsCRP HRS Variables Model 1 Model 2 OR (95%CI) P OR (95%CI) P Low-Risk Very low-stable MHNW 1.00 (Reference) 1.00 (Reference) MHOO 0.72 (0.57 ~ 0.90) 0.005 0.79 (0.62 ~ 1.01) 0.057 MUNW 0.29 (0.18 ~ 0.48) <.001 0.33 (0.20 ~ 0.54) <.001 MUOO 0.12 (0.09 ~ 0.16) <.001 0.14 (0.10 ~ 0.19) <.001 Low-growth MHNW 1.00 (Reference) 1.00 (Reference) MHOO 1.07 (0.85 ~ 1.34) 0.582 1.13 (0.90 ~ 1.43) 0.291 MUNW 0.48 (0.31 ~ 0.74) <.001 0.52 (0.34 ~ 0.81) 0.003 MUOO 0.50 (0.40 ~ 0.63) <.001 0.55 (0.43 ~ 0.70) <.001 Medium-Risk Middle-growth MHNW 1.00 (Reference) 1.00 (Reference) MHOO 1.17 (0.91 ~ 1.51) 0.208 1.20 (0.94 ~ 1.55) 0.148 MUNW 0.90 (0.61 ~ 1.33) 0.597 0.92 (0.62 ~ 1.36) 0.675 MUOO 1.35 (1.06 ~ 1.71) 0.014 1.43 (1.12 ~ 1.82) 0.005 High-Risk High-growth MHNW 1.00 (Reference) 1.00 (Reference) MHOO 1.19 (0.85 ~ 1.66) 0.302 1.12 (0.80 ~ 1.57) 0.506 MUNW 4.21 (2.82 ~ 6.28) <.001 3.92 (2.61 ~ 5.87) <.001 MUOO 4.12 (3.05 ~ 5.58) <.001 3.66 (2.69 ~ 4.98) <.001 Very high-growth MHNW 1.00 (Reference) 1.00 (Reference) MHOO 0.61 (0.35 ~ 1.07) 0.087 0.54 (0.31 ~ 0.96) 0.035 MUNW 3.30 (1.84 ~ 5.91) <.001 2.65 (1.47 ~ 4.80) 0.001 MUOO 3.83 (2.42 ~ 6.06) <.001 3.01 (1.88 ~ 4.80) <.001 OR: Odds Ratio, CI: Confidence Interval Model1: Adjust: age, gender Model2: Adjust: age, gender, education, drink, smoke, SES，hsCRP Additional Declarations There is NO Competing Interest. Supplementary Files checklist.docx checklist SupplementaryMaterial.pdf Supplementary Material Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6138718","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":428888639,"identity":"743382c1-8518-4ae2-b636-3264da1440ec","order_by":0,"name":"Lei Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIie3RPQrCQBCG4U8CsVncdm08w9hLcpVIwMoiB1DJAaJ1xFPYW0wIaCOxDSRFKmvtLCyMf3U2neC+zTbzwA4DmEw/WM9Ch0EjgIUmsa16GDRpQ/AkSNuQriC+BCdnVSwZ190cchM2fUxQElPhr8vM68TnA1TJzSQVNaF8SpbgPUh5GuROWVsCYudDZjrEDpKIfK9fHuulmIXKG4iU6ba63R23V0TD6sKLgYwbyLdxqN4H0r0O4EK93oW2MJlMpv/pAQVjRCg4P02OAAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Liu","suffix":""},{"id":428888640,"identity":"b0a830c9-8b87-42a2-9d91-ebdef56d70f1","order_by":1,"name":"Heng Wang","email":"","orcid":"","institution":"Department of Ophthalmology, First Hospital of China Medical University, Shenyang, China","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Wang","suffix":""},{"id":428888641,"identity":"b48edc90-1896-4747-be21-b359e1fa7b15","order_by":2,"name":"Jun Li","email":"","orcid":"","institution":"Department of Ophthalmology, First Hospital of China Medical University, Shenyang, China","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Li","suffix":""},{"id":428888642,"identity":"306e794c-514f-4c3f-886b-15a55a0e5978","order_by":3,"name":"Shunming Liu","email":"","orcid":"","institution":"Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Shunming","middleName":"","lastName":"Liu","suffix":""},{"id":428888643,"identity":"c95b55ed-de4a-4966-91e7-d8d4f2907024","order_by":4,"name":"Ching-Yu Cheng","email":"","orcid":"","institution":"Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore","correspondingAuthor":false,"prefix":"","firstName":"Ching-Yu","middleName":"","lastName":"Cheng","suffix":""},{"id":428888644,"identity":"72573b13-73cf-49b4-8106-5aaa28415685","order_by":5,"name":"Moluan Zhang","email":"","orcid":"","institution":"School of Medicine, Gifu University, Gifu City, Japan","correspondingAuthor":false,"prefix":"","firstName":"Moluan","middleName":"","lastName":"Zhang","suffix":""},{"id":428888645,"identity":"2f3857c8-636f-4690-8e1b-e814e3c82137","order_by":6,"name":"Jinghua Jiao","email":"","orcid":"","institution":"Department of Anesthesiology, Guangzhou Eighth People's Hospital, Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinghua","middleName":"","lastName":"Jiao","suffix":""},{"id":428888646,"identity":"a4ab69a6-414e-47b9-9574-f462add56344","order_by":7,"name":"Han Zhang","email":"","orcid":"","institution":"Department of Ophthalmology, First Hospital of China Medical University, Shenyang, China","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-03-02 10:10:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6138718/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6138718/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82510135,"identity":"fc42a82c-8d0a-4f59-b32b-9803149cc9b1","added_by":"auto","created_at":"2025-05-12 10:33:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":460211,"visible":true,"origin":"","legend":"\u003cp\u003eMultimorbidity Trajectories of CHARLS, ELSA, HRS\u003c/p\u003e\n\u003cp\u003eThe multimorbidity trajectories of CHARLS Wave 1 - Wave 4, ELSA Wave 4 - Wave 8, and HRS Wave 10 - Wave 14 were determined using the GBTM method in each cohort. The choice of trajectory model was based on the following criteria: (1) Observed improvement in the trajectory fitting parameters (2) Each trajectory group includes at least 5% of the sample size in the cohort (3) The average posterior probability (AvePP) is higher than 70% (4) Visual confirmation of different trajectories. The solid line represents the trajectory fitting line, and the dashed line represents the actual average of each group.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6138718/v1/84ed77b843d648407a8f35f5.jpg"},{"id":82510138,"identity":"03214765-31e8-4fc1-8338-0dd6212e6156","added_by":"auto","created_at":"2025-05-12 10:33:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":435013,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of Metabolic Obesity Transition Phenotype and Multimorbidity Trajectory in CHARLS, ELSA, HRS\u003c/p\u003e\n\u003cp\u003eMHNW: Metabolically Healthy Normal Weight, MHOO: Metabolically Healthy Obesity/Overweight, MUNW: Metabolically Unhealthy Normal Weight, MUOO: Metabolically Unhealthy Obesity/Overweight. The reference group is MHNW, and the model is adjusted according to baseline age, gender, education level, smoking, drinking, socioeconomic status (SES), and hsCRP levels. The numbers represent the odds ratios of metabolic obesity progression phenotype with multimorbidity trajectories, and the bold asterisks represent associations that remain significant after correction for multiple testing.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6138718/v1/bdbeae011cf842e645bc958d.jpg"},{"id":82512263,"identity":"c116741f-f44b-48a8-9a59-54ddf4433813","added_by":"auto","created_at":"2025-05-12 10:57:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2365840,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6138718/v1/4b023af6-9eed-4a38-bfe6-a963549a4edc.pdf"},{"id":82511080,"identity":"0e7c50d0-94b9-4d54-93d5-843514a6e45a","added_by":"auto","created_at":"2025-05-12 10:41:01","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":37679,"visible":true,"origin":"","legend":"checklist","description":"","filename":"checklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-6138718/v1/8fa0ab01d822ca51e6836270.docx"},{"id":82509827,"identity":"27d3a2ed-0a57-4fd8-91a1-608221b1b9b3","added_by":"auto","created_at":"2025-05-12 10:25:01","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":927107,"visible":true,"origin":"","legend":"Supplementary Material","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6138718/v1/604e3c0b0bf34de52565dbe0.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Metabolic Obesity Phenotypes and Their Transitions as Determinants of Multimorbidity Trajectories: Evidence from Global Aging Cohorts","fulltext":[{"header":"Research in Context","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEvidence before this study\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe searched PubMed and Web of Science for articles published in English using the keywords “metabolic obesity phenotypes,” “multimorbidity trajectories,” “obesity” “metabolic heterogeneity” “BMI,” and “longitudinal cohorts” from inception up to October 15, 2024. Previous studies have established that metabolic obesity phenotypes are important predictors of multimorbidity. However, limited research has explored the dynamic relationships between metabolic obesity phenotypes and multimorbidity trajectories, particularly incorporating transitions between phenotypes over time. While a few studies examined associations with single metabolic components, the nonlinear effects of these components on multimorbidity risks remain underexplored. Furthermore, the modifying roles of subgroups based on age, gender, and SES in the association between metabolic health and multimorbidity trajectories are not well understood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAdded value of this study\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study leverages longitudinal data from three national aging cohorts (CHARLS, ELSA, HRS) to provide robust evidence of the associations between metabolic obesity phenotypes, their transitions, and multimorbidity trajectories. It demonstrates that metabolically unhealthy phenotypes are strongly linked to high-risk multimorbidity trajectories, with stable metabolically unhealthy obesity (MUOO) showing the greatest risk. Subgroup analyses reveal differential risks by age, gender, and SES, and nonlinear analyses identify critical thresholds for components such as HbA1C and BMI. This study emphasizes that persistent metabolic unhealth significantly elevates the risk of unfavorable multimorbidity outcomes. By validating findings using metabolic syndrome criteria, the robustness of the results is enhanced.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eImplications of all the available evidence\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings underscore the critical need for proactive metabolic health management, especially targeting individuals with metabolically unhealthy phenotypes to prevent high-risk multimorbidity trajectories. These results also highlight the importance of tailored interventions addressing subgroup-specific risks, such as for younger individuals, males, and those with varying SES levels. Furthermore, the nonlinear relationships observed for specific metabolic components suggest that intervention strategies might benefit from targeting specific thresholds to optimize health outcomes. This research supports integrating metabolic health interventions into broader multimorbidity management strategies and advocates for future studies to refine risk prediction and explore causal pathways in diverse populations.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eMultimorbidity (i.e. a person suffering from at least two chronic diseases simultaneously) has become a significant public health challenge in aging societies, affecting approximately one-third of the global adult population, with the situation particularly pronounced in low- and middle-income countries (LMIC)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Although existing cross-sectional studies provide information on the prevalence and clustering of multimorbidity\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, there is a lack of exploration of its dynamic evolution. By repeatedly measuring individual multimorbidity statuses through longitudinal cohort studies, a more comprehensive construction of its progression trajectories can be made, revealing the patterns of diseases changing over time. Studies have shown that multimorbidity trajectory patterns are influenced by various factors, including race, education level, and regional poverty levels\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Longitudinal analysis can not only clarify the roles of these influencing factors but also help to identify high-risk groups early, providing a scientific basis for the development of precise prevention strategies. This approach offers a critical foundation for reducing public health challenges, enhancing the efficient allocation of healthcare resources, and guiding the development of evidence-based policies.\u003c/p\u003e \u003cp\u003eObesity is a global epidemic. In the 21st century, many LMIC have also begun to see a trend of rising obesity rates, in addition to high-income countries\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The pathogenic role of adipocyte hypertrophy (a response to excess calorie intake) on the metabolic system has been well established\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, and these metabolic disorders are closely related to the occurrence of multimorbidity\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, epidemiological surveys show that about one-third of overweight and obese individuals are metabolically healthy, and these individuals are classified as MHOO. Others are classified as Metabolically Unhealthy Overweight/Obesity (MUOO). This heterogeneity is also observed in individuals with normal weight, who are classified as Metabolically Healthy Normal Weight (MHNW) and Metabolically Unhealthy Normal Weight (MUNW)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The impact of obesity and metabolism on chronic diseases has been well described\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. At present, the association between the heterogeneity of metabolic obesity phenotypes and the progression of multimorbidity has not been fully studied. Considering the current trends of aging and increasing obesity prevalence, this research gap is particularly concerning.\u003c/p\u003e \u003cp\u003eIn this study, we use global aging data from the China Health and Retirement Longitudinal Study (CHARLS)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, the Health and Retirement Study (HRS)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and the English Longitudinal Study of Ageing (ELSA)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e to investigate the relationship between metabolic heterogeneity of obesity and multimorbidity trajectories. In addition, we propose that transitioning from a healthy metabolic state to an unhealthy metabolic state, compared to maintaining a stable metabolic state, will further accelerate the progression of multimorbidity. Our study aims to help better understand the association between metabolic heterogeneity of obesity and the development of multimorbidity trajectories, and provide insights and clinical evidence for public health systems and governments to control the increasingly severe development of multimorbidity in the elderly population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThis study uses data from three similarly designed large-scale prospective cohort studies (CHARLS, ELSA, and HRS). Detailed design of the cohorts has been published in other journals and is briefly described in the supplementary material. To make the cohorts comparable, we chose data from the same time period. For detailed follow-up time points, see the \u003cb\u003eSupplementary Methods\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe screening process of the study can be found in the supplementary material. Individuals who participated in blood tests and questionnaires at baseline were included in the study. Those under the age of 45 or unable to obtain sufficient disease diagnostic data were excluded. Further data missing that led to inability to determine the baseline and progression of metabolic obesity status and BMI less than 18.5 kg/m\u003csup\u003e2\u003c/sup\u003e were also excluded, as well as individuals who lacked any covariates. According to the inclusion and exclusion criteria, 4,064 individuals from CHARLS, 3,468 from ELSA, and 3,745 from HRS were included in the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition for metabolic heterogeneity of obesity\u003c/h3\u003e\n\u003cp\u003eIn CHARLS and ELSA, metabolic status was assessed through four metabolic criteria\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, with individuals meeting two or more criteria defined as metabolically unhealthy: (1) elevated blood pressure: systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;85 mmHg or use of anti-hypertensive drugs; (2) poor glucose control: fasting blood glucose (FBG)\u0026thinsp;\u0026ge;\u0026thinsp;5.6 mmol/L or hemoglobin A1c (HbA\u003csub\u003e1\u003c/sub\u003ec)\u0026thinsp;\u0026ge;\u0026thinsp;6.0% or use of hypoglycemic drugs; (3) elevated triglycerides (TG): TG\u0026thinsp;\u0026ge;\u0026thinsp;1.7 mmol/L or use of lipid-lowering drugs; (4) reduced high-density lipoprotein cholesterol (HDL-C): HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.03 mmol/L in men or \u0026lt;\u0026thinsp;1.29 mmol/L in women or use of lipid-lowering drugs. In HRS, the reliance on dried blood spot collection instead of venous blood sampling results in the unavailability of certain biomarkers, including fasting blood glucose and triglycerides, which limits the comprehensiveness of metabolic assessments. We defined individuals meeting two or more of the three criteria as metabolically unhealthy: (1) elevated blood pressure: SBP\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg or DBP\u0026thinsp;\u0026ge;\u0026thinsp;85 mmHg or use of antihypertensive drugs; (2) poor glucose control: hemoglobin A1c (HbA\u003csub\u003e1\u003c/sub\u003ec)\u0026thinsp;\u0026ge;\u0026thinsp;6.0% or use of hypoglycemic drugs; (3) reduced HDL-C: HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.03 mmol/L in men or \u0026lt;\u0026thinsp;1.29 mmol/L in women or use of lipid-lowering drugs. According to the definitions of obesity in different countries, in CHARLS, BMI greater than 24 kg/m\u003csup\u003e2\u003c/sup\u003e was defined as obesity, while in ELSA and HRS, BMI greater than 25 kg/m\u003csup\u003e2\u003c/sup\u003e was defined as obesity\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Combining individuals' metabolic and obesity statuses, individuals were divided into four metabolic obesity phenotypes: MHNW, MUNW, MHOO, and MUOO\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Further, we determined the progression of individuals' metabolic obesity phenotypes, thereby determining changes in individuals' metabolic obesity phenotypes, such as MHNW-MUNW, MHOO-MUOO, and so on.\u003c/p\u003e\n\u003ch3\u003eDefinition for multimorbidity\u003c/h3\u003e\n\u003cp\u003eInformation related to multimorbidity was extracted from questionnaires completed by individuals during the follow-up, including whether they were diagnosed by a doctor with hypertension, dyslipidemia, diabetes, cancer, chronic lung disease, liver disease, heart disease, stroke, chronic kidney disease, digestive system disease, asthma, arthritis or rheumatism, Parkinson's, mental and emotional problems, memory-related diseases, etc. The questionnaires of CHARLS, ELSA, and HRS varied in multiple follow-ups, and the specific questionnaires can be found in the supplementary material (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). We calculated the number of multimorbidity at each follow-up as the individual's current multimorbidity status. multimorbidity trajectories were identified using Group-Based Trajectory Modeling (GBTM), For detailed information on the introduction and analysis of the GBTM model, see the \u003cb\u003eSupplementary Methods\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The results showed that the trajectory grouping results of all cohorts support the assumption of 5 trajectories. Furthermore, we assigned labels and grouped them by the starting number of multimorbidity and growth patterns of different trajectories. At the beginning of the follow-up, the number of multimorbidity less than 1 was deemed \"low\", between 1 and 2 was defined as \"middl\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ee\u003c/span\u003e\", and above 2 was defined as \"high\". The growth of the number of multimorbidity within the complete follow-up cycle is less than 1 defined as \"stable\", and greater than 1 defined as \"growth\".\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eDemographic statistics include individuals' age and gender at baseline and education level. To coordinate the differences brought by different countries' education systems, education level is divided into below high school, high school, college and others. Other covariates include alcohol consumption (never/ever), smoking (never/ever), hsCRP level (mg/L), and socioeconomic status (SES). Considering the influence of SES on various chronic diseases and mortality, we used the total family wealth after three divisions (Q1: lower, Q2: medium, Q3: higher) to represent individual SES, and the hsCRP value extracted from the baseline blood test to represent the individual's chronic inflammation level. All covariates were obtained at baseline. The description of the covariates can be found in the supplementary material (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eTo compare the results of the three cohorts, each stage of the analysis process was conducted separately on each dataset based on the same standards and procedures. Continuous variables are expressed as mean (standard deviation [SD]) or median (interquartile range [IQR]). Categorical variables are expressed as numbers (percentages). Missing data in CHARLS, ELSA, HRS Cohort was shown in Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e. Baseline features were compared using one-way ANOVA or Kruskal-Wallis Rank Sum Test for continuous variables, and chi-square test for categorical variables. We used a logistic regression model to analyze the association between baseline metabolic obesity phenotype and multimorbidity progression trajectory, calculating OR values and confidence intervals with MHNW as a reference. Model 1 adjusted for age and gender, while Model 2 adjusted for all covariates (age, gender, education level, smoking, drinking, SES, hsCRP level). Further, we used a similar method to analyze the association between transitions in metabolic obesity status between two metabolic obesity data collections and multimorbidity progression trajectory. Restricted Cubic Splines (RCS) curves were used to analyze the non-linear relationship between the continuous components of metabolism and obesity and the progression trajectory of multimorbidity. In addition, we conducted subgroup analyses to detect possible differences among different age, gender, and SES subgroups. To verify the robustness of our research results, we conducted sensitivity analyses. We adopted the International Diabetes Federation's definition of metabolic syndrome as metabolic unhealth and conducted the main analysis\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. All analyses were performed using R software (version 4.4.1) and Stata (version 18.0). As multiple tests were conducted, we used the Bonferroni method to adjust the p-values within the cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRole of funding\u003c/h2\u003e \u003cp\u003eThe study sponsor has no role in study design, data analysis and interpretation of data, the writing of manuscript, or the decision to submit the paper for publication.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cem\u003eEthical statement\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSince this study relies on secondary analysis of publicly available datasets, ethical approvals for the original surveys were obtained as follows: The HRS was authorized by the National Institute on Aging and the Social Security Administration (NIA U01AG009740). The CHARLS was approved by the Ethical Review Committee of Peking University (IRB00001052-11015). The ELSA was sanctioned by the National Research and Ethics Service Committee South Central-Berkshire. All participants in these studies provided written informed consent, as specified in the original survey documentation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eBaseline characteristics of the study population\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable-1 shows the descriptive statistics of each cohort divided according to trajectory grouping. At baseline, 11,277 eligible individuals were included, of which 4,064 were from CHARLS, 3,468 from ELSA, and 3,745 from HRS. In CHARLS, individuals with a high-risk multimorbidity trajectory are more likely to be female, older, and have lower economic status. The baseline characteristics of ELSA and HRS are relatively similar, with older individuals, females, those with lower education level, non-drinkers, smokers, and those with lower economic status more likely to have a high-risk multimorbidity trajectory. In all cohorts, individuals with higher BMI, hsCRP, SBP, HbA\u003csub\u003e1\u003c/sub\u003eC as well as TG at baseline, but lower HDL-C levels at baseline are more likely to have a high-risk multimorbidity trajectory. In the three cohorts, individuals with baseline statuses of MHNW and MHOO generally decrease in proportion as the multimorbidity trajectory level increases, while it is just the opposite for MUOO.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTrajectory analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe trajectory analysis results are shown in Figure-1.\u003c/p\u003e\n\u003cp\u003eWe observed different multimorbidity progression patterns in different cohorts. Based on their progression trends and the number of multimorbidity at baseline, we named each trajectory. At the same time, we observed similar progression patterns in different categories between cohorts. Based on their current multimorbidity burden and future expectations on individuals, we divided the trajectories into \"low risk\", \"medium risk\", \"high risk\" groups. In CHARLS, we observed the low-risk group: \"Very low-stable\", \"Low-stable\"; medium-risk group: \"Low-growth\", \"Middle-stable\"; high-risk group: \"High-growth\". In ELSA, we observed the low-risk group: \"Low-stable\"; medium-risk group: \"Middle-growth\", \"High-stable\"; high-risk group: \"High-growth\", \"Very high-stable\". In HRS, we observed: low-risk group: \"Very low-stable\", \"Low-growth\"; medium-risk group: \"Middle-growth\"; high-risk group: \" High-growth\", \"Very high-growth\". The detailed fitting parameters of the trajectory model can be found in the supplementary material (Supplementary Table S4).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe association between the metabolic obesity phenotype at baseline and the\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003emultimorbidity trajectory\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable-2 shows the logistic regression results between baseline metabolic obesity phenotype and multimorbidity trajectory level. Similar results were found in all cohorts, and the results remained stable after adjusting for all covariates. Compared with individuals with MHNW metabolic phenotype, other phenotypes have a lower risk of low-risk multimorbidity trajectory and a higher risk of high-risk trajectory. In all cohorts, the risk of individuals with MU group (MUOO+MUNW) showing a low-risk multimorbidity trajectory is significantly lower than those with the MH group (MHOO+MHNW), including CHARLS (MUNW, OR: 0.65 (0.53 ~ 0.80); MUOO, OR: 0.30 (0.24 ~ 0.38)); ELSA (MUNW, OR: 0.28 (0.20 ~ 0.39); MUOO, OR: 0.26 (0.20 ~ 0.32)); HRS (MUNW, OR: 0.33 (0.20 ~ 0.54); MUOO, OR: 0.14 (0.10 ~ 0.19)). No consistent results were found in the analysis of metabolic obesity phenotype and medium-risk multimorbidity trajectory. The risk of progression to high-risk trajectory in each cohort is basically consistent. The relative risk of MU is significantly higher than that of MH. The highest risk is mostly in the MUOO group. In the outcomes of the worst trajectory in each cohort, CHARLS (MUOO, OR: 4.03 (3.18 ~ 5.11)); ELSA (MUOO, OR: 4.73 (2.64 ~ 8.50)); HRS (MUOO, OR: 3.01 (1.88 ~ 4.80)). It should be noted that we have not detected any phenotype that has a protective effect relative to MHNW individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSubgroup Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn subgroup analysis (Supplementary Table S8-10), we analyzed different subgroups stratified by age, sex, and SES. After correction by the Bonferroni method, we found that the risk of the subgroup with baseline age ≤ 60 and males progressing to high-risk subgroup was higher. In the CHARLS cohort, the risk of progressing to a high-risk subgroup was higher in high SES status (Q3), and in ELSA and HRS, the risk of progressing to a high-risk subgroup was highest in medium SES status, followed by high SES status. We used total family wealth as a measure of SES, acknowledging that this approach might reflect differing economic and financial frameworks across countries.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAssociations of\u0026nbsp;\u003c/em\u003e\u003cem\u003emetabolic obesity phenotype\u003c/em\u003e\u003cem\u003e\u0026nbsp;transitions with multimorbidity trajectories\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe metabolic obesity phenotype of individuals progresses over time, and we have detected all 16 metabolic obesity model progression patterns in all cohorts, with individuals with a stable MUOO phenotype accounting for the highest proportion in all cohorts (Supplementary Figure S1-S3). We used logistic regression to detect the association between metabolic obesity progression phenotype and all trajectories, with stable MHNW as a reference, as shown in Figure-2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor individuals with stable metabolic unhealthiness (MU-MU), the likelihood of progressing to a low-risk trajectory is significantly lower than other progression phenotypes (CHARLS (MUOO-MUOO, OR: 0.24 (0.18 ~ 0.32)), ELSA (MUOO-MUOO, OR: 0.15 (0.11 ~ 0.20)), HRS (MUOO-MUOO, OR: 0.05 (0.03 ~ 0.07))). For CHARLS progressing to \"very low-stable\", ELSA progressing to \"low-stable\", and HRS progressing to \"very low-stable\" outcomes, individuals with stable obesity who have experienced metabolic unhealthiness have a significantly reduced likelihood. For outcomes progressing to medium-risk trajectories, we found no consistent and meaningful progression phenotype risk differences. For outcomes progressing to high-risk trajectories in all cohorts, any phenotype that has experienced metabolic unhealthy exposure significantly increases the risk, (CHARLS (MHOO-MUNW, OR: 6.82 (2.48 ~ 18.73)), ELSA (MUOO-MUNW, OR: 9.18 (3.54 ~ 23.80)), HRS (MUNW-MUOO, OR: 9.65 (3.30 ~ 28.24))). Among them, individuals who are continuously metabolically unhealthy have a higher risk of developing a high-risk trajectory than those who are temporarily unhealthy.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRCS and sensitivity analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the nonlinear association between various components constituting the metabolic obesity phenotype and high-risk trajectories, and the results after adjusting for all covariates are shown mainly in Supplementary Figure S4. The increase in HbA\u003csub\u003e1\u003c/sub\u003eC is nonlinearly related to the high-risk trajectory in all three cohorts, and the increase in BMI and the decrease in HDL‐C are significant in the entire cohort, but no consistent nonlinear trend is observed. The increase in triglycerides is nonlinearly related to the high-risk trajectory in CHARLS and ELSA. Interestingly, we observed that in HRS, the risk of HbA\u003csub\u003e1\u003c/sub\u003eC starts to decline when it is close to 8%, and the risk of triglycerides in ELSA starts to decline when it is close to 2 mmol/L, which may mean that their contributions to the overall risk decrease after reaching the inflection point. After using the standards of metabolic syndrome as the definition, we conducted the main analyses (Supplementary Table S5), and the results remained basically consistent.\u003c/p\u003e\n\n\n\n\n\n\n\n"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we used follow-up data from large-scale, prospective cohorts from three continents to systematically analyze the association between metabolic obesity phenotype and multimorbidity progression patterns. At baseline, compared with MHNW, all other phenotypes have a higher risk of rapid multimorbidity progression and a lower risk of slow multimorbidity progression. In the analysis of progression phenotypes, exposure to metabolic unhealthiness significantly increases the risk of rapid progression of individual multimorbidity, even if the metabolic unhealthiness is corrected; the risk of continuous exposure is even higher. Further subgroup analysis revealed that individuals with a younger baseline age and males exhibited a higher risk of progressing to high-risk multimorbidity trajectories. Additionally, the RCS curve analysis did not identify a consistent nonlinear relationship across all cohorts, except for HbA\u003csub\u003e1\u003c/sub\u003eC. Our study aims to help better understand the association between obesity metabolic heterogeneity and the progression of multimorbidity, and provide insights and clinical evidence for the public health system and government to control the increasingly severe development of multimorbidity in the elderly.\u003c/p\u003e\u003cp\u003eThe analysis at baseline shows that compared with metabolically healthy normal weight (MHNW), all other phenotypes have a higher risk of rapid multimorbidity progression. Among them, individuals with metabolic unhealthiness (MUOO+MUNW) have a significantly higher risk of rapid multimorbidity progression compared to metabolically healthy individuals (MHOO+MHNW). Of which, MUOO is the worst phenotype in metabolic obesity, and its risk of rapid multimorbidity progression is several times that of other phenotypes. For the risk of low-risk trajectories, the MHOO phenotype is basically no different from MHNW, suggesting that metabolic factors play a decisive role in it. Metabolic and obesity states change over time, and we found that about 35% of individuals will undergo such transitions. A single assessment of metabolic obesity phenotype is not enough to summarize the individual's situation in the entire follow-up cohort. Therefore, we further determined the phenotype change of individuals after four to five years of follow-up. In CHARLS, individuals who have experienced temporary metabolic unhealthiness have increased the risk of rapid multimorbidity progression by three to four times, and continuously unhealthy individuals can reach four to six times. In ELSA and HRS, this risk can be as high as nine times. This suggests that even short-term exposure to metabolic unhealthiness can significantly increase the risk of rapid progression of multimorbidity, and continuous exposure is the most dangerous group characteristic. It should be noted that the risk of rapid multimorbidity progression will increase for all phenotypes except MHNW, indicating that there is no \"Healthy Obesity Phenotype\"\u003csup\u003e18\u003c/sup\u003e. For people who can maintain metabolic health, keeping a normal weight is also very important. BMI is unable to provide information on fat distribution and the percentage of muscle mass, which is even more evident in the elderly population with sarcopenia, necessitating a reevaluation of the value of BMI as a measure of obesity in the elderly\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eExisting studies have found that metabolic unhealthiness and obesity increase the risk of multiple chronic diseases; we found that metabolic unhealthiness and obesity are associated with a faster progression of chronic diseases\u003csup\u003e20-26\u003c/sup\u003e. Considering the heterogeneity of patients with multimorbidity, the mechanisms and pathophysiology behind each disease that constitutes multimorbidity are very complex. Despite this, pathophysiological studies of multimorbidity have proposed possible \"common\" mechanisms, that is, there may be the same mechanisms behind different diseases in multimorbidity\u003csup\u003e1\u003c/sup\u003e. Obesity is associated with more than 250 genetic variants and multiple clinical diseases. Cohort studies have shown that obesity is closely related to 21 non-overlapping diseases of multiple organ systems\u003csup\u003e6\u003c/sup\u003e. A meta-analysis of prospective studies found that although MHOO faces a higher risk of cardiovascular events compared to MHNW, their risk is lower than that of MUOO and MUNW participants, suggesting the potential importance of metabolic and obesity dual factors in the development of multimorbidity\u003csup\u003e6\u003c/sup\u003e. Treating obesity and correcting metabolic unhealthiness may be a feasible measure to reduce multimorbidity at the population level.\u003c/p\u003e\u003cp\u003eGlobal free trade and urbanization-driven economic growth are major drivers of obesity trends\u003csup\u003e27\u003c/sup\u003e. Studies have shown that adjusting dietary structure to improve obesity and metabolic status is safe and effective for most people\u003csup\u003e5\u003c/sup\u003e. Countries such as China, UK, and US are reducing the incidence of obesity through their own efforts. Public propaganda, taxation of sugary drinks, and removal of trans fatty acids from processed foods have all been effective\u003csup\u003e28-30\u003c/sup\u003e. Using plant-based meat substitutes and insects as alternative protein sources can benefit both human and planetary health. Further research is needed to assess the impact of these foods on obesity compared to traditional diets\u003csup\u003e27,31\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor individuals who find it difficult to achieve weight loss goals through healthy lifestyles, we believe that restoring metabolic health should be a priority short-term goal. Transitioning from MUOO to MHOO can significantly reduce the risk of rapid multimorbidity progression, and both can be achieved through lifestyle interventions and drug treatments, which are obvious medical benefits for obese patients\u003csup\u003e5\u003c/sup\u003e. A unified metabolic health standard should be established as soon as possible to quickly identify metabolically unhealthy individuals among adults, especially MUNW, as they are exposed to a multimorbidity progression risk similar to MUOO individuals but lack attention. Through population-level metabolic health screening, we can identify individuals who may have rapid multimorbidity progression in the future and implement early interventions to delay multimorbidity progression.\u003c/p\u003e\u003cp\u003eThe present study has several strengths. First, we used large cohort studies from different continents and races to provide a global perspective for the study. Second, metabolic obesity progresses in different patterns in different cohorts, suggesting that different countries and races need specific health policies to prevent multimorbidity. Our study of phenotype transition during follow-up reveals the important role of metabolism in multimorbidity, and early identification and timely intervention of metabolically unhealthy individuals are very important.\u0026nbsp;\u003c/p\u003e\u003cp\u003eHowever, our study also has some limitations. First, the current definition of metabolic syndrome/unhealthiness is still not unified. We have chosen a highly recognized definition, which may cause bias when comparing our study with other similar studies. Second, we used logistic regression instead of the Cox proportional hazard model in our study, which may not reflect the advantages of cohort studies. Considering that the follow-up time is mainly in years, a large number of individuals will have exactly the same follow-up data time points, which may weaken the statistical power of the Cox model\u003csup\u003e32\u003c/sup\u003e, so we prefer to use logistic regression. Third, because metabolic damage requires multiple blood test results to confirm, only cohorts from China, the UK, and the US were included. The lack of cohorts from the southern hemisphere and low-income countries may affect the representativeness of our conclusions. Fourth, our study did not use imputed data for analysis. We believe that missing data for individuals in the included studies may not occur randomly (for example, individuals with multimorbidities are more likely to be absent from blood tests), which violates the assumptions of commonly used multiple imputation. This may weaken the representativeness of our study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that metabolically unhealthy individuals (MUOO, MUNW) are significantly more likely to follow high-risk multimorbidity trajectories across multiple cohorts, whereas metabolically healthy individuals exhibit a comparatively lower risk. Notably, prolonged exposure to metabolic unhealthiness markedly increases the likelihood of transitioning into a high-risk trajectory. These findings underscore the critical role of metabolic health in mitigating multimorbidity among middle-aged and older adults. They also highlight the need for public health strategies aimed at promoting and sustaining metabolic health. Future research should investigate the underlying mechanisms linking metabolic dysfunction to multimorbidity and evaluate effective interventions to reduce multimorbidity risk through metabolic health improvements.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHZ and LL conceptualised and designed the study. HW, JL and JHJ managed, analysed and verified the data. HW, SML and LL prepared the first draft. HW, CYC, JL and LL interpreted the data, and HW, JHJ, HZ, and LL were responsible for editing and proofreading the manuscript. All authors contributed to the critical revision of the manuscript and read and approved the final version of the manuscript. All authors had full access to all the data in the study and accepted responsibility for the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData for this study were obtained from several major cohort studies, through the CHARLS website (https://charls.pku.edu.cn/), ELSA website (https://www.elsa-project.ac.uk/), and HRS website (https://hrs.isr.umich.edu/data-products).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest regarding this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSkou ST, Mair FS, Fortin M, et al. Multimorbidity. \u003cem\u003eNat Rev Dis Primers\u003c/em\u003e 2022; \u003cstrong\u003e8\u003c/strong\u003e(1): 48.\u003c/li\u003e\n\u003cli\u003eCezard G, McHale CT, Sullivan F, Bowles JKF, Keenan K. Studying trajectories of multimorbidity: a systematic scoping review of longitudinal approaches and evidence. \u003cem\u003eBMJ Open\u003c/em\u003e 2021; \u003cstrong\u003e11\u003c/strong\u003e(11): e048485.\u003c/li\u003e\n\u003cli\u003eBarnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. \u003cem\u003eLancet\u003c/em\u003e 2012; \u003cstrong\u003e380\u003c/strong\u003e(9836): 37-43.\u003c/li\u003e\n\u003cli\u003eTrends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19\u0026middot;2 million participants. \u003cem\u003eLancet\u003c/em\u003e 2016; \u003cstrong\u003e387\u003c/strong\u003e(10026): 1377-96.\u003c/li\u003e\n\u003cli\u003eStefan N, H\u0026auml;ring HU, Hu FB, Schulze MB. Metabolically healthy obesity: epidemiology, mechanisms, and clinical implications. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e 2013; \u003cstrong\u003e1\u003c/strong\u003e(2): 152-62.\u003c/li\u003e\n\u003cli\u003eKivim\u0026auml;ki M, Strandberg T, Pentti J, et al. Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e 2022; \u003cstrong\u003e10\u003c/strong\u003e(4): 253-63.\u003c/li\u003e\n\u003cli\u003eHe D, Qiu Y, Yan M, et al. Associations of metabolic heterogeneity of obesity with frailty progression: Results from two prospective cohorts. \u003cem\u003eJ Cachexia Sarcopenia Muscle\u003c/em\u003e 2023; \u003cstrong\u003e14\u003c/strong\u003e(1): 632-41.\u003c/li\u003e\n\u003cli\u003eKivim\u0026auml;ki M, Kuosma E, Ferrie JE, et al. Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe. \u003cem\u003eLancet Public Health\u003c/em\u003e 2017; \u003cstrong\u003e2\u003c/strong\u003e(6): e277-e85.\u003c/li\u003e\n\u003cli\u003ePietzner M, Stewart ID, Raffler J, et al. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. \u003cem\u003eNat Med\u003c/em\u003e 2021; \u003cstrong\u003e27\u003c/strong\u003e(3): 471-9.\u003c/li\u003e\n\u003cli\u003eCheong CY, Yap P, Yap KB, Ng TP. Associations of Inflammatory, Metabolic, Malnutrition, and Frailty Indexes with Multimorbidity Incidence and Progression, and Mortality Impact: Singapore Longitudinal Aging Study. \u003cem\u003eGerontology\u003c/em\u003e 2023; \u003cstrong\u003e69\u003c/strong\u003e(4): 416-27.\u003c/li\u003e\n\u003cli\u003eZhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). \u003cem\u003eInt J Epidemiol\u003c/em\u003e 2014; \u003cstrong\u003e43\u003c/strong\u003e(1): 61-8.\u003c/li\u003e\n\u003cli\u003eSonnega A, Faul JD, Ofstedal MB, Langa KM, Phillips JW, Weir DR. Cohort Profile: the Health and Retirement Study (HRS). \u003cem\u003eInt J Epidemiol\u003c/em\u003e 2014; \u003cstrong\u003e43\u003c/strong\u003e(2): 576-85.\u003c/li\u003e\n\u003cli\u003eSteptoe A, Breeze E, Banks J, Nazroo J. Cohort profile: the English longitudinal study of ageing. \u003cem\u003eInt J Epidemiol\u003c/em\u003e 2013; \u003cstrong\u003e42\u003c/strong\u003e(6): 1640-8.\u003c/li\u003e\n\u003cli\u003eGao M, Lv J, Yu C, et al. Metabolically healthy obesity, transition to unhealthy metabolic status, and vascular disease in Chinese adults: A cohort study. \u003cem\u003ePLoS Med\u003c/em\u003e 2020; \u003cstrong\u003e17\u003c/strong\u003e(10): e1003351.\u003c/li\u003e\n\u003cli\u003eChen H, Zhou Y, Huang L, Xu X, Yuan C. Multimorbidity burden and developmental trajectory in relation to later-life dementia: A prospective study. \u003cem\u003eAlzheimers Dement\u003c/em\u003e 2023; \u003cstrong\u003e19\u003c/strong\u003e(5): 2024-33.\u003c/li\u003e\n\u003cli\u003eNagin DS, Jones BL, Passos VL, Tremblay RE. Group-based multi-trajectory modeling. \u003cem\u003eStat Methods Med Res\u003c/em\u003e 2018; \u003cstrong\u003e27\u003c/strong\u003e(7): 2015-23.\u003c/li\u003e\n\u003cli\u003eAlberti KG, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. \u003cem\u003eCirculation\u003c/em\u003e 2009; \u003cstrong\u003e120\u003c/strong\u003e(16): 1640-5.\u003c/li\u003e\n\u003cli\u003eSmith GI, Mittendorfer B, Klein S. Metabolically healthy obesity: facts and fantasies. \u003cem\u003eJ Clin Invest\u003c/em\u003e 2019; \u003cstrong\u003e129\u003c/strong\u003e(10): 3978-89.\u003c/li\u003e\n\u003cli\u003eNevill AM, Duncan MJ, Myers T. BMI is dead; long live waist-circumference indices: But which index should we choose to predict cardio-metabolic risk? \u003cem\u003eNutr Metab Cardiovasc Dis\u003c/em\u003e 2022; \u003cstrong\u003e32\u003c/strong\u003e(7): 1642-50.\u003c/li\u003e\n\u003cli\u003eAshworth M, Durbaba S, Whitney D, Crompton J, Wright M, Dodhia H. Journey to multimorbidity: longitudinal analysis exploring cardiovascular risk factors and sociodemographic determinants in an urban setting. \u003cem\u003eBMJ Open\u003c/em\u003e 2019; \u003cstrong\u003e9\u003c/strong\u003e(12): e031649.\u003c/li\u003e\n\u003cli\u003eCanizares M, Hogg-Johnson S, Gignac MAM, Glazier RH, Badley EM. Increasing Trajectories of Multimorbidity Over Time: Birth Cohort Differences and the Role of Changes in Obesity and Income. \u003cem\u003eJ Gerontol B Psychol Sci Soc Sci\u003c/em\u003e 2018; \u003cstrong\u003e73\u003c/strong\u003e(7): 1303-14.\u003c/li\u003e\n\u003cli\u003eFreisling H, Viallon V, Lennon H, et al. Lifestyle factors and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study. \u003cem\u003eBMC Med\u003c/em\u003e 2020; \u003cstrong\u003e18\u003c/strong\u003e(1): 5.\u003c/li\u003e\n\u003cli\u003eJackson CA, Dobson A, Tooth L, Mishra GD. Body mass index and socioeconomic position are associated with 9-year trajectories of multimorbidity: A population-based study. \u003cem\u003ePrev Med\u003c/em\u003e 2015; \u003cstrong\u003e81\u003c/strong\u003e: 92-8.\u003c/li\u003e\n\u003cli\u003eLappenschaar M, Hommersom A, Lucas PJ, et al. Multilevel temporal Bayesian networks can model longitudinal change in multimorbidity. \u003cem\u003eJ Clin Epidemiol\u003c/em\u003e 2013; \u003cstrong\u003e66\u003c/strong\u003e(12): 1405-16.\u003c/li\u003e\n\u003cli\u003eBhaskaran K, Dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3\u0026middot;6 million adults in the UK. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e 2018; \u003cstrong\u003e6\u003c/strong\u003e(12): 944-53.\u003c/li\u003e\n\u003cli\u003eFlegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. \u003cem\u003eJama\u003c/em\u003e 2013; \u003cstrong\u003e309\u003c/strong\u003e(1): 71-82.\u003c/li\u003e\n\u003cli\u003eMalik VS, Willet WC, Hu FB. Nearly a decade on - trends, risk factors and policy implications in global obesity. \u003cem\u003eNat Rev Endocrinol\u003c/em\u003e 2020; \u003cstrong\u003e16\u003c/strong\u003e(11): 615-6.\u003c/li\u003e\n\u003cli\u003eWang Y, Zhao L, Gao L, Pan A, Xue H. Health policy and public health implications of obesity in China. \u003cem\u003eLancet Diabetes Endocrinol\u003c/em\u003e 2021; \u003cstrong\u003e9\u003c/strong\u003e(7): 446-61.\u003c/li\u003e\n\u003cli\u003eSilver LD, Ng SW, Ryan-Ibarra S, et al. Changes in prices, sales, consumer spending, and beverage consumption one year after a tax on sugar-sweetened beverages in Berkeley, California, US: A before-and-after study. \u003cem\u003ePLoS Med\u003c/em\u003e 2017; \u003cstrong\u003e14\u003c/strong\u003e(4): e1002283.\u003c/li\u003e\n\u003cli\u003eGhebreyesus TA, Frieden T. Trans fat: everyone must join the fight to eliminate this invisible killer from the world\u0026apos;s food supply forever. \u003cem\u003eBmj\u003c/em\u003e 2024; \u003cstrong\u003e386\u003c/strong\u003e: q1525.\u003c/li\u003e\n\u003cli\u003eWillett W, Rockstr\u0026ouml;m J, Loken B, et al. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. \u003cem\u003eLancet\u003c/em\u003e 2019; \u003cstrong\u003e393\u003c/strong\u003e(10170): 447-92.\u003c/li\u003e\n\u003cli\u003eFisher LD, Lin DY. Time-dependent covariates in the Cox proportional-hazards regression model. \u003cem\u003eAnnu Rev Public Health\u003c/em\u003e 1999; \u003cstrong\u003e20\u003c/strong\u003e: 145-57.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable-1\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 85.7143%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHARLS \u0026nbsp;(n=4,064)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003eTotal (n = 4064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003eVery low-stable (n = 797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003eLow-stable (n = 851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003eLow-growth (n = 823)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003eMiddle-stable (n = 935)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003eHigh-growth (n = 658)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eAge, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e58.30 \u0026plusmn; 8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e56.18 \u0026plusmn; 8.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e57.14 \u0026plusmn; 8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e58.43 \u0026plusmn; 8.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e59.64 \u0026plusmn; 8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e60.27 \u0026plusmn; 7.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eGender, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e2178 (53.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e385 (48.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e476 (55.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e435 (52.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e504 (53.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e378 (57.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1886 (46.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e412 (51.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e375 (44.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e388 (47.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e431 (46.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e280 (42.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eEducation, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Below highschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e3732 (91.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e700 (87.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e803 (94.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e769 (93.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e858 (91.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e602 (91.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Highschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e304 (7.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e91 (11.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e41 (4.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e50 (6.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e70 (7.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e52 (7.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; College\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e28 (0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e6 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e7 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e4 (0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e7 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e4 (0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eDrink, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e2461 (60.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e475 (59.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e543 (63.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e478 (58.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e558 (59.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e407 (61.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1603 (39.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e322 (40.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e308 (36.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e345 (41.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e377 (40.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e251 (38.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eSmoke, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e2493 (61.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e471 (59.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e541 (63.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e495 (60.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e583 (62.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e403 (61.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1571 (38.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e326 (40.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e310 (36.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e328 (39.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e352 (37.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e255 (38.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eSES, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1338 (32.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e208 (26.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e253 (29.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e262 (31.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e356 (38.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e259 (39.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1404 (34.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e286 (35.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e292 (34.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e290 (35.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e325 (34.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e211 (32.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1322 (32.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e303 (38.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e306 (35.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e271 (32.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e254 (27.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e188 (28.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eBMI, Mean \u0026plusmn; SD, kg/ m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e24.12 \u0026plusmn; 3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e23.23 \u0026plusmn; 3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e23.55 \u0026plusmn; 3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e23.96 \u0026plusmn; 3.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e24.50 \u0026plusmn; 3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e25.57 \u0026plusmn; 3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003ehsCRP, Mean \u0026plusmn; SD, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e2.59 \u0026plusmn; 7.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e2.33 \u0026plusmn; 9.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e2.11 \u0026plusmn; 5.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e2.22 \u0026plusmn; 4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e3.30 \u0026plusmn; 9.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e2.95 \u0026plusmn; 6.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eSBP, Mean \u0026plusmn; SD, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e130.86 \u0026plusmn;\u0026nbsp;21.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e123.46 \u0026plusmn; 16.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e126.22 \u0026plusmn; 19.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e131.17 \u0026plusmn; 20.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e135.78 \u0026plusmn; 22.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e138.42 \u0026plusmn; 22.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eDBP, Mean \u0026plusmn; SD, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e76.28 \u0026plusmn; 12.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e73.29 \u0026plusmn; 10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e74.23 \u0026plusmn; 11.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e76.60 \u0026plusmn; 12.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e78.19 \u0026plusmn; 12.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e79.41 \u0026plusmn; 12.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eHeight, Mean \u0026plusmn; SD, m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.58 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.59 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.58 \u0026plusmn;\u0026nbsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.58 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.58 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e1.58 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eWeight, Mean \u0026plusmn; SD, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e60.42 \u0026plusmn; 10.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e58.99 \u0026plusmn; 9.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e58.75 \u0026plusmn; 10.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e60.00 \u0026plusmn; 10.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e61.19 \u0026plusmn; 10.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e63.74 \u0026plusmn; 11.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eGlucose, Mean \u0026plusmn; SD, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e6.20 \u0026plusmn; 1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e5.79 \u0026plusmn; 1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e5.89 \u0026plusmn; 1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e6.12 \u0026plusmn; 1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e6.44 \u0026plusmn; 2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e6.86 \u0026plusmn;\u0026nbsp;3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eHbA1C, Mean \u0026plusmn; SD, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e5.29 \u0026plusmn; 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e5.10 \u0026plusmn; 0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e5.17 \u0026plusmn; 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e5.29 \u0026plusmn; 0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e5.34 \u0026plusmn; 0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e5.59 \u0026plusmn; 1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eHDL-C, Mean \u0026plusmn; SD, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.30 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.36 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.34 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.31 \u0026plusmn; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.27 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e1.24 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eTG, Mean \u0026plusmn; SD, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.53 \u0026plusmn; 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.29 \u0026plusmn; 0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.42 \u0026plusmn; 1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.53 \u0026plusmn; 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.67 \u0026plusmn; 1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e1.74 \u0026plusmn; 1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eStatus 1, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1278 (31.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e355 (44.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e330 (38.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e260 (31.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e223 (23.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e110 (16.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e579 (14.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e128 (16.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e135 (15.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e119 (14.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e115 (12.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e82 (12.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e942 (23.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e179 (22.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e193 (22.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e205 (24.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e235 (25.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e130 (19.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1265 (31.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e135 (16.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e193 (22.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e239 (29.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e362 (38.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e336 (51.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eStatus 2, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1316 (32.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e356 (44.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e360 (42.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e262 (31.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e238 (25.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e100 (15.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e751 (18.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e165 (20.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e171 (20.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e159 (19.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e161 (17.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e95 (14.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e763 (18.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e126 (15.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e137 (16.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e170 (20.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e191 (20.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e139 (21.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1234 (30.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e150 (18.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e183 (21.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e232 (28.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e345 (36.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e324 (49.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 85.7143%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eELSA \u0026nbsp;(n=3,468)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003eTotal (n = 3468)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003eLow-stable (n = 1112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003eMiddle-growth (n = 1203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003eHigh-stable (n = 176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003eHigh-growth (n = 720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003eVery high-stable (n = 257)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eAge, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e64.07 \u0026plusmn; 8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e60.43 \u0026plusmn;\u0026nbsp;6.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e64.29 \u0026plusmn; 7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e66.34 \u0026plusmn; 8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e67.08 \u0026plusmn; 7.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e68.73 \u0026plusmn; 8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eGender, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1901 (54.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e564 (50.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e631 (52.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e98 (55.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e441 (61.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e167 (64.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1567 (45.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e548 (49.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e572 (47.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e78 (44.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e279 (38.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e90 (35.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eEducation, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Below highschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e882 (25.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e204 (18.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e294 (24.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e53 (30.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e228 (31.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e103 (40.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Highschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1685 (48.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e589 (52.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e594 (49.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e80 (45.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e320 (44.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e102 (39.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; College\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e636 (18.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e263 (23.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e222 (18.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e26 (14.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e95 (13.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e30 (11.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e265 (7.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e56 (5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e93 (7.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e17 (9.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e77 (10.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e22 (8.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eDrink, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e290 (8.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e55 (4.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e83 (6.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e20 (11.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e88 (12.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e44 (17.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e3178 (91.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1057 (95.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1120 (93.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e156 (88.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e632 (87.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e213 (82.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eSmoke, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1441 (41.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e503 (45.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e501 (41.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e80 (45.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e288 (40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e69 (26.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e2027 (58.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e609 (54.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e702 (58.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e96 (54.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e432 (60.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e188 (73.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eSES, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1020 (29.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e238 (21.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e344 (28.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e45 (25.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e254 (35.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e139 (54.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1143 (32.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e378 (33.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e392 (32.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e72 (40.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e228 (31.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e73 (28.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1248 (35.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e470 (42.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e447 (37.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e57 (32.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e230 (31.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e44 (17.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eBMI, Mean \u0026plusmn; SD, kg/ m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e27.99 \u0026plusmn; 4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e27.06 \u0026plusmn; 4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e27.84 \u0026plusmn; 4.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e28.56 \u0026plusmn; 5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e28.91 \u0026plusmn; 5.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e29.76 \u0026plusmn; 4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003ehsCRP, Mean \u0026plusmn; SD, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e3.35 \u0026plusmn; 6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e2.64 \u0026plusmn; 5.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e3.33 \u0026plusmn; 6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e3.50 \u0026plusmn; 6.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e3.63 \u0026plusmn; 5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e5.56 \u0026plusmn; 9.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eSBP, Mean \u0026plusmn; SD, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e132.20 \u0026plusmn; 16.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e128.26 \u0026plusmn;\u0026nbsp;15.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e133.17 \u0026plusmn; 16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e133.77 \u0026plusmn; 17.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e134.68 \u0026plusmn; 16.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e136.74 \u0026plusmn; 17.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eDBP, Mean \u0026plusmn; SD, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e75.09 \u0026plusmn; 10.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e75.40 \u0026plusmn; 9.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e75.82 \u0026plusmn; 10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e74.17 \u0026plusmn; 10.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e74.29 \u0026plusmn; 10.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e73.22 \u0026plusmn; 11.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eHeight, Mean \u0026plusmn; SD, m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.66 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.68 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.67 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.66 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.65 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e1.64 \u0026plusmn; 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eWeight, Mean \u0026plusmn; SD, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e77.69 \u0026plusmn; 14.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e76.37 \u0026plusmn; 13.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e77.52 \u0026plusmn; 14.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e79.06 \u0026plusmn; 17.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e78.98 \u0026plusmn; 15.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e79.72 \u0026plusmn; 14.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eGlucose, Mean \u0026plusmn; SD, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e4.88 \u0026plusmn; 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e4.76 \u0026plusmn; 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e4.86 \u0026plusmn; 0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e4.95 \u0026plusmn; 1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e5.02 \u0026plusmn; 1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e5.20 \u0026plusmn; 1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eHbA1C, Mean \u0026plusmn; SD, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e5.83 \u0026plusmn; 0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e5.67 \u0026plusmn; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e5.77 \u0026plusmn; 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e5.97 \u0026plusmn; 0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e5.98 \u0026plusmn; 0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e6.25 \u0026plusmn; 0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eHDL-C, Mean \u0026plusmn; SD, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.57 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.60 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.59 \u0026plusmn; 0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.54 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.53 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e1.49 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eTG, Mean \u0026plusmn; SD, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.70 \u0026plusmn; 0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.58 \u0026plusmn; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.69 \u0026plusmn; 0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.92 \u0026plusmn; 1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.80 \u0026plusmn; 0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e1.82 \u0026plusmn; 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eStatus 1, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e586 (16.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e302 (27.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e198 (16.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e17 (9.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e56 (7.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e13 (5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e946 (27.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e448 (40.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e343 (28.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e20 (11.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e115 (15.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e20 (7.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e349 (10.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e66 (5.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e130 (10.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e28 (15.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e101 (14.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e24 (9.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1587 (45.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e296 (26.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e532 (44.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e111 (63.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e448 (62.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e200 (77.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eStatus 2, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e637 (18.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e311 (27.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e208 (17.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e21 (11.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e76 (10.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e21 (8.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1088 (31.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e488 (43.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e393 (32.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e36 (20.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e139 (19.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e32 (12.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e320 (9.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e60 (5.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e121 (10.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e22 (12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e91 (12.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e26 (10.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1423 (41.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e253 (22.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e481 (39.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e97 (55.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e414 (57.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e178 (69.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 85.7143%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHRS \u0026nbsp;(n=3,745)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003eTotal (n = 3745)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003eVery low-stable (n = 635)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003eLow-growth (n = 892)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003eMiddle-growth (n = 974)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003eHigh-growth (n = 902)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003eVery high-growth (n = 342)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eAge, Mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e63.96 \u0026plusmn; 9.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e59.87 \u0026plusmn; 8.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e61.76 \u0026plusmn; 9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e64.64 \u0026plusmn; 9.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e67.07 \u0026plusmn; 9.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e67.12 \u0026plusmn; 9.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eGender, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e2258 (60.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e326 (51.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e491 (55.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e605 (62.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e591 (65.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e245 (71.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1487 (39.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e309 (48.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e401 (44.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e369 (37.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e311 (34.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e97 (28.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eEducation, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Below highschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e566 (15.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e69 (10.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e130 (14.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e130 (13.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e147 (16.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e90 (26.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Highschool\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e2190 (58.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e339 (53.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e476 (53.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e597 (61.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e578 (64.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e200 (58.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; College\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e989 (26.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e227 (35.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e286 (32.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e247 (25.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e177 (19.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e52 (15.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eDrink, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1391 (37.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e180 (28.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e277 (31.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e350 (35.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e412 (45.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e172 (50.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e2354 (62.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e455 (71.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e615 (68.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e624 (64.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e490 (54.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e170 (49.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eSmoke, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1757 (46.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e341 (53.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e435 (48.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e446 (45.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e401 (44.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e134 (39.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1988 (53.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e294 (46.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e457 (51.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e528 (54.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e501 (55.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e208 (60.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eSES, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Q1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1188 (31.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e167 (26.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e264 (29.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e279 (28.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e311 (34.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e167 (48.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Q2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1254 (33.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e208 (32.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e295 (33.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e322 (33.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e331 (36.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e98 (28.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; Q3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1303 (34.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e260 (40.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e333 (37.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e373 (38.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e260 (28.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e77 (22.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eBMI, Mean \u0026plusmn; SD, kg/ m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e29.96 \u0026plusmn; 5.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e28.14 \u0026plusmn; 5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e29.31 \u0026plusmn; 5.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e30.28 \u0026plusmn; 5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e30.89 \u0026plusmn; 6.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e31.63 \u0026plusmn; 6.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003ehsCRP, Mean \u0026plusmn; SD, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e3.13 \u0026plusmn; 5.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e2.13 \u0026plusmn; 3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e2.66 \u0026plusmn; 3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e3.13 \u0026plusmn; 4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e3.81 \u0026plusmn; 8.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e4.47 \u0026plusmn; 9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eSBP, Mean \u0026plusmn; SD, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e129.13 \u0026plusmn; 20.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e123.96 \u0026plusmn; 18.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e128.18 \u0026plusmn; 20.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e130.99 \u0026plusmn; 19.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e131.06 \u0026plusmn; 20.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e130.82 \u0026plusmn; 20.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eDBP, Mean \u0026plusmn; SD, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e80.19 \u0026plusmn; 12.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e79.19 \u0026plusmn; 11.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e80.51 \u0026plusmn; 12.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e81.36 \u0026plusmn; 11.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e79.85 \u0026plusmn; 12.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e78.74 \u0026plusmn; 11.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eHeight, Mean \u0026plusmn; SD, m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.66 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.68 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.67 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.65 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.64 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e1.63 \u0026plusmn; 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eWeight, Mean \u0026plusmn; SD, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e82.22 \u0026plusmn; 17.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e79.33 \u0026plusmn; 16.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e81.78 \u0026plusmn;\u0026nbsp;16.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e82.72 \u0026plusmn; 17.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e83.54 \u0026plusmn; 18.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e83.86 \u0026plusmn; 18.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eHbA1C, Mean \u0026plusmn; SD, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e6.05 \u0026plusmn; 1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e5.67 \u0026plusmn; 0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e5.87 \u0026plusmn; 0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e6.08 \u0026plusmn; 1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e6.29 \u0026plusmn; 1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e6.47 \u0026plusmn; 1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eHDL-C, Mean \u0026plusmn; SD, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.50 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1.52 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.53 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.50 \u0026plusmn; 0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e1.46 \u0026plusmn; 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e1.46 \u0026plusmn;\u0026nbsp;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eStatus 1, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e485 (12.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e157 (24.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e144 (16.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e109 (11.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e54 (5.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e21 (6.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1271 (33.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e359 (56.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e413 (46.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e316 (32.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e152 (16.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e31 (9.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e216 (5.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e21 (3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e32 (3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e47 (4.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e84 (9.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e32 (9.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1773 (47.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e98 (15.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e303 (33.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e502 (51.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e612 (67.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e258 (75.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003eStatus 2, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e503 (13.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e162 (25.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e147 (16.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e105 (10.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e72 (7.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e17 (4.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1457 (38.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e391 (61.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e487 (54.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e342 (35.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e190 (21.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e47 (13.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e216 (5.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e18 (2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e25 (2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e63 (6.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e73 (8.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e37 (10.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15.3061%;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e1569 (41.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.2393%;\"\u003e\n \u003cp\u003e64 (10.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e233 (26.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e464 (47.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7671%;\"\u003e\n \u003cp\u003e567 (62.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.817%;\"\u003e\n \u003cp\u003e241 (70.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.8114%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable-2\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHARLS\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 30px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 16px;\"\u003e\n \u003cp\u003eLow-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eVery low-stable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.69 (0.54 ~ 0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.66 (0.52 ~ 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.67 (0.54 ~ 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.65 (0.53 ~ 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.31 (0.25 ~ 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.30 (0.24 ~ 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eLow-stable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.81 (0.64 ~ 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.82 (0.65 ~ 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.76 (0.62 ~ 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.75 (0.61 ~ 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.50 (0.41 ~ 0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.49 (0.40 ~ 0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 16px;\"\u003e\n \u003cp\u003eMedium-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eLow-growth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.02 (0.80 ~ 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.03 (0.81 ~ 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.09 (0.88 ~ 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.09 (0.89 ~ 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.92 (0.75 ~ 1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.93 (0.76 ~ 1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eMiddle-stable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.25 (0.97 ~ 1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.26 (0.98 ~ 1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.51 (1.22 ~ 1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.52 (1.24 ~ 1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.93 (1.59 ~ 2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.97 (1.62 ~ 2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 16px;\"\u003e\n \u003cp\u003eHigh-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eHigh-growth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.90 (1.40 ~ 2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.93 (1.42 ~ 2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.57 (1.20 ~ 2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.59 (1.21 ~ 2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.90 (3.08 ~ 4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e4.03 (3.18 ~ 5.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eOR: Odds Ratio, CI: Confidence Interval\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;Model1: Adjust: age, gender\u003cbr\u003e\u0026nbsp;\u003cbr\u003eModel2: Adjust: age, gender, education, drink, smoke, SES，hsCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eELSA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 30px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 16px;\"\u003e\n \u003cp\u003eLow-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eLow-stable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.83 (0.66 ~ 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.86 (0.69 ~ 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.26 (0.19 ~ 0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.28 (0.20 ~ 0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.23 (0.19 ~ 0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.26 (0.20 ~ 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 16px;\"\u003e\n \u003cp\u003eMedium-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eMiddle-growth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.09 (0.88 ~ 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.09 (0.88 ~ 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.11 (0.84 ~ 1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.13 (0.85 ~ 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.94 (0.77 ~ 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.96 (0.78 ~ 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eHigh-stable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.72 (0.38 ~ 1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.71 (0.37 ~ 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e2.66 (1.42 ~ 4.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e2.72 (1.45 ~ 5.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e2.35 (1.39 ~ 3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e2.37 (1.39 ~ 4.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 16px;\"\u003e\n \u003cp\u003eHigh-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eHigh-growth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.36 (0.97 ~ 1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.35 (0.96 ~ 1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.37 (2.34 ~ 4.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.29 (2.27 ~ 4.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.48 (2.57 ~ 4.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.39 (2.49 ~ 4.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eVery high-stable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (0.49 ~ 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.95 (0.46 ~ 1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e2.61 (1.30 ~ 5.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e2.19 (1.08 ~ 4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e5.74 (3.23 ~ 10.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e4.73 (2.64 ~ 8.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eOR: Odds Ratio, CI: Confidence Interval\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;Model1: Adjust: age, gender\u003cbr\u003e\u0026nbsp;\u003cbr\u003eModel2: Adjust: age, gender, education, drink, smoke, SES，hsCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHRS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 30px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 16px;\"\u003e\n \u003cp\u003eLow-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eVery low-stable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.72 (0.57 ~ 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.79 (0.62 ~ 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.29 (0.18 ~ 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.33 (0.20 ~ 0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.12 (0.09 ~ 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.14 (0.10 ~ 0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eLow-growth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.07 (0.85 ~ 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.13 (0.90 ~ 1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.48 (0.31 ~ 0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.52 (0.34 ~ 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.50 (0.40 ~ 0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.55 (0.43 ~ 0.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 16px;\"\u003e\n \u003cp\u003eMedium-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eMiddle-growth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.17 (0.91 ~ 1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.20 (0.94 ~ 1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.90 (0.61 ~ 1.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.92 (0.62 ~ 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.35 (1.06 ~ 1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.43 (1.12 ~ 1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 16px;\"\u003e\n \u003cp\u003eHigh-Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eHigh-growth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.19 (0.85 ~ 1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.12 (0.80 ~ 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e4.21 (2.82 ~ 6.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.92 (2.61 ~ 5.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e4.12 (3.05 ~ 5.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.66 (2.69 ~ 4.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eVery high-growth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MHOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.61 (0.35 ~ 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.54 (0.31 ~ 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.30 (1.84 ~ 5.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e2.65 (1.47 ~ 4.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp; MUOO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.83 (2.42 ~ 6.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e3.01 (1.88 ~ 4.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 83px;\"\u003e\n \u003cp\u003eOR: Odds Ratio, CI: Confidence Interval\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;Model1: Adjust: age, gender\u003cbr\u003e\u0026nbsp;\u003cbr\u003eModel2: Adjust: age, gender, education, drink, smoke, SES，hsCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6138718/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6138718/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: The high prevalence of multimorbidity poses significant challenges to the health burden of the elderly population and healthcare systems, understanding its trajectories is critical for intervention strategies. Metabolic obesity phenotypes are considered key predictors of multimorbidity. This study aimed to analyze the associations between metabolic obesity phenotypes and their transitions with multimorbidity trajectories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Longitudinal data from three cohort studies (CHARLS, ELSA, and HRS) were used, and trajectories of multimorbidity were identified through trajectory analysis. Baseline metabolic obesity phenotypes were classified into Metabolically Healthy Normal Weight (MHNW), Metabolically Healthy Obesity/Overweight (MHOO), Metabolically Unhealthy Normal Weight (MUNW), and Metabolically Unhealthy Obesity/Overweight (MUOO). The Group-Based Trajectory Modeling method was used to construct multimorbidity trajectories, perform logistic regression analysis on trajectory groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e: In baseline analysis, compared with the MH (both MHNW and MHOO) group, the likelihood of MU group individuals in the low-risk trajectory significantly decreased, and the risk in the high-risk trajectory significantly increased, especially in CHARLS (OR=4.03), ELSA (OR=4.73), HRS (OR=3.01). The analysis of changes in metabolic obesity phenotypes showed that individuals with stable metabolic unhealth had the lowest risk in the low-risk trajectory, and the risk of developing high-risk trajectories significantly increased for phenotypes that had been exposed to metabolic unhealth. In particular, in CHARLS, ELSA, and HRS, individuals continuously exposed to metabolic unhealth significantly increased the risk of developing high-risk trajectories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretations\u003c/strong\u003e: Metabolic obesity phenotypes and their changes have significant impacts on multimorbidity trajectories, especially the strong association between metabolic unhealthy status and high-risk multimorbidity trajectories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings: \u003c/strong\u003eThis study was funded by GDPH Supporting Fund for Talent Program (KY0120220263), LiaoNing Revitalization Talents Program (XLYC2203192), and Guangzhou School (hospital) Enterprise Joint Funding Project (2025A03J3901).\u003c/p\u003e","manuscriptTitle":"Metabolic Obesity Phenotypes and Their Transitions as Determinants of Multimorbidity Trajectories: Evidence from Global Aging Cohorts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-12 10:24:56","doi":"10.21203/rs.3.rs-6138718/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0e4ad00d-a710-449e-a41d-9194ae757802","owner":[],"postedDate":"May 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":45692654,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes"},{"id":45692655,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Obesity"}],"tags":[],"updatedAt":"2025-08-06T12:08:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-12 10:24:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6138718","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6138718","identity":"rs-6138718","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}