All Cause and Cardiovascular Mortality Risk in Metabolically Healthy and Unhealthy Obesity: Results From NHANES 1999-2018

All Cause and Cardiovascular Mortality Risk in Metabolically Healthy and Unhealthy Obesity: Results From NHANES 1999-2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article All Cause and Cardiovascular Mortality Risk in Metabolically Healthy and Unhealthy Obesity: Results From NHANES 1999-2018 Chaoqiang Dong, Chan Yang, Xuemei Wan, Xuelei Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6181171/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Metabolically healthy obese (MHO) is not static; it can transform into Metabolically Unhealthy Obesity (MUO) over time. Whether MHO individuals at the baseline, who appear to defy the expected health consequences of obesity, indeed experience a lower risk of mortality when compared to their counterparts with MUO. Methods In a cohort study using data from the National Health and Nutrition Examination Survey (NHANES) thorough 1999–2018, metabolic health and obesity status were assessed at baseline. Participants were followed up for 112-months follow-up to ascertain cardiovascular and all-cause mortality events. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) adjusting for potential confounders. Stratified analyses and sensitivity analyses were performed. Results In a cohort of 18,869 participants, with a median 112-month follow-up, 2581 all-cause deaths and 842 cardiovascular deaths were observed. MHO individuals had significantly higher survival rates for both all-cause and cardiovascular mortality compared to MUO participants (p < 0.05). Adjusted multivariable models consistently showed that MHO individuals had lower rates of all-cause mortality (HR 0.85; 95% CI 0.76, 0.95) and cardiovascular mortality (HR 0.85; 95% CI 0.76, 0.94). There were no significant interactions between MHO and these factors except for a slight decrease in the association strength among older participants, drinkers, former smokers, and those with self-reported hypertension. Sensitivity analyses, including the exclusion of shorter follow-up times and baseline cardiovascular disease cases, consistently supported the protective effect of MHO against both all-cause mortality (HR, 0.84 to 0.92) and cardiovascular mortality (HR, 0.86 to 0.92) compared to MUO. Conclusion our extensive analysis of a large cohort spanning nearly a decade highlights that individuals with MHO consistently exhibit significantly lower rates of all-cause and cardiovascular mortality compared to their MUO counterparts. These findings emphasize the critical role of metabolic health in evaluating mortality risk among obese individuals and underscore the potential for more personalized healthcare strategies and public health policies to address obesity-related health outcomes. Metabolically healthy obesity Metabolically unhealthy obesity Cardiovascular mortality All-cause mortality Cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Obesity has emerged as a global public health challenge, with its prevalence steadily increasing across the world [ 1 , 2 , 3 ]. It is a complex condition associated with a range of health risks, including cardiovascular diseases (CVD) and premature mortality, even in some cases, weight loss may not reduce the risk of CVD [ 3 , 4 ]. Recent research has unveiled a multifaceted aspect of obesity, challenging the traditional view that all obese individuals face similar health outcomes [ 5 , 6 ]. Within the realm of obesity research, the concept of Metabolically Healthy Obesity (MHO) has gained prominence [ 7 , 8 , 9 ]. MHO refers to individuals who, despite being classified as obese based on their Body Mass Index (BMI), exhibit a metabolically healthy profile. This metabolic health is characterized by the absence of insulin resistance, dyslipidemia, hypertension, and elevated blood sugar levels, which are typically associated with obesity-related health risks [ 7 , 8 , 9 ]. Existing studies indicate that MHO is not necessarily truly healthy; compared to non-obese individuals, MHO individuals have a significantly increased risk of cardiovascular events and mortality [ 10 , 11 , 12 ]. Furthermore, MHO is not static; it can transform into Metabolically Unhealthy Obesity (MUO) or metabolic syndrome over time [ 13 ]. The intriguing question that has emerged is whether MHO individuals at the baseline, who appear to defy the expected health consequences of obesity, indeed experience a lower risk of mortality, particularly from cardiovascular diseases, when compared to their counterparts with MUO. This question has significant implications for healthcare, public health policy, and our understanding of obesity as a complex and heterogeneous condition. This study delves into a comprehensive study that explores the intricate associations between MHO and all-cause as well as cardiovascular disease mortality among adults in the United States. By examining a large and diverse cohort over an extended period, this research aims to shed light on the potential protective effects of metabolic health within the context of obesity. Understanding these associations may pave the way for more personalized healthcare strategies and inform public health policies to mitigate the impact of obesity-related health risks in a more nuanced manner. 2. Methods 2.1 Study design and participants The NHANES, conducted regularly by the Centers for Disease Control and Prevention (CDC) and the CDC's National Center for Health Statistics, is a cross-sectional sampling poll that provides a nationally representative sample of the noninstitutionalized US civilian population. Leveraging NHANES data granted us access to a diverse and comprehensive cohort of individuals, facilitating an in-depth exploration of various health-related factors within the adult population. Trained medical professionals collected five types of information, including demographics, dietary, examination, laboratory, and questionnaire data. This cohort study utilized data from ten NHANES cycles spanning 1999 to 2018, encompassing a total of 59,204 adults aged 18 years and older. We excluded 1,670 participants who self-reported pregnancy, resulting in 57,534 remaining individuals. After applying the inclusion criteria for Metabolically Healthy Obesity (MHO), we identified 18,902 individuals with MHO status. The final MHO analyses included 18,869 participants, following the exclusion of 33 individuals lost to follow-up. Throughout this study, we adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines for cohort studies. 2.2 Definition of Metabolic Health Obese Firstly, individuals with a body mass index (BMI) equal to or exceeding 30 kg/m² were categorized as obese, calculated as the ratio of weight to height squared. Secondly, to ascertain metabolic health status, we applied the Harmonized Criteria for Metabolic Syndrome (MetS) [ 7 , 14 ], considering individuals to be metabolically healthy if they exhibited two or fewer of the following indicators: systolic blood pressure ≥ 130mmHg or diastolic blood pressure ≥ 85mmHg, triglyceride levels ≥ 1.69 mmol/L (mM), HDL-cholesterol levels < 1.04 mM (in men) or < 1.29 mM (in women), fasting plasma glucose levels ≥ 5.6 mM, and waist circumference (WC) ≥ 102 cm (in men) or ≥ 88 cm (in women). Conversely, those meeting three or more of these diagnostic criteria were classified as MUO. 2.3 Assessment of Covariates Fundamental demographic factors, including age, sex, race, ethnicity, education levels, and family income to poverty ratio, were self-reported during interviews. Participants' alcohol consumption was categorized into three groups: nondrinkers, moderate drinkers (consuming fewer than two drinks per day for men and fewer than one drink per day for women), and heavy drinkers (consuming two or more drinks per day for men and one or more drinks per day for women) [ 15 ]. Physical activity (PA) was defined as engaging in moderate- to vigorous-intensity sports, fitness programs, or recreational activities for more than 600 minutes per week; otherwise, participants were considered inactive [ 16 ]. PA was assessed using the Metabolic Equivalent (MET), a standard indicator of relative energy metabolism during various activities. Healthy Eating Index (HEI) 2015 scores were calculated in alignment with the US Dietary Guidelines for Americans (DGA) 2015–2020 [ 17 ]. Participants' self-reported history of physician-diagnosed hypertension was recorded. Additionally, levels of HbA1c, triglycerides, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol were measured at recruitment. 2.4 Ascertainment of Mortality We acquired death statistics by cross-referencing the cohort database with the publicly accessible National Death Index (NDI) database ( https://www.cdc.gov/nchs/data-linkage/mortality.htm ) up to December 31, 2019. All recorded causes of death were classified as 'all-cause mortality.' Cardiovascular disease (CVD) mortality was identified using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, with codes I00 to I09, I11, I13, I20 to I51, and I60 to I69. 2.5 Statistical Analysis In consideration of the intricate examination design employed in NHANES, all analyses in this study incorporated weighted test weights, clustering, and stratification adjustments. Person-years were calculated for each participant from their enrollment date until either the date of death or the conclusion of the follow-up period on December 31, 2019, whichever came first. To assess the relationships between Metabolically Healthy Obese (MHO) status and the risks of cardiovascular disease (CVD) and all-cause mortality, we employed weighted Kaplan-Meier plots to visualize survival probabilities and utilized multivariable Cox proportional hazards regression models to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). Proportional hazards assumptions were evaluated using Schoenfeld residuals, revealing no violations. Our analysis comprised three multivariable models. Model 1 adjusted for age (continuous, years), sex (male or female), and race/ethnicity (self-reported as Mexican American, non-Hispanic Black, non-Hispanic White, other Hispanic, or other race/multi-racial). Model 2 further incorporated adjustments for educational level, family income to poverty ratio, body mass index (BMI), drinking status, physical activity, smoking status, HbA1c level, Healthy Eating Index 2015 (HEI 2015) quartiles, total energy intake quartiles, self-reported hypertension, total cholesterol (TC) quartiles, triglycerides (TG) quartiles, high-density lipoprotein cholesterol (HDL-C) quartiles, and low-density lipoprotein cholesterol (LDL-C) quartiles. Model 3 combined adjustments from both Model 1 and Model 2. Multiple imputation was applied to address missing values. Stratified analyses were conducted based on age (< 60 or ≥ 60), sex, race/ethnicity, drinking status, physical activity, smoking status, HbA1c level, self-reported hypertension, and diabetes duration (< 10 or ≥ 10) to mitigate the influence of these factors, with relationships assessed using P-values. Several sensitivity analyses were performed to validate our findings. Firstly, participants who died within the initial 24 months of follow-up were excluded to reduce potential reverse causation bias. Additionally, participants with a history of CVD were excluded from the primary analyses. Sensitivity analysis employing restricted cubic spline (RCS) analysis was also conducted. All statistical analyses were performed using R 4.2.1, with statistical significance set at a two-sided P < .05. Data analysis was conducted between October 1, 2022, and June 20, 2023. 3. Results 3.1 Baseline characteristics The final analysis (Fig. 1 ) comprised a total of 18,869 participants, out of which 10,103 (53.5%) exhibited MHO, while 8,766 (46.5%) had MUO. Participants with MHO at baseline, in comparison to those with MUO, demonstrated the following characteristics: they were younger, more likely to be female, had higher education levels (College and above), belonged to middle-income families in relation to the poverty ratio, were more likely to be nondrinkers and never smokers, engaged in active physical activity, had lower levels of triglycerides (TG), total cholesterol (TC), and low-density lipoprotein (LDL), lower HbA1c levels, higher Healthy Eating Index (HEI) scores, and consumed a higher total energy intake. Additionally, they were also more likely to have no hypertension (Table 1 ). Table 1 Participant characteristics. Numbers are N (%) unless otherwise specified. Some sub-categories, such as education level, may not add up due to missing data. Characteristic Total MHO MUO P value Total N 18869 10103 (53.5) 8766 (46.5) Age 47.6 (47.2,48.0) 42.8 (42.4,43.2) 53.3 (52.8,53.7) < 0.0001 BMI 35.7 (35.5,35.9) 35.6 (35.4,35.8) 35.8 (35.6,36.1) 0.2 Sex < 0.0001 Female 10484 (55.6) 6075 (57.6) 4409 (48.5) Male 8385 (44.4) 4028 (42.4) 4357 (51.5) Ethnicty < 0.0001 Mexican American 3658 (19.4) 2094 (10.7) 1564 (8.0) Non-Hispanic Black 5031 (26.7) 2655 (14.9) 2376 (14.1) Non-Hispanic White 7610 (40.3) 3879 (63.0) 3731 (69.2) Other Hispanic 1589 (8.4) 901 (6.4) 688 (4.5) Other Race - Including Multi-Racial 981 (5.2) 574 (5.0) 407 (4.1) Education 0.04 Less than high school 5329 (28.3) 2833 (18.3) 2496 (17.9) College and above 8646 (45.9) 4531 (53.9) 4115 (56.3) High school 4878 (25.9) 2729 (27.8) 2149 (25.8) Family income to poverty ratio < 0.0001 3.0 5854 (34.0) 2900 (43.4) 2954 (50.2) 1.0–3.0 7538 (43.8) 4034 (39.5) 3504 (37.2) Drink < 0.0001 Heavy 2305 (26.7) 1116 (26.2) 1189 (34.9) Moderate 1004 (11.6) 457 (11.1) 547 (14.3) None 5332 (61.7) 3212 (62.7) 2120 (50.8) Physical activity 0.2 Active 7326 (71.5) 4068 (73.6) 3258 (72.2) Inactive 2922 (28.5) 1589 (26.4) 1333 (27.8) Smoke < 0.0001 Former 2483 (25) 1084 (21.0) 1399 (32.2) Never 5630 (56.7) 3226 (58.8) 2404 (52.8) Now 1813 (18.3) 1069 (20.2) 744 (15.1) TC, mmol/L < 0.0001 Quartile 1 (< 4.24) 2340 (24.1) 1347 (25.0) 993 (20.7) Quartile 2 (4.24–4.91 ) 2443 (25.2) 1339 (25.8) 1104 (23.6) Quartile 3 (4.91–5.64) 2433 (25.1) 1250 (24.3) 1183 (26.8) Quartile 4 (5.64–15.83) 2486 (25.6) 1199 (25.0) 1287 (28.8) TG, mmol/L < 0.0001 Quartile 1 ( 1.897) 1159 (25.1) 150 (10.9) 1009 (33.2) HDL, mmol/L < 0.0001 Quartile 1 ( 1.40) 2656 (27.4) 973 (19.4) 1683 (34.8) LDL, mmol/L 0.05 Quartile 1 ( 3.569) 1128 (25.2) 313 (21.8) 815 (26.5) HbA1c, % < 0.0001 =7.0 1021 (10.4) 370 (5.5) 651 (11.1) HEI 0.003 Quartile 1 ( 58.252793) 2416 (25) 1219 (21.5) 1197 (26.1) Total energy intakes, kcal 0.2 Quartile 1 ( 2652.25) 2416 (25) 1295 (26.5) 1121 (29.0) Self_reported hypertention < 0.0001 No 5953 (58.2) 4167 (75.8) 1786 (41.8) Yes 4278 (41.8) 1479 (24.2) 2799 (58.2) 3.2 MHO and mortality The median (IQR) follow-up period was 112 months. Among the participants, 2581 cases of all-cause death and 842 cases of cardiovascular death were identified. Using MUO as the reference group and based on weighted Kaplan-Meier plot, it was observed that, at the end of the follow-up period, individuals with MHO had a significantly higher likelihood of survival for both all-cause mortality and cardiovascular mortality (p < 0.05, Fig. 2 ). Furthermore, in the multivariable Cox proportional hazards regression models analysis, after adjusting for various confounding factors, individuals with MHO, compared to participants with MUO at baseline, consistently showed lower rates of all-cause mortality (HR 0.85; 95% CI 0.76, 0.95) and cardiovascular mortality (HR 0.85; 95% CI 0.76, 0.94) in model 3 (Fig. 3 ). 3.3 Stratified Analyses Consistent findings were observed in the stratified analyses based on age (< 60), sex (Female or Male), race/ethnicity (Non-Hispanic White or other Hispanic), drinking status (nondrinker), physical activity (active or inactive), smoking status (never smoker or current smoker), and self-reported hypertension (no). No statistically significant interactions were identified between MHO and these stratification variables in relation to the risk of all-cause mortality, even after conducting several tests (all Pinteraction > 0.05) (Fig. 4 ). While, the strength of the association between MHO and all-cause mortality exhibited a slight decrease in participants who were older (HR, 0.93 [95% CI, 0.89–0.97] for adults aged < 60 years; 0.96 [95% CI, 0.87–1.05] for those aged ≥ 60 years). Similarly, the association between MHO and all-cause mortality showed a slight decrease in participants who were drinkers (HR, 0.92 [95% CI, 0.84–1.01]), former smokers (HR, 0.90 [95% CI, 0.80–1.03]), and those who self-reported hypertension (HR, 0.95 [95% CI, 0.87–1.04]) (Fig. 4 ). Unsuprisingly, no statistically significant interactions were identified between MHO and these stratification variables in relation to the risk of cardiovascular mortality, even after conducting several tests (all Pinteraction > 0.05) (Fig. 4 ). 3.4 Sensitivity Analyses All sensitivity analyses consistently yielded similar results, demonstrating the robustness of our findings. After excluding participants with a follow-up time of less than 2 years (24 months) and those diagnosed with cardiovascular disease (CVD) at baseline (Table 3 ), Metabolically Healthy Obesity (MHO) continued to show a significantly protective effect against both all-cause mortality (HR, 0.84, 95% CI, 0.78, 0.91) and cardiovascular mortality (HR, 0.86, 95% CI, 0.79, 0.93) compared to Metabolically Unhealthy Obesity (MUO). Furthermore, even after excluding participants with a baseline diagnosis of CVD, MHO still exhibited relatively lower risks of all-cause mortality (HR, 0.92, 95% CI, 0.85, 0.99) and cardiovascular mortality (HR, 0.92, 95% CI, 0.85, 0.99) compared to MUO. Table 3 Hazard ratios of All-Cause and Cardiovascular Mortality by MHO Among US Adults in NHANES 1999–2018. Hazard ratio (95% CIs) for model 3 was adjusted for age (continuous), sex (male of female), and race and ethnicity (self-reported Mexican American, non-Hispanic Black, non-Hispanic White, or other), educational level (< high school, high school or equivalent, or college or above), family income-to-poverty ratio ( 3.0), drinking status (nondrinker, moderate drinker, or heavy drinker), physical activity (inactive or active), smoking status (never smoker, former smoker, or current smoker), HbA1c ( 7%), HEI (in quartiles), total energy intakes (in quartiles), self-reported hypertension (yes or no), TC (in quartiles), TG (in quartiles), HDL-C (in quartiles) and LDL-C (in quartiles). Hazard ratio (95% CI) Characteristic MUO MHO All-cause mortality Model 3 (excluding < 24 months follow-up) 1 0.84 (0.78, 0.91) Model 3 (excluding CVD) 1 0.92 (0.85, 0.99) Cardiovascular mortality Model 3 (excluding < 24 months follow-up) 1 0.86 (0.79, 0.93) Model 3 (excluding CVD) 1 0.92 (0.85, 0.99) 4. Discussion The findings of this comprehensive cohort study, spanning nearly two decades and involving a substantial sample size, shed light on the intriguing relationship between MHO and mortality outcomes. Our study adds valuable insights to the ongoing debate surrounding the heterogeneity of obesity and its health implications. The principal observation from our analysis is that individuals classified as MHO consistently demonstrated significantly lower rates of all-cause and cardiovascular mortality compared to their MUO counterparts at baseline. This robust association held true even after meticulous adjustments for a range of potential confounding factors. These results align with previous research suggesting that metabolic health plays a pivotal role in modifying the adverse health outcomes traditionally associated with obesity [ 7 , 13 , 14 , 18 ]. Unlike previous studies, our research utilizes the widely representative NHANES database with a large sample size and extended follow-up period (up to 20 years). To our knowledge, this study also represents the first direct exploration of the relationship between MHO and all-cause as well as cardiovascular mortality rates when compared to MUO individuals. MHO individuals, characterized by the absence of key metabolic risk factors, appear to possess a certain level of protection against the mortality risks posed by obesity [ 7 ]. This emphasizes the importance of evaluating obesity not solely based on BMI but also considering metabolic health status when assessing health risks [ 19 , 20 ]. It is important to note that the term metabolically healthy does not imply complete absence of health risks [ 7 ]. Although MHO individuals may not exhibit the typical metabolic abnormalities, they can still have other health issues and remain at a higher risk compared to metabolically healthy non-obese individuals [ 11 , 12 , 21 ]. This study's extended time frame allows us to gain a deeper understanding of the long-term health trajectories of individuals with MHO. Over the course of nearly two decades, these individuals exhibited a consistent and substantial reduction in all-cause and cardiovascular mortality compared to their MUO counterparts. Metabolic health outcomes can evolve as individuals age, and this evolution is influenced by a complex interplay of genetic, lifestyle, and physiological factors. For example, as individuals age, there is a natural decline in insulin sensitivity, which can lead to impaired glucose control. Metabolic health outcomes, such as fasting glucose levels and insulin resistance, may worsen over time in both MHO and non-obese individuals [ 22 , 23 ]. This decline can be influenced by factors like reduced physical activity, changes in body composition, and genetic predisposition. Age-related increases in blood pressure are common. Metabolic health parameters, such as systolic and diastolic blood pressure, tend to rise with age [ 24 , 25 ]. For individuals with MHO, maintaining healthy blood pressure levels becomes increasingly important as they age to sustain their protective effect against cardiovascular mortality. Age-related changes in lipid profiles are observed, including elevated triglycerides and reductions in HDL-C. MHO individuals may experience shifts in lipid parameters over time, potentially impacting their metabolic health and cardiovascular risk [ 26 , 27 ]. Thus, for individuals with MHO, maintaining or enhancing metabolic health as they age may require proactive efforts, including regular health screenings, lifestyle modifications, and personalized healthcare plans. Recognizing the dynamic nature of metabolic health over time underscores the importance of long-term healthcare strategies that adapt to an individual's changing needs. How to translate research findings into clinical practice is crucial. Recognizing the presence of MHO and its potential protective effect on mortality risk underscores the need for more nuanced and personalized approaches in public health strategies. A one-size-fits-all approach to obesity may not be effective, as different subgroups of obese individuals may have varying health risks. Healthcare providers should consider metabolic health when assessing the health risks of obese patients. MHO individuals may require different preventive and treatment strategies than those with MUO, emphasizing the importance of targeted interventions based on individual metabolic profiles. It is essential to acknowledge the limitations of our study. The observational nature of the study precludes causal inferences. While we adjusted for various confounding factors, residual confounding remains possible. Additionally, the classification of MHO and MUO is not universally standardized, and differences in criteria may affect the results. Further investigation is warranted to delve deeper into the mechanisms underlying the protective effect of MHO. Understanding the metabolic factors contributing to this association can provide valuable insights into potential therapeutic targets and interventions. Our stratified analyses revealed slight variations in the association between MHO and mortality among specific subgroups, such as older individuals, drinkers, former smokers, and those with self-reported hypertension. While the overall trend of reduced mortality risk for MHO individuals remained consistent, these nuances may warrant further investigation to tailor interventions for these subgroups. Additionally, the study relied on self-reported data for the assessment of obesity and metabolic health, which introduces the possibility of misclassification and reporting bias. Utilizing more objective measures, such as clinical assessments and laboratory tests, would enhance the accuracy of the results. Moreover, this study was observational in nature, and therefore, causal relationships cannot be established. Longitudinal studies and interventional research are needed to investigate the potential mechanisms underlying the observed associations and to determine causality. 5. Conclusion In conclusion, our study contributes robust evidence to the evolving discourse on obesity and its associated health risks. The consistent and substantial reduction in all-cause and cardiovascular mortality among individuals with MHO underscores the pivotal role of metabolic health in shaping obesity-related health outcomes. These findings advocate for a more holistic approach to assessing and managing obesity, one that incorporates both BMI and metabolic health status. Recognizing the complexity of obesity and its heterogeneity is essential for refining healthcare strategies, public health policies, and clinical practice to effectively mitigate obesity-related health risks and improve overall population health. Declarations Ethical Approval Ethical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study. Consent to Publish All authors have contributed significantly, and all authors are in agreement with the content of the manuscript. Funding This work was supported by a grant from National Natural Science Foundation of China (Grant Number. 82474453，82004341); Sichuan Science and Technology Program (Grant Number. 2024YFFK0151). Data availability https://www.cdc.gov/nchs/ Declaration of Competing Interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Availability of data and materials The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Acknowledgements: We are grateful to the participants and to the people involved in the National Health and Nutrition Examination Survey study. Author Contribution Chaoqiang Dong: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Xuemei Wang: Writing – original draft, Visualization, Supervision, Methodology, Conceptualization. Xuelei Zhou: Writing – review & editing, Visualization, Validation, Supervision, Methodology, Funding acquisition, Conceptualization. Chan Yang: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Formal analysis, Funding acquisition. Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 Supplementary Material 2 References NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet. 2017;390(10113):2627–42. https://doi.org/10.1016/S0140-6736( . NCD Risk Factor Collaboration (NCD-RisC). 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Metabolically Healthy Obesity, Transition to Metabolic Syndrome, and Cardiovascular Risk. J Am Coll Cardiol. 2018;71(17):1857–65. https://doi.org/10.1016/j.jacc.2018.02.055 . Hoddy KK, Axelrod CL, Mey JT, et al. Insulin resistance persists despite a metabolically healthy obesity phenotype. Obes (Silver Spring). 2022;30(1):39–44. https://doi.org/10.1002/oby.23312 . Kalyani RR, Egan JM. Diabetes and altered glucose metabolism with aging. Endocrinol Metab Clin North Am. 2013;42(2):333–47. https://doi.org/10.1016/j.ecl.2013.02.010 . Satoh M, Metoki H, Asayama K, et al. Age-Related Trends in Home Blood Pressure, Home Pulse Rate, and Day-to-Day Blood Pressure and Pulse Rate Variability Based on Longitudinal Cohort Data: The Ohasama Study. J Am Heart Assoc. 2019;8(15):e012121. https://doi.org/10.1161/JAHA.119.012121 . Gurven M, Blackwell AD, Rodríguez DE, Stieglitz J, Kaplan H. Does blood pressure inevitably rise with age? longitudinal evidence among forager horticulturalists. Hypertension. 2012;60(1):25–33. https://doi.org/10.1161/HYPERTENSIONAHA.111.189100 . Markovič R, Grubelnik V, Vošner HB, et al. Age-Related Changes in Lipid and Glucose Levels Associated with Drug Use and Mortality: An Observational Study. J Pers Med. 2022;12(2). https://doi.org/10.3390/jpm12020280 . Feng L, Nian S, Tong Z, et al. Age-related trends in lipid levels: a large-scale cross-sectional study of the general Chinese population. BMJ Open. 2020;10(3):e034226. https://doi.org/10.1136/bmjopen-2019-034226 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial1.pdf SupplementaryMaterial2.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6181171","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445869415,"identity":"ed8139cd-fc4f-4bce-bd25-20ea7f189559","order_by":0,"name":"Chaoqiang Dong","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chaoqiang","middleName":"","lastName":"Dong","suffix":""},{"id":445869416,"identity":"2f057607-a68a-4e94-af6b-201536586ffb","order_by":1,"name":"Chan Yang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Chan","middleName":"","lastName":"Yang","suffix":""},{"id":445869417,"identity":"204ed0e6-4658-4405-a70d-fb32a12bde64","order_by":2,"name":"Xuemei Wan","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xuemei","middleName":"","lastName":"Wan","suffix":""},{"id":445869419,"identity":"d9c66dd7-d9fb-4d9e-9826-d81653011599","order_by":3,"name":"Xuelei Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYDACCQglw8DMfICBB1UQvxYeBma2BCBpQIoWBh4D4rTIz24+Js3bZsPDz87z8cPbtj/yug3MB2/zMNjl4dLCOOdYmuTMtjQeyWbezZJz2wwMtx1gS7bmYUguxqWFWSLHTOJj22Eeg8O825h52wwSzA7wmEnzMBxIbMChhQ2kJbHtP4/9YZ5nUC383/Bq4YHYcoDHgJmHDWYLG14tEhJpyZYzziXzSBxmM5acc87YcBuQYTnHIBmnFvkZycDwKbOT4+8//PDDmzI5ebPjzQ9vvKmww6kFC2AGEQbEqx8Fo2AUjIJRgAkAylZI9eptyJgAAAAASUVORK5CYII=","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xuelei","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-03-07 23:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6181171/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6181171/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82051582,"identity":"855fdfcf-7699-496a-863e-1fdb60f7a994","added_by":"auto","created_at":"2025-05-06 10:02:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182190,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eParticipant Flowchart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6181171/v1/58b8e1c0c19245331d4a435a.png"},{"id":82051585,"identity":"e5ceafe6-8dc8-47da-9faa-ae47976d8993","added_by":"auto","created_at":"2025-05-06 10:02:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":309312,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival probability from weighted Kaplan-Meier plots.\u003cstrong\u003e \u003c/strong\u003eA. all-cause mortality. B. cardiovascular mortality. The x-axis represents the follow-up time, while the y-axis displays the estimated survival probability. The orange curve represents no-MHO, that is MUO, while the light blue curve represents MHO.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6181171/v1/4fc26bccb3a69d30681073c2.png"},{"id":82053285,"identity":"7b7401d7-003a-4250-9fd3-3c708995a049","added_by":"auto","created_at":"2025-05-06 10:10:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":265913,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between MHO and all-cause as well as cardiovascular mortality. MUO was used as the reference group. The crude model was unadjusted for any factors. Model 1 was adjusted for age (continuous), sex (male or female), and race and ethnicity (self-reported Mexican American, non-Hispanic Black, non-Hispanic White, or other). Model 2 included additional adjustments for educational level (\u0026lt; high school, high school or equivalent, or college and above), family income-to-poverty ratio (\u0026lt;1.0, 1.0 - 3.0, or \u0026gt;3.0), drinking status (nondrinker, moderate drinker, or heavy drinker), physical activity (inactive or active), smoking status (never smoker, former smoker, or current smoker), HbA1c (\u0026lt;7% or \u0026gt;7%), HEI in quartiles, total energy intakes in quartiles, self-reported hypertension (yes or no), TC in quartiles, TG in quartiles, HDL in quartiles, and LDL in quartiles. Model 3 encompassed all the confounding factors present in both Model 1 and Model 2. This comprehensive adjustment allowed us to thoroughly examine and elucidate the relationship between MHO and all-cause as well as cardiovascular mortality. HR: hazard ratio，CI: confidence interval.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6181171/v1/a9392ded865a29b10953c6db.png"},{"id":82051588,"identity":"a5cdb2da-f734-4397-8c24-0a9e9cc82ac6","added_by":"auto","created_at":"2025-05-06 10:02:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":387234,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations of MHO With all-cause and CVD Mortality in Various Subgroups Among US Adults with Diabetes in NHANES 1999-2018. Seven subgroup were stratified by age (\u0026lt;60 or ≥ 60), sex (Female or Male), race/ethnicity (Non-Hispanic White or other Hispanic), drinking status (drinker or nondrinker), physical activity (active or inactive), smoking status (former/never smoker or current smoker), self-reported hypertension (yes or no). Hazard ratio (95% CIs) was adjusted for age (continuous), sex (male of female), and race and ethnicity (self-reported Mexican American, non-Hispanic Black, non-Hispanic White, or other), educational level (\u0026lt; high school, high school or equivalent, or college or above), family income-to-poverty ratio (\u0026lt;1.0, 1.0-3.0, or \u0026gt;3.0), drinking status (nondrinker, moderate drinker, or heavy drinker), physical activity (inactive or active), smoking status (never smoker, former smoker, or current smoker), HbA1c (\u0026lt;7% or \u0026gt;7%), HEI (in quartiles), total energy intakes (in quartiles), self-reported hypertension (yes or no), TC (in quartiles), TG (in quartiles), HDL-C (in quartiles) and LDL-C (in quartiles).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6181171/v1/c47a0a3b37b5047949a22d35.png"},{"id":93909253,"identity":"e2e63061-9c79-4981-be45-e70e5c7babdf","added_by":"auto","created_at":"2025-10-20 07:39:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1963868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6181171/v1/1e02f79c-6a05-48fc-8569-402454b8e3b6.pdf"},{"id":82053283,"identity":"1bfd35db-d43c-4381-be4d-e2bcb3defd30","added_by":"auto","created_at":"2025-05-06 10:10:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":126398,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6181171/v1/1cb26d9875095ba7fe4883a6.pdf"},{"id":82053287,"identity":"2d28f74a-70f5-415f-b564-589fefa5758a","added_by":"auto","created_at":"2025-05-06 10:10:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":428067,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6181171/v1/3fdc40466796f3c87778016f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"All Cause and Cardiovascular Mortality Risk in Metabolically Healthy and Unhealthy Obesity: Results From NHANES 1999-2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eObesity has emerged as a global public health challenge, with its prevalence steadily increasing across the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is a complex condition associated with a range of health risks, including cardiovascular diseases (CVD) and premature mortality, even in some cases, weight loss may not reduce the risk of CVD [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Recent research has unveiled a multifaceted aspect of obesity, challenging the traditional view that all obese individuals face similar health outcomes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin the realm of obesity research, the concept of Metabolically Healthy Obesity (MHO) has gained prominence [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. MHO refers to individuals who, despite being classified as obese based on their Body Mass Index (BMI), exhibit a metabolically healthy profile. This metabolic health is characterized by the absence of insulin resistance, dyslipidemia, hypertension, and elevated blood sugar levels, which are typically associated with obesity-related health risks [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExisting studies indicate that MHO is not necessarily truly healthy; compared to non-obese individuals, MHO individuals have a significantly increased risk of cardiovascular events and mortality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Furthermore, MHO is not static; it can transform into Metabolically Unhealthy Obesity (MUO) or metabolic syndrome over time [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The intriguing question that has emerged is whether MHO individuals at the baseline, who appear to defy the expected health consequences of obesity, indeed experience a lower risk of mortality, particularly from cardiovascular diseases, when compared to their counterparts with MUO. This question has significant implications for healthcare, public health policy, and our understanding of obesity as a complex and heterogeneous condition. This study delves into a comprehensive study that explores the intricate associations between MHO and all-cause as well as cardiovascular disease mortality among adults in the United States. By examining a large and diverse cohort over an extended period, this research aims to shed light on the potential protective effects of metabolic health within the context of obesity. Understanding these associations may pave the way for more personalized healthcare strategies and inform public health policies to mitigate the impact of obesity-related health risks in a more nuanced manner.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and participants\u003c/h2\u003e \u003cp\u003eThe NHANES, conducted regularly by the Centers for Disease Control and Prevention (CDC) and the CDC's National Center for Health Statistics, is a cross-sectional sampling poll that provides a nationally representative sample of the noninstitutionalized US civilian population. Leveraging NHANES data granted us access to a diverse and comprehensive cohort of individuals, facilitating an in-depth exploration of various health-related factors within the adult population. Trained medical professionals collected five types of information, including demographics, dietary, examination, laboratory, and questionnaire data. This cohort study utilized data from ten NHANES cycles spanning 1999 to 2018, encompassing a total of 59,204 adults aged 18 years and older. We excluded 1,670 participants who self-reported pregnancy, resulting in 57,534 remaining individuals. After applying the inclusion criteria for Metabolically Healthy Obesity (MHO), we identified 18,902 individuals with MHO status. The final MHO analyses included 18,869 participants, following the exclusion of 33 individuals lost to follow-up. Throughout this study, we adhered to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guidelines for cohort studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Definition of Metabolic Health Obese\u003c/h2\u003e \u003cp\u003eFirstly, individuals with a body mass index (BMI) equal to or exceeding 30 kg/m\u0026sup2; were categorized as obese, calculated as the ratio of weight to height squared. Secondly, to ascertain metabolic health status, we applied the Harmonized Criteria for Metabolic Syndrome (MetS) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], considering individuals to be metabolically healthy if they exhibited two or fewer of the following indicators: systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;85mmHg, triglyceride levels\u0026thinsp;\u0026ge;\u0026thinsp;1.69 mmol/L (mM), HDL-cholesterol levels\u0026thinsp;\u0026lt;\u0026thinsp;1.04 mM (in men) or \u0026lt;\u0026thinsp;1.29 mM (in women), fasting plasma glucose levels\u0026thinsp;\u0026ge;\u0026thinsp;5.6 mM, and waist circumference (WC)\u0026thinsp;\u0026ge;\u0026thinsp;102 cm (in men) or \u0026ge;\u0026thinsp;88 cm (in women). Conversely, those meeting three or more of these diagnostic criteria were classified as MUO.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Assessment of Covariates\u003c/h2\u003e \u003cp\u003eFundamental demographic factors, including age, sex, race, ethnicity, education levels, and family income to poverty ratio, were self-reported during interviews. Participants' alcohol consumption was categorized into three groups: nondrinkers, moderate drinkers (consuming fewer than two drinks per day for men and fewer than one drink per day for women), and heavy drinkers (consuming two or more drinks per day for men and one or more drinks per day for women) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Physical activity (PA) was defined as engaging in moderate- to vigorous-intensity sports, fitness programs, or recreational activities for more than 600 minutes per week; otherwise, participants were considered inactive [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. PA was assessed using the Metabolic Equivalent (MET), a standard indicator of relative energy metabolism during various activities. Healthy Eating Index (HEI) 2015 scores were calculated in alignment with the US Dietary Guidelines for Americans (DGA) 2015\u0026ndash;2020 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Participants' self-reported history of physician-diagnosed hypertension was recorded. Additionally, levels of HbA1c, triglycerides, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol were measured at recruitment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Ascertainment of Mortality\u003c/h2\u003e \u003cp\u003eWe acquired death statistics by cross-referencing the cohort database with the publicly accessible National Death Index (NDI) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/data-linkage/mortality.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/data-linkage/mortality.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) up to December 31, 2019. All recorded causes of death were classified as 'all-cause mortality.' Cardiovascular disease (CVD) mortality was identified using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, with codes I00 to I09, I11, I13, I20 to I51, and I60 to I69.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eIn consideration of the intricate examination design employed in NHANES, all analyses in this study incorporated weighted test weights, clustering, and stratification adjustments. Person-years were calculated for each participant from their enrollment date until either the date of death or the conclusion of the follow-up period on December 31, 2019, whichever came first.\u003c/p\u003e \u003cp\u003eTo assess the relationships between Metabolically Healthy Obese (MHO) status and the risks of cardiovascular disease (CVD) and all-cause mortality, we employed weighted Kaplan-Meier plots to visualize survival probabilities and utilized multivariable Cox proportional hazards regression models to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). Proportional hazards assumptions were evaluated using Schoenfeld residuals, revealing no violations.\u003c/p\u003e \u003cp\u003eOur analysis comprised three multivariable models. Model 1 adjusted for age (continuous, years), sex (male or female), and race/ethnicity (self-reported as Mexican American, non-Hispanic Black, non-Hispanic White, other Hispanic, or other race/multi-racial). Model 2 further incorporated adjustments for educational level, family income to poverty ratio, body mass index (BMI), drinking status, physical activity, smoking status, HbA1c level, Healthy Eating Index 2015 (HEI 2015) quartiles, total energy intake quartiles, self-reported hypertension, total cholesterol (TC) quartiles, triglycerides (TG) quartiles, high-density lipoprotein cholesterol (HDL-C) quartiles, and low-density lipoprotein cholesterol (LDL-C) quartiles. Model 3 combined adjustments from both Model 1 and Model 2. Multiple imputation was applied to address missing values.\u003c/p\u003e \u003cp\u003eStratified analyses were conducted based on age (\u0026lt;\u0026thinsp;60 or \u0026ge;\u0026thinsp;60), sex, race/ethnicity, drinking status, physical activity, smoking status, HbA1c level, self-reported hypertension, and diabetes duration (\u0026lt;\u0026thinsp;10 or \u0026ge;\u0026thinsp;10) to mitigate the influence of these factors, with relationships assessed using P-values.\u003c/p\u003e \u003cp\u003eSeveral sensitivity analyses were performed to validate our findings. Firstly, participants who died within the initial 24 months of follow-up were excluded to reduce potential reverse causation bias. Additionally, participants with a history of CVD were excluded from the primary analyses. Sensitivity analysis employing restricted cubic spline (RCS) analysis was also conducted.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R 4.2.1, with statistical significance set at a two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;.05. Data analysis was conducted between October 1, 2022, and June 20, 2023.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e \u003cp\u003eThe final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) comprised a total of 18,869 participants, out of which 10,103 (53.5%) exhibited MHO, while 8,766 (46.5%) had MUO. Participants with MHO at baseline, in comparison to those with MUO, demonstrated the following characteristics: they were younger, more likely to be female, had higher education levels (College and above), belonged to middle-income families in relation to the poverty ratio, were more likely to be nondrinkers and never smokers, engaged in active physical activity, had lower levels of triglycerides (TG), total cholesterol (TC), and low-density lipoprotein (LDL), lower HbA1c levels, higher Healthy Eating Index (HEI) scores, and consumed a higher total energy intake. Additionally, they were also more likely to have no hypertension (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics. Numbers are N (%) unless otherwise specified. Some sub-categories, such as education level, may not add up due to missing data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMHO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMUO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003evalue\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e \u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10103 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8766 (46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.6 (47.2,48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.8 (42.4,43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.3 (52.8,53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.7 (35.5,35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.6 (35.4,35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.8 (35.6,36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10484 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6075 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4409 (48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8385 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4028 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4357 (51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicty\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3658 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2094 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1564 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5031 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2655 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2376 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7610 (40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3879 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3731 (69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1589 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e901 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e688 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e981 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e574 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e407 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5329 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2833 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2496 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8646 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4531 (53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4115 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4878 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2729 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2149 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily income to poverty ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3835 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2278 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1557 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5854 (34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2900 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2954 (50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.0\u0026ndash;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7538 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4034 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3504 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrink\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2305 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1116 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1189 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1004 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e457 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e547 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5332 (61.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3212 (62.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2120 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7326 (71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4068 (73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3258 (72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2922 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1589 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1333 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoke\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2483 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1084 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1399 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5630 (56.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3226 (58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2404 (52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1813 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1069 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e744 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC, mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1 (\u0026lt;\u0026thinsp;4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2340 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1347 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e993 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2 (4.24\u0026ndash;4.91 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2443 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1339 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1104 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3 (4.91\u0026ndash;5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2433 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1250 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1183 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4 (5.64\u0026ndash;15.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2486 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1199 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1287 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG, mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1 (\u0026lt;\u0026thinsp;0.903)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1146 (24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e531 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e615 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2 (0.903\u0026ndash;1.310 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1143 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e423 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e720 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3 (1.310\u0026ndash;1.897)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1161 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e372 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e789 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4 (\u0026gt;\u0026thinsp;1.897)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1159 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1009 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL, mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1 (\u0026lt;\u0026thinsp;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2425 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1761 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e664 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2 (1.01\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2390 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1229 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1161 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3 (1.19\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2230 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1171 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1059 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4 (\u0026gt;\u0026thinsp;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2656 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e973 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1683 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL, mmol/L\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1 (\u0026lt;\u0026thinsp;2.353 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1079 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e353 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e726 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2 (2.353\u0026ndash;2.948)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1144 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e406 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e738 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3 (2.948\u0026ndash;3.569)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1131 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e376 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e755 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4 (\u0026gt;\u0026thinsp;3.569)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1128 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e313 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e815 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c, %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8784 (89.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4854 (94.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3930 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1021 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e370 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e651 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHEI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1 (\u0026lt;\u0026thinsp;39.509571)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2416 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1393 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1023 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2 (39.509571\u0026ndash;48.583837 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2416 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1327 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1089 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3 (48.583837\u0026ndash;58.252793)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2416 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1311 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1105 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4 (\u0026gt;\u0026thinsp;58.252793)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2416 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1219 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1197 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal energy intakes, kcal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 1 (\u0026lt;\u0026thinsp;1443.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2414 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1301 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1113 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 2 (1443.00-1964.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2412 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1326 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1086 (23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 3 (1964.00-2652.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2422 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1328 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1094 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile 4 (\u0026gt;\u0026thinsp;2652.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2416 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1295 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1121 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf_reported hypertention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5953 (58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4167 (75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1786 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4278 (41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1479 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2799 (58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 MHO and mortality\u003c/h2\u003e \u003cp\u003eThe median (IQR) follow-up period was 112 months. Among the participants, 2581 cases of all-cause death and 842 cases of cardiovascular death were identified. Using MUO as the reference group and based on weighted Kaplan-Meier plot, it was observed that, at the end of the follow-up period, individuals with MHO had a significantly higher likelihood of survival for both all-cause mortality and cardiovascular mortality (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, in the multivariable Cox proportional hazards regression models analysis, after adjusting for various confounding factors, individuals with MHO, compared to participants with MUO at baseline, consistently showed lower rates of all-cause mortality (HR 0.85; 95% CI 0.76, 0.95) and cardiovascular mortality (HR 0.85; 95% CI 0.76, 0.94) in model 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Stratified Analyses\u003c/h2\u003e \u003cp\u003eConsistent findings were observed in the stratified analyses based on age (\u0026lt;\u0026thinsp;60), sex (Female or Male), race/ethnicity (Non-Hispanic White or other Hispanic), drinking status (nondrinker), physical activity (active or inactive), smoking status (never smoker or current smoker), and self-reported hypertension (no). No statistically significant interactions were identified between MHO and these stratification variables in relation to the risk of all-cause mortality, even after conducting several tests (all Pinteraction\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). While, the strength of the association between MHO and all-cause mortality exhibited a slight decrease in participants who were older (HR, 0.93 [95% CI, 0.89\u0026ndash;0.97] for adults aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years; 0.96 [95% CI, 0.87\u0026ndash;1.05] for those aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years). Similarly, the association between MHO and all-cause mortality showed a slight decrease in participants who were drinkers (HR, 0.92 [95% CI, 0.84\u0026ndash;1.01]), former smokers (HR, 0.90 [95% CI, 0.80\u0026ndash;1.03]), and those who self-reported hypertension (HR, 0.95 [95% CI, 0.87\u0026ndash;1.04]) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Unsuprisingly, no statistically significant interactions were identified between MHO and these stratification variables in relation to the risk of cardiovascular mortality, even after conducting several tests (all Pinteraction\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eAll sensitivity analyses consistently yielded similar results, demonstrating the robustness of our findings. After excluding participants with a follow-up time of less than 2 years (24 months) and those diagnosed with cardiovascular disease (CVD) at baseline (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), Metabolically Healthy Obesity (MHO) continued to show a significantly protective effect against both all-cause mortality (HR, 0.84, 95% CI, 0.78, 0.91) and cardiovascular mortality (HR, 0.86, 95% CI, 0.79, 0.93) compared to Metabolically Unhealthy Obesity (MUO). Furthermore, even after excluding participants with a baseline diagnosis of CVD, MHO still exhibited relatively lower risks of all-cause mortality (HR, 0.92, 95% CI, 0.85, 0.99) and cardiovascular mortality (HR, 0.92, 95% CI, 0.85, 0.99) compared to MUO.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratios of All-Cause and Cardiovascular Mortality by MHO Among US Adults in NHANES 1999\u0026ndash;2018. Hazard ratio (95% CIs) for model 3 was adjusted for age (continuous), sex (male of female), and race and ethnicity (self-reported Mexican American, non-Hispanic Black, non-Hispanic White, or other), educational level (\u0026lt;\u0026thinsp;high school, high school or equivalent, or college or above), family income-to-poverty ratio (\u0026lt;\u0026thinsp;1.0, 1.0\u0026ndash;3.0, or \u0026gt;\u0026thinsp;3.0), drinking status (nondrinker, moderate drinker, or heavy drinker), physical activity (inactive or active), smoking status (never smoker, former smoker, or current smoker), HbA1c (\u0026lt;\u0026thinsp;7% or \u0026gt;\u0026thinsp;7%), HEI (in quartiles), total energy intakes (in quartiles), self-reported hypertension (yes or no), TC (in quartiles), TG (in quartiles), HDL-C (in quartiles) and LDL-C (in quartiles).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMUO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMHO\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 (excluding\u0026thinsp;\u0026lt;\u0026thinsp;24 months follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.78, 0.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 (excluding CVD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.85, 0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCardiovascular mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 (excluding\u0026thinsp;\u0026lt;\u0026thinsp;24 months follow-up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86 (0.79, 0.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 (excluding CVD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.85, 0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe findings of this comprehensive cohort study, spanning nearly two decades and involving a substantial sample size, shed light on the intriguing relationship between MHO and mortality outcomes. Our study adds valuable insights to the ongoing debate surrounding the heterogeneity of obesity and its health implications. The principal observation from our analysis is that individuals classified as MHO consistently demonstrated significantly lower rates of all-cause and cardiovascular mortality compared to their MUO counterparts at baseline. This robust association held true even after meticulous adjustments for a range of potential confounding factors.\u003c/p\u003e \u003cp\u003eThese results align with previous research suggesting that metabolic health plays a pivotal role in modifying the adverse health outcomes traditionally associated with obesity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Unlike previous studies, our research utilizes the widely representative NHANES database with a large sample size and extended follow-up period (up to 20 years). To our knowledge, this study also represents the first direct exploration of the relationship between MHO and all-cause as well as cardiovascular mortality rates when compared to MUO individuals. MHO individuals, characterized by the absence of key metabolic risk factors, appear to possess a certain level of protection against the mortality risks posed by obesity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This emphasizes the importance of evaluating obesity not solely based on BMI but also considering metabolic health status when assessing health risks [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It is important to note that the term metabolically healthy does not imply complete absence of health risks [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although MHO individuals may not exhibit the typical metabolic abnormalities, they can still have other health issues and remain at a higher risk compared to metabolically healthy non-obese individuals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study's extended time frame allows us to gain a deeper understanding of the long-term health trajectories of individuals with MHO. Over the course of nearly two decades, these individuals exhibited a consistent and substantial reduction in all-cause and cardiovascular mortality compared to their MUO counterparts. Metabolic health outcomes can evolve as individuals age, and this evolution is influenced by a complex interplay of genetic, lifestyle, and physiological factors. For example, as individuals age, there is a natural decline in insulin sensitivity, which can lead to impaired glucose control. Metabolic health outcomes, such as fasting glucose levels and insulin resistance, may worsen over time in both MHO and non-obese individuals [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This decline can be influenced by factors like reduced physical activity, changes in body composition, and genetic predisposition. Age-related increases in blood pressure are common. Metabolic health parameters, such as systolic and diastolic blood pressure, tend to rise with age [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. For individuals with MHO, maintaining healthy blood pressure levels becomes increasingly important as they age to sustain their protective effect against cardiovascular mortality. Age-related changes in lipid profiles are observed, including elevated triglycerides and reductions in HDL-C. MHO individuals may experience shifts in lipid parameters over time, potentially impacting their metabolic health and cardiovascular risk [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Thus, for individuals with MHO, maintaining or enhancing metabolic health as they age may require proactive efforts, including regular health screenings, lifestyle modifications, and personalized healthcare plans. Recognizing the dynamic nature of metabolic health over time underscores the importance of long-term healthcare strategies that adapt to an individual's changing needs.\u003c/p\u003e \u003cp\u003eHow to translate research findings into clinical practice is crucial. Recognizing the presence of MHO and its potential protective effect on mortality risk underscores the need for more nuanced and personalized approaches in public health strategies. A one-size-fits-all approach to obesity may not be effective, as different subgroups of obese individuals may have varying health risks. Healthcare providers should consider metabolic health when assessing the health risks of obese patients. MHO individuals may require different preventive and treatment strategies than those with MUO, emphasizing the importance of targeted interventions based on individual metabolic profiles. It is essential to acknowledge the limitations of our study. The observational nature of the study precludes causal inferences. While we adjusted for various confounding factors, residual confounding remains possible. Additionally, the classification of MHO and MUO is not universally standardized, and differences in criteria may affect the results. Further investigation is warranted to delve deeper into the mechanisms underlying the protective effect of MHO. Understanding the metabolic factors contributing to this association can provide valuable insights into potential therapeutic targets and interventions. Our stratified analyses revealed slight variations in the association between MHO and mortality among specific subgroups, such as older individuals, drinkers, former smokers, and those with self-reported hypertension. While the overall trend of reduced mortality risk for MHO individuals remained consistent, these nuances may warrant further investigation to tailor interventions for these subgroups. Additionally, the study relied on self-reported data for the assessment of obesity and metabolic health, which introduces the possibility of misclassification and reporting bias. Utilizing more objective measures, such as clinical assessments and laboratory tests, would enhance the accuracy of the results. Moreover, this study was observational in nature, and therefore, causal relationships cannot be established. Longitudinal studies and interventional research are needed to investigate the potential mechanisms underlying the observed associations and to determine causality.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our study contributes robust evidence to the evolving discourse on obesity and its associated health risks. The consistent and substantial reduction in all-cause and cardiovascular mortality among individuals with MHO underscores the pivotal role of metabolic health in shaping obesity-related health outcomes. These findings advocate for a more holistic approach to assessing and managing obesity, one that incorporates both BMI and metabolic health status. Recognizing the complexity of obesity and its heterogeneity is essential for refining healthcare strategies, public health policies, and clinical practice to effectively mitigate obesity-related health risks and improve overall population health.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical review and approval were not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have contributed significantly, and all authors are in agreement with the content of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from National Natural Science Foundation of China (Grant Number. 82474453，82004341); Sichuan Science and Technology Program (Grant Number. 2024YFFK0151).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ehttps://www.cdc.gov/nchs/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the participants and to the people involved in the National Health and Nutrition Examination Survey study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChaoqiang Dong: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Xuemei Wang: Writing \u0026ndash; original draft, Visualization, Supervision, Methodology, Conceptualization. Xuelei Zhou: Writing \u0026ndash; review \u0026amp; editing, Visualization, Validation, Supervision, Methodology, Funding acquisition, Conceptualization. Chan Yang: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Visualization, Validation, Methodology, Formal analysis, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElectronic supplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBelow is the link to the electronic supplementary material.\u003c/p\u003e\n\u003cp\u003eSupplementary Material 1\u003c/p\u003e\n\u003cp\u003eSupplementary Material 2\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNCD Risk Factor Collaboration (NCD-RisC). 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Age-related trends in lipid levels: a large-scale cross-sectional study of the general Chinese population. BMJ Open. 2020;10(3):e034226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2019-034226\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2019-034226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metabolically healthy obesity, Metabolically unhealthy obesity, Cardiovascular mortality, All-cause mortality, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-6181171/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6181171/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMetabolically healthy obese (MHO) is not static; it can transform into Metabolically Unhealthy Obesity (MUO) over time. Whether MHO individuals at the baseline, who appear to defy the expected health consequences of obesity, indeed experience a lower risk of mortality when compared to their counterparts with MUO.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn a cohort study using data from the National Health and Nutrition Examination Survey (NHANES) thorough 1999\u0026ndash;2018, metabolic health and obesity status were assessed at baseline. Participants were followed up for 112-months follow-up to ascertain cardiovascular and all-cause mortality events. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) adjusting for potential confounders. Stratified analyses and sensitivity analyses were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn a cohort of 18,869 participants, with a median 112-month follow-up, 2581 all-cause deaths and 842 cardiovascular deaths were observed. MHO individuals had significantly higher survival rates for both all-cause and cardiovascular mortality compared to MUO participants (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Adjusted multivariable models consistently showed that MHO individuals had lower rates of all-cause mortality (HR 0.85; 95% CI 0.76, 0.95) and cardiovascular mortality (HR 0.85; 95% CI 0.76, 0.94). There were no significant interactions between MHO and these factors except for a slight decrease in the association strength among older participants, drinkers, former smokers, and those with self-reported hypertension. Sensitivity analyses, including the exclusion of shorter follow-up times and baseline cardiovascular disease cases, consistently supported the protective effect of MHO against both all-cause mortality (HR, 0.84 to 0.92) and cardiovascular mortality (HR, 0.86 to 0.92) compared to MUO.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eour extensive analysis of a large cohort spanning nearly a decade highlights that individuals with MHO consistently exhibit significantly lower rates of all-cause and cardiovascular mortality compared to their MUO counterparts. These findings emphasize the critical role of metabolic health in evaluating mortality risk among obese individuals and underscore the potential for more personalized healthcare strategies and public health policies to address obesity-related health outcomes.\u003c/p\u003e","manuscriptTitle":"All Cause and Cardiovascular Mortality Risk in Metabolically Healthy and Unhealthy Obesity: Results From NHANES 1999-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 10:02:27","doi":"10.21203/rs.3.rs-6181171/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"52a9e59c-7bff-4369-8f76-fa7bc43be344","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T07:39:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 10:02:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6181171","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6181171","identity":"rs-6181171","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}