U-Shaped Association Between Body Roundness Index and All-Cause Mortality in Hypertensive Adults: NHANES 1999–2018 Cohort Study

U-Shaped Association Between Body Roundness Index and All-Cause Mortality in Hypertensive Adults: NHANES 1999–2018 Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article U-Shaped Association Between Body Roundness Index and All-Cause Mortality in Hypertensive Adults: NHANES 1999–2018 Cohort Study Jing Wang, Li Fu, Tingting Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7174810/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 Previous studies have shown that obesity is associated with an increased risk of various cardiovascular diseases. The Body Roundness Index (BRI) is a novel indicator for assessing body fat and visceral fat. However, the relationship between BRI and all-cause and cardiovascular mortality in individuals with hypertension remains unclear. This study aims to investigate the association between BRI and all-cause and cardiovascular mortality among US adults with hypertension. Methods This study utilized data from the National Health and Nutrition Examination Survey (NHANES) (1999–2018). The study population consisted of 20,532 hypertensive adults. Cox proportional hazards models were used to assess the association between BRI and all-cause and cardiovascular mortality. A generalized additive model were employed to evaluate potential nonlinear relationships between BRI and mortality. Results Among the 20,532 hypertensive adults (mean age: 59.5 ± 15.9 years), a total of 5,044 (25.4%) participants died during follow-up. BRI exhibited a U-shaped association with all-cause mortality, with an inflection point at 5.09. Below the inflection point, each unit increase in BRI was associated with a decreased risk of all-cause mortality (HR = 0.82, 95% CI: 0.79–0.86, P < 0.0001); above the inflection point, each unit increase in BRI was associated with an increased risk (HR = 1.05, 95% CI: 1.04–1.07, P < 0.0001). A similar U-shaped relationship was observed for cardiovascular mortality, with an inflection point at 4.97 (HR = 0.87 [0.80, 0.94], P = 0.0006 below the inflection point; HR = 1.23 [1.12, 1.36], P < 0.0001 above the inflection point). After adjusting for age, sex, race, and education level, both the lowest and highest BRI tertiles were associated with higher all-cause mortality. Conclusion Among US adults with hypertension, BRI demonstrates a U-shaped relationship with all-cause and cardiovascular mortality. Further research is needed to validate these findings. Body Roundness Index hypertension all-cause mortality cardiovascular mortality NHANES Figures Figure 1 Figure 2 Figure 3 Background Hypertension is worldwide and is a primary contributor to cardiovascular disease (CVD) morbidity and mortality, as well as all-cause death[ 1 , 2 ]. Obesity, especially visceral obesity, is widely acknowledged as a significant risk factor linked to cardiovascular events and overall mortality. Increasing evidence suggests that visceral fat poses a far greater health risk compared to subcutaneous fat, as it is more strongly associated with various diseases [ 3 , 4 ]. Traditional anthropometric indices, particularly Body Mass Index (BMI), are widely used but have significant limitations[ 5 ]. BMI fails to distinguish between fat mass and lean mass or capture critical aspects of body fat distribution, notably visceral adiposity, which is strongly linked to metabolic dysregulation and cardiovascular risk[ 6 ]. Waist circumference (WC) and waist-to-height ratio (WHtR) offer improvements by focusing on central adiposity but may not fully characterize body shape complexity[ 7 , 8 ]. The Body Roundness Index (BRI) was proposed by Thomas et al (2013)[ 9 ]. BRI is calculated using both height and waist circumference. Evidence suggests BRI outperforms BMI and may be comparable or superior to WC and WHtR in predicting cardiometabolic risk factors, type 2 diabetes, and hypertension incidence[ 2 , 10 ]. While BRI shows promise in predicting incident disease, data on its association with hard clinical endpoints, particularly mortality, is more limited and somewhat conflicting. Crucially, the relationship between BRI and mortality risk specifically within hypertensive adults – a population inherently at elevated cardiovascular risk – has not been thoroughly investigated using large-scale, nationally representative data[ 11 ]. Therefore, this study aims to prospectively investigate the association between BRI and the risks of cardiovascular mortality and all-cause mortality in a large, nationally representative sample of U.S. hypertensive adults participating in the National Health and Nutrition Examination Survey (NHANES) 1999–2018. Methods Study Design and Study participants This population-based cross-sectional study utilized data from NHANES (1999–2018), a nationally representative survey conducted by the Centers for Disease Control and Prevention (CDC) to assess the health and nutritional status of the US population. NHANES employs a stratified, multistage sampling design to ensure a representative sample of the U.S. civilian noninstitutionalized population, collecting comprehensive data on demographics, socio-economic status, dietary habits, and health-related outcomes. The NHANES study protocol was approved by the Research Ethics Review Committee of the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC). All participants provided written informed consent, as mandated by the NCHS Research Ethics Review Board (ERB). This secondary analysis exclusively used de-identified NHANES data, thus requiring no additional ethical approval or consent. Further details are available on the NHANES website ( https://www.cdc.gov/nchs/nhanes/ ). Participants were adults (≥ 18 years) with hypertension from the NHANES database. Hypertension was defined as (1) systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg, (2) current use of antihypertensive medication, or (3) self-reported hypertension. Exclusion criteria included missing BRI and mortality data. The final analytical sample comprised 20,532 individuals. The flowchart shows the detailed inclusion and exclusion of participants (Fig. 1 .). Definition of body roundness index The Body Roundness Index (BRI), introduced by Thomas et al. in 2013, serves as an anthropometric metric designed to evaluate fat distribution, with particular emphasis on visceral adiposity. In contrast to the Body Mass Index (BMI), which does not differentiate between adipose tissue and lean muscle mass, BRI conceptualizes the human torso as an elliptical shape, thus offering a refined representation of body morphology. Elevated BRI values correspond to a more rounded body shape, which has been correlated with higher levels of body fat, especially visceral fat—a known risk factor for various chronic diseases. The calculation of BRI relies on anthropometric measurements including waist circumference (Wc) and height, which were obtained from the NHANES database's body measurement records. The index values were subsequently derived by applying the established BRI formula. Body Roundness Index (BRI)[ 12 ]: Calculated as BRI = 364.2–365.5 × Covariates Definitions We included a range of baseline covariates known or suspected to confound the relationship between adiposity and mortality. Demographic variables were age (years) and sex (male or female). Race/ethnicity was categorized as Non-Hispanic White, Non-Hispanic Black, Hispanic (any race), or Other (including multi-racial). Socioeconomic status was approximated by education level (less than high school, high school graduate/GED, some college or higher). Lifestyle factors included cigarette smoking status (never, former, current smoker), alcohol use (classified as non-drinker, moderate drinker, or heavy drinker based on self-reported frequency and quantity of alcohol consumption), and total physical activity level, expressed in metabolic equivalent (MET) hours per week (derived from self-reported leisure and household activities). We also adjusted for the presence of diabetes mellitus, defined by self-reported physician diagnosis or use of glucose-lowering medications. These covariates were selected a priori due to their associations with both body composition and mortality risk. Outcome Definitions Outcome Definitions All-cause mortality: Obtained via linkage between NHANES and the National Death Index (NDI), with follow-up from survey participation until death or December 31, 2019. Cardiovascular mortality: Determined using death certificate data based on ICD-10 codes. Statistical Analysis Continuous variables are presented as mean ± standard deviation (SD), while categorical variables are expressed as number and weighted percentages.BRI was categorized into tertiles based on its distribution within the study population. Differences in baseline characteristics across BRI tertiles were assessed using one-way ANOVA for continuous variables and the Chi-square test for categorical variables. A P-value < 0.05 was considered statistically significant. To examine the association between BRI and mortality risk in hypertensive patients, Cox proportional hazards models were employed. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. Two multivariable-adjusted models were constructed: Model I: Adjusted for age, sex, ethnicity, marital status, education level, and poverty income ratio. Model II: Further adjusted for smoking status, alcohol use, total physical activity (MET/week), and diabetes mellitus. To explore potential non-linear relationships between BRI and mortality, we performed a segmented Cox regression analysis. This analysis allowed for the identification of potential threshold effects of BRI on mortality risk. The best-fitting breakpoint (K) was determined based on the maximization of the log-likelihood function. Likelihood ratio tests were used to compare the segmented regression model to a linear Cox model. To determine the cumulative survival of long-term all-cause and cardiovascular mortality in different body round index groups, survival analysis by the Kaplan–Meier method was performed and the significance was determined by the log-rank test. All the statistical analyses were performed using the EmpowerStats ( www.empowerstats.com , X&Y solutions, Inc. Boston MA) and R software version 4.2.0 ( http://www.r-project.org ). The two-sided alpha level was set at 0.05. Results Baseline Characteristics of study participants Table 1 outlines characteristics of participants stratified by Body Roundness Index (BRI) tertiles (Low, Middle, High), each group consisting of 6,844 individuals. Significant differences were observed across tertiles in continuous variables including age, poverty income ratio, body mass index (BMI), waist circumference, white blood cell count (WBC), and follow-up time (all P < 0.001). Notably, the High BRI group exhibited the highest mean BMI (37.44 ± 6.34 kg/m²) and waist circumference (119.75 ± 12.41 cm), whereas these values were lowest in the Low BRI group. Sex distribution differed significantly among groups ( P < 0.001), with a greater proportion of females in the High BRI tertile (61.98%). Significant differences were also observed in ethnicity, marital status, education level, poverty income status, smoking and alcohol use, physical activity levels, and diabetes mellitus prevalence across tertiles (all P < 0.001). The prevalence of diabetes was markedly higher in the High BRI group (42.09%) compared to the Low BRI group (15.97%). All-cause mortality differed significantly among the tertiles ( P < 0.001), whereas cardiovascular mortality did not reach statistical significance ( P = 0.082). Table 1 Baseline characteristics of study participants Body Roundness Index tertile Low Middle High P-value N 6844 6844 6844 Age (years) 57.61 ± 17.12 61.15 ± 15.28 58.64 ± 15.12 < 0.001 Poverty income ratio 2.62 ± 1.63 2.53 ± 1.59 2.29 ± 1.53 < 0.001 Body Mass Index(kg/m2) 24.44 ± 3.13 29.47 ± 3.03 37.44 ± 6.34 < 0.001 Waist(cm) 88.89 ± 8.59 102.82 ± 6.99 119.75 ± 12.41 < 0.001 A Body Shape Index 0.08 ± 0.00 0.08 ± 0.00 0.08 ± 0.00 < 0.001 WBC 6.94 ± 3.11 7.26 ± 2.12 7.88 ± 5.51 < 0.001 Sex < 0.001 Male 3912 (57.16%) 3623 (52.94%) 2602 (38.02%) Female 2932 (42.84%) 3221 (47.06%) 4242 (61.98%) Ethnicity < 0.001 Non-Hispanic White 3087 (45.11%) 3103 (45.34%) 3024 (44.18%) Non-Hispanic Black 1926 (28.14%) 1574 (23.00%) 1804 (26.36%) Mexican American 687 (10.04%) 1125 (16.44%) 1147 (16.76%) Other Hispanic 397 (5.80%) 550 (8.04%) 568 (8.30%) Other Race 747 (10.91%) 492 (7.19%) 301 (4.40%) Marital status < 0.001 Married/Living with Partner 3963 (59.00%) 4164 (61.44%) 3754 (55.52%) Widowed/Divorced/Separated 1930 (28.73%) 2030 (29.95%) 2215 (32.76%) Never married 824 (12.27%) 583 (8.60%) 793 (11.73%) EDU < 0.001 Below high school 778 (11.37%) 1081 (15.79%) 1058 (15.46%) High school 2740 (40.04%) 2768 (40.44%) 2888 (42.20%) Above high school 3312 (48.39%) 2987 (43.64%) 2895 (42.30%) Missing 14 (0.20%) 8 (0.12%) 3 (0.04%) Poverty income ratio < 0.001 Poor 1137 (16.61%) 1187 (17.34%) 1435 (20.97%) Nearly poor 1636 (23.90%) 1759 (25.70%) 1924 (28.11%) Middle income 1730 (25.28%) 1711 (25.00%) 1642 (23.99%) High income 1713 (25.03%) 1582 (23.12%) 1237 (18.07%) Missing 628 (9.18%) 605 (8.84%) 606 (8.85%) SMOKE < 0.001 Never 3242 (47.37%) 3381 (49.40%) 3546 (51.81%) Former 1830 (26.74%) 2299 (33.59%) 2188 (31.97%) Now 1652 (24.14%) 1117 (16.32%) 1067 (15.59%) Missing 120 (1.75%) 47 (0.69%) 43 (0.63%) Alcohol use < 0.001 Never 833 (12.17%) 987 (14.42%) 1117 (16.32%) Former 1206 (17.62%) 1443 (21.08%) 1616 (23.61%) Mild 2294 (33.52%) 2184 (31.91%) 1887 (27.57%) Moderate 825 (12.05%) 738 (10.78%) 733 (10.71%) Heavy 1034 (15.11%) 900 (13.15%) 866 (12.65%) Missing 652 (9.53%) 592 (8.65%) 625 (9.13%) Total physical activity (MET/week) < 0.001 =600 3289 (48.06%) 2958 (43.22%) 2617 (38.24%) Missing 1845 (26.96%) 2269 (33.15%) 2682 (39.19%) Diabetes Mellitus < 0.001 No 5195 (76.31%) 4223 (62.05%) 3307 (48.77%) Diabetes Mellitus 1087 (15.97%) 1946 (28.59%) 2854 (42.09%) IFG(Impaired Fasting Glycaemia) 315 (4.63%) 418 (6.14%) 414 (6.11%) IGT(Impaired Glucose Tolerance) 211 (3.10%) 219 (3.22%) 206 (3.04%) Mean ± SD for continuous variables; (%) for categorical variables; Abbreviation: WBC: white blood cell; EDU: education recoded. Association Between BRI and Mortality In multivariable Cox proportional hazards models using the middle tertile of the Body Roundness Index (BRI) as the reference(Table 2 .), Adjust I model (controlling for age, sex, ethnicity, and education) revealed that individuals in the low BRI tertile had a significantly increased risk of all-cause mortality (HR = 1.11, 95% CI: 1.04–1.19, P = 0.0016), and those in the high BRI tertile exhibited an even higher risk (HR = 1.21, 95% CI: 1.13–1.29, P < 0.001). After further adjustment in Adjust II model (including smoking status, alcohol use, physical activity, and diabetes), the elevated risk persisted in the low BRI group (HR = 1.16, 95% CI: 1.09–1.25, P < 0.001) and remained significant but attenuated in the high BRI group (HR = 1.11, 95% CI: 1.03–1.19, P = 0.004). For cardiovascular mortality, the high BRI tertile was associated with a significantly increased risk in Adjust I (HR = 1.26, 95% CI: 1.12–1.42, P < 0.001), whereas the low BRI tertile showed no significant difference ( P = 0.6649); these results were consistent in Adjust II with a significant risk increase in the high BRI group (HR = 1.16, 95% CI: 1.03–1.30, P = 0.017) and no significant change for the low tertile ( P = 0.227). These findings indicate that higher BRI is significantly associated with increased risks of all-cause and cardiovascular mortality. Table 2 Associations of Body Roundness Index (BRI) Tertiles with All-Cause and Cardiovascular Mortality Risk Using Cox Proportional Hazards Models Exposure Adjust I HR (95%CI) P value Adjust II HR (95%CI) P value All-cause mortality Body Roundness Index tertile Middle Reference Reference Low 1.11 (1.04, 1.19) a 0.0016 b 1.16 (1.09, 1.25) < 0.0001 High 1.21 (1.13, 1.29) < 0.0001 1.11 (1.03, 1.19) 0.0040 Cardiovascular mortality Body Roundness Index tertile Middle Reference Reference Low 1.03 (0.91, 1.15) 0.6649 1.08 (0.96, 1.21) 0.2266 High 1.26 (1.12, 1.42) < 0.0001 1.16 (1.03, 1.30) 0.0167 The hazard ratio was calculated by weighted Multivariate Cox proportional hazards regression. Adjust I model: adjusted for age (years), sex, ethnicity, and education (recoded); Adjust II model: further adjusted for smoking (recoded), alcohol use (recoded), total physical activity (MET/week), and diabetes mellitus; Time variable in Cox model: PERMTH_INT. a Values are hazard ratio (95% confidence interval). b Values are P-value. Dose-response relationship between BRI and mortality A Cox proportional hazards model(Table 3 ) was used to examine the association between Body Roundness Index (BRI) and all-cause as well as cardiovascular mortality. In Model I, treating BRI as a continuous linear variable showed no significant association with all-cause mortality (HR = 1.00, 95% CI: 0.98–1.01, P = 0.7543) and a borderline association with cardiovascular mortality (HR = 1.02, 95% CI: 1.00–1.05, P = 0.0660). Model II employed a threshold effect analysis identifying inflection points at 5.09 for all-cause mortality and 4.97 for cardiovascular mortality. Below the threshold, BRI was significantly negatively associated with all-cause mortality (HR = 0.82, 95% CI: 0.79–0.86, P < 0.001) and cardiovascular mortality (HR = 0.87, 95% CI: 0.80–0.94, P = 0.0006); above the threshold, BRI showed a significant positive association with both all-cause mortality (HR = 1.05, 95% CI: 1.04–1.07, P < 0.001) and cardiovascular mortality (HR = 1.07, 95% CI: 1.03–1.10, P < 0.001). The difference in effect sizes between the two segments was statistically significant for all-cause mortality (HR = 1.28, 95% CI: 1.21–1.35, P < 0.001) and cardiovascular mortality (HR = 1.23, 95% CI: 1.12–1.36, P < 0.001). Covariates adjusted for included age, sex, ethnicity, education level, smoking status, alcohol use, total physical activity (MET/week), and diabetes mellitus, with follow-up time variable PERMTH_INT. These findings suggest a nonlinear relationship between BRI and mortality risk, presenting a significant threshold effect. Table 3 Associations of Body Roundness Index (BRI) Tertiles with All-Cause and Cardiovascular Mortality Risk Using Cox Proportional Hazards Models. Outcome: All-cause mortality Cardiovascular mortality 模型 I 一条直线效应 1.00 (0.98, 1.01) 0.7543 1.02 (1.00, 1.05) 0.0660 模型 II 折点(K) 5.09 4.97 < K 段效应 1 0.82 (0.79, 0.86) K 段效应 2 1.05 (1.04, 1.07) < 0.0001 1.07 (1.03, 1.10) < 0.0001 2与1的效应差 1.28 (1.21, 1.35) < 0.0001 1.23 (1.12, 1.36) < 0.0001 对数似然比检验 < 0.001 < 0.001 A generalized additive model (Fig. 2 ) revealed a U-shaped relationship between BRI and all-cause mortality ( P < 0.0001), with an inflection point at 5.09. Below the inflection point, each unit increase in BRI reduced mortality risk (HR = 0.82, 95% CI: 0.79–0.86, P < 0.0001); above it, each unit increased risk (HR = 1.05, 95% CI: 1.04–1.07, P < 0.0001). For cardiovascular mortality, the inflection point was 4.97 (HR = 0.87 [0.80, 0.94], P = 0.0006 below; HR = 1.23 [1.12, 1.36], P < 0.0001 above). The cumulative hazard curves for all-cause mortality and cardiovascular mortality stratified by Body Roundness Index (BRI) tertiles are presented in Figs. 3 (A) and Figs. 3 ༈B༉, respectively. For all-cause mortality, the cumulative hazard was lowest in the middle BRI tertile group, whereas both the low and high BRI tertile groups exhibited higher cumulative hazard over the follow-up period, with the low BRI group showing a slightly greater cumulative hazard than the high BRI group towards the end of follow-up. A similar pattern was observed for cardiovascular mortality, where the middle BRI tertile again had the lowest cumulative hazard, and the high BRI tertile demonstrated the highest cumulative hazard throughout the follow-up period. These results indicate that both low and high BRI are associated with increased risk of all-cause and cardiovascular death, consistent with a U-shaped relationship between BRI and mortality outcomes in hypertensive individuals. Discussion This study found a U-shaped relationship between BRI and mortality in hypertensive adults, with an inflection point at 5.09, suggesting that both excessively high and low BRI values may increase mortality risk. This compilation of studies highlights the evolving understanding of body composition and its impact on health outcomes, specifically focusing on the body roundness index (BRI) as a predictive tool. Across diverse populations and disease states, these investigations suggest BRI's potential to refine risk stratification beyond traditional measures like BMI[ 13 ]. The first study (Chen et al., 2025)[ 14 ] investigated the utility of BRI in a nationally representative sample of US adults diagnosed with metabolic syndrome (MetS). The research, comprising 10,527 participants from NHANES (2001–2016), examined the association between BRI (exposure) and both cardiovascular disease (CVD) prevalence and all-cause/cardiovascular-specific mortality (outcomes). The analysis revealed a significant positive correlation between higher BRI quartiles and increased CVD prevalence. Moreover, a U-shaped relationship was observed between BRI and mortality, with a threshold effect at a BRI value of 6.89. Above this threshold, elevated BRI was associated with increased cardiovascular and all-cause mortality risks. Importantly, the study found that BMI, in unadjusted models, exhibited a paradoxical inverse relationship with mortality, which became non-significant after accounting for confounders. The authors concluded that BRI might be a more reliable predictor of cardiovascular outcomes and mortality in MetS patients compared to BMI. They highlighted that these findings may differ across different age groups or genders[ 15 , 16 ]. The second study (Wang et al., 2025) [ 17 ]investigated the utility of BRI in a sample of US adults diagnosed with either diabetes or pre-diabetes. The analysis encompassed 15,848 participants from the National Health and Nutrition Examination Survey (NHANES) database spanning from 2003 to 2018. The analysis revealed a significant association between BRI and both all-cause mortality and cardiovascular disease mortality. Both all-cause and cardiovascular mortality was associated with U-shaped association. In the fully adjusted model, the association between lower BRI and all-cause mortality was significantly inverse. Conversely, higher BRI was more strongly positively correlated with all-cause mortality and cardiovascular mortality. The two-piecewise Cox regression analysis revealed that the inflection points for the risk of all-cause and cardiovascular disease mortality associated with BRI are 5.54 and 5.21, respectively. The third study (Zhang et al., 2024)[ 14 ], examined trends in BRI in 32,995 US adults, along with the association between BRI and all-cause mortality using NHANES data from 1999–2018. This national cohort found an increasing trend of BRI during nearly 20-year period among US adults, and importantly, a U-shaped association between BRI and all-cause mortality. This analysis was able to identify those more impacted, such as woman, older individuals and those of the Mexican race. While these studies are informative, some important considerations remain. There is a need for further research to fully understand the complex interactions between obesity and patient outcomes, as there can be variability in findings across different age groups and genders[ 18 ]. To enhance the generalizability and robustness of these findings, larger, multinational samples and the incorporation of additional confounders are recommended. The link between BRI and higher mortality rates can be explained from both epidemiological and clinical perspectives. Epidemiologically, higher BRI levels have been consistently linked to greater risks of cardiovascular disease, metabolic disorders, and even cancer [ 3 , 19 – 21 ], which likely contribute to increased overall mortality. Clinically, excess visceral fat is known to worsen insulin resistance and elevate the likelihood of cardiometabolic diseases—even in individuals whose weight falls within the normal range [ 22 , 23 ]. The possible mechanisms of the relationship between BRI and mortality are as follows: Firstly, BRI reflects the degree of fat accumulation in the body, especially in the abdominal area. Abdominal fat is considered to have a high metabolic activity and can release inflammatory factors and substances that promote atherosclerosis. These pathological processes are closely related to an increased risk of cardiovascular diseases and all-cause mortality[ 18 ]. Secondly, An increase in BRI is usually accompanied by the accumulation of visceral fat. Visceral fat is not only a risk factor for metabolic disorders, but also exerts a greater burden on key organs such as the liver and heart through mechanical compression and abnormal metabolic regulation, leading to impaired organ function and an increased risk of death[ 18 ]. Thirdly, Individuals with a high BRI usually have insulin resistance and energy metabolism disorders, which not only increase the risk of diabetes but also may aggravate cardiovascular diseases, affecting overall health status and lifespan[ 24 ]. The study utilized a large, nationally representative sample and accounted for numerous potential confounding factors during the analysis. Nevertheless, there are some limitations to consider. First, the data used to assess the relationship between BRI and mortality were derived from the US population, which may limit the generalizability of the findings to other populations; thus, further external validation in racially and ethnically diverse cohorts is required. Second, the optimal BRI cut-off values could differ across subpopulations, indicating the need for additional research to determine an appropriate BRI range rather than relying on a single threshold. Third, although multiple confounders were controlled for, residual confounding from unidentified factors cannot be entirely excluded. Finally, due to the observational nature of the study design, causal inference between BRI and mortality outcomes cannot be established. Conclusion In summary, this study demonstrates a clear U-shaped relationship between body roundness index and all-cause mortality among adults with hypertension. Both very low BRI (indicating a lean or possibly frail body composition) and very high BRI (indicating central obesity) were associated with higher mortality, whereas intermediate BRI values conferred the greatest survival benefit. These findings highlight the importance of a balanced body composition in hypertensive individuals and suggest that neither extreme leanness nor extreme adiposity is ideal in this context. Further research should validate these findings and define optimal BRI ranges for risk reduction. Declarations Competing interests The authors declare no competing interests. Ethics approval and consent to participate NHANES protocols are approved by the National Center for Health Statistics institutional review board, and all participants gave informed consent. The data we used are de-identified and publicly available; thus, our analysis was exempt from additional ethical review. Consent for publication Not applicable. Funding Not applicable. Author Contribution Jing Wang contributed to the study conception and design, as well as the acquisition, analysis, and interpretation of data. Jing Wang also drafted the manuscript. Li Fu was responsible for the interpretation of the results. Tingting Wang provided supervision and revised the manuscript. All authors reviewed and approved the final version of the manuscript. As the guarantor of this work, Jing Wang had full access to all study data and takes responsibility for the integrity of the data and the accuracy of the data analysis. Acknowledgement The authors extend their sincere thanks to both the participants and the staff of the NHANES for their invaluable contributions to this research. Data Availability The data underlying the results presented in the study are available from“https://www.cdc.gov/nchs/nhanes/”. All relevant data are available without restrictions to ensure the reproducibility of the study.The data used in this study are publicly available from the NHANES database (https://wwwn.cdc.gov/nchs/nhanes/default.aspx) and the National Death Index (https://www.cdc.gov/nchs/data-linkage/mortality-public.htm). 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Chen Z, Cheang I, Zhu X, Qu Q, Chen S, Xing Y, Zhou Y, Zhang H, Li X: Associations of body roundness index with cardiovascular disease and mortality among patients with metabolic syndrome . Diabetes, Obesity and Metabolism 2025, 27 (6):3285-3298. Additional Declarations No competing interests reported. Supplementary Files Supplementary.doc 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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02:28:02","extension":"html","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114889,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7174810/v1/518ed36105c9ad0e6c35443f.html"},{"id":91931022,"identity":"59b1d706-6593-4ff4-8c9e-43bacd5942ab","added_by":"auto","created_at":"2025-09-23 02:28:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79699,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of study participants. Abbreviation: NHANES, National Health and Nutrition Examination Survey.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7174810/v1/fd0f23a86a878fa874d67178.jpg"},{"id":91931017,"identity":"f89f6467-4404-4696-bd47-454d7f3af8dc","added_by":"auto","created_at":"2025-09-23 02:28:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40662,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationships between body round index and the probability of all-cause (A) and CVD (B) mortality. A nonlinear association between body round index and all-cause and cause-specific (CVD) mortality was found (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) in a generalized additive model. The solid line and dashed line represent the estimated values and their corresponding 95% confidence intervals.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7174810/v1/d4dba3f2b78336d9c3b953b7.jpg"},{"id":91931018,"identity":"2db9527d-e442-4817-9f54-fa21dbc7f8bf","added_by":"auto","created_at":"2025-09-23 02:28:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45401,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eCumulative Hazard Curves for all-cause mortality and cardiovascular mortality by body roundness index (BRI) tertiles. (A) All-cause mortality, (B) cardiovascular mortality\u003c/u\u003e\u003cu\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7174810/v1/c0c562afca2f8b0f0d3330ab.jpg"},{"id":93005190,"identity":"f1c5ae4c-ee46-4713-b636-a664c268d8cf","added_by":"auto","created_at":"2025-10-08 06:31:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2256206,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7174810/v1/3b74fbe1-a80b-4f49-af85-31dc8b25ad9d.pdf"},{"id":91931021,"identity":"9f3febe2-5212-493f-8811-89dfdcb276c4","added_by":"auto","created_at":"2025-09-23 02:28:02","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":252122,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.doc","url":"https://assets-eu.researchsquare.com/files/rs-7174810/v1/2991ca119d463048b1845e3e.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"U-Shaped Association Between Body Roundness Index and All-Cause Mortality in Hypertensive Adults: NHANES 1999–2018 Cohort Study","fulltext":[{"header":"Background","content":"\u003cp\u003eHypertension is worldwide and is a primary contributor to cardiovascular disease (CVD) morbidity and mortality, as well as all-cause death[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eObesity, especially visceral obesity, is widely acknowledged as a significant risk factor linked to cardiovascular events and overall mortality. Increasing evidence suggests that visceral fat poses a far greater health risk compared to subcutaneous fat, as it is more strongly associated with various diseases\u003c/span\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTraditional anthropometric indices, particularly Body Mass Index (BMI), are widely used but have significant limitations[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. BMI fails to distinguish between fat mass and lean mass or capture critical aspects of body fat distribution, notably visceral adiposity, which is strongly linked to metabolic dysregulation and cardiovascular risk[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Waist circumference (WC) and waist-to-height ratio (WHtR) offer improvements by focusing on central adiposity but may not fully characterize body shape complexity[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Body Roundness Index (BRI) was proposed by Thomas et al (2013)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. BRI is calculated using both height and waist circumference. Evidence suggests BRI outperforms BMI and may be comparable or superior to WC and WHtR in predicting cardiometabolic risk factors, type 2 diabetes, and hypertension incidence[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile BRI shows promise in predicting incident disease, data on its association with hard clinical endpoints, particularly mortality, is more limited and somewhat conflicting. Crucially, the relationship between BRI and mortality risk specifically within hypertensive adults \u0026ndash; a population inherently at elevated cardiovascular risk \u0026ndash; has not been thoroughly investigated using large-scale, nationally representative data[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, this study aims to prospectively investigate the association between BRI and the risks of cardiovascular mortality and all-cause mortality in a large, nationally representative sample of U.S. hypertensive adults participating in the National Health and Nutrition Examination Survey (NHANES) 1999\u0026ndash;2018.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Study participants\u003c/h2\u003e\u003cp\u003eThis population-based cross-sectional study utilized data from NHANES (1999\u0026ndash;2018), a nationally representative survey conducted by the Centers for Disease Control and Prevention (CDC) to assess the health and nutritional status of the US population. NHANES employs a stratified, multistage sampling design to ensure a representative sample of the U.S. civilian noninstitutionalized population, collecting comprehensive data on demographics, socio-economic status, dietary habits, and health-related outcomes. The NHANES study protocol was approved by the Research Ethics Review Committee of the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC). All participants provided written informed consent, as mandated by the NCHS Research Ethics Review Board (ERB). This secondary analysis exclusively used de-identified NHANES data, thus requiring no additional ethical approval or consent. Further details are available on the NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e\u003cp\u003eParticipants were adults (\u0026ge;\u0026thinsp;18 years) with hypertension from the NHANES database. Hypertension was defined as (1) systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, (2) current use of antihypertensive medication, or (3) self-reported hypertension. Exclusion criteria included missing BRI and mortality data. The final analytical sample comprised 20,532 individuals. The flowchart shows the detailed inclusion and exclusion of participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefinition of body roundness index\u003c/h3\u003e\n\u003cp\u003eThe Body Roundness Index (BRI), introduced by Thomas et al. in 2013, serves as an anthropometric metric designed to evaluate fat distribution, with particular emphasis on visceral adiposity. In contrast to the Body Mass Index (BMI), which does not differentiate between adipose tissue and lean muscle mass, BRI conceptualizes the human torso as an elliptical shape, thus offering a refined representation of body morphology. Elevated BRI values correspond to a more rounded body shape, which has been correlated with higher levels of body fat, especially visceral fat\u0026mdash;a known risk factor for various chronic diseases. The calculation of BRI relies on anthropometric measurements including waist circumference (Wc) and height, which were obtained from the NHANES database's body measurement records. The index values were subsequently derived by applying the established BRI formula.\u003c/p\u003e\u003cp\u003eBody Roundness Index (BRI)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]: Calculated as BRI\u0026thinsp;=\u0026thinsp;364.2\u0026ndash;365.5 \u0026times; \u003cimg 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\" style=\"width: 261px; height: 39.3789px;\" width=\"261\" height=\"39.3789\"\u003e\u003c/p\u003e\n\u003ch3\u003eCovariates Definitions\u003c/h3\u003e\n\u003cp\u003eWe included a range of baseline covariates known or suspected to confound the relationship between adiposity and mortality. Demographic variables were age (years) and sex (male or female). Race/ethnicity was categorized as Non-Hispanic White, Non-Hispanic Black, Hispanic (any race), or Other (including multi-racial). Socioeconomic status was approximated by education level (less than high school, high school graduate/GED, some college or higher). Lifestyle factors included cigarette smoking status (never, former, current smoker), alcohol use (classified as non-drinker, moderate drinker, or heavy drinker based on self-reported frequency and quantity of alcohol consumption), and total physical activity level, expressed in metabolic equivalent (MET) hours per week (derived from self-reported leisure and household activities). We also adjusted for the presence of diabetes mellitus, defined by self-reported physician diagnosis or use of glucose-lowering medications. These covariates were selected a priori due to their associations with both body composition and mortality risk.\u003c/p\u003e\n\u003ch3\u003eOutcome Definitions\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eOutcome Definitions\u003c/div\u003e\u003cp\u003eAll-cause mortality: Obtained via linkage between NHANES and the National Death Index (NDI), with follow-up from survey participation until death or December 31, 2019. Cardiovascular mortality: Determined using death certificate data based on ICD-10 codes.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), while categorical variables are expressed as number and weighted percentages.BRI was categorized into tertiles based on its distribution within the study population. Differences in baseline characteristics across BRI tertiles were assessed using one-way ANOVA for continuous variables and the Chi-square test for categorical variables. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eTo examine the association between BRI and mortality risk in hypertensive patients, Cox proportional hazards models were employed. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. Two multivariable-adjusted models were constructed: Model I: Adjusted for age, sex, ethnicity, marital status, education level, and poverty income ratio. Model II: Further adjusted for smoking status, alcohol use, total physical activity (MET/week), and diabetes mellitus.\u003c/p\u003e\u003cp\u003eTo explore potential non-linear relationships between BRI and mortality, we performed a segmented Cox regression analysis. This analysis allowed for the identification of potential threshold effects of BRI on mortality risk. The best-fitting breakpoint (K) was determined based on the maximization of the log-likelihood function. Likelihood ratio tests were used to compare the segmented regression model to a linear Cox model.\u003c/p\u003e\u003cp\u003eTo determine the cumulative survival of long-term all-cause and cardiovascular mortality in different body round index groups, survival analysis by the Kaplan\u0026ndash;Meier method was performed and the significance was determined by the log-rank test.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAll the statistical analyses were performed using the EmpowerStats (\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.empowerstats.com\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eX\u0026amp;Y solutions, Inc. Boston MA) and R software version 4.2.0 (\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e). The two-sided alpha level was set at 0.05.\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics of study participants\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines characteristics of participants stratified by Body Roundness Index (BRI) tertiles (Low, Middle, High), each group consisting of 6,844 individuals. Significant differences were observed across tertiles in continuous variables including age, poverty income ratio, body mass index (BMI), waist circumference, white blood cell count (WBC), and follow-up time (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the High BRI group exhibited the highest mean BMI (37.44\u0026thinsp;\u0026plusmn;\u0026thinsp;6.34 kg/m\u0026sup2;) and waist circumference (119.75\u0026thinsp;\u0026plusmn;\u0026thinsp;12.41 cm), whereas these values were lowest in the Low BRI group. Sex distribution differed significantly among groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a greater proportion of females in the High BRI tertile (61.98%). Significant differences were also observed in ethnicity, marital status, education level, poverty income status, smoking and alcohol use, physical activity levels, and diabetes mellitus prevalence across tertiles (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The prevalence of diabetes was markedly higher in the High BRI group (42.09%) compared to the Low BRI group (15.97%). All-cause mortality differed significantly among the tertiles (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), whereas cardiovascular mortality did not reach statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.082).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline characteristics of study participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody Roundness Index tertile\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6844\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\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.61\u0026thinsp;\u0026plusmn;\u0026thinsp;17.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.15\u0026thinsp;\u0026plusmn;\u0026thinsp;15.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.64\u0026thinsp;\u0026plusmn;\u0026thinsp;15.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoverty income ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody Mass Index(kg/m2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.44\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.44\u0026thinsp;\u0026plusmn;\u0026thinsp;6.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist(cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88.89\u0026thinsp;\u0026plusmn;\u0026thinsp;8.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102.82\u0026thinsp;\u0026plusmn;\u0026thinsp;6.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119.75\u0026thinsp;\u0026plusmn;\u0026thinsp;12.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA Body Shape Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.94\u0026thinsp;\u0026plusmn;\u0026thinsp;3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.88\u0026thinsp;\u0026plusmn;\u0026thinsp;5.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\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;0.001\u003c/p\u003e\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\u003e3912 (57.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3623 (52.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2602 (38.02%)\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\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2932 (42.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3221 (47.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4242 (61.98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity\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;0.001\u003c/p\u003e\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\u003e3087 (45.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3103 (45.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3024 (44.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1926 (28.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1574 (23.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1804 (26.36%)\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\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e687 (10.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1125 (16.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1147 (16.76%)\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\u003e397 (5.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e550 (8.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e568 (8.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e747 (10.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e492 (7.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e301 (4.40%)\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\u003eMarital status\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;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/Living with Partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3963 (59.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4164 (61.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3754 (55.52%)\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\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1930 (28.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2030 (29.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2215 (32.76%)\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 married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e824 (12.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e583 (8.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e793 (11.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEDU\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;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBelow high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e778 (11.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1081 (15.79%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1058 (15.46%)\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\u003e2740 (40.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2768 (40.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2888 (42.20%)\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\u003eAbove high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3312 (48.39%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2987 (43.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2895 (42.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (0.20%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (0.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (0.04%)\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\u003ePoverty income ratio\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;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1137 (16.61%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1187 (17.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1435 (20.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNearly poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1636 (23.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1759 (25.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1924 (28.11%)\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\u003eMiddle income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1730 (25.28%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1711 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1642 (23.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1713 (25.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1582 (23.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1237 (18.07%)\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\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e628 (9.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e605 (8.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e606 (8.85%)\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\u003eSMOKE\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;0.001\u003c/p\u003e\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\u003e3242 (47.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3381 (49.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3546 (51.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1830 (26.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2299 (33.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2188 (31.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1652 (24.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1117 (16.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1067 (15.59%)\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\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120 (1.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (0.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43 (0.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol use\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;0.001\u003c/p\u003e\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\u003e833 (12.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e987 (14.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1117 (16.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1206 (17.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1443 (21.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1616 (23.61%)\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\u003eMild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2294 (33.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2184 (31.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1887 (27.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e825 (12.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e738 (10.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e733 (10.71%)\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\u003eHeavy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1034 (15.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e900 (13.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e866 (12.65%)\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\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e652 (9.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e592 (8.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e625 (9.13%)\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\u003eTotal physical activity (MET/week)\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;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1710 (24.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1617 (23.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1545 (22.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;=600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3289 (48.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2958 (43.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2617 (38.24%)\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\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1845 (26.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2269 (33.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2682 (39.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes Mellitus\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;0.001\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\u003e5195 (76.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4223 (62.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3307 (48.77%)\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\u003eDiabetes Mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1087 (15.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1946 (28.59%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2854 (42.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIFG(Impaired Fasting Glycaemia)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e315 (4.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e418 (6.14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e414 (6.11%)\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\u003eIGT(Impaired Glucose Tolerance)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e211 (3.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e219 (3.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e206 (3.04%)\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\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables; (%) for categorical variables; Abbreviation: WBC: white blood cell; EDU: education recoded.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssociation Between BRI and Mortality\u003c/h3\u003e\n\u003cp\u003eIn multivariable Cox proportional hazards models using the middle tertile of the Body Roundness Index (BRI) as the reference(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.), Adjust I model (controlling for age, sex, ethnicity, and education) revealed that individuals in the low BRI tertile had a significantly increased risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 1.04\u0026ndash;1.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0016), and those in the high BRI tertile exhibited an even higher risk (HR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 1.13\u0026ndash;1.29, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After further adjustment in Adjust II model (including smoking status, alcohol use, physical activity, and diabetes), the elevated risk persisted in the low BRI group (HR\u0026thinsp;=\u0026thinsp;1.16, 95% CI: 1.09\u0026ndash;1.25, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and remained significant but attenuated in the high BRI group (HR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 1.03\u0026ndash;1.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). For cardiovascular mortality, the high BRI tertile was associated with a significantly increased risk in Adjust I (HR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.12\u0026ndash;1.42, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas the low BRI tertile showed no significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6649); these results were consistent in Adjust II with a significant risk increase in the high BRI group (HR\u0026thinsp;=\u0026thinsp;1.16, 95% CI: 1.03\u0026ndash;1.30, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) and no significant change for the low tertile (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.227). These findings indicate that higher BRI is significantly associated with increased risks of all-cause and cardiovascular mortality.\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations of Body Roundness Index (BRI) Tertiles with All-Cause and Cardiovascular Mortality Risk Using Cox Proportional Hazards Models\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdjust I\u003c/p\u003e\u003cp\u003eHR (95%CI) \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdjust II\u003c/p\u003e\u003cp\u003eHR (95%CI) \u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll-cause mortality\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\u003eBody Roundness Index tertile\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\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11 (1.04, 1.19)\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e 0.0016\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16 (1.09, 1.25)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21 (1.13, 1.29)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11 (1.03, 1.19) 0.0040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular mortality\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\u003eBody Roundness Index tertile\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\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03 (0.91, 1.15) 0.6649\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08 (0.96, 1.21) 0.2266\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.26 (1.12, 1.42)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.16 (1.03, 1.30) 0.0167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe hazard ratio was calculated by weighted Multivariate Cox proportional hazards regression.\u003c/p\u003e\u003cp\u003eAdjust I model: adjusted for age (years), sex, ethnicity, and education (recoded);\u003c/p\u003e\u003cp\u003eAdjust II model: further adjusted for smoking (recoded), alcohol use (recoded), total physical activity (MET/week), and diabetes mellitus;\u003c/p\u003e\u003cp\u003eTime variable in Cox model: PERMTH_INT.\u003c/p\u003e\u003cp\u003ea Values are hazard ratio (95% confidence interval). b Values are P-value.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDose-response relationship between BRI and mortality\u003c/h2\u003e\u003cp\u003eA Cox proportional hazards model(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was used to examine the association between Body Roundness Index (BRI) and all-cause as well as cardiovascular mortality. In Model I, treating BRI as a continuous linear variable showed no significant association with all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.00, 95% CI: 0.98\u0026ndash;1.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7543) and a borderline association with cardiovascular mortality (HR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.00\u0026ndash;1.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0660). Model II employed a threshold effect analysis identifying inflection points at 5.09 for all-cause mortality and 4.97 for cardiovascular mortality. Below the threshold, BRI was significantly negatively associated with all-cause mortality (HR\u0026thinsp;=\u0026thinsp;0.82, 95% CI: 0.79\u0026ndash;0.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cardiovascular mortality (HR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.80\u0026ndash;0.94, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0006); above the threshold, BRI showed a significant positive association with both all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.04\u0026ndash;1.07, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cardiovascular mortality (HR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 1.03\u0026ndash;1.10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The difference in effect sizes between the two segments was statistically significant for all-cause mortality (HR\u0026thinsp;=\u0026thinsp;1.28, 95% CI: 1.21\u0026ndash;1.35, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cardiovascular mortality (HR\u0026thinsp;=\u0026thinsp;1.23, 95% CI: 1.12\u0026ndash;1.36, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Covariates adjusted for included age, sex, ethnicity, education level, smoking status, alcohol use, total physical activity (MET/week), and diabetes mellitus, with follow-up time variable PERMTH_INT. These findings suggest a nonlinear relationship between BRI and mortality risk, presenting a significant threshold effect.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations of Body Roundness Index (BRI) Tertiles with All-Cause and Cardiovascular Mortality Risk Using Cox Proportional Hazards Models.\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\u003cp\u003eOutcome:\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll-cause mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCardiovascular mortality\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e模型 I\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\u003e一条直线效应\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.98, 1.01) 0.7543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.02 (1.00, 1.05) 0.0660\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e模型 II\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\u003e折点(K)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; K 段效应 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.82 (0.79, 0.86)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.87 (0.80, 0.94) 0.0006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;K 段效应 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.05 (1.04, 1.07)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.07 (1.03, 1.10)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2与1的效应差\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.28 (1.21, 1.35)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.23 (1.12, 1.36)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e对数似然比检验\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA generalized additive model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed a U-shaped relationship between BRI and all-cause mortality (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with an inflection point at 5.09. Below the inflection point, each unit increase in BRI reduced mortality risk (HR\u0026thinsp;=\u0026thinsp;0.82, 95% CI: 0.79\u0026ndash;0.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); above it, each unit increased risk (HR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.04\u0026ndash;1.07, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). For cardiovascular mortality, the inflection point was 4.97 (HR\u0026thinsp;=\u0026thinsp;0.87 [0.80, 0.94], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0006 below; HR\u0026thinsp;=\u0026thinsp;1.23 [1.12, 1.36], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 above).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe cumulative hazard curves for all-cause mortality and cardiovascular mortality stratified by Body Roundness Index (BRI) tertiles are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(A) and Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e༈B༉, respectively. For all-cause mortality, the cumulative hazard was lowest in the middle BRI tertile group, whereas both the low and high BRI tertile groups exhibited higher cumulative hazard over the follow-up period, with the low BRI group showing a slightly greater cumulative hazard than the high BRI group towards the end of follow-up. A similar pattern was observed for cardiovascular mortality, where the middle BRI tertile again had the lowest cumulative hazard, and the high BRI tertile demonstrated the highest cumulative hazard throughout the follow-up period. These results indicate that both low and high BRI are associated with increased risk of all-cause and cardiovascular death, consistent with a U-shaped relationship between BRI and mortality outcomes in hypertensive individuals.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study found a U-shaped relationship between BRI and mortality in hypertensive adults, with an inflection point at 5.09, suggesting that both excessively high and low BRI values may increase mortality risk.\u003c/p\u003e\u003cp\u003eThis compilation of studies highlights the evolving understanding of body composition and its impact on health outcomes, specifically focusing on the body roundness index (BRI) as a predictive tool. Across diverse populations and disease states, these investigations suggest BRI's potential to refine risk stratification beyond traditional measures like BMI[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe first study (Chen et al., 2025)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] investigated the utility of BRI in a nationally representative sample of US adults diagnosed with metabolic syndrome (MetS). The research, comprising 10,527 participants from NHANES (2001\u0026ndash;2016), examined the association between BRI (exposure) and both cardiovascular disease (CVD) prevalence and all-cause/cardiovascular-specific mortality (outcomes). The analysis revealed a significant positive correlation between higher BRI quartiles and increased CVD prevalence. Moreover, a U-shaped relationship was observed between BRI and mortality, with a threshold effect at a BRI value of 6.89. Above this threshold, elevated BRI was associated with increased cardiovascular and all-cause mortality risks. Importantly, the study found that BMI, in unadjusted models, exhibited a paradoxical inverse relationship with mortality, which became non-significant after accounting for confounders. The authors concluded that BRI might be a more reliable predictor of cardiovascular outcomes and mortality in MetS patients compared to BMI. They highlighted that these findings may differ across different age groups or genders[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe second study (Wang et al., 2025) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]investigated the utility of BRI in a sample of US adults diagnosed with either diabetes or pre-diabetes. The analysis encompassed 15,848 participants from the National Health and Nutrition Examination Survey (NHANES) database spanning from 2003 to 2018. The analysis revealed a significant association between BRI and both all-cause mortality and cardiovascular disease mortality. Both all-cause and cardiovascular mortality was associated with U-shaped association. In the fully adjusted model, the association between lower BRI and all-cause mortality was significantly inverse. Conversely, higher BRI was more strongly positively correlated with all-cause mortality and cardiovascular mortality. The two-piecewise Cox regression analysis revealed that the inflection points for the risk of all-cause and cardiovascular disease mortality associated with BRI are 5.54 and 5.21, respectively.\u003c/p\u003e\u003cp\u003eThe third study (Zhang et al., 2024)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], examined trends in BRI in 32,995 US adults, along with the association between BRI and all-cause mortality using NHANES data from 1999\u0026ndash;2018. This national cohort found an increasing trend of BRI during nearly 20-year period among US adults, and importantly, a U-shaped association between BRI and all-cause mortality. This analysis was able to identify those more impacted, such as woman, older individuals and those of the Mexican race.\u003c/p\u003e\u003cp\u003eWhile these studies are informative, some important considerations remain. There is a need for further research to fully understand the complex interactions between obesity and patient outcomes, as there can be variability in findings across different age groups and genders[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To enhance the generalizability and robustness of these findings, larger, multinational samples and the incorporation of additional confounders are recommended.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eThe link between BRI and higher mortality rates can be explained from both epidemiological and clinical perspectives. Epidemiologically, higher BRI levels have been consistently linked to greater risks of cardiovascular disease, metabolic disorders, and even cancer\u003c/span\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ewhich likely contribute to increased overall mortality. Clinically, excess visceral fat is known to worsen insulin resistance and elevate the likelihood of cardiometabolic diseases\u0026mdash;even in individuals whose weight falls within the normal range\u003c/span\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe possible mechanisms of the relationship between BRI and mortality are as follows: Firstly, BRI reflects the degree of fat accumulation in the body, especially in the abdominal area. Abdominal fat is considered to have a high metabolic activity and can release inflammatory factors and substances that promote atherosclerosis. These pathological processes are closely related to an increased risk of cardiovascular diseases and all-cause mortality[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Secondly, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAn\u003c/span\u003e increase in BRI is usually accompanied by the accumulation of visceral fat. Visceral fat is not only a risk factor for metabolic disorders, but also exerts a greater burden on key organs such as the liver and heart through mechanical compression and abnormal metabolic regulation, leading to impaired organ function and an increased risk of death[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Thirdly, Individuals with a high BRI usually have insulin resistance and energy metabolism disorders, which not only increase the risk of diabetes but also may aggravate cardiovascular diseases, affecting overall health status and lifespan[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe study utilized a large, nationally representative sample and accounted for numerous potential confounding factors during the analysis. Nevertheless, there are some limitations to consider. First, the data used to assess the relationship between BRI and mortality were derived from the US population, which may limit the generalizability of the findings to other populations; thus, further external validation in racially and ethnically diverse cohorts is required. Second, the optimal BRI cut-off values could differ across subpopulations, indicating the need for additional research to determine an appropriate BRI range rather than relying on a single threshold. Third, although multiple confounders were controlled for, residual confounding from unidentified factors cannot be entirely excluded. Finally, due to the observational nature of the study design, causal inference between BRI and mortality outcomes cannot be established.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study demonstrates a clear U-shaped relationship between body roundness index and all-cause mortality among adults with hypertension. Both very low BRI (indicating a lean or possibly frail body composition) and very high BRI (indicating central obesity) were associated with higher mortality, whereas intermediate BRI values conferred the greatest survival benefit. These findings highlight the importance of a balanced body composition in hypertensive individuals and suggest that neither extreme leanness nor extreme adiposity is ideal in this context. Further research should validate these findings and define optimal BRI ranges for risk reduction.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNHANES protocols are approved by the National Center for Health Statistics institutional review board, and all participants gave informed consent. The data we used are de-identified and publicly available; thus, our analysis was exempt from additional ethical review.\u003c/span\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNot applicable.\u003c/span\u003e\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJing Wang contributed to the study conception and design, as well as the acquisition, analysis, and interpretation of data. Jing Wang also drafted the manuscript. Li Fu was responsible for the interpretation of the results. Tingting Wang provided supervision and revised the manuscript. All authors reviewed and approved the final version of the manuscript. As the guarantor of this work, Jing Wang had full access to all study data and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors extend their sincere thanks to both the participants and the staff of the NHANES for their invaluable contributions to this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data underlying the results presented in the study are available from\u0026ldquo;https://www.cdc.gov/nchs/nhanes/\u0026rdquo;. All relevant data are available without restrictions to ensure the reproducibility of the study.The data used in this study are publicly available from the NHANES database (https://wwwn.cdc.gov/nchs/nhanes/default.aspx) and the National Death Index (https://www.cdc.gov/nchs/data-linkage/mortality-public.htm).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXu J, Zhang L, Wu Q, Zhou Y, Jin Z, Li Z, Zhu Y: \u003cstrong\u003eBody roundness index is a superior indicator to associate with the cardio\u003c/strong\u003e\u003cstrong\u003e‐\u003c/strong\u003e\u003cstrong\u003emetabolic risk: evidence from a cross\u003c/strong\u003e\u003cstrong\u003e‐\u003c/strong\u003e\u003cstrong\u003esectional study with 17,000 Eastern-China adults\u003c/strong\u003e. \u003cem\u003eBMC CARDIOVASC DISOR\u003c/em\u003e 2021, \u003cstrong\u003e21\u003c/strong\u003e(1):97.\u003c/li\u003e\n\u003cli\u003eZhou D, Liu X, Huang Y, Feng Y: \u003cstrong\u003eA nonlinear association 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Diabetes \u0026amp; endocrinology\u003c/em\u003e 2019, \u003cstrong\u003e7\u003c/strong\u003e(9):715-725.\u003c/li\u003e\n\u003cli\u003eGao W, Jin L, Li D, Zhang Y, Zhao W, Zhao Y, Gao J, Zhou L, Chen P, Dong G: \u003cstrong\u003eThe association between the body roundness index and the risk of colorectal cancer: a cross-sectional study\u003c/strong\u003e. \u003cem\u003eLIPIDS HEALTH DIS\u003c/em\u003e 2023, \u003cstrong\u003e22\u003c/strong\u003e(1):53.\u003c/li\u003e\n\u003cli\u003eEndukuru CK, Gaur GS, Dhanalakshmi Y, Sahoo J, Vairappan B: \u003cstrong\u003eCut-off values and clinical efficacy of body roundness index and other novel anthropometric indices in identifying metabolic syndrome and its components among Southern-Indian adults\u003c/strong\u003e. \u003cem\u003eDIABETOL INT\u003c/em\u003e 2022, \u003cstrong\u003e13\u003c/strong\u003e(1):188-200.\u003c/li\u003e\n\u003cli\u003eXu J, Zhang L, Wu Q, Zhou Y, Jin Z, Li Z, Zhu Y: \u003cstrong\u003eBody roundness index is a superior indicator to associate with the cardio\u003c/strong\u003e\u003cstrong\u003e‐\u003c/strong\u003e\u003cstrong\u003emetabolic risk: evidence from a cross\u003c/strong\u003e\u003cstrong\u003e‐\u003c/strong\u003e\u003cstrong\u003esectional study with 17,000 Eastern-China adults\u003c/strong\u003e. \u003cem\u003eBMC CARDIOVASC DISOR\u003c/em\u003e 2021, \u003cstrong\u003e21\u003c/strong\u003e(1):97.\u003c/li\u003e\n\u003cli\u003eSakers A, De Siqueira MK, Seale P, Villanueva CJ: \u003cstrong\u003eAdipose-tissue plasticity in health and disease\u003c/strong\u003e. \u003cem\u003eCELL\u003c/em\u003e 2022, \u003cstrong\u003e185\u003c/strong\u003e(3):419-446.\u003c/li\u003e\n\u003cli\u003eChait A, den Hartigh LJ: \u003cstrong\u003eAdipose Tissue Distribution, Inflammation and Its Metabolic Consequences, Including Diabetes and Cardiovascular Disease\u003c/strong\u003e. \u003cem\u003eFRONT CARDIOVASC MED\u003c/em\u003e 2020, \u003cstrong\u003e7\u003c/strong\u003e:22.\u003c/li\u003e\n\u003cli\u003eChen Z, Cheang I, Zhu X, Qu Q, Chen S, Xing Y, Zhou Y, Zhang H, Li X: \u003cstrong\u003eAssociations of body roundness index with cardiovascular disease and mortality among patients with metabolic syndrome\u003c/strong\u003e. \u003cem\u003eDiabetes, Obesity and Metabolism\u003c/em\u003e 2025, \u003cstrong\u003e27\u003c/strong\u003e(6):3285-3298.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Body Roundness Index, hypertension, all-cause mortality, cardiovascular mortality, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-7174810/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7174810/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePrevious studies have shown that obesity is associated with an increased risk of various cardiovascular diseases. The Body Roundness Index (BRI) is a novel indicator for assessing body fat and visceral fat. However, the relationship between BRI and all-cause and cardiovascular mortality in individuals with hypertension remains unclear.\u003c/span\u003e This study aims to \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003einvestigate the association between BRI and all-cause and cardiovascular mortality among US adults with hypertension.\u003c/span\u003e\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study utilized data from the National Health and Nutrition Examination Survey (NHANES) (1999\u0026ndash;2018). The study population consisted of 20,532 hypertensive adults. Cox proportional hazards models were used to assess the association between BRI and all-cause and cardiovascular mortality. A generalized additive model were employed to evaluate potential nonlinear relationships between BRI and mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong the 20,532 hypertensive adults (mean age: 59.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.9 years), a total of 5,044 (25.4%) participants died during follow-up. BRI exhibited a U-shaped association with all-cause mortality, with an inflection point at 5.09. Below the inflection point, each unit increase in BRI was associated with a decreased risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;0.82, 95% CI: 0.79\u0026ndash;0.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); above the inflection point, each unit increase in BRI was associated with an increased risk (HR\u0026thinsp;=\u0026thinsp;1.05, 95% CI: 1.04\u0026ndash;1.07, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). A similar U-shaped relationship was observed for cardiovascular mortality, with an inflection point at 4.97 (HR\u0026thinsp;=\u0026thinsp;0.87 [0.80, 0.94], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0006 below the inflection point; HR\u0026thinsp;=\u0026thinsp;1.23 [1.12, 1.36], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 above the inflection point). After adjusting for age, sex, race, and education level, both the lowest and highest BRI tertiles were associated with higher all-cause mortality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAmong US adults with hypertension, BRI demonstrates a U-shaped relationship with all-cause and cardiovascular mortality. Further research is needed to validate these findings.\u003c/p\u003e","manuscriptTitle":"U-Shaped Association Between Body Roundness Index and All-Cause Mortality in Hypertensive Adults: NHANES 1999–2018 Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 02:27:57","doi":"10.21203/rs.3.rs-7174810/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":"467f880a-61f1-4fdb-a8fc-fdc50c393f96","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T06:23:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 02:27:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7174810","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7174810","identity":"rs-7174810","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}