Body roundness index and all-cause and CVD mortality: findings from Japanese adults and meta-analysis

Body roundness index and all-cause and CVD mortality: findings from Japanese adults and meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Body roundness index and all-cause and CVD mortality: findings from Japanese adults and meta-analysis Koji Suzuki, Kazuma Murakami, Ryosuke Fujii, Yoshiki Tsuboi, Hiroshi Okumiyama This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5977532/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: Although body roundness index (BRI) is gaining attention as an indicator of abdominal obesity, evidence on this indicator is still sparse. We aimed to summarize basic information about BRI in a Japanese population and to examine associations of BRI with all-cause and cardiovascular disease (CVD) mortality with a meta-analysis. Methods: This population-based cohort study included participants (mean age of 58.4 years, 37.8% men) in health check-up programs between 2004 and 2018, and we followed up until December 31, 2023. BRI was calculated by a conventional formula for height (cm) and waist circumference (cm). Mortality was ascertained by death certifications. CVD mortality was defined as mortality with ICD-10 codes of I00-I99. Hazard ratios were estimated for all-cause and CVD mortality using Cox proportional hazards regression models. Results: During the follow-up period (median: 13.3 years), 206 individuals died, and 47 individuals died by CVD. Women had a wider distribution of BRI (median: 3.72; IQR: 2.84, 4.88) compared with men (median: 3.54; IQR: 2.88, 4.19). BRI increased from the 40-49 age group (median: 3.24; IQR: 2.42, 4.08) to those over 70 years old (median: 4.22; IQR: 3.20, 5.32). Compared with Q1, HRs (95% CI) in Q3 were lower for both all-cause mortality (HR: 0.64, 95% CI: 0.43, 0.96) and CVD mortality (HR: 0.27, 95% CI: 0.09, 0.78). Meta-analysis also showed similar associations. Conclusions: BRI varies across age groups and between sexes in a Japanese population. Both our results and this meta-analysis suggest that BRI has U-shaped associations with all-cause and CVD mortality. Health sciences/Risk factors Health sciences/Diseases/Cardiovascular diseases body roundness index abdominal obesity cardiovascular disease mortality meta-analysis Figures Figure 1 Figure 2 Figure 3 Introduction Body roundness index (BRI) is a novel anthropometric indicator developed to assess body composition, focusing on central obesity [ 1 ]. Unlike traditional measures such as body mass index (BMI), which uses only height and weight, BRI incorporates waist circumference with height to provide a more accurate representation of fat distribution [ 1 , 2 ]. Researchers have proposed that BRI can predict visceral fat levels better than BMI [ 3 ]. Central obesity has been identified as more strongly associated with adverse health outcomes compared to general obesity [ 4 – 6 ]. Thus, by providing a single numerical value based on body shape, BRI aims to simplify the assessment of obesity-related health risks. As obesity continues to rise globally, BRI is a promising tool for improving the detection and management of obesity-related health risks [ 7 – 9 ]. Evidence on BRI has grown, focusing on its effectiveness as a tool for predicting health outcomes related to obesity [ 8 ]. There are consistent results that BRI is a stronger predictor of abdominal obesity, cardiovascular disease (CVD), and metabolic risks compared to BMI [ 10 – 13 ]. Researchers have explored its utility in different populations, demonstrating its ability to capture complex relationships between body shape and disease risk, including nonlinear associations with mortality. Recent studies have highlighted the clinical utility of BRI as a robust predictor of mortality and cardiovascular disease (CVD) [ 14 , 15 ]. A study revealed a nonlinear relationship between BRI and all-cause and cardiovascular mortality [ 14 ]. Another recent study further demonstrated that BRI outperformed BMI in predicting all-cause mortality among middle-aged Chinese adults [ 15 ]. Together, these findings support BRI as a practical tool for identifying at-risk individuals and guiding preventive strategies globally. Despite its promise, research on BRI is limited by methodological and contextual challenges. Short follow-up periods limit insights into long-term risks associated with differences in BRI, particularly for chronic diseases and older populations. Another limitation is population specificity. Research often focuses on specific populations (e.g., rural China, and U.S. cohorts), making it difficult to generalize findings to other demographics, ethnic groups, or geographic regions [ 14 , 15 ]. Cultural and environmental differences, such as diet and healthcare access, further constrain BRI’s applicability to diverse populations [ 16 ]. Broader studies from different settings are essential to accumulate evidence on BRI and validate BRI's utility on a global scale. In this study, for the Japanese population, we aimed to describe basic information of BRI across different demographic valuables and to examine the associations between BRI and all-cause and CVD mortality by incorporating an updated meta-analysis. Methods Study Participants Yakumo study was launched in 1982 in Yakumo Town, Hokkaido, and has continued for more than 40 years [ 17 , 18 ]. Every August, more than 500 residents aged 40 and more participate in a health check-up program. This cohort study was conducted based on the Yakumo Study, with 1,880 participants who participated in the health check-up program between 2004 and 2018 and were followed until December 31, 2023. For individuals who participated in more than one visit between 2004 and 2018, data from the first visit were used. Out of all eligible participants, 10 participants with less than 8 months of follow-up period and 2 participants with incomplete anthropometric data were excluded. Additionally, 462 participants with a history of cancer, cardiovascular disease, or diabetes were excluded, resulting in a final analysis of 1,772 participants. Written informed consent was obtained from all participants. This study adhered to the Declaration of Helsinki. The protocol of this study was approved by the Ethics Review Committee at Fujita Health University. Definition of BRI BRI was calculated according to the formula as 364.2-365.5 × √(1-[waist circumference (cm)/2π] 2 /[0.5 × height (cm)] 2 ) [ 1 ]. Waist circumference (cm) and height (cm) were measured during the health check-up program by trained public health nurses. Since there is no clear reference range for BRI, we divided participants into quartiles to examine the association with all-cause mortality and CVD mortality. Assessment of mortality After the baseline data from 2004 to 2018, death certificates were reviewed and extracted by municipal officers. Based on this information, we assigned the ICD International Classification of Diseases, Tenth Revision (ICD-10) codes. CVD mortality was defined as deaths with ICD-10 codes I00-I99 (i.e., diseases of the circulatory system). Covariates Self-administered questionnaires were used to obtain information on participants’ lifestyle such as smoking (current, former, and never), drinking (current, former, and never), exercise per week (almost none, one or two hours, three or four hours, and more than five hours) and past medical history of cancer, cardiovascular disease, and diabetes (none, under treatment, previously treated, and never treated). For tobacco smoking and drinking habits, those who answered “current” or “former” were defined as smoking experience and drinking alcohol. Regarding exercise habits, those who answered more than “one or two hours” was defined as regular exercise habits. Persons who responded other than “none” were defined as having a medical history. Inclusion/Exclusion criteria for meta-analysis For meta-analysis, we reviewed the two databases (PubMed and Cochrane) with a specific search of (“Body roundness index”) AND (“mortality”). As shown in PRISMA flowchart in Supplementary Materials, two possible publications were finally selected in our meta-analysis. In this meta-analysis, a random-effects model was used to pool previous results using generalized linear mixed-effects models for each quartile. The heterogeneity was evaluated by the I 2 and the Q statistic. Statistical analysis Cox proportional hazard model was performed to estimate hazard ratios (HR) and 95% confidence intervals (CI) of all-cause and CVD mortality across baseline BRI quartiles. Age, sex, smoking experience, drinking habits, and regular exercise habit at the first visit were included in a fully adjusted model. Nonlinear associations between baseline BRI and mortality were examined by Cox-PH models with cubic spline. We performed stratified analyses by sex (men and women) and age (< 65 years and ≥ 65 years) by recategorizing quartiles in each subgroup. Additionally, we analyzed with arbitrary BRI groups (< 3, 3–4, 4–5, and ≥ 5) for setting a practical cut-off point. All statistical analysis was performed by R package (ver.4.3.0). Statistical significance was made by two-sided tests and considered as P < 0.05. Results Baseline characteristics of 1,772 participants according to BRI quartiles are summarized (Table 1 ). During a median follow-up period of 13.3 years (IQR: 8.3, 18.3), 206 all-cause deaths and 47 CVD deaths were identified. Median age was higher among Q4 (62.0 years old) compared with Q1 (54.0 years old). The proportion of women decreased from Q1 (63.4%) to Q3 (53.7%) and then rose in Q4 (76.7%). Height was slightly low in Q4 (153.0 cm), while weight, waist circumference, and BMI was high, with Q4 showing the highest median values (62.8 kg, 92.4 cm, and 26.8 kg/m²). Table 1 Basic characteristics of study participants Q1: <2.85 (n = 445) Q2: 2.85–3.65 (n = 443) Q3: 3.65–4.57 (n = 441) Q4: ≥4.57 (n = 443) Age (years) 54.0 (46.0, 61.0) 57.0 (50.0, 64.0) 60.0 (52.0, 66.0) 62.0 (56.0, 69.0) Women (%) 282 (63.4%) 244 (55.1%) 237 (53.7%) 340 (76.7%) Current and ever smoker (%) 233 (52.4%) 233 (52.6%) 232 (52.6%) 147 (33.2%) Alcohol drinking (%) 211 (47.4%) 230 (51.9%) 214 (48.5%) 143 (32.3%) Habitual exercise (%) 132 (29.7%) 170 (38.4%) 158 (35.8%) 143 (32.3%) Height (cm) 158.8 (153.3, 164.7) 158.8 (153.1, 164.6) 158.5 (152.1, 164.3) 153.0 (148.1, 159.1) Weight (kg) 50.5 (45.8, 56.6) 57.0 (50.9, 63.0) 61.6 (54.8, 69.2) 62.8 (55.2, 71.3) Waist circumference (cm) 69.6 (66.0, 73.7) 78.5 (75.0, 81.3) 85.0 (82.0, 88.0) 92.4 (88.5, 97.0) Body mass index (kg/m²) 20.1 (18.8, 21.5) 22.7 (21.2, 23.9) 24.7 (23.1, 26.2) 26.8 (24.7, 29.2) Body roundness index 2.3 (2.0, 2.6) 3.2 (3.1, 3.4) 4.0 (3.8, 4.3) 5.3 (4.9, 6.0) Data are presented in median (interquartile range), or number (percentage) BMI, body mass index; BRI, body roundness index Basic comparisons of BRI with demographic traits Figure 1 and Supplementary Table 1 compared BRI by various demographic factors (mortality status, sex, and age groups). Figure 1 A showed that higher BRI values were associated with increased mortality, as deceased individuals had a higher density at elevated BRI levels. Figure 1 B highlighted differences in BRI by sex, with men displaying higher density at lower BRI values, indicating a leaner body composition, while women show greater density at moderate to higher BRI levels, suggesting higher roundness. Women had a wider distribution of BRI (median: 3.72; IQR: 2.84, 4.88) compared with men (median: 3.54; IQR: 2.88, 4.19). Figure 1 C revealed that BRI increases with age, with density peaks progressively shifting upward from the 40–49 age group (median: 3.24; IQR: 2.42, 4.08) to those over 70 (median: 4.22; IQR: 3.20, 5.32), indicating greater body roundness in older age groups. Figure 1 D shows the ranges of BRI calculated by height and waist circumference and the area of BRI quartiles. This panel easily assess individual’s BRI. Associations of BRI with all-cause and CVD mortality Table 2 summarized the hazard ratios (HRs) of all-cause and CVD mortality across BRI tertiles. In the sex- and age-adjusted model, the HR (95% CI) in Q2 and Q3 was 0.66 (0.43, 0.99) and 0.61 (0.40, 0.91), while no significant association was observed in Q4 with the HR (95% CI) of 0.75 (0.51, 1.11). In the fully adjusted model, the HR in Q2 and Q3 was similar to the sex- and age-adjusted model, with HRs of 0.66 (0.44, 1.01) and 0.64 (0.43, 0.96). For CVD mortality, in the sex- and age-adjusted model, Q3 had an HR (95% CI) of 0.25 (0.08, 0.71). After adjusting for all potential confounders, the HR for Q3 remained similar at 0.27 (0.09, 0.78). With arbitrary BRI groups (< 3, 3–4, 4–5, and ≥ 5), there were reduced risks of all-cause mortality (HR: 0.65, 95% CI: 0.43, 0.98) and CVD mortality (HR: 0.39, 95% CI: 0.15, 1.01) among those with BRI of 4–5 compared with BRI of < 3 (Supplementary Table S2). Table 2 Associations of BRI quartiles with all-cause and cardiovascular disease mortality BRI tertiles BRI Mortality rates a Unadjusted Sex- and age-adjusted Fully adjusted b All-cause mortality Q1 < 2.85 85.7 Ref. Ref. Ref. Q2 2.85–3.65 75.1 0.86 (0.56, 1.30) 0.66 (0.43, 0.99) 0.66 (0.44, 1.01) Q3 3.65–4.57 84.6 0.97 (0.65, 1.45) 0.61 (0.40, 0.91) 0.64 (0.43, 0.96) Q4 ≥ 4.57 121.0 1.41 (0.97, 2.05) 0.75 (0.51, 1.11) 0.77 (0.52, 1.14) CVD mortality Q1 < 2.85 20.5 Ref. Ref. Ref. Q2 2.85–3.65 21.0 1.00 (0.44, 2.27) 0.72 (0.32, 1.63) 0.76 (0.33, 1.73) Q3 3.65–4.57 8.8 0.42 (0.15, 1.21) 0.25 (0.08, 0.71) 0.27 (0.09, 0.78) Q4 ≥ 4.57 33.4 1.65 (0.78, 3.46) 0.80 (0.37, 1.74) 0.79 (0.36, 1.75) a Mortality rates are shown per 10,000 person-years. b Adjusted for age, sex, smoking experience, drinking habits, and habitual exercise The restricted cubic spline model showed a curvilinear association between BRI and HRs of all-cause and CVD mortality (Fig. 2 ). The HRs decreased as the BRI increased and bottomed out around 4.5 for all-cause mortality and 4.0 for CVD mortality. After BRI exceeds the bottom points, the HRs increased again even after adjustment for potential confounders. For all-cause mortality, compared with a BRI of 4.5, the HR was higher at a BRI of 6.5 or more. Stratified analysis The results of sex- and age-stratified analyses were summarized in Supplementary Tables S3 and S4. In sex-stratified analysis, compared with Q1, the HR of CVD mortality in Q3 was 0.10 (0.01, 0.86). In age-stratified analysis, among those who aged over 65 years, the HRs of all-cause mortality were 0.49 (0.29, 0.81) in Q2 and 0.62 (0.40, 0.97) in Q3 compared with Q1. Furthermore, for CVD mortality, the HR in Q2 was 0.18 (0.04, 0.82), respectively. No association was observed in a younger age group (40–65 yrs). Meta-analysis A meta-analysis with BRI quartiles (Q1 as reference) showed that the HR of all-cause mortality was lower in Q3 (HR: 0.81; 95% CI: 0.65, 0.96) (Fig. 3 and Supplementary Table S5). Meanwhile, Q2 and Q4 showed marginal associations with all-cause mortality (HR: 0.88; 95% CI: 0.76, 1.01 and HR: 0.87; 95% CI: 0.75, 1.00), suggesting Q3 of BRI could reduce the risk of all-cause mortality compared with Q1. For CVD mortality, the hazard would be decreased in Q2 with HRs of (HR: 0.91; 95% CI: 0.83, 0.98). Although slight heterogeneities across studies were observed in the meta-analysis for CVD mortality, heterogeneities were more cautious in the analysis for all-cause mortality. Discussion This study aimed to summarize basic characteristics of BRI across demographic variables and examine its associations with all-cause and CVD mortality in a Japanese population, incorporating the meta-analysis. In our analyses in a Japanese population, moderate BRI levels (Q2 and Q3) had the lowest HRs for both all-cause and CVD mortality. We also found a U-shaped relationship between BRI and mortality, with the lowest hazard between BRI around 4.5. Although a meta-analysis showed heterogeneity driven by our study, the results supported that moderate BRI had reduced risks of all-cause and CVD mortality compared to Q1. Epidemiological studies on the association between BRI and mortality have been reported over the past couple of years in general populations in China and the United States [ 14 , 15 , 19 , 20 ]. They are motivated by the problem that BMI fails to reflect an individual’s visceral fat accumulation, although previous studies have observed an association between BMI and all-cause and CVD mortality [ 21 ]. For example, in the United States, a study among ~ 30,000 NHANES participants found a U-shaped association between BRI and all-cause mortality [ 14 ]. Compared to the third quintile (BRI: 4.5 to 5.5), there was a 25% increased risk in the first quintile (BRI: 3.4 or less) and 49% in the fifth quintile (BRI: 6.9 or more). The same NHANES study in different waves also found a 17% risk reduction for all-cause mortality and a 22% risk reduction for CVD mortality in the third quartile (BRI: 4.88 to 6.38) compared to the first quartile (BRI: 3.58 or less) [ 19 ]. Furthermore, another study among the hypertensive NHANES participants also found a 13% risk reduction in all-cause mortality in the third quartile (BRI: 5.85 to 7.37) compared to the first quartile (BRI: 4.61 or less) [ 22 ]. Among 70,000 Chinese general middle-aged adults, a study showed U-shaped associations between BRI and all-cause and CVD mortality. In the third quartile of BRI (BRI: 3.4 to 3.7), the risk for all-cause and CVD mortality was reduced with 9% and 7% compared to the first quartile (BRI: 2.5 or less) [ 12 ]. Compared with these previous studies in China and the US, our results also showed the lowest risk (HR) in the third quartile of BRI (BRI: 3.65–4.57) when referred to the first quartile (BRI: 2.85 or lower). Furthermore, all the forementioned studies confirmed the non-linear associations between continuous BRI and all-cause mortality and CVD mortality by analysis using a cubic spline. Similarly, we observed a U-shaped association between BRI and mortality in the Japanese population. A cautious point is that studies on BRI rely on use of the NHANES dataset, which may exaggerate the results. Therefore, it is meaningful that the results were replicated in the completely different population (in this case, Japanese population). Our study performed a meta-analysis using two previous studies (with quartered groups) in the general population [ 12 , 19 ]. Although heterogeneity was observed between studies, the results showed a risk reduction in all-cause and CVD mortality in Q2 and Q3 of BRI. In other words, the results suggest that moderate BRI is beneficial in lowering the risk of death, regardless of race. However, when considering the practice of BRI, a major issue would be different distributions of BRI across various populations. Our study considered the population with a BRI of less than 2.9 as the first quartile and the population with a BRI of 4.6 or more as the fourth quartile. Although a study by Wu et al. was also performed in a Japanese population, they reported that people with a BRI of 2.1 or less were categorized as the first quartile and people with a BRI of 3.2 or more as the fourth quartile [ 13 ]. It is indicative that the distribution of BRI differed even among the same Japanese population. In this case, the difference would come from chronological age of the target populations. In fact, Wu et al. included a large number of young people (mean age: 44.2 years), whereas the present study middle-aged and older participants (mean age: 58.4 years). A plausible reason underlying this difference of BRI across age groups would be dysfunction in adipose tissue among older individuals. Furthermore, compared the BRI values between Japanese and US general populations, the mean BRI was 5.2 in the NHANES and 3.8 in our study group. Interestingly, the mean age of the NHANES participants was 46.7 years, while our study were 58.4 years. In other words, the NHANES population had a higher BRI regardless of age, which implies that BRI would be affected not simply by age. From other viewpoints, as described in our study, BRI was relatively higher in women than in men. This phenomenon may result in the impact of sex hormones on body composition and eating behavior, especially in a middle-aged population. In summary, BRI values may be affected by complex combinations of lifestyles and biophysical phenomena, not simply by age. In similar to BMI, it is difficult to establish a single cut-off value for BRI across racial and ethnic groups, which will be a major challenge in the future. Shifting into marginal focus of the original scope of this study, but we would like to discuss the difference between BMI and BRI. Initially, BRI was proposed to reflect visceral fat accumulation more appropriately than BMI [ 1 ]. In the present study population, we also examined associations between mortality and conventional BMI classifications (underweight (BMI 25.0)) and found no significant association with all-cause or CVD mortality. In addition, when BMI was included as a covariate in the multivariable regression model, BRI estimates did not differ for either all-cause or CVD mortality (Supplementary Table S6). In other words, even when conditioning on BMI (theoretically, when comparing within subgroups with the same BMI), there were differences in survival probabilities between BRI quartiles. Previous studies have reported that BRI is more strongly associated with the number of cardiometabolic-related risk factors than BMI [ 23 ]. Our results cannot determine whether BRI is superior to BMI, but it may be potentially meaningful as a different biological indicator. The strength of this study is a comprehensive approach, including a descriptive summary of BRI and a survival analysis with longer follow-ups in a Japanese population with middle-aged adults, and a meta-analysis with previous studies. Aging may contribute to dysfunction of adipose tissue and increased BRI in the elderly. Our stratified results also supported that moderate BRI in the elderly was associated with a decreased risk of all-cause mortality related to metabolic abnormalities. Additionally, we pointed out the sex differences of BRI and heterogenous effects of BRI on mortality between sex. This may be due to sex hormone effects on bio composition and appetite. Furthermore, our meta-analysis will be meaningful for summarizing different studies across ethnic groups at the current stage. However, there are limitations that should be mentioned. First, measurements of waist in different study years may introduce errors. Second, we did not analyze how much longitudinal changes in BRI affect mortality. Although previous studies on BRI have also focused on trajectories of BRI, they analyzed subgroups which had a constant BRI for the multiple time points (i.e., constantly high, moderate, and low BRI groups). Third, the generalizability of the results in an absolute BRI value is difficult because BRI distribution varied in different populations, while discussion on the basis of BRI quartiles could be possible. Forth, the results may be biased by unmeasured confounding. For example, variables associated with both BRI and mortality were not collected in this study (e.g., educational attainment). In this study, we found that higher BRI are observed particularly in older age groups and women among Japanese adults. The time-to-event analysis showed reduced risks of all-cause and CVD mortality among Q3 of BRI in this population. A curvilinear relationship was observed between BRI and mortality, with the lowest HRs around a BRI of 4.0–4.5, suggesting moderate BRI levels may reduce the risk of all-cause and CVD mortality. The meta-analysis also confirmed that individuals with moderate BRI quartiles (Q2 or Q3) had lower mortality risks. Further studies are expected to be conducted from using multidirectional approaches among diverse populations. Declarations Competing Interests Nothing to declare Acknowledgements We would like to thank the residents for their cooperation and the staff at the Health Examination Program for the Residents of Yakumo, Hokkaido, Japan, for their substantial support. The authors used the DeepL Translator (DeepL SE, cologne, Germany) to improve readability of the manuscript. Source of funding N.A. Author contribution RF, YT, and HO collected data; KM and RF performed statistical analysis and drafted original manuscript; KS organized health check-up program and supervised this study. Data availability Data available on request from authors References Thomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring). 2013;21:2264-71. Maessen MF, Eijsvogels TM, Verheggen RJ, Hopman MT, Verbeek AL, de Vegt F. Entering a new era of body indices: the feasibility of a body shape index and body roundness index to identify cardiovascular health status. PLoS One. 2014;9:e107212. Li H, Zhang Y, Luo H, Lin R. 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Tao L, Miao L, Guo YJ, Liu YL, Xiao LH, Yang ZJ. Associations of body roundness index with cardiovascular and all-cause mortality: NHANES 2001-2018. J Hum Hypertens. 2024;38:120-127. Xu J, Zhang L, Wu Q, Zhou Y, Jin Z, Li Z, et al. Body roundness index is a superior indicator to associate with the cardio-metabolic risk: evidence from a cross-sectional study with 17,000 Eastern-China adults. BMC Cardiovasc Disord. 2021;21:97. Additional Declarations There is NO conflict of interest to disclose Supplementary Files SupplementaryTables.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5977532","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":421955246,"identity":"aea37df3-58f2-40cb-a8e1-d93f436dae0d","order_by":0,"name":"Koji Suzuki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYBACCTB5QEKOvYEh4QCQmQAR5yGsxZjnAFDLARK0MCT2HADTMC14gGT/GePPPGcs0nskEh4e/lDDkGdwgPnhBwaZOzi1SEvkGBjz3JDIBWoBOuwYQ7HBATZjCQaeZzi1yEnwGCTzfJDI3Q/WwvY/ccMBBjOgXw7j1sJ/xuAwUEs6D1jLPwagFvZveLVIM+QYNgMdlgDWcrANpIUHvy2SM9KKGeeckTDs4XmQcOBsH0PizMM8xUATcPtF4vzhzR/eHKuT52HPSf5Q8Y0hse94+8YPH3twhxgS4EmA0MxADIkmgoAdWdUPorSMglEwCkbByAAA419aoTfSgIEAAAAASUVORK5CYII=","orcid":"","institution":"Fujita Health University","correspondingAuthor":true,"prefix":"","firstName":"Koji","middleName":"","lastName":"Suzuki","suffix":""},{"id":421955247,"identity":"d19f85c0-e8a2-4ccb-9702-10ffd6ee81fa","order_by":1,"name":"Kazuma Murakami","email":"","orcid":"","institution":"Fujita Health University","correspondingAuthor":false,"prefix":"","firstName":"Kazuma","middleName":"","lastName":"Murakami","suffix":""},{"id":421955248,"identity":"1d7b0124-68c2-47c5-bf17-366d9355ee1a","order_by":2,"name":"Ryosuke Fujii","email":"","orcid":"https://orcid.org/0000-0003-1730-2059","institution":"Fujita Health University School of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ryosuke","middleName":"","lastName":"Fujii","suffix":""},{"id":421955249,"identity":"7401f4dd-d6d6-4677-8a62-832c5b80091c","order_by":3,"name":"Yoshiki Tsuboi","email":"","orcid":"https://orcid.org/0000-0001-8145-3949","institution":"Fujita Health University, Japan","correspondingAuthor":false,"prefix":"","firstName":"Yoshiki","middleName":"","lastName":"Tsuboi","suffix":""},{"id":421955250,"identity":"472ffbe1-d2c1-484e-ade7-a424f4eb218f","order_by":4,"name":"Hiroshi Okumiyama","email":"","orcid":"","institution":"Fujita Health University","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Okumiyama","suffix":""}],"badges":[],"createdAt":"2025-02-07 04:00:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5977532/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5977532/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77681934,"identity":"68c3c9f1-b2a0-467f-b203-d775baba8bec","added_by":"auto","created_at":"2025-03-04 08:44:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150593,"visible":true,"origin":"","legend":"\u003cp\u003eDistributions of body roundness index (BRI) between mortality status (\u003cstrong\u003eA\u003c/strong\u003e) and sex (\u003cstrong\u003eB\u003c/strong\u003e), and across age groups (\u003cstrong\u003eC\u003c/strong\u003e). Range of BRI calculated by height and waist circumference and quartile ranges (\u003cstrong\u003eD\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5977532/v1/17a6611228e266081a3b99dc.png"},{"id":77681931,"identity":"e6b479f0-78b0-4ec0-8956-0a2a2b7900f1","added_by":"auto","created_at":"2025-03-04 08:44:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":115274,"visible":true,"origin":"","legend":"\u003cp\u003eNon-linear associations of body roundness index (BRI) with all-cause (\u003cstrong\u003eA\u003c/strong\u003e) and cardiovascular disease (CVD) mortality (\u003cstrong\u003eB\u003c/strong\u003e). Grey-shaded areas indicate the 95% confidence intervals.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5977532/v1/441a63c86083417bca68c9f8.png"},{"id":77681944,"identity":"6369ff7b-27a3-47d1-a68e-2d8cd77e57cf","added_by":"auto","created_at":"2025-03-04 08:44:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":460523,"visible":true,"origin":"","legend":"\u003cp\u003eMeta-analysis with previous studies from Chinese and US populations for the associations of body roundness index (BRI) with all-cause (\u003cstrong\u003eA\u003c/strong\u003e) and cardiovascular disease (CVD) mortality (\u003cstrong\u003eB\u003c/strong\u003e). The size of black squares indicates the weights of each study. The red diamonds indicate overall effect sizes.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5977532/v1/f7f52c3cdb545c6990059a93.png"},{"id":87425472,"identity":"0c71a55c-eb1d-41d1-bea3-3c2aaa4a4036","added_by":"auto","created_at":"2025-07-23 16:24:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1375882,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5977532/v1/37a779f0-82ac-491e-920e-54b336766662.pdf"},{"id":77682697,"identity":"fe1a1eea-34b7-4bbc-892a-e963dffbd366","added_by":"auto","created_at":"2025-03-04 08:52:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":69169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5977532/v1/31ed1a1e938e252d502aeb70.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Body roundness index and all-cause and CVD mortality: findings from Japanese adults and meta-analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBody roundness index (BRI) is a novel anthropometric indicator developed to assess body composition, focusing on central obesity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Unlike traditional measures such as body mass index (BMI), which uses only height and weight, BRI incorporates waist circumference with height to provide a more accurate representation of fat distribution [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Researchers have proposed that BRI can predict visceral fat levels better than BMI [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Central obesity has been identified as more strongly associated with adverse health outcomes compared to general obesity [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Thus, by providing a single numerical value based on body shape, BRI aims to simplify the assessment of obesity-related health risks. As obesity continues to rise globally, BRI is a promising tool for improving the detection and management of obesity-related health risks [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence on BRI has grown, focusing on its effectiveness as a tool for predicting health outcomes related to obesity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There are consistent results that BRI is a stronger predictor of abdominal obesity, cardiovascular disease (CVD), and metabolic risks compared to BMI [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Researchers have explored its utility in different populations, demonstrating its ability to capture complex relationships between body shape and disease risk, including nonlinear associations with mortality. Recent studies have highlighted the clinical utility of BRI as a robust predictor of mortality and cardiovascular disease (CVD) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A study revealed a nonlinear relationship between BRI and all-cause and cardiovascular mortality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Another recent study further demonstrated that BRI outperformed BMI in predicting all-cause mortality among middle-aged Chinese adults [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Together, these findings support BRI as a practical tool for identifying at-risk individuals and guiding preventive strategies globally.\u003c/p\u003e \u003cp\u003eDespite its promise, research on BRI is limited by methodological and contextual challenges. Short follow-up periods limit insights into long-term risks associated with differences in BRI, particularly for chronic diseases and older populations. Another limitation is population specificity. Research often focuses on specific populations (e.g., rural China, and U.S. cohorts), making it difficult to generalize findings to other demographics, ethnic groups, or geographic regions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Cultural and environmental differences, such as diet and healthcare access, further constrain BRI\u0026rsquo;s applicability to diverse populations [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Broader studies from different settings are essential to accumulate evidence on BRI and validate BRI's utility on a global scale.\u003c/p\u003e \u003cp\u003eIn this study, for the Japanese population, we aimed to describe basic information of BRI across different demographic valuables and to examine the associations between BRI and all-cause and CVD mortality by incorporating an updated meta-analysis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants\u003c/h2\u003e \u003cp\u003eYakumo study was launched in 1982 in Yakumo Town, Hokkaido, and has continued for more than 40 years [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Every August, more than 500 residents aged 40 and more participate in a health check-up program. This cohort study was conducted based on the Yakumo Study, with 1,880 participants who participated in the health check-up program between 2004 and 2018 and were followed until December 31, 2023. For individuals who participated in more than one visit between 2004 and 2018, data from the first visit were used. Out of all eligible participants, 10 participants with less than 8 months of follow-up period and 2 participants with incomplete anthropometric data were excluded. Additionally, 462 participants with a history of cancer, cardiovascular disease, or diabetes were excluded, resulting in a final analysis of 1,772 participants. Written informed consent was obtained from all participants. This study adhered to the Declaration of Helsinki. The protocol of this study was approved by the Ethics Review Committee at Fujita Health University.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of BRI\u003c/h3\u003e\n\u003cp\u003eBRI was calculated according to the formula as 364.2-365.5 \u0026times; \u0026radic;(1-[waist circumference (cm)/2π]\u003csup\u003e2\u003c/sup\u003e/[0.5 \u0026times; height (cm)]\u003csup\u003e2\u003c/sup\u003e) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Waist circumference (cm) and height (cm) were measured during the health check-up program by trained public health nurses. Since there is no clear reference range for BRI, we divided participants into quartiles to examine the association with all-cause mortality and CVD mortality.\u003c/p\u003e\n\u003ch3\u003eAssessment of mortality\u003c/h3\u003e\n\u003cp\u003eAfter the baseline data from 2004 to 2018, death certificates were reviewed and extracted by municipal officers. Based on this information, we assigned the ICD International Classification of Diseases, Tenth Revision (ICD-10) codes. CVD mortality was defined as deaths with ICD-10 codes I00-I99 (i.e., diseases of the circulatory system).\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eSelf-administered questionnaires were used to obtain information on participants\u0026rsquo; lifestyle such as smoking (current, former, and never), drinking (current, former, and never), exercise per week (almost none, one or two hours, three or four hours, and more than five hours) and past medical history of cancer, cardiovascular disease, and diabetes (none, under treatment, previously treated, and never treated). For tobacco smoking and drinking habits, those who answered \u0026ldquo;current\u0026rdquo; or \u0026ldquo;former\u0026rdquo; were defined as smoking experience and drinking alcohol. Regarding exercise habits, those who answered more than \u0026ldquo;one or two hours\u0026rdquo; was defined as regular exercise habits. Persons who responded other than \u0026ldquo;none\u0026rdquo; were defined as having a medical history.\u003c/p\u003e\n\u003ch3\u003eInclusion/Exclusion criteria for meta-analysis\u003c/h3\u003e\n\u003cp\u003eFor meta-analysis, we reviewed the two databases (PubMed and Cochrane) with a specific search of (\u0026ldquo;Body roundness index\u0026rdquo;) AND (\u0026ldquo;mortality\u0026rdquo;). As shown in PRISMA flowchart in Supplementary Materials, two possible publications were finally selected in our meta-analysis. In this meta-analysis, a random-effects model was used to pool previous results using generalized linear mixed-effects models for each quartile. The heterogeneity was evaluated by the \u003cem\u003eI\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e and the \u003cem\u003eQ\u003c/em\u003e statistic.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCox proportional hazard model was performed to estimate hazard ratios (HR) and 95% confidence intervals (CI) of all-cause and CVD mortality across baseline BRI quartiles. Age, sex, smoking experience, drinking habits, and regular exercise habit at the first visit were included in a fully adjusted model. Nonlinear associations between baseline BRI and mortality were examined by Cox-PH models with cubic spline. We performed stratified analyses by sex (men and women) and age (\u0026lt;\u0026thinsp;65 years and \u0026ge;\u0026thinsp;65 years) by recategorizing quartiles in each subgroup. Additionally, we analyzed with arbitrary BRI groups (\u0026lt;\u0026thinsp;3, 3\u0026ndash;4, 4\u0026ndash;5, and \u0026ge;\u0026thinsp;5) for setting a practical cut-off point. All statistical analysis was performed by R package (ver.4.3.0). Statistical significance was made by two-sided tests and considered as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline characteristics of 1,772 participants according to BRI quartiles are summarized (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). During a median follow-up period of 13.3 years (IQR: 8.3, 18.3), 206 all-cause deaths and 47 CVD deaths were identified. Median age was higher among Q4 (62.0 years old) compared with Q1 (54.0 years old). The proportion of women decreased from Q1 (63.4%) to Q3 (53.7%) and then rose in Q4 (76.7%). Height was slightly low in Q4 (153.0 cm), while weight, waist circumference, and BMI was high, with Q4 showing the highest median values (62.8 kg, 92.4 cm, and 26.8 kg/m\u0026sup2;).\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\u003eBasic 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1: \u0026lt;2.85 (n\u0026thinsp;=\u0026thinsp;445)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2: 2.85\u0026ndash;3.65 (n\u0026thinsp;=\u0026thinsp;443)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3: 3.65\u0026ndash;4.57 (n\u0026thinsp;=\u0026thinsp;441)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4: \u0026ge;4.57 (n\u0026thinsp;=\u0026thinsp;443)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e54.0 (46.0, 61.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.0 (50.0, 64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.0 (52.0, 66.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.0 (56.0, 69.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282 (63.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244 (55.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e237 (53.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e340 (76.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent and ever smoker (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233 (52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e233 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147 (33.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol drinking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e211 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230 (51.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214 (48.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHabitual exercise (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (29.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (38.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158.8 (153.3, 164.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.8 (153.1, 164.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158.5 (152.1, 164.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e153.0 (148.1, 159.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.5 (45.8, 56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.0 (50.9, 63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.6 (54.8, 69.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.8 (55.2, 71.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.6 (66.0, 73.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.5 (75.0, 81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.0 (82.0, 88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.4 (88.5, 97.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.1 (18.8, 21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.7 (21.2, 23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.7 (23.1, 26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.8 (24.7, 29.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody roundness index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.3 (2.0, 2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2 (3.1, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0 (3.8, 4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.3 (4.9, 6.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are presented in median (interquartile range), or number (percentage)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eBMI, body mass index; BRI, body roundness index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eBasic comparisons of BRI with demographic traits\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table\u0026nbsp;1 compared BRI by various demographic factors (mortality status, sex, and age groups). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA showed that higher BRI values were associated with increased mortality, as deceased individuals had a higher density at elevated BRI levels. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB highlighted differences in BRI by sex, with men displaying higher density at lower BRI values, indicating a leaner body composition, while women show greater density at moderate to higher BRI levels, suggesting higher roundness. Women had a wider distribution of BRI (median: 3.72; IQR: 2.84, 4.88) compared with men (median: 3.54; IQR: 2.88, 4.19). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC revealed that BRI increases with age, with density peaks progressively shifting upward from the 40\u0026ndash;49 age group (median: 3.24; IQR: 2.42, 4.08) to those over 70 (median: 4.22; IQR: 3.20, 5.32), indicating greater body roundness in older age groups. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD shows the ranges of BRI calculated by height and waist circumference and the area of BRI quartiles. This panel easily assess individual\u0026rsquo;s BRI.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of BRI with all-cause and CVD mortality\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarized the hazard ratios (HRs) of all-cause and CVD mortality across BRI tertiles. In the sex- and age-adjusted model, the HR (95% CI) in Q2 and Q3 was 0.66 (0.43, 0.99) and 0.61 (0.40, 0.91), while no significant association was observed in Q4 with the HR (95% CI) of 0.75 (0.51, 1.11). In the fully adjusted model, the HR in Q2 and Q3 was similar to the sex- and age-adjusted model, with HRs of 0.66 (0.44, 1.01) and 0.64 (0.43, 0.96). For CVD mortality, in the sex- and age-adjusted model, Q3 had an HR (95% CI) of 0.25 (0.08, 0.71). After adjusting for all potential confounders, the HR for Q3 remained similar at 0.27 (0.09, 0.78). With arbitrary BRI groups (\u0026lt;\u0026thinsp;3, 3\u0026ndash;4, 4\u0026ndash;5, and \u0026ge;\u0026thinsp;5), there were reduced risks of all-cause mortality (HR: 0.65, 95% CI: 0.43, 0.98) and CVD mortality (HR: 0.39, 95% CI: 0.15, 1.01) among those with BRI of 4\u0026ndash;5 compared with BRI of \u0026lt;\u0026thinsp;3 (Supplementary Table S2).\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 BRI quartiles with all-cause and cardiovascular disease mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBRI tertiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMortality rates\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSex- and age-adjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFully adjusted\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.85\u0026ndash;3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86 (0.56, 1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66 (0.43, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66 (0.44, 1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65\u0026ndash;4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.65, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61 (0.40, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.64 (0.43, 0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.41 (0.97, 2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75 (0.51, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77 (0.52, 1.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCVD mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.85\u0026ndash;3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.44, 2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.72 (0.32, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76 (0.33, 1.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65\u0026ndash;4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42 (0.15, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25 (0.08, 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.27 (0.09, 0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65 (0.78, 3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80 (0.37, 1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.79 (0.36, 1.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eMortality rates are shown per 10,000 person-years.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eAdjusted for age, sex, smoking experience, drinking habits, and habitual exercise\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 restricted cubic spline model showed a curvilinear association between BRI and HRs of all-cause and CVD mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The HRs decreased as the BRI increased and bottomed out around 4.5 for all-cause mortality and 4.0 for CVD mortality. After BRI exceeds the bottom points, the HRs increased again even after adjustment for potential confounders. For all-cause mortality, compared with a BRI of 4.5, the HR was higher at a BRI of 6.5 or more.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStratified analysis\u003c/h2\u003e \u003cp\u003eThe results of sex- and age-stratified analyses were summarized in Supplementary Tables S3 and S4. In sex-stratified analysis, compared with Q1, the HR of CVD mortality in Q3 was 0.10 (0.01, 0.86). In age-stratified analysis, among those who aged over 65 years, the HRs of all-cause mortality were 0.49 (0.29, 0.81) in Q2 and 0.62 (0.40, 0.97) in Q3 compared with Q1. Furthermore, for CVD mortality, the HR in Q2 was 0.18 (0.04, 0.82), respectively. No association was observed in a younger age group (40\u0026ndash;65 yrs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMeta-analysis\u003c/h2\u003e \u003cp\u003eA meta-analysis with BRI quartiles (Q1 as reference) showed that the HR of all-cause mortality was lower in Q3 (HR: 0.81; 95% CI: 0.65, 0.96) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table S5). Meanwhile, Q2 and Q4 showed marginal associations with all-cause mortality (HR: 0.88; 95% CI: 0.76, 1.01 and HR: 0.87; 95% CI: 0.75, 1.00), suggesting Q3 of BRI could reduce the risk of all-cause mortality compared with Q1. For CVD mortality, the hazard would be decreased in Q2 with HRs of (HR: 0.91; 95% CI: 0.83, 0.98). Although slight heterogeneities across studies were observed in the meta-analysis for CVD mortality, heterogeneities were more cautious in the analysis for all-cause mortality.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to summarize basic characteristics of BRI across demographic variables and examine its associations with all-cause and CVD mortality in a Japanese population, incorporating the meta-analysis. In our analyses in a Japanese population, moderate BRI levels (Q2 and Q3) had the lowest HRs for both all-cause and CVD mortality. We also found a U-shaped relationship between BRI and mortality, with the lowest hazard between BRI around 4.5. Although a meta-analysis showed heterogeneity driven by our study, the results supported that moderate BRI had reduced risks of all-cause and CVD mortality compared to Q1.\u003c/p\u003e \u003cp\u003eEpidemiological studies on the association between BRI and mortality have been reported over the past couple of years in general populations in China and the United States [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. They are motivated by the problem that BMI fails to reflect an individual\u0026rsquo;s visceral fat accumulation, although previous studies have observed an association between BMI and all-cause and CVD mortality [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. For example, in the United States, a study among ~\u0026thinsp;30,000 NHANES participants found a U-shaped association between BRI and all-cause mortality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Compared to the third quintile (BRI: 4.5 to 5.5), there was a 25% increased risk in the first quintile (BRI: 3.4 or less) and 49% in the fifth quintile (BRI: 6.9 or more). The same NHANES study in different waves also found a 17% risk reduction for all-cause mortality and a 22% risk reduction for CVD mortality in the third quartile (BRI: 4.88 to 6.38) compared to the first quartile (BRI: 3.58 or less) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, another study among the hypertensive NHANES participants also found a 13% risk reduction in all-cause mortality in the third quartile (BRI: 5.85 to 7.37) compared to the first quartile (BRI: 4.61 or less) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Among 70,000 Chinese general middle-aged adults, a study showed U-shaped associations between BRI and all-cause and CVD mortality. In the third quartile of BRI (BRI: 3.4 to 3.7), the risk for all-cause and CVD mortality was reduced with 9% and 7% compared to the first quartile (BRI: 2.5 or less) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Compared with these previous studies in China and the US, our results also showed the lowest risk (HR) in the third quartile of BRI (BRI: 3.65\u0026ndash;4.57) when referred to the first quartile (BRI: 2.85 or lower). Furthermore, all the forementioned studies confirmed the non-linear associations between continuous BRI and all-cause mortality and CVD mortality by analysis using a cubic spline. Similarly, we observed a U-shaped association between BRI and mortality in the Japanese population. A cautious point is that studies on BRI rely on use of the NHANES dataset, which may exaggerate the results. Therefore, it is meaningful that the results were replicated in the completely different population (in this case, Japanese population).\u003c/p\u003e \u003cp\u003eOur study performed a meta-analysis using two previous studies (with quartered groups) in the general population [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Although heterogeneity was observed between studies, the results showed a risk reduction in all-cause and CVD mortality in Q2 and Q3 of BRI. In other words, the results suggest that moderate BRI is beneficial in lowering the risk of death, regardless of race. However, when considering the practice of BRI, a major issue would be different distributions of BRI across various populations. Our study considered the population with a BRI of less than 2.9 as the first quartile and the population with a BRI of 4.6 or more as the fourth quartile. Although a study by Wu et al. was also performed in a Japanese population, they reported that people with a BRI of 2.1 or less were categorized as the first quartile and people with a BRI of 3.2 or more as the fourth quartile [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It is indicative that the distribution of BRI differed even among the same Japanese population. In this case, the difference would come from chronological age of the target populations. In fact, Wu et al. included a large number of young people (mean age: 44.2 years), whereas the present study middle-aged and older participants (mean age: 58.4 years). A plausible reason underlying this difference of BRI across age groups would be dysfunction in adipose tissue among older individuals. Furthermore, compared the BRI values between Japanese and US general populations, the mean BRI was 5.2 in the NHANES and 3.8 in our study group. Interestingly, the mean age of the NHANES participants was 46.7 years, while our study were 58.4 years. In other words, the NHANES population had a higher BRI regardless of age, which implies that BRI would be affected not simply by age. From other viewpoints, as described in our study, BRI was relatively higher in women than in men. This phenomenon may result in the impact of sex hormones on body composition and eating behavior, especially in a middle-aged population. In summary, BRI values may be affected by complex combinations of lifestyles and biophysical phenomena, not simply by age. In similar to BMI, it is difficult to establish a single cut-off value for BRI across racial and ethnic groups, which will be a major challenge in the future.\u003c/p\u003e \u003cp\u003eShifting into marginal focus of the original scope of this study, but we would like to discuss the difference between BMI and BRI. Initially, BRI was proposed to reflect visceral fat accumulation more appropriately than BMI [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the present study population, we also examined associations between mortality and conventional BMI classifications (underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5), normal weight (18.5\u0026ndash;25.0), and obese (\u0026gt;\u0026thinsp;25.0)) and found no significant association with all-cause or CVD mortality. In addition, when BMI was included as a covariate in the multivariable regression model, BRI estimates did not differ for either all-cause or CVD mortality (Supplementary Table S6). In other words, even when conditioning on BMI (theoretically, when comparing within subgroups with the same BMI), there were differences in survival probabilities between BRI quartiles. Previous studies have reported that BRI is more strongly associated with the number of cardiometabolic-related risk factors than BMI [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our results cannot determine whether BRI is superior to BMI, but it may be potentially meaningful as a different biological indicator.\u003c/p\u003e \u003cp\u003eThe strength of this study is a comprehensive approach, including a descriptive summary of BRI and a survival analysis with longer follow-ups in a Japanese population with middle-aged adults, and a meta-analysis with previous studies. Aging may contribute to dysfunction of adipose tissue and increased BRI in the elderly. Our stratified results also supported that moderate BRI in the elderly was associated with a decreased risk of all-cause mortality related to metabolic abnormalities. Additionally, we pointed out the sex differences of BRI and heterogenous effects of BRI on mortality between sex. This may be due to sex hormone effects on bio composition and appetite. Furthermore, our meta-analysis will be meaningful for summarizing different studies across ethnic groups at the current stage. However, there are limitations that should be mentioned. First, measurements of waist in different study years may introduce errors. Second, we did not analyze how much longitudinal changes in BRI affect mortality. Although previous studies on BRI have also focused on trajectories of BRI, they analyzed subgroups which had a constant BRI for the multiple time points (i.e., constantly high, moderate, and low BRI groups). Third, the generalizability of the results in an absolute BRI value is difficult because BRI distribution varied in different populations, while discussion on the basis of BRI quartiles could be possible. Forth, the results may be biased by unmeasured confounding. For example, variables associated with both BRI and mortality were not collected in this study (e.g., educational attainment).\u003c/p\u003e \u003cp\u003eIn this study, we found that higher BRI are observed particularly in older age groups and women among Japanese adults. The time-to-event analysis showed reduced risks of all-cause and CVD mortality among Q3 of BRI in this population. A curvilinear relationship was observed between BRI and mortality, with the lowest HRs around a BRI of 4.0\u0026ndash;4.5, suggesting moderate BRI levels may reduce the risk of all-cause and CVD mortality. The meta-analysis also confirmed that individuals with moderate BRI quartiles (Q2 or Q3) had lower mortality risks. Further studies are expected to be conducted from using multidirectional approaches among diverse populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNothing to declare\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the residents for their cooperation and the staff at the Health Examination Program for the Residents of Yakumo, Hokkaido, Japan, for their substantial support. The authors used the DeepL Translator (DeepL SE, cologne, Germany) to improve readability of the manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eSource of funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.A.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRF, YT, and HO collected data; KM and RF performed statistical analysis and drafted original manuscript; KS organized health check-up program and supervised this study.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available on request from authors\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring). 2013;21:2264-71. \u003c/li\u003e\n\u003cli\u003eMaessen MF, Eijsvogels TM, Verheggen RJ, Hopman MT, Verbeek AL, de Vegt F. Entering a new era of body indices: the feasibility of a body shape index and body roundness index to identify cardiovascular health status. PLoS One. 2014;9:e107212.\u003c/li\u003e\n\u003cli\u003eLi H, Zhang Y, Luo H, Lin R. The lipid accumulation product is a powerful tool to diagnose metabolic dysfunction-associated fatty liver disease in the United States adults. Front Endocrinol (Lausanne). 2022;13:977625.\u003c/li\u003e\n\u003cli\u003eLi X, Lian Y, Ping W, Wang K, Jiang L, Li S. Abdominal obesity and digestive system cancer: a systematic review and meta-analysis of prospective studies. BMC Public Health. 2023;23:2343.\u003c/li\u003e\n\u003cli\u003eHuang W, Gan Z, Gao Z, Lin Q, Li X, Xie W, et al. Discrepancies between general and central obesity in arterial stiffness: observational studies and Mendelian randomization study. BMC Med. 2024;22:325.\u003c/li\u003e\n\u003cli\u003eJayedi A, Soltani S, Zargar MS, Khan TA, Shab-Bidar S. Central fatness and risk of all cause mortality: systematic review and dose-response meta-analysis of 72 prospective cohort studies. BMJ. 2020;23:370.\u003c/li\u003e\n\u003cli\u003eMotamed N, Rabiee B, Hemasi GR, Ajdarkosh H, Khonsari MR, Maadi M, et al. Body Roundness Index and Waist-to-Height Ratio are Strongly Associated With Non-Alcoholic Fatty Liver Disease: A Population-Based Study. Hepat Mon. 2016;16:e39575.\u003c/li\u003e\n\u003cli\u003eStefanescu A, Revilla L, Lopez T, Sanchez SE, Williams MA, Gelaye B. Using A Body Shape Index (ABSI) and Body Roundness Index (BRI) to predict risk of metabolic syndrome in Peruvian adults. J Int Med Res. 2020;48:300060519848854.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Obesity and overweight fact sheet 2016.\u003c/li\u003e\n\u003cli\u003eLiu PJ, Ma F, Lou HP, Zhu YN. Body roundness index and body adiposity index: two new anthropometric indices to identify metabolic syndrome among Chinese postmenopausal women. Climacteric. 2016;19:433-9.\u003c/li\u003e\n\u003cli\u003eLi Y, Gui J, Liu H, Guo LL, Li J, Lei Y, et al. Predicting metabolic syndrome by obesity- and lipid-related indices in mid-aged and elderly Chinese: a population-based cross-sectional study. Front Endocrinol (Lausanne). 2023;14:120113.\u003c/li\u003e\n\u003cli\u003eDing J, Chen X, Shi Z, Bai K, Shi S. Association of body roundness index and its trajectories with all-cause and cardiovascular mortality among a Chinese middle-aged and older population: A retrospective cohort study. Front Public Health. 2023;11:1107158.\u003c/li\u003e\n\u003cli\u003eWu L, Pu H, Zhang M, Hu H, Wan Q. Non-linear relationship between the body roundness index and incident type 2 diabetes in Japan: a secondary retrospective analysis. J Transl Med. 2022;20:110.\u003c/li\u003e\n\u003cli\u003eZhang X, Ma N, Lin Q, Chen K, Zheng F, Wu J, et al. Body Roundness Index and All-Cause Mortality Among US Adults. JAMA Netw Open. 2024;7:e2415051.\u003c/li\u003e\n\u003cli\u003eYang M, Liu J, Shen Q, Chen H, Liu Y, Wang N, et al. Body Roundness Index Trajectories and the Incidence of Cardiovascular Disease: Evidence From the China Health and Retirement Longitudinal Study. J Am Heart Assoc. 2024;13:e034768.\u003c/li\u003e\n\u003cli\u003eRico-Mart\u0026iacute;n S, Calder\u0026oacute;n-Garc\u0026iacute;a JF, S\u0026aacute;nchez-Rey P, Franco-Antonio C, Mart\u0026iacute;nez Alvarez M, S\u0026aacute;nchez Mu\u0026ntilde;oz-Torrero JF. Effectiveness of body roundness index in predicting metabolic syndrome: A systematic review and meta-analysis. Obes Rev. 2020;21:e13023.\u003c/li\u003e\n\u003cli\u003eSuzuki K, Yamada H, Fujii R, Munetsuna E, Yamazaki M, Ando Y, et al. Circulating microRNA-27a and -133a are negatively associated with incident hypertension: a five-year longitudinal population-based study. Biomarkers. 2022;27:496-502.\u003c/li\u003e\n\u003cli\u003eFujii R, Tsuboi Y, Maeda K, Ishihara Y, Suzuki K. Analysis of Repeated Measurements of Serum Carotenoid Levels and All-Cause and Cause-Specific Mortality in Japan. JAMA Netw Open. 2021;4:e2113369.\u003c/li\u003e\n\u003cli\u003eZhou D, Liu X, Huang Y, Feng Y. A nonlinear association between body roundness index and all-cause mortality and cardiovascular mortality in general population. Public Health Nutr. 2022;25:3008-3015.\u003c/li\u003e\n\u003cli\u003eWu J, Lu D, Chen X. Association of body roundness index with abdominal aortic calcification among middle aged and elderly population: findings from NHANES. Front Cardiovasc Med. 2024;11:1475579.\u003c/li\u003e\n\u003cli\u003eBhaskaran K, Dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3\u0026middot;6 million adults in the UK. Lancet Diabetes Endocrinol. 2018;6:944-953.\u003c/li\u003e\n\u003cli\u003eTao L, Miao L, Guo YJ, Liu YL, Xiao LH, Yang ZJ. Associations of body roundness index with cardiovascular and all-cause mortality: NHANES 2001-2018. J Hum Hypertens. 2024;38:120-127.\u003c/li\u003e\n\u003cli\u003eXu J, Zhang L, Wu Q, Zhou Y, Jin Z, Li Z, et al. Body roundness index is a superior indicator to associate with the cardio-metabolic risk: evidence from a cross-sectional study with 17,000 Eastern-China adults. BMC Cardiovasc Disord. 2021;21:97.\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, abdominal obesity, cardiovascular disease, mortality, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-5977532/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5977532/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003eAlthough body roundness index (BRI) is gaining attention as an indicator of abdominal obesity, evidence on this indicator is still sparse. We aimed to summarize basic information about BRI in a Japanese population and to examine associations of BRI with all-cause and cardiovascular disease (CVD) mortality with a meta-analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eThis population-based cohort study included participants (mean age of 58.4 years, 37.8% men) in health check-up programs between 2004 and 2018, and we followed up until December 31, 2023. BRI was calculated by a conventional formula for height (cm) and waist circumference (cm). Mortality was ascertained by death certifications. CVD mortality was defined as mortality with ICD-10 codes of I00-I99. Hazard ratios were estimated for all-cause and CVD mortality using Cox proportional hazards regression models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eDuring the follow-up period (median: 13.3 years), 206 individuals died, and 47 individuals died by CVD. Women had a wider distribution of BRI (median: 3.72; IQR: 2.84, 4.88) compared with men (median: 3.54; IQR: 2.88, 4.19). BRI increased from the 40-49 age group (median: 3.24; IQR: 2.42, 4.08) to those over 70 years old (median: 4.22; IQR: 3.20, 5.32). Compared with Q1, HRs (95% CI) in Q3 were lower for both all-cause mortality (HR: 0.64, 95% CI: 0.43, 0.96) and CVD mortality (HR: 0.27, 95% CI: 0.09, 0.78). Meta-analysis also showed similar associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eBRI varies across age groups and between sexes in a Japanese population. Both our results and this meta-analysis suggest that BRI has U-shaped associations with all-cause and CVD mortality.\u003c/p\u003e","manuscriptTitle":"Body roundness index and all-cause and CVD mortality: findings from Japanese adults and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-04 08:44:26","doi":"10.21203/rs.3.rs-5977532/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":"7b84ba1b-e309-468a-839b-57ca20d4f0ee","owner":[],"postedDate":"March 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44979154,"name":"Health sciences/Risk factors"},{"id":44979155,"name":"Health sciences/Diseases/Cardiovascular diseases"}],"tags":[],"updatedAt":"2025-07-23T16:16:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-04 08:44:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5977532","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5977532","identity":"rs-5977532","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}