Association between body roundness index and rheumatoid arthritis: a cross-sectional study based on NHANES

Association between body roundness index and rheumatoid arthritis: a cross-sectional study based on NHANES | 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 Association between body roundness index and rheumatoid arthritis: a cross-sectional study based on NHANES Zong Jiang, Xin Cai, Xiaoling Yao, Weiya Lan, Jia Liu, Fang Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5884438/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 The Body Roundness Index (BRI) has been identified as a potentially superior measure of body fat distribution such as body mass index (BMI) and waist circumference (WC). However, its relationship with rheumatoid arthritis (RA) has yet to be thoroughly investigated. This study examines the association between BRI and RA risk using data from the National Health and Nutrition Examination Survey (NHANES). Methods The analysis included 28,559 adults, excluding those with missing values for BRI or RA status. BRI was calculated using height and WC measurements, while RA was self-reported by participants. Multivariate logistic regression was utilized to assess the relationship between BRI and RA, while controlling for sociodemographic variables and pertinent comorbid conditions. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were employed to assess the predictive accuracy of BRI, BMI, and WC concerning RA. Results An elevated BRI demonstrated a notable correlation with a heightened risk of RA. With each unit increase in BRI, there was a corresponding 10% increase in the likelihood of RA after complete adjustment (OR: 1.10, 95% CI: 1.08–1.12, P < 0.001). A clear dose-response relationship was identified among the BRI quartiles, where individuals in the highest quartile exhibited a 76% increased risk (OR: 1.76, 95% CI: 1.50–2.07, P < 0.001). Subgroup analysis indicated a more pronounced association among participants exhibiting hyperlipidemia (P for interaction = 0.012). Threshold analysis revealed a BRI value of 4.61 as the critical inflection point, beyond which each unit increase correlated with a 12% elevated risk of RA (OR: 1.12, P < 0.001). ROC analysis revealed that BRI exhibited the highest AUC of 0.637 in predicting RA risk, surpassing WC at 0.622 and BMI at 0.594. Conclusions BRI serves as a strong indicator of RA risk, demonstrating enhanced predictive accuracy when contrasted with conventional metrics like BMI and WC. The results indicate that BRI may function as a valuable instrument for assessing the risk of RA, especially in those exhibiting hyperlipidemia. Health sciences/Diseases/Nutrition disorders Health sciences/Diseases/Rheumatic diseases Risk Body roundness index Rheumatoid arthritis Cross-sectional study NHANES Figures Figure 1 Figure 2 Figure 3 Introduction RA is a chronic, systemic autoimmune disorder marked by persistent synovial inflammation, progressive joint damage, and the involvement of multiple organ systems 1 . This condition results in considerable pain, functional limitations, and a heightened likelihood of associated health issues, including cardiovascular disease, metabolic disorders, and osteoporosis, thereby imposing a significant strain on healthcare systems worldwide 2 . Despite advancements in treatment options, including biologics and immunomodulatory therapies, therapeutic responses remain highly variable, and the long-term efficacy of these treatments is often constrained by adverse effects 3 ; 4 . Thus, a deeper understanding of the risk factors associated with RA and the exploration of novel mechanisms underlying its pathogenesis are essential for improving early detection, prevention, and management strategies. Obesity, particularly the accumulation of visceral fat, has emerged as a significant risk factor for RA 5 . Visceral fat contributes to RA pathogenesis through several mechanisms, including the promotion of chronic low-grade inflammation, immune system dysregulation, and altered secretion of adipokines that influence autoimmune responses 6 . Traditional methods of assessing obesity, such as BMI and WC, have notable limitations, particularly in evaluating fat distribution and specifically visceral fat 7 . For instance, BMI does not distinguish between fat and lean mass, while WC, although indicative of central obesity, fails to precisely measure visceral fat—an important factor linked to metabolic and inflammatory processes 8 ; 9 . To address these constraints, the BRI has been established as a more precise and thorough measure for evaluating obesity, especially in relation to the buildup of visceral fat 10 . BRI integrates both height and waist circumference, providing a more accurate assessment of fat distribution compared to conventional indices 11 . Research indicates that BRI serves as a more robust indicator of health risks associated with obesity, encompassing cardiovascular disease, diabetes, and metabolic syndrome 12–14 . However, while BRI’s potential in detecting metabolic disorders has been explored, its relationship with RA remains under-researched. To date, no studies have established a link between BRI and RA in the general population. This research seeks to fill this gap by employing data from the NHANES to perform a cross-sectional analysis of a representative sample of the U.S. population. This study aims to investigate the connection between BRI and RA, offering essential data that will deepen our comprehension of this relationship and lay the groundwork for subsequent research endeavors. Methods Study population Continuously surveying a cross-section of Americans, the NHANES compiles detailed information on people's physical and dietary well-being. The Multi-Stage Stratified Probability Sampling Design is Used by NHANES Biennially to Guarantee a Diverse and Representative Sample 15 . The research protocol was authorized by the NCHS Ethics Review Board, and all subjects willingly gave their informed consent. Detailed methodology and dataset information are publicly available in the NHANES database. For this study, data from the 1999–2023 NHANES cycles were utilized, initially including 119,555 participants. Participants were excluded if they had missing BRI data (n = 32,416), lacked RA information (n = 41,740), or had incomplete covariate data (n = 16,840). After applying these exclusions, the final sample included 28,559 adult participants (Fig. 1 ). Assessment of BRI and RA Two critical bodily measurements—height and weight center—formed the basis of the formula used to determine BRI 16 . Medical experts from Mobile Examination Centers (MEC) took these readings. Participants reported their RA status using a standardized questionnaire. The first question asked of participants was if they had ever been informed by a doctor or other healthcare provider that they had arthritis. using the "Yes" or "No" choices. When asked whether they had arthritis, participants could select "Osteoarthritis," "RA," or "Don't know" as their form of arthritis if the answer was "Yes." Prior studies have shown that self-reported RA data collected through NHANES is reliable. Covariates In accordance with previous research, several covariates were included that could potentially influence the relationship between BRI and RA 17 . Age, gender, race, poverty-to-income ratio (PIR), marital status, education level, smoking status, alcohol intake, hyperlipidemia, diabetes, and asthma were all included as confounders (Supplementary Material 1). Statistical analysis Frequency and percentage distributions were utilized to summarize categorical variables, whereas continuous variables were expressed in terms of means and standard deviations. A multivariate regression analysis was conducted to investigate the relationship between BRI and RA. Three models were developed: Model 1 was a baseline model without any covariates, Model 2 incorporated adjustments for sociodemographic variables such as age and gender, while Model 3 expanded upon Model 2 by also accounting for health-related behaviors and comorbid conditions, including smoking status, alcohol consumption, and so on. A segmented linear regression model was utilized to evaluate the possible threshold effect of BRI on RA incidence, while a smooth curve-fitting approach was applied to investigate the nonlinear relationship between BRI and RA. In order to assess the predictive efficacy of BRI, BMI, and WC concerning the risk of RA, ROC curves and AUC values were computed. Subgroup analyses and interaction tests were conducted to investigate the variability among various population groups. All statistical analyses were performed utilizing EmpowerStats (version 4.2) and R (version 4.4.1). P < 0.05 was deemed to indicate statistical significance. Results Baseline characteristics Of the total sample, 41.72% were aged 20–39 years, 33.47% were aged 40–59 years, and 24.80% were aged 60 years or older (Table 1 ). The gender distribution was 53.92% male and 46.08% female. In terms of race, non-Hispanic Whites comprised the largest group (47.66%), followed by non-Hispanic Blacks (19.23%), Mexican Americans (17.30%), other Hispanics (8.08%), and other racial groups (7.72%). With regard to education, 44.78% of participants had a high school education or less, 29.60% had some college education, and 25.62% were college graduates or higher. Health-related characteristics indicated that 28.02% of participants had hypertension, 8.89% had diabetes, 27.45% had hyperlipidemia, and 13.10% had asthma. The prevalence of smoking and alcohol consumption was 48.94% and 16.69%, respectively. Stratifying participants by BRI quartiles (Q1–Q4) revealed significant differences across all variables (P < 0.001). Participants in higher BRI quartiles tended to be older, male, and had lower educational levels. Furthermore, higher BRI levels were associated with an increased prevalence of hypertension, diabetes, hyperlipidemia, smoking, and alcohol use (P < 0.001 for all). Table 1 Characteristics of the study population Variable Total Body roundness index P -value Q1 (1.35–4.54) Q2 (4.54–5.94) Q3 (5.94–7.60) Q4 (7.60–20.80) Age (years) < 0.001 20–39 11916 (41.72) 4290 (60.08) 2968 (41.57) 2241 (31.39) 2417 (33.85) 40–59 9560 (33.47) 1950 (27.31) 2501 (35.03) 2587 (36.23) 2522 (35.32) ≥ 60 7083 (24.80) 900 (12.61) 1670 (23.39) 2312 (32.38) 2201 (30.83) Gender (%) < 0.001 Male 15398 (53.92) 3354 (46.97) 4107 (57.53) 4297 (60.18) 3640 (50.98) Female 13161 (46.08) 3786 (53.03) 3032 (42.47) 2843 (39.82) 3500 (49.02) Race (%) < 0.001 Mexican American 4942 (17.30) 761 (10.66) 1314 (18.41) 1523 (21.33) 1344 (18.82) Other Hispanic 2307 (8.08) 504 (7.06) 615 (8.61) 615 (8.61) 573 (8.03) Non-Hispanic White 13612 (47.66) 3579 (50.13) 3292 (46.11) 3365 (47.13) 3376 (47.28) Non-Hispanic Black 5493 (19.23) 1447 (20.27) 1250 (17.51) 1248 (17.48) 1548 (21.68) Other Race 2205 (7.72) 849 (11.89) 668 (9.36) 389 (5.45) 299 (4.19) Educational Attainment (%) < 0.001 High school or less 12788 (44.78) 2686 (37.62) 3125 (43.77) 3452 (48.35) 3525 (49.37) Some college 8453 (29.60) 2151 (30.13) 1993 (27.92) 2056 (28.80) 2253 (31.55) College graduate or higher 7318 (25.62) 2303 (32.25) 2021 (28.31) 1632 (22.86) 1362 (19.08) Marital Status (%) < 0.001 Married 15380 (53.85) 3143 (44.02) 4044 (56.65) 4310 (60.36) 3883 (54.38) Other 5994 (20.99) 1322 (18.52) 1415 (19.82) 1539 (21.55) 1718 (24.06) Never married 4949 (17.33) 2012 (28.18) 1098 (15.38) 789 (11.05) 1050 (14.71) Living with partner 2236 (7.83) 663 (9.29) 582 (8.15) 502 (7.03) 489 (6.85) PIR (%) 1.3 and ≤ 3.5 10657 (37.32) 2528 (35.41) 2560 (35.86) 2775 (38.87) 2794 (39.13) > 3.5 10047 (35.18) 2698 (37.79) 2695 (37.75) 2446 (34.26) 2208 (30.92) Smoking status (%) < 0.001 Yes 13976 (48.94) 3250 (45.52) 3438 (48.16) 3655 (51.19) 3633 (50.88) No 14583 (51.06) 3890 (54.48) 3701 (51.84) 3485 (48.81) 3507 (49.12) Drinking status (%) < 0.001 Yes 4767 (16.69) 1006 (14.09) 1126 (15.77) 1296 (18.15) 1339 (18.75) No 23792 (83.31) 6134 (85.91) 6013 (84.23) 5844 (81.85) 5801 (81.25) Hypertension (%) < 0.001 Yes 8003 (28.02) 898 (12.58) 1600 (22.41) 2379 (33.32) 3126 (43.78) No 20556 (71.98) 6242 (87.42) 5539 (77.59) 4761 (66.68) 4014 (56.22) Diabetes (%) < 0.001 Yes 2540 (8.89) 163 (2.28) 417 (5.84) 681 (9.54) 1279 (17.91) No 26019 (91.11) 6977 (97.72) 6722 (94.16) 6459 (90.46) 5861 (82.09) Hyperlipidemia (%) < 0.001 Yes 7840 (27.45) 1007 (14.10) 1885 (26.40) 2392 (33.50) 2556 (35.80) No 20719 (72.55) 6133 (85.90) 5254 (73.60) 4748 (66.50) 4584 (64.20) Asthma (%) < 0.001 Yes 3741 (13.10) 964 (13.50) 831 (11.64) 822 (11.51) 1124 (15.74) No 24818 (86.90) 6176 (86.50) 6308 (88.36) 6318 (88.49) 6016 (84.26) Rheumatoid arthritis (%) < 0.001 Yes 1867 (6.54) 241 (3.38) 334 (4.68) 512 (7.17) 780 (10.92) No 26692 (93.46) 6899 (96.62) 6805 (95.32) 6628 (92.83) 6360 (89.08) WC (mean ± SD) 97.82 ± 15.81 79.45 ± 5.65 91.84 ± 3.90 101.54 ± 4.25 118.44 ± 11.06 < 0.001 BMI (mean ± SD) 28.51 ± 6.44 22.14 ± 2.38 25.99 ± 2.31 29.33 ± 2.67 36.59 ± 5.92 < 0.001 Abbreviations: PIR, family poverty income ratio; FBG, fasting blood glucose; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; BMI, body mass index; WC, waist circumstance. Associations between BRI and RA Table 2 presents the association between BRI and the risk of RA, with OR and 95%CI across three models. For continuous BRI, each unit increase in BRI was associated with a 17% higher risk of RA in Model 1 (OR: 1.17, 95% CI: 1.16–1.19, P < 0.001), a 14% higher risk in Model 2 (OR: 1.14, 95% CI: 1.12–1.16, P < 0.001), and a 10% higher risk in Model 3 after full adjustment (OR: 1.10, 95% CI: 1.08–1.12, P < 0.001). When BRI was categorized into quartiles, a clear positive trend was observed across all models (P for trend < 0.001). Participants in the Q4 exhibited a higher risk of RA in Model 1 (OR: 3.51, 95% CI: 3.03–4.07, P < 0.001), Model 2 (OR: 2.23, 95% CI: 1.91–2.60, P < 0.001), and Model 3 (OR: 1.76, 95% CI: 1.50–2.07, P < 0.001). These findings indicate a robust positive relationship between higher BRI levels and RA risk, with the highest risk observed in the upper BRI quartile. Table 2 Association between BRI and RA risk Variables Odd ratio (95% confidence interval), P -value Model 1 Model 2 Model 3 Continuous BRI 1.17 (1.16, 1.19) < 0.001 1.14 (1.12, 1.16) < 0.001 1.10 (1.08, 1.12) < 0.001 Quartile of BRI Q1 (1.35–4.54) Reference Reference Reference Q2 (4.54–5.94) 1.41 (1.19, 1.66) < 0.001 1.06 (0.89, 1.27) 0.494 1.01 (0.85, 1.21) 0.881 Q3 (5.94–7.60) 2.21 (1.89, 2.59) < 0.001 1.37 (1.16, 1.62) < 0.001 1.23 (1.04, 1.45) 0.017 Q4 (7.60–20.80) 3.51 (3.03, 4.07) < 0.001 2.23 (1.91, 2.60) < 0.001 1.76 (1.50, 2.07) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Model 1: adjusted for no covariates Model 2: adjusted for age; gender; race; education_adults; marital_status; family_PIR Model 3: adjusted for age; gender; race; education_adults; marital_status; family_PIR; SMQ020; drinking; hypertension; diabetes; hyperlipidemia; asthma BRI, body roundness index; CI: confidence interval; OR: odds ratio; PIR, family poverty income ratio Subgroup analyses Table 3 presents the stratified associations between BRI and RA risk across various subgroups. In each subgroup, BRI was associated with an increased risk of RA (P < 0.001). Among different age groups, the odds ratios (ORs) ranged from 1.06 (95% CI: 1.00–1.11) for participants aged 20–39 years to 1.13 (95% CI: 1.10–1.16) for those aged 60 years or older. Similar associations were observed in both males (OR: 1.13, 95% CI: 1.09–1.16) and females (OR: 1.08, 95% CI: 1.05–1.11), as well as across various socioeconomic strata, smoking and drinking status, hypertension, diabetes, and asthma. In these subgroups, the ORs ranged from 1.08 to 1.14. Notably, a significant interaction was found for hyperlipidemia (P for interaction = 0.012), where the association between BRI and RA was stronger among participants with hyperlipidemia (OR: 1.14, 95% CI: 1.11–1.17) (OR: 1.08, 95% CI: 1.05–1.10). Table 3 Stratified associations between BRI and RA by age, Gender, hypertension, smoking status, and drinking status Characteristics OR (95% CI) P- value P for interaction Age (years) 0.057 20–39 1.06 (1.00, 1.11) 0.036 40–59 1.07 (1.04, 1.11) < 0.001 ≥ 60 1.13 (1.10, 1.16) < 0.001 Gender 0.078 Male 1.13 (1.09, 1.16) < 0.001 Female 1.08 (1.05, 1.11) < 0.001 PIR 0.806 ≤ 1.3 1.11 (1.07, 1.14) 1.3 and ≤ 3.5 1.09 (1.06, 1.13) 3.5 1.11 (1.06, 1.16) < 0.001 Smoking status 0.156 Yes 1.09 (1.07, 1.12) < 0.001 No 1.12 (1.09, 1.16) < 0.001 Drinking status 0.355 Yes 1.08 (1.04, 1.12) < 0.001 No 1.11 (1.09, 1.14) < 0.001 Hypertension 0.073 Yes 1.12 (1.09, 1.15) < 0.001 No 1.08 (1.04, 1.11) < 0.001 Diabetes 0.256 Yes 1.12 (1.08, 1.17) < 0.001 No 1.10 (1.07, 1.12) < 0.001 Hyperlipidemia 0.012 Yes 1.14 (1.11, 1.17) < 0.001 No 1.08 (1.05, 1.10) < 0.001 Asthma 0.773 Yes 1.09 (1.04, 1.13) < 0.001 No 1.11 (1.08, 1.13) < 0.001 PIR, family poverty income ratio Analysis of curve fitting and threshold effects Model III analysis revealed a threshold effect in the relationship between BRI and RA risk (Fig. 2 and Table 4 ). A critical inflection point was identified at a BRI value of 4.61. Below this threshold, the association was not significant (OR = 0.95, 95% CI: 0.83–1.09, P = 0.436), indicating that lower BRI values had minimal influence on RA risk. However, for BRI values greater than or equal to 4.61, each unit increase was associated with a 12% higher RA risk (OR = 1.12, 95% CI: 1.09–1.14, P < 0.001), reflecting a strong positive relationship in this range. The likelihood ratio test confirmed the statistical significance of the threshold effect (P = 0.031), highlighting the nonlinear nature of the association between BRI and RA risk. Table 4 Threshold effect analyses of the effects of BRI on the risk of RA OR (95% CI) P value Inflection points of BRI 4.61 < 4.61 0.95 (0.83, 1.09) 0.436 ≥ 4.61 1.12 (1.09, 1.14) < 0.001 P for log-likelihood ratio test 0.031 Adjusted for age, gender, race, education_adults, marital_status, family_PIR, SMQ020, drinking, hypertension, diabetes, hyperlipidemia, asthma ROC curve analysis Figure 3 displays the ROC curves for RA risk prediction using BRI, WC, and BMI. Among these three indices, BRI exhibited the highest predictive performance, with an AUC of 0.637, indicating superior discrimination between individuals with and without RA. WC followed with an AUC of 0.622, while BMI demonstrated the lowest predictive ability (AUC = 0.594). These findings suggest that BRI offers a modest advantage over WC and BMI in predicting RA risk. As a body composition measure, BRI may more effectively capture factors associated with RA risk compared to traditional indices such as BMI, emphasizing its potential as a valuable tool for RA risk stratification. Discussion Our study, drawing on data from the NHANES 1999–2023 cohort, included 28,559 adult subjects and explored the association between the BRI and the risk of RA. The results indicated a notable positive correlation between BRI and the risk of RA, where each unit increase in BRI was linked to a 10% elevated risk of developing RA (OR: 1.10, 95% CI: 1.08–1.12). Quartile analysis revealed that elevated BRI levels correlated with a heightened risk of RA, with the Q4 exhibiting a significantly increased risk in comparison to the Q1. Subgroup analyses revealed that the relationship between BRI and RA risk remained stable across various age groups, genders, and health conditions, including hypertension and diabetes. Notably, the relationship was more pronounced in individuals with hyperlipidemia. Furthermore, nonlinear analysis revealed that the association between BRI and RA risk became significant only when BRI exceeded a threshold of 4.61. ROC curve analysis highlighted that BRI outperformed both BMI and WC in predicting RA risk, with an AUC of 0.637, suggesting its potential as a valuable tool for RA risk assessment. RA, a systemic autoimmune disorder, has been increasingly linked to obesity, particularly visceral fat, in recent years 18–20 . Conventional metrics such as BMI and WC have limitations in accurately reflecting fat distribution, especially visceral fat, which is more closely associated with RA 16 . In contrast, BRI, a body shape index based on height, offers a more precise reflection of body fat distribution, particularly visceral fat 16 . Existing literature supports the hypothesis that visceral fat contributes significantly to the pathogenesis of RA by promoting chronic low-grade inflammation and activating the immune system 21 ; 22 . Our results corroborate these findings, confirming the positive correlation between BRI and RA. Compared to traditional indices, BRI offers superior sensitivity in predicting RA risk, which also found a strong association between BRI and RA risk, with BRI surpassing BMI and WC in predictive capability. In addition, our study demonstrates that the relationship between BRI and RA risk remained robust even after adjusting for various potential confounders, thereby reinforcing the reliability and utility of BRI as an effective tool for RA risk assessment. An intriguing aspect of our findings is the identification of a nonlinear relationship between BRI and RA risk. Specifically, the association was not significant until BRI surpassed a threshold value of 4.61, beyond which the risk of RA increased significantly. This threshold effect implies that lower BRI levels may not substantially impact RA risk, but once visceral fat reaches a certain accumulation level, the risk escalates 6 ; 23 . This observation is consistent with prior research, such as the work by Wang x et al, which also reported a threshold effect of obesity on RA risk, wherein significant increases in RA risk were observed only when BMI or WC exceeded specific thresholds 24 . Our findings further substantiate this nonlinear effect, suggesting that BRI may serve as a more predictive measure within certain ranges of body composition. In the subgroup analysis, a notable interaction effect was observed between hyperlipidemia and BRI concerning the risk of RA (P for interaction = 0.012). The correlation between BRI and RA risk exhibited greater intensity in individuals presenting with hyperlipidemia. This indicates that hyperlipidemia could intensify the accumulation of visceral fat, consequently heightening the risk of RA 25 . Hyperlipidemia has the potential to modify adipocyte functionality and enhance the release of pro-inflammatory cytokines, consequently exacerbating chronic inflammation 26 , which could be pivotal in the onset and advancement of rheumatoid arthritis. Although no notable interaction effects were detected in other subgroups, including gender, age, smoking, and alcohol consumption, this finding remains clinically important. It indicates that BRI could serve as a more sensitive marker for RA risk, particularly in individuals with hyperlipidemia. Further supporting the utility of BRI, the ROC curve analysis revealed that BRI outperformed both WC (0.622) and BMI (0.594) in predicting RA risk, with an AUC of 0.637. This suggests that BRI offers superior discriminatory power for RA risk prediction, highlighting its potential as a more reliable tool for assessing RA risk, especially in cases where traditional measures like BMI and WC fail to adequately capture fat distribution. Unlike BMI and WC, which predominantly reflect overall body weight and abdominal fat, BRI provides a more precise indication of visceral fat accumulation—a key determinant of RA risk. Thus, BRI could serve as an important clinical tool for early RA risk screening. The association between BRI and RA may be explained through several interconnected mechanisms, particularly those related to inflammation. Obesity, especially the accumulation of visceral fat, is a well-established driver of chronic, low-grade systemic inflammation 27 . Adipose tissue not only functions as an energy reservoir but also acts as an active endocrine organ, secreting adipokines such as leptin, adiponectin, and chemerin, which influence immune responses and inflammatory processes 28–30 . Leptin, a key adipokine, plays a significant role in promoting inflammation by enhancing macrophage phagocytic activity and increasing the production of pro-inflammatory cytokines 31 . These cytokines are critical for initiating and maintaining inflammatory responses, which in turn alter immune cell activity. In the context of obesity, macrophages are predominantly polarized toward a pro-inflammatory M1 phenotype. M1 macrophages secrete inflammatory cytokines and reactive oxygen species (ROS), which recruit and activate CD4 + T-cells. These T-cells subsequently differentiate into Th1 and Th17 subsets, intensifying the inflammatory response 32 ; 33 . In contrast, anti-inflammatory Th2 and Treg cell populations are typically reduced in obesity, resulting in a shift toward a predominantly pro-inflammatory immune environment. This imbalance in immune cell populations may represent a crucial pathway through which obesity contributes to the onset and progression of RA 34 . Furthermore, adipokines produced by adipose tissue are closely linked to RA pathophysiology. Elevated adiponectin levels in RA patients have been shown to promote bone erosion by inducing a pro-inflammatory state in both osteoblasts and osteoclasts 35 . Additionally, leptin, which is increased in the early stages of RA, is associated with heightened ROS production, further supporting its role in driving RA-related inflammation 36 . This study, leveraging large-scale NHANES data and validated through multiple adjusted models, provides valuable insights. Nevertheless, it is essential to acknowledge various constraints that must be taken into account. The cross-sectional design of this study limits the capacity to draw causal inferences regarding the relationship between BRI and RA. Therefore, it is essential to conduct future longitudinal studies to determine the predictive significance of BRI in the initiation and advancement of RA. Second, despite controlling for a variety of potential confounders, unmeasured factors—such as genetic and environmental influences—may still affect the precision of the findings. Lastly, the data examined in this study was confined to adults in the United States, and the applicability of the findings to other populations, especially those from diverse racial or cultural backgrounds, necessitates additional validation. Conclusion Our study reveals a significant positive correlation between BRI and RA risk, with the association being more pronounced in individuals with hyperlipidemia. Moreover, BRI demonstrates superior predictive capacity. As a non-invasive, easy-to-administer tool for body shape assessment, BRI holds promise as a valuable clinical marker for early RA screening and risk evaluation. Future research should focus on elucidating the causal mechanisms underlying the BRI-RA relationship and examining its applicability across diverse populations. Declarations Acknowledgements Not applicable. Author contributions ZJ, XC, XL, and WY aided the writing, empirical analysis, and conceptualization of the article. ZJ, JL, and XC gathered and examined the data, and written the article. In addition to modifying the article’s format and meticulously revising and polishing the wording, FT and JL gathered pertinent literature and data. The article’s submission was reviewed and approved by all authors. Funding This research were funded by the National Natural Science Foundation of China(82160917), Guizhou Provincial Basic Research Program(Natural Science)(Fundamentals of Qian Kehe-ZK General 435), and supported by the Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine ‘Three Voyages’ Talent Training Project (SHRC-KY2024017). Data availability This study analyzed publicly available datasets. These data can be found here:https://www.cdc.gov/nchs/nhanes/. Ethics approval and consent to participate Not applicable. Consent for publication Not Applicable. Competing interests The authors declare that they have no competing interests. References Di Matteo, A., Bathon, J. M. & Emery, P. Rheumatoid Arthritis. Lancet . 402 , 2019–2033 (2023). Figus, F. A., Piga, M., Azzolin, I., McConnell, R. & Iagnocco, A. Rheumatoid Arthritis: Extra-Articular Manifestations and Comorbidities. Autoimmun. Rev. 20 , 102776 (2021). Aletaha, D. 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Update On Nhanes Dietary Data: Focus On Collection, Release, Analytical Considerations, and Uses to Inform Public Policy. Adv. Nutr. 7 , 121–134 (2016). Thomas, D. M. et al. Relationships Between Body Roundness with Body Fat and Visceral Adipose Tissue Emerging From a New Geometrical Model. Obesity . 21 , 2264–2271 (2013). Wang, X., Guo, Z., Wang, M. & Xiang, C. Association Between Body Roundness Index and Risk of Osteoarthritis: A Cross-Sectional Study. Lipids Health Dis. 23 , 334 (2024). Neumann, E. et al. Adipokines and Autoimmunity in Inflammatory Arthritis. Cells . 10 , (2021). Lee, J. S. et al. The Association of Obesity and the Risk of Rheumatoid Arthritis According to Abdominal Obesity Status: A Nationwide Population-Based Study in Korea. Rheumatol. Int. 44 , 2863–2871 (2024). Maisha, J. A., El-Gabalawy, H. S. & O'Neil, L. J. Modifiable Risk Factors Linked to the Development of Rheumatoid Arthritis: Evidence, Immunological Mechanisms and Prevention. Front. Immunol. 14 , 1221125 (2023). Farrag, Y. et al. Adipokines as Potential Pharmacological Targets for Immune Inflammatory Rheumatic Diseases: Focus On Rheumatoid Arthritis, Osteoarthritis, and Intervertebral Disc Degeneration. Pharmacol. Res. 205 , 107219 (2024). Braga, G. C., Simoes, J., Teixeira, D. S. Y., Filho, J. & Bagatini, M. D. The Impacts of Obesity in Rheumatoid Arthritis and Insights Into Therapeutic Purinergic Modulation. Int. Immunopharmacol. 136 , 112357 (2024). Ajeganova, S., Andersson, M. L. & Hafstrom, I. Association of Obesity with Worse Disease Severity in Rheumatoid Arthritis as Well as with Comorbidities: A Long-Term Followup From Disease Onset. Arthritis Care Res. 65 , 78–87 (2013). Wang, X., Xie, L. & Yang, S. Association Between Weight-Adjusted-Waist Index and the Prevalence of Rheumatoid Arthritis and Osteoarthritis: A Population-Based Study. Bmc Musculoskelet. Disord. 24 , 595 (2023). Steiner, G. & Urowitz, M. B. Lipid Profiles in Patients with Rheumatoid Arthritis: Mechanisms and the Impact of Treatment. Semin. Arthritis Rheum. 38 , 372–381 (2009). Esteve, E., Ricart, W. & Fernandez-Real, J. M. Dyslipidemia and Inflammation: An Evolutionary Conserved Mechanism. Clin. Nutr. 24 , 16–31 (2005). Cifuentes, M. et al. Low-Grade Chronic Inflammation: A Shared Mechanism for Chronic Diseases. Physiology . 40 , 0 (2025). Coppack, S. W. Pro-Inflammatory Cytokines and Adipose Tissue. Proc. Nutr. Soc. 60 , 349–356 (2001). Xie, C. & Chen, Q. Adipokines: New Therapeutic Target for Osteoarthritis? Curr. Rheumatol. Rep. 21 , 71 (2019). Romacho, T., Elsen, M., Rohrborn, D. & Eckel, J. Adipose Tissue and its Role in Organ Crosstalk. Acta Physiol. 210 , 733–753 (2014). Kiernan, K. & MacIver, N. J. The Role of the Adipokine Leptin in Immune Cell Function in Health and Disease. Front. Immunol. 11 , 622468 (2020). Castoldi, A., Naffah, D. S. C., Camara, N. O. & Moraes-Vieira, P. M. The Macrophage Switch in Obesity Development. Front. Immunol. 6 , 637 (2015). Chen, S. et al. Macrophages in Immunoregulation and Therapeutics. Signal Transduct. Target. Ther. 8 , 207 (2023). Liu, R. & Nikolajczyk, B. S. Tissue Immune Cells Fuel Obesity-Associated Inflammation in Adipose Tissue and Beyond. Front. Immunol. 10 , 1587 (2019). Qian, J. et al. Adiponectin Aggravates Bone Erosion by Promoting Osteopontin Production in Synovial Tissue of Rheumatoid Arthritis. Arthritis Res. Ther. 20 , 26 (2018). Sun, X. et al. Leptin-Induced Migration and Angiogenesis in Rheumatoid Arthritis is Mediated by Reactive Oxygen Species. Febs Open Bio . 7 , 1899–1908 (2017). Additional Declarations No competing interests reported. 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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-5884438","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":407050895,"identity":"a481723c-2293-47e2-be17-91240760b751","order_by":0,"name":"Zong Jiang","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zong","middleName":"","lastName":"Jiang","suffix":""},{"id":407050896,"identity":"fa3b7ca0-067e-431e-beec-356300489811","order_by":1,"name":"Xin Cai","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Cai","suffix":""},{"id":407050897,"identity":"7479cf57-37b3-4dcb-bdca-678002c0d744","order_by":2,"name":"Xiaoling Yao","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaoling","middleName":"","lastName":"Yao","suffix":""},{"id":407050898,"identity":"4d457695-c975-44a2-a805-940f0b9aae1a","order_by":3,"name":"Weiya Lan","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Weiya","middleName":"","lastName":"Lan","suffix":""},{"id":407050899,"identity":"8329f717-ad61-46bc-a14f-a753880e2149","order_by":4,"name":"Jia Liu","email":"","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""},{"id":407050900,"identity":"46e6f426-b511-4d3b-812b-611cb9e837cb","order_by":5,"name":"Fang Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIie2PP0vEMBiHWwLtEsmagB8iUKgeHM1XeUvhpuLcwSFyEDdde1/E+S0Bp6jrgQ6dxOGG3uKfRcwtOpm7UTAPvL/h5ffwJkkSifxVOj+MkGEEOaeM6XCb7sL5EZemkWO3OBY9HqhI50oxOjuXGsKKyu/sC54/VXoNktfmgcoE02nbBq7Qs8Up3j43Fz2ArM0jPSGaiNVN6GFtWUyZbQgHhJ0y05iRo5DCNqXET9tkvNZYm3sqEfYovC3GwdiKUptqcHiAst6UyXBlgeeGJNA1VPTDMviX/LotJny1Sln29vEuK8XYcpi2AcWTcR+1/lmk+pfmN2Tyofa1IpFI5B/zBehLXBOKEhHbAAAAAElFTkSuQmCC","orcid":"","institution":"Guizhou University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Fang","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2025-01-23 02:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5884438/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5884438/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74944710,"identity":"7e2d9e19-3c17-4f9a-876e-83b90985cbe2","added_by":"auto","created_at":"2025-01-28 15:21:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87706,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of participants' selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNHANES: National Health and Nutrition Examination Survey.\u003c/p\u003e","description":"","filename":"OnlineFigure1Flowchartofparticipantsselection.png","url":"https://assets-eu.researchsquare.com/files/rs-5884438/v1/40d15b3bad303f72abf271a7.png"},{"id":74944713,"identity":"3a3ee591-1360-481c-81a8-bb2c5b6808c3","added_by":"auto","created_at":"2025-01-28 15:21:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between BRI and RA\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"OnlineFigure2AssociationbetweenBRIandRA.png","url":"https://assets-eu.researchsquare.com/files/rs-5884438/v1/182ed7403c3199d10ca93ea9.png"},{"id":74944714,"identity":"02c5c091-7608-44fd-976a-f797a1bed002","added_by":"auto","created_at":"2025-01-28 15:21:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65706,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve for the evaluation of the prediction model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBRI, body roundness index; WC, waist circumference; BMI, body mass index\u003c/p\u003e","description":"","filename":"OnlineFigure3ROCcurvefortheevaluationofthepredictionmodel.png","url":"https://assets-eu.researchsquare.com/files/rs-5884438/v1/ba9b10cfb943d5e2a37c23b6.png"},{"id":76248148,"identity":"7f9cc45e-428e-49c7-a8f8-293b99a0d901","added_by":"auto","created_at":"2025-02-14 02:46:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1601684,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5884438/v1/31aaf983-b4f2-44eb-96a7-c11f4c43176d.pdf"},{"id":74945868,"identity":"a5188f3d-60f8-4b01-a131-2b72c7947964","added_by":"auto","created_at":"2025-01-28 15:29:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14821,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5884438/v1/0a991aabc035ba8de04a42ab.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between body roundness index and rheumatoid arthritis: a cross-sectional study based on NHANES","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRA is a chronic, systemic autoimmune disorder marked by persistent synovial inflammation, progressive joint damage, and the involvement of multiple organ systems\u003csup\u003e1\u003c/sup\u003e. This condition results in considerable pain, functional limitations, and a heightened likelihood of associated health issues, including cardiovascular disease, metabolic disorders, and osteoporosis, thereby imposing a significant strain on healthcare systems worldwide\u003csup\u003e2\u003c/sup\u003e. Despite advancements in treatment options, including biologics and immunomodulatory therapies, therapeutic responses remain highly variable, and the long-term efficacy of these treatments is often constrained by adverse effects\u003csup\u003e3\u003c/sup\u003e; \u003csup\u003e4\u003c/sup\u003e. Thus, a deeper understanding of the risk factors associated with RA and the exploration of novel mechanisms underlying its pathogenesis are essential for improving early detection, prevention, and management strategies.\u003c/p\u003e \u003cp\u003eObesity, particularly the accumulation of visceral fat, has emerged as a significant risk factor for RA\u003csup\u003e5\u003c/sup\u003e. Visceral fat contributes to RA pathogenesis through several mechanisms, including the promotion of chronic low-grade inflammation, immune system dysregulation, and altered secretion of adipokines that influence autoimmune responses\u003csup\u003e6\u003c/sup\u003e. Traditional methods of assessing obesity, such as BMI and WC, have notable limitations, particularly in evaluating fat distribution and specifically visceral fat\u003csup\u003e7\u003c/sup\u003e. For instance, BMI does not distinguish between fat and lean mass, while WC, although indicative of central obesity, fails to precisely measure visceral fat\u0026mdash;an important factor linked to metabolic and inflammatory processes\u003csup\u003e8\u003c/sup\u003e; \u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo address these constraints, the BRI has been established as a more precise and thorough measure for evaluating obesity, especially in relation to the buildup of visceral fat\u003csup\u003e10\u003c/sup\u003e. BRI integrates both height and waist circumference, providing a more accurate assessment of fat distribution compared to conventional indices\u003csup\u003e11\u003c/sup\u003e. Research indicates that BRI serves as a more robust indicator of health risks associated with obesity, encompassing cardiovascular disease, diabetes, and metabolic syndrome\u003csup\u003e12\u0026ndash;14\u003c/sup\u003e. However, while BRI\u0026rsquo;s potential in detecting metabolic disorders has been explored, its relationship with RA remains under-researched. To date, no studies have established a link between BRI and RA in the general population.\u003c/p\u003e \u003cp\u003eThis research seeks to fill this gap by employing data from the NHANES to perform a cross-sectional analysis of a representative sample of the U.S. population. This study aims to investigate the connection between BRI and RA, offering essential data that will deepen our comprehension of this relationship and lay the groundwork for subsequent research endeavors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eContinuously surveying a cross-section of Americans, the NHANES compiles detailed information on people's physical and dietary well-being. The Multi-Stage Stratified Probability Sampling Design is Used by NHANES Biennially to Guarantee a Diverse and Representative Sample\u003csup\u003e15\u003c/sup\u003e. The research protocol was authorized by the NCHS Ethics Review Board, and all subjects willingly gave their informed consent. Detailed methodology and dataset information are publicly available in the NHANES database.\u003c/p\u003e \u003cp\u003eFor this study, data from the 1999\u0026ndash;2023 NHANES cycles were utilized, initially including 119,555 participants. Participants were excluded if they had missing BRI data (n\u0026thinsp;=\u0026thinsp;32,416), lacked RA information (n\u0026thinsp;=\u0026thinsp;41,740), or had incomplete covariate data (n\u0026thinsp;=\u0026thinsp;16,840). After applying these exclusions, the final sample included 28,559 adult participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of BRI and RA\u003c/h3\u003e\n\u003cp\u003eTwo critical bodily measurements\u0026mdash;height and weight center\u0026mdash;formed the basis of the formula used to determine BRI\u003csup\u003e16\u003c/sup\u003e. Medical experts from Mobile Examination Centers (MEC) took these readings.\u003c/p\u003e \u003cp\u003eParticipants reported their RA status using a standardized questionnaire. The first question asked of participants was if they had ever been informed by a doctor or other healthcare provider that they had arthritis. using the \"Yes\" or \"No\" choices. When asked whether they had arthritis, participants could select \"Osteoarthritis,\" \"RA,\" or \"Don't know\" as their form of arthritis if the answer was \"Yes.\" Prior studies have shown that self-reported RA data collected through NHANES is reliable.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eIn accordance with previous research, several covariates were included that could potentially influence the relationship between BRI and RA\u003csup\u003e17\u003c/sup\u003e. Age, gender, race, poverty-to-income ratio (PIR), marital status, education level, smoking status, alcohol intake, hyperlipidemia, diabetes, and asthma were all included as confounders (Supplementary Material 1).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFrequency and percentage distributions were utilized to summarize categorical variables, whereas continuous variables were expressed in terms of means and standard deviations. A multivariate regression analysis was conducted to investigate the relationship between BRI and RA. Three models were developed: Model 1 was a baseline model without any covariates, Model 2 incorporated adjustments for sociodemographic variables such as age and gender, while Model 3 expanded upon Model 2 by also accounting for health-related behaviors and comorbid conditions, including smoking status, alcohol consumption, and so on.\u003c/p\u003e \u003cp\u003eA segmented linear regression model was utilized to evaluate the possible threshold effect of BRI on RA incidence, while a smooth curve-fitting approach was applied to investigate the nonlinear relationship between BRI and RA. In order to assess the predictive efficacy of BRI, BMI, and WC concerning the risk of RA, ROC curves and AUC values were computed. Subgroup analyses and interaction tests were conducted to investigate the variability among various population groups. All statistical analyses were performed utilizing EmpowerStats (version 4.2) and R (version 4.4.1). P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eOf the total sample, 41.72% were aged 20\u0026ndash;39 years, 33.47% were aged 40\u0026ndash;59 years, and 24.80% were aged 60 years or older (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The gender distribution was 53.92% male and 46.08% female. In terms of race, non-Hispanic Whites comprised the largest group (47.66%), followed by non-Hispanic Blacks (19.23%), Mexican Americans (17.30%), other Hispanics (8.08%), and other racial groups (7.72%). With regard to education, 44.78% of participants had a high school education or less, 29.60% had some college education, and 25.62% were college graduates or higher. Health-related characteristics indicated that 28.02% of participants had hypertension, 8.89% had diabetes, 27.45% had hyperlipidemia, and 13.10% had asthma. The prevalence of smoking and alcohol consumption was 48.94% and 16.69%, respectively. Stratifying participants by BRI quartiles (Q1\u0026ndash;Q4) revealed significant differences across all variables (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Participants in higher BRI quartiles tended to be older, male, and had lower educational levels. Furthermore, higher BRI levels were associated with an increased prevalence of hypertension, diabetes, hyperlipidemia, smoking, and alcohol use (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all).\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\u003eCharacteristics of the study population\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eBody roundness index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1 (1.35\u0026ndash;4.54)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2 (4.54\u0026ndash;5.94)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3 (5.94\u0026ndash;7.60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4 (7.60\u0026ndash;20.80)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003e20\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11916 (41.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4290 (60.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2968 (41.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2241 (31.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2417 (33.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9560 (33.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1950 (27.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2501 (35.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2587 (36.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2522 (35.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7083 (24.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e900 (12.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1670 (23.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2312 (32.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2201 (30.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15398 (53.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3354 (46.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4107 (57.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4297 (60.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3640 (50.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13161 (46.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3786 (53.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3032 (42.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2843 (39.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3500 (49.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4942 (17.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e761 (10.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1314 (18.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1523 (21.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1344 (18.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2307 (8.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e504 (7.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e615 (8.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e615 (8.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e573 (8.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13612 (47.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3579 (50.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3292 (46.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3365 (47.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3376 (47.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5493 (19.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1447 (20.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1250 (17.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1248 (17.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1548 (21.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2205 (7.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e849 (11.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e668 (9.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e389 (5.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e299 (4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational Attainment (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eHigh school or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12788 (44.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2686 (37.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3125 (43.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3452 (48.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3525 (49.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8453 (29.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2151 (30.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1993 (27.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2056 (28.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2253 (31.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7318 (25.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2303 (32.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2021 (28.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1632 (22.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1362 (19.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15380 (53.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3143 (44.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4044 (56.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4310 (60.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3883 (54.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5994 (20.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1322 (18.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1415 (19.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1539 (21.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1718 (24.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4949 (17.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2012 (28.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1098 (15.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e789 (11.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1050 (14.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2236 (7.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e663 (9.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e582 (8.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e502 (7.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e489 (6.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u0026le;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7855 (27.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1914 (26.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1884 (26.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1919 (26.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2138 (29.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.3 and \u0026le;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10657 (37.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2528 (35.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2560 (35.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2775 (38.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2794 (39.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10047 (35.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2698 (37.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2695 (37.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2446 (34.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2208 (30.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13976 (48.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3250 (45.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3438 (48.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3655 (51.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3633 (50.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14583 (51.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3890 (54.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3701 (51.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3485 (48.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3507 (49.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking status (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4767 (16.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1006 (14.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1126 (15.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1296 (18.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1339 (18.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23792 (83.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6134 (85.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6013 (84.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5844 (81.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5801 (81.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8003 (28.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e898 (12.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1600 (22.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2379 (33.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3126 (43.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20556 (71.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6242 (87.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5539 (77.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4761 (66.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4014 (56.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2540 (8.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163 (2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e417 (5.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e681 (9.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1279 (17.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26019 (91.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6977 (97.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6722 (94.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6459 (90.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5861 (82.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHyperlipidemia (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7840 (27.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1007 (14.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1885 (26.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2392 (33.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2556 (35.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20719 (72.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6133 (85.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5254 (73.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4748 (66.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4584 (64.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsthma (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3741 (13.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e964 (13.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e831 (11.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e822 (11.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1124 (15.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24818 (86.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6176 (86.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6308 (88.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6318 (88.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6016 (84.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRheumatoid arthritis (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1867 (6.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e241 (3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e334 (4.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e512 (7.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e780 (10.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26692 (93.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6899 (96.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6805 (95.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6628 (92.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6360 (89.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWC (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.82\u0026thinsp;\u0026plusmn;\u0026thinsp;15.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.84\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e118.44\u0026thinsp;\u0026plusmn;\u0026thinsp;11.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003cb\u003eBMI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.51\u0026thinsp;\u0026plusmn;\u0026thinsp;6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.99\u0026thinsp;\u0026plusmn;\u0026thinsp;2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.59\u0026thinsp;\u0026plusmn;\u0026thinsp;5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\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\u003cp\u003eAbbreviations: PIR, family poverty income ratio; FBG, fasting blood glucose; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; BMI, body mass index; WC, waist circumstance.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociations between BRI and RA\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the association between BRI and the risk of RA, with OR and 95%CI across three models. For continuous BRI, each unit increase in BRI was associated with a 17% higher risk of RA in Model 1 (OR: 1.17, 95% CI: 1.16\u0026ndash;1.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a 14% higher risk in Model 2 (OR: 1.14, 95% CI: 1.12\u0026ndash;1.16, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a 10% higher risk in Model 3 after full adjustment (OR: 1.10, 95% CI: 1.08\u0026ndash;1.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When BRI was categorized into quartiles, a clear positive trend was observed across all models (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Participants in the Q4 exhibited a higher risk of RA in Model 1 (OR: 3.51, 95% CI: 3.03\u0026ndash;4.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Model 2 (OR: 2.23, 95% CI: 1.91\u0026ndash;2.60, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Model 3 (OR: 1.76, 95% CI: 1.50\u0026ndash;2.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings indicate a robust positive relationship between higher BRI levels and RA risk, with the highest risk observed in the upper BRI quartile.\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\u003eAssociation between BRI and RA risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eOdd ratio (95% confidence interval), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContinuous BRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17 (1.16, 1.19)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 (1.12, 1.16)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (1.08, 1.12)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuartile of BRI\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (1.35\u0026ndash;4.54)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (4.54\u0026ndash;5.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.41 (1.19, 1.66)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06 (0.89, 1.27) 0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.85, 1.21) 0.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (5.94\u0026ndash;7.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21 (1.89, 2.59)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37 (1.16, 1.62)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23 (1.04, 1.45) 0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (7.60\u0026ndash;20.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.51 (3.03, 4.07)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.23 (1.91, 2.60)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76 (1.50, 2.07)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: adjusted for no covariates\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2: adjusted for age; gender; race; education_adults; marital_status; family_PIR\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3: adjusted for age; gender; race; education_adults; marital_status; family_PIR; SMQ020; drinking; hypertension; diabetes; hyperlipidemia; asthma\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBRI, body roundness index; CI: confidence interval; OR: odds ratio; PIR, family poverty income ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSubgroup analyses\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the stratified associations between BRI and RA risk across various subgroups. In each subgroup, BRI was associated with an increased risk of RA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among different age groups, the odds ratios (ORs) ranged from 1.06 (95% CI: 1.00\u0026ndash;1.11) for participants aged 20\u0026ndash;39 years to 1.13 (95% CI: 1.10\u0026ndash;1.16) for those aged 60 years or older. Similar associations were observed in both males (OR: 1.13, 95% CI: 1.09\u0026ndash;1.16) and females (OR: 1.08, 95% CI: 1.05\u0026ndash;1.11), as well as across various socioeconomic strata, smoking and drinking status, hypertension, diabetes, and asthma. In these subgroups, the ORs ranged from 1.08 to 1.14. Notably, a significant interaction was found for hyperlipidemia (P for interaction\u0026thinsp;=\u0026thinsp;0.012), where the association between BRI and RA was stronger among participants with hyperlipidemia (OR: 1.14, 95% CI: 1.11\u0026ndash;1.17) (OR: 1.08, 95% CI: 1.05\u0026ndash;1.10).\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\u003eStratified associations between BRI and RA by age, Gender, hypertension, smoking status, and drinking status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.00, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.04, 1.11)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.10, 1.16)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.09, 1.16)\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 \u003ctd align=\"left\" colname=\"c4\"\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08 (1.05, 1.11)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePIR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.07, 1.14)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.3 and \u0026le;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.06, 1.13)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.06, 1.16)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.07, 1.12)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.09, 1.16)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDrinking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08 (1.04, 1.12)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.09, 1.14)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.09, 1.15)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08 (1.04, 1.11)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.08, 1.17)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.07, 1.12)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHyperlipidemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14 (1.11, 1.17)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08 (1.05, 1.10)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsthma\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.04, 1.13)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.08, 1.13)\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePIR, family poverty income ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of curve fitting and threshold effects\u003c/h2\u003e \u003cp\u003eModel III analysis revealed a threshold effect in the relationship between BRI and RA risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A critical inflection point was identified at a BRI value of 4.61. Below this threshold, the association was not significant (OR\u0026thinsp;=\u0026thinsp;0.95, 95% CI: 0.83\u0026ndash;1.09, P\u0026thinsp;=\u0026thinsp;0.436), indicating that lower BRI values had minimal influence on RA risk. However, for BRI values greater than or equal to 4.61, each unit increase was associated with a 12% higher RA risk (OR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 1.09\u0026ndash;1.14, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reflecting a strong positive relationship in this range. The likelihood ratio test confirmed the statistical significance of the threshold effect (P\u0026thinsp;=\u0026thinsp;0.031), highlighting the nonlinear nature of the association between BRI and RA risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect analyses of the effects of BRI on the risk of RA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\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\u003eInflection points of BRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.61\u003c/p\u003e \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\u0026lt;\u0026thinsp;4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.83, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (1.09, 1.14)\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 \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for log-likelihood ratio test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAdjusted for age, gender, race, education_adults, marital_status, family_PIR, SMQ020, drinking, hypertension, diabetes, hyperlipidemia, asthma\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eROC curve analysis\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the ROC curves for RA risk prediction using BRI, WC, and BMI. Among these three indices, BRI exhibited the highest predictive performance, with an AUC of 0.637, indicating superior discrimination between individuals with and without RA. WC followed with an AUC of 0.622, while BMI demonstrated the lowest predictive ability (AUC\u0026thinsp;=\u0026thinsp;0.594). These findings suggest that BRI offers a modest advantage over WC and BMI in predicting RA risk. As a body composition measure, BRI may more effectively capture factors associated with RA risk compared to traditional indices such as BMI, emphasizing its potential as a valuable tool for RA risk stratification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study, drawing on data from the NHANES 1999\u0026ndash;2023 cohort, included 28,559 adult subjects and explored the association between the BRI and the risk of RA. The results indicated a notable positive correlation between BRI and the risk of RA, where each unit increase in BRI was linked to a 10% elevated risk of developing RA (OR: 1.10, 95% CI: 1.08\u0026ndash;1.12). Quartile analysis revealed that elevated BRI levels correlated with a heightened risk of RA, with the Q4 exhibiting a significantly increased risk in comparison to the Q1. Subgroup analyses revealed that the relationship between BRI and RA risk remained stable across various age groups, genders, and health conditions, including hypertension and diabetes. Notably, the relationship was more pronounced in individuals with hyperlipidemia. Furthermore, nonlinear analysis revealed that the association between BRI and RA risk became significant only when BRI exceeded a threshold of 4.61. ROC curve analysis highlighted that BRI outperformed both BMI and WC in predicting RA risk, with an AUC of 0.637, suggesting its potential as a valuable tool for RA risk assessment.\u003c/p\u003e \u003cp\u003eRA, a systemic autoimmune disorder, has been increasingly linked to obesity, particularly visceral fat, in recent years\u003csup\u003e18\u0026ndash;20\u003c/sup\u003e. Conventional metrics such as BMI and WC have limitations in accurately reflecting fat distribution, especially visceral fat, which is more closely associated with RA\u003csup\u003e16\u003c/sup\u003e. In contrast, BRI, a body shape index based on height, offers a more precise reflection of body fat distribution, particularly visceral fat\u003csup\u003e16\u003c/sup\u003e. Existing literature supports the hypothesis that visceral fat contributes significantly to the pathogenesis of RA by promoting chronic low-grade inflammation and activating the immune system\u003csup\u003e21\u003c/sup\u003e; \u003csup\u003e22\u003c/sup\u003e. Our results corroborate these findings, confirming the positive correlation between BRI and RA. Compared to traditional indices, BRI offers superior sensitivity in predicting RA risk, which also found a strong association between BRI and RA risk, with BRI surpassing BMI and WC in predictive capability.\u003c/p\u003e \u003cp\u003eIn addition, our study demonstrates that the relationship between BRI and RA risk remained robust even after adjusting for various potential confounders, thereby reinforcing the reliability and utility of BRI as an effective tool for RA risk assessment. An intriguing aspect of our findings is the identification of a nonlinear relationship between BRI and RA risk. Specifically, the association was not significant until BRI surpassed a threshold value of 4.61, beyond which the risk of RA increased significantly. This threshold effect implies that lower BRI levels may not substantially impact RA risk, but once visceral fat reaches a certain accumulation level, the risk escalates\u003csup\u003e6\u003c/sup\u003e; \u003csup\u003e23\u003c/sup\u003e. This observation is consistent with prior research, such as the work by Wang x et al, which also reported a threshold effect of obesity on RA risk, wherein significant increases in RA risk were observed only when BMI or WC exceeded specific thresholds\u003csup\u003e24\u003c/sup\u003e. Our findings further substantiate this nonlinear effect, suggesting that BRI may serve as a more predictive measure within certain ranges of body composition.\u003c/p\u003e \u003cp\u003eIn the subgroup analysis, a notable interaction effect was observed between hyperlipidemia and BRI concerning the risk of RA (P for interaction\u0026thinsp;=\u0026thinsp;0.012). The correlation between BRI and RA risk exhibited greater intensity in individuals presenting with hyperlipidemia. This indicates that hyperlipidemia could intensify the accumulation of visceral fat, consequently heightening the risk of RA\u003csup\u003e25\u003c/sup\u003e. Hyperlipidemia has the potential to modify adipocyte functionality and enhance the release of pro-inflammatory cytokines, consequently exacerbating chronic inflammation\u003csup\u003e26\u003c/sup\u003e, which could be pivotal in the onset and advancement of rheumatoid arthritis. Although no notable interaction effects were detected in other subgroups, including gender, age, smoking, and alcohol consumption, this finding remains clinically important. It indicates that BRI could serve as a more sensitive marker for RA risk, particularly in individuals with hyperlipidemia.\u003c/p\u003e \u003cp\u003eFurther supporting the utility of BRI, the ROC curve analysis revealed that BRI outperformed both WC (0.622) and BMI (0.594) in predicting RA risk, with an AUC of 0.637. This suggests that BRI offers superior discriminatory power for RA risk prediction, highlighting its potential as a more reliable tool for assessing RA risk, especially in cases where traditional measures like BMI and WC fail to adequately capture fat distribution. Unlike BMI and WC, which predominantly reflect overall body weight and abdominal fat, BRI provides a more precise indication of visceral fat accumulation\u0026mdash;a key determinant of RA risk. Thus, BRI could serve as an important clinical tool for early RA risk screening.\u003c/p\u003e \u003cp\u003eThe association between BRI and RA may be explained through several interconnected mechanisms, particularly those related to inflammation. Obesity, especially the accumulation of visceral fat, is a well-established driver of chronic, low-grade systemic inflammation\u003csup\u003e27\u003c/sup\u003e. Adipose tissue not only functions as an energy reservoir but also acts as an active endocrine organ, secreting adipokines such as leptin, adiponectin, and chemerin, which influence immune responses and inflammatory processes\u003csup\u003e28\u0026ndash;30\u003c/sup\u003e. Leptin, a key adipokine, plays a significant role in promoting inflammation by enhancing macrophage phagocytic activity and increasing the production of pro-inflammatory cytokines\u003csup\u003e31\u003c/sup\u003e. These cytokines are critical for initiating and maintaining inflammatory responses, which in turn alter immune cell activity. In the context of obesity, macrophages are predominantly polarized toward a pro-inflammatory M1 phenotype. M1 macrophages secrete inflammatory cytokines and reactive oxygen species (ROS), which recruit and activate CD4\u0026thinsp;+\u0026thinsp;T-cells. These T-cells subsequently differentiate into Th1 and Th17 subsets, intensifying the inflammatory response\u003csup\u003e32\u003c/sup\u003e; \u003csup\u003e33\u003c/sup\u003e. In contrast, anti-inflammatory Th2 and Treg cell populations are typically reduced in obesity, resulting in a shift toward a predominantly pro-inflammatory immune environment. This imbalance in immune cell populations may represent a crucial pathway through which obesity contributes to the onset and progression of RA\u003csup\u003e34\u003c/sup\u003e. Furthermore, adipokines produced by adipose tissue are closely linked to RA pathophysiology. Elevated adiponectin levels in RA patients have been shown to promote bone erosion by inducing a pro-inflammatory state in both osteoblasts and osteoclasts\u003csup\u003e35\u003c/sup\u003e. Additionally, leptin, which is increased in the early stages of RA, is associated with heightened ROS production, further supporting its role in driving RA-related inflammation\u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study, leveraging large-scale NHANES data and validated through multiple adjusted models, provides valuable insights. Nevertheless, it is essential to acknowledge various constraints that must be taken into account. The cross-sectional design of this study limits the capacity to draw causal inferences regarding the relationship between BRI and RA. Therefore, it is essential to conduct future longitudinal studies to determine the predictive significance of BRI in the initiation and advancement of RA. Second, despite controlling for a variety of potential confounders, unmeasured factors\u0026mdash;such as genetic and environmental influences\u0026mdash;may still affect the precision of the findings. Lastly, the data examined in this study was confined to adults in the United States, and the applicability of the findings to other populations, especially those from diverse racial or cultural backgrounds, necessitates additional validation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study reveals a significant positive correlation between BRI and RA risk, with the association being more pronounced in individuals with hyperlipidemia. Moreover, BRI demonstrates superior predictive capacity. As a non-invasive, easy-to-administer tool for body shape assessment, BRI holds promise as a valuable clinical marker for early RA screening and risk evaluation. Future research should focus on elucidating the causal mechanisms underlying the BRI-RA relationship and examining its applicability across diverse populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZJ, XC, XL, and WY aided the writing, empirical analysis, and conceptualization of the article. ZJ, JL, and XC gathered and examined the data, and written the article. In addition to modifying the article\u0026rsquo;s format and meticulously revising and polishing the wording, FT and JL gathered pertinent literature and data. The article\u0026rsquo;s submission was reviewed and approved by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research were funded by the National Natural Science Foundation of China(82160917),\u0026nbsp;Guizhou Provincial Basic Research Program(Natural Science)(Fundamentals of Qian Kehe-ZK General 435), and supported by the Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine \u0026lsquo;Three Voyages\u0026rsquo; Talent Training Project (SHRC-KY2024017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study analyzed publicly available datasets. These data can be found here:https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDi Matteo, A., Bathon, J. M. \u0026amp; Emery, P. Rheumatoid Arthritis. \u003cem\u003eLancet\u003c/em\u003e. \u003cb\u003e402\u003c/b\u003e, 2019\u0026ndash;2033 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFigus, F. A., Piga, M., Azzolin, I., McConnell, R. \u0026amp; Iagnocco, A. Rheumatoid Arthritis: Extra-Articular Manifestations and Comorbidities. \u003cem\u003eAutoimmun. Rev.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 102776 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAletaha, D. Precision Medicine and Management of Rheumatoid Arthritis. \u003cem\u003eJ. Autoimmun.\u003c/em\u003e \u003cb\u003e110\u003c/b\u003e, 102405 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMankia, K., Di Matteo, A. \u0026amp; Emery, P. Prevention and Cure: The Major Unmet Needs in the Management of Rheumatoid Arthritis. \u003cem\u003eJ. Autoimmun.\u003c/em\u003e \u003cb\u003e110\u003c/b\u003e, 102399 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaker, J. F. et al. 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Leptin-Induced Migration and Angiogenesis in Rheumatoid Arthritis is Mediated by Reactive Oxygen Species. \u003cem\u003eFebs Open Bio\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, 1899\u0026ndash;1908 (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Risk, Body roundness index, Rheumatoid arthritis, Cross-sectional study, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-5884438/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5884438/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Body Roundness Index (BRI) has been identified as a potentially superior measure of body fat distribution such as body mass index (BMI) and waist circumference (WC). However, its relationship with rheumatoid arthritis (RA) has yet to be thoroughly investigated. This study examines the association between BRI and RA risk using data from the National Health and Nutrition Examination Survey (NHANES).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe analysis included 28,559 adults, excluding those with missing values for BRI or RA status. BRI was calculated using height and WC measurements, while RA was self-reported by participants. Multivariate logistic regression was utilized to assess the relationship between BRI and RA, while controlling for sociodemographic variables and pertinent comorbid conditions. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were employed to assess the predictive accuracy of BRI, BMI, and WC concerning RA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAn elevated BRI demonstrated a notable correlation with a heightened risk of RA. With each unit increase in BRI, there was a corresponding 10% increase in the likelihood of RA after complete adjustment (OR: 1.10, 95% CI: 1.08\u0026ndash;1.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A clear dose-response relationship was identified among the BRI quartiles, where individuals in the highest quartile exhibited a 76% increased risk (OR: 1.76, 95% CI: 1.50\u0026ndash;2.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analysis indicated a more pronounced association among participants exhibiting hyperlipidemia (P for interaction\u0026thinsp;=\u0026thinsp;0.012). Threshold analysis revealed a BRI value of 4.61 as the critical inflection point, beyond which each unit increase correlated with a 12% elevated risk of RA (OR: 1.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ROC analysis revealed that BRI exhibited the highest AUC of 0.637 in predicting RA risk, surpassing WC at 0.622 and BMI at 0.594.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eBRI serves as a strong indicator of RA risk, demonstrating enhanced predictive accuracy when contrasted with conventional metrics like BMI and WC. The results indicate that BRI may function as a valuable instrument for assessing the risk of RA, especially in those exhibiting hyperlipidemia.\u003c/p\u003e","manuscriptTitle":"Association between body roundness index and rheumatoid arthritis: a cross-sectional study based on NHANES","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 15:20:59","doi":"10.21203/rs.3.rs-5884438/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":"6356d7db-649c-4ccb-b2d0-6343f6819406","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43410762,"name":"Health sciences/Diseases/Nutrition disorders"},{"id":43410763,"name":"Health sciences/Diseases/Rheumatic diseases"}],"tags":[],"updatedAt":"2025-02-17T07:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-28 15:20:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5884438","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5884438","identity":"rs-5884438","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}