The Time Effect of the Impact of the Body Roundness Index on Kidney Disease: A Cluster Analysis Based on Longitudinal Data

The Time Effect of the Impact of the Body Roundness Index on Kidney Disease: A Cluster Analysis Based on Longitudinal Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Time Effect of the Impact of the Body Roundness Index on Kidney Disease: A Cluster Analysis Based on Longitudinal Data Xiaobin Liu, Jirong Wang, Zaiyun Yang, Shengyu Huang, Feng Zhu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7245888/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 Central obesity is a key modifiable risk factor for kidney disease. The Body Round-ness Index (BRI) is useful for assessing central obesity and associated metabolic risks. This study aimed to investigate the correlation between body roundness index (BRI) and the risk of kidney disease and explore the possibility of using BRI monitoring to identify high-risk groups. Methods This study used longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) including 2,160 participants. K-means clustering identified patterns in BRI changes, and logistic regression was used to assess the association between BRI and kidney disease risk. Subgroup and restricted cubic spline analyses were conducted to explore non-linear relationships and influencing factors. Results The best clustering of BRI was achieved by dividing participants into four groups. Significant differences were observed in demographic, physical, and clinical characteristics. Logistic regression revealed that groups B (P < 0.001) and C (P = 0.007) had significantly higher kidney disease risk compared to group A. No significant difference was observed for group D. Subgroup analysis showed elevated kidney disease risk in group B across all subgroups, and higher risk in women and those under 60 in group C. A non-linear, U-shaped relationship between BRI and kidney disease risk was observed, with both high and low BRI levels increasing risk. Conclusions The impact of BRI on kidney disease is time-dependent. Both persistently high and low BRI levels are risk factors, highlighting the value of BRI monitoring for early identification and prevention of high-risk populations. Body Roundness Index (BRI) Central obesity CHARLS Database Cluster analysis Kidney disease Figures Figure 1 Figure 2 Figure 3 1. Introduction Kidney disease has become an increasingly severe global public health issue, with its high morbidity and mortality rates imposing a significant burden on healthcare systems and societies worldwide. According to the Global Burden of Disease (GBD) study, kidney disease, including acute kidney injury (AKI), chronic kidney disease (CKD), and other non-specific renal disorders, has become one of the leading chronic non-communicable diseases contributing to the global health burden, following cardiovascular diseases, diabetes, and cancer [ 1 , 2 ] . In particular, CKD has seen a marked rise in prevalence and related mortality over the past few decades. Similarly, the incidence of AKI has increased annually and is linked to an increased risk of transitioning into CKD, exacerbating the progressive decline of kidney health [ 3 ] . Despite advancements in the screening, diagnosis, and treatment of kidney disease, many patients are diagnosed only when the disease has progressed to end-stage renal disease (ESRD), as early symptoms are often subtle. This late diagnosis leads to irreversible kidney damage and multi-system complications [ 4 , 5 ] . Research indicates that modifiable risk factors such as metabolic abnormalities, central obesity, hypertension, diabetes, and lifestyle factors play critical roles in the onset and progression of kidney disease [ 6 ] . Therefore, identifying high-risk populations for kidney disease, exploring modifiable risk factors related to metabolic abnormalities, and implementing effective interventions to slow disease progression are of significant clinical and public health importance in reducing the global burden of kidney disease. Among the multiple pathogenic mechanisms in the development of kidney disease, central obesity has been identified as an important, modifiable risk factor [ 7 , 8 ] . Central obesity can accelerate kidney function decline by exacerbating insulin resistance, triggering chronic low-grade inflammation, promoting oxidative stress, and causing endothelial dysfunction. However, traditional obesity indicators (such as body mass index [BMI] and waist circumference [WC]) have certain limitations in reflecting fat distribution patterns and related metabolic burdens, making it difficult to accurately capture fat redistribution characteristics closely associated with kidney disease risk [ 9 ] . In recent years, the Body Roundness Index (BRI), a novel measure that incorporates the geometric relationship between waist circumference and height, has been proposed as a more accurate way to quantify the extent of central obesity and its associated metabolic risks. Compared to traditional indicators, BRI has demonstrated superior predictive performance in forecasting cardiovascular and metabolic abnormalities [ 10 , 11 ] . BRI has been significantly associated with the occurrence of metabolic syndrome and cardiovascular events. However, existing studies are predominantly cross-sectional and insufficient to explore the long-term effects of dynamic changes in obesity indicators on kidney disease risk. Given that obesity and metabolic disorders have cumulative and time-dependent effects, integrating the dynamic changes of BRI into research frameworks could help capture the potential contribution of changes in these indicators over time to kidney damage. Thus, this study aims to use longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011, 2013, and 2015 to identify different dynamic patterns of BRI using clustering methods like k-means and prospectively evaluate the impact of various BRI trajectories on kidney disease risk. Unlike previous studies, this research attempts to systematically explore the relationship between dynamic changes in BRI and kidney disease from a temporal perspective, thereby providing scientific evidence to optimize obesity management strategies and kidney disease prevention interventions. 2. Methods 2.1 Study Population and Design CHARLS is a nationally representative survey targeting Chinese households and individuals aged 45 years and older. The survey utilizes computer-assisted personal interviews (CAPI) to collect data through face-to-face interviews, covering demographic information and health-related issues. It is conducted by the National School of Development at Peking University and includes participants from 28 provinces across China, with the aim of collecting population and health-related data for older adults [ 12 ] . CHARLS has received approval from the Biomedical Ethics Committee of Peking University, and all participants provided written informed consent. The ethics approval number is IRB00001052-11015. This study was conducted in accordance with the World Medical Association Declaration of Helsinki guidelines. This study analyzed the data from three rounds of surveys (2011, 2013, and 2015) with complete population characteristics. Individuals with pre-existing renal impairment before 2011 or those missing key information required to calculate BRI during follow-up were excluded. The final cohort included 2,160 participants. This study is a secondary analysis of the CHARLS data, conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. 2.2 BRI Calculation Formula and Kidney Disease Diagnostic Criteria The Body Roundness Index (BRI) is calculated using the following formula [ 13 ] : $$\:BRI=364.2-365.5\times\:\sqrt{1-{（\frac{\text{W}\text{C}}{2{\pi\:}}）}^{2}/{\left(0.5\text{H}\right)}^{2}}$$ Where WC refers to waist circumference, and H refers to height. The diagnosis of kidney disease was based on participants’ self-reported doctor diagnoses from the 2015, 2018, and 2021 follow-up surveys. 2.3 Covariates Assessment Demographic information includes: age, gender, residence (urban or rural), marital status, education level, smoking, and alcohol consumption. Blood sample data includes: blood glucose, white blood cell count (WBC), C-reactive protein (CRP), blood urea nitrogen (BUN), uric acid, and other biomarkers. Medical history includes: hypertension, diabetes, hyperlipidemia, cancer, chronic lung disease, chronic heart disease, etc. 2.4 Dynamic Trend Clustering Analysis To analyze the dynamic patterns of BRI, a longitudinal k-means clustering method was applied. First, the optimal number of clusters was determined based on the Calinski-Harabasz index. Then, based on the clustering results, a line graph was created to visually display the dynamic trend of BRI in each cluster group, highlighting the distinct change patterns. 2.5 Demographic and Baseline Characteristics of Participants After determining the clustering results, demographic and baseline characteristics of participants in each cluster were analyzed, including gender, age, household registration type (urban or rural), marital status, education level, smoking and drinking habits, etc. Normally distributed continuous variables were expressed as mean ± standard deviation (SD), and analysis of variance (ANOVA) was used to assess differences between categories. For skewed continuous variables, median and interquartile range (IQR) were reported, and the Kruskal-Wallis test was used for non-parametric comparison of differences between categories. Categorical variables were presented as frequencies and percentages, with chi-square analysis used to assess differences between categories. 2.6 Logistic Regression Models Logistic regression models were used to calculate the odds ratios (OR) and 95% confidence intervals (95% CI) for the risk of kidney disease between different BRI trajectory groups. To further explore the association between dynamic BRI trends and kidney disease risk, multivariable adjusted models were constructed after controlling for potential confounders. All models were based on logistic regression to evaluate the independent associations between BRI dynamic trend groups and kidney disease incidence. Results were presented as odds ratios (OR) and 95% confidence intervals (CI). Data analysis was performed using R language, with a significance level set at P < 0.05. 2.7 Subgroup Analysis Subgroup analyses were performed to examine the effect of BRI trajectory groups on kidney disease outcomes in different populations. Stratified analyses were conducted based on gender, age, household registration type, education level, smoking status, and alcohol consumption. 2.8 Restricted Cubic Splines (RCS) Analysis RCS analysis were employed to explore potential nonlinear relationships between average BRI and kidney disease outcomes. The average BRI was calculated as the mean value of BRI from the 2011, 2013, and 2015 surveys. 3. Results The participant selection process is shown in Fig. 1 . 3.1 Clustering Analysis Results and Basic Demographic Characteristics Based on the Calinski-Harabasz index, the clustering analysis of BRI yielded the best results when the population was divided into four groups (shown in Fig. 2 . A). Further, the dynamic trends for each of the four groups were plotted (shown in Fig. 2 . B, C). Significant differences in baseline characteristics were observed across the groups in terms of demographic, physical indicators, underlying health conditions, and laboratory tests (p < 0.05) (Table 1 ). Group A had generally good health status and normal metabolic indicators. Group B consisted mostly of males with poor lifestyle habits (smoking, alcohol consumption) and consistently low BMI. Group C was characterized by persistent central obesity, high metabolic load, and inflammatory status. Group D was distinguished by older age, weight loss, high burden of chronic diseases, and early renal function decline risk. Table 1 Patient demographics and baseline characteristics Characteristic clusters p-value A, N = 8511 B, N = 8261 C, N = 3601 D, N = 1231 Age(year) 58 (51, 64) 58 (51, 64) 58 (51, 65) 62 (56, 68) < 0.001 Sex < 0.001 Male 332 (39.0%) 511 (61.9%) 69 (19.2%) 59 (48.0%) Female 519 (61.0%) 315 (38.1%) 291 (80.8%) 64 (52.0%) Hukou < 0.001 agricultural area 717 (84.3%) 742 (89.8%) 301 (83.6%) 120 (97.6%) non-agricultural area 134 (15.7%) 84 (10.2%) 59 (16.4%) 3 (2.4%) Marriage 0.233 single status 105 (12.3%) 93 (11.3%) 56 (15.6%) 15 (12.2%) marital status 746 (87.7%) 733 (88.7%) 304 (84.4%) 108 (87.8%) Education < 0.001 none 425 (49.9%) 395 (47.8%) 190 (52.8%) 88 (71.5%) primary education 346 (40.7%) 367 (44.4%) 144 (40.0%) 33 (26.8%) advanced education 80 (9.4%) 64 (7.7%) 26 (7.2%) 2 (1.6%) Smoke < 0.001 Never 611 (71.9%) 406 (49.4%) 312 (86.7%) 75 (61.0%) Yes 239 (28.1%) 416 (50.6%) 48 (13.3%) 48 (39.0%) Drink < 0.001 Never 605 (71.2%) 478 (58.2%) 304 (84.4%) 65 (52.8%) Once a month or less than once a month 178 (20.9%) 252 (30.7%) 39 (10.8%) 50 (40.7%) More than once a month 67 (7.9%) 92 (11.2%) 17 (4.7%) 8 (6.5%) BMI 23.8 (22.4, 25.5) 20.7 (19.2, 22.0) 27.8 (26.2, 29.7) 21.8 (19.3, 24.1) < 0.001 Waist Measurement (cm) 87 (82, 91) 77 (72, 81) 97 (92, 102) 80 (74, 86) < 0.001 Basic Illness Hypertension 196 (23.0%) 118 (14.3%) 138 (38.3%) 22 (17.9%) < 0.001 Dyslipidemia 51 (6.0%) 35 (4.2%) 53 (14.7%) 4 (3.3%) < 0.001 Diabetes or High Blood Sugar 33 (3.9%) 21 (2.5%) 30 (8.3%) 2 (1.6%) < 0.001 Cancer or Malignant Tumor 6 (0.7%) 6 (0.7%) 5 (1.4%) 0 (0.0%) 0.53 Chronic Lung Diseases 71 (8.3%) 91 (11.0%) 35 (9.7%) 21 (17.1%) 0.016 Liver Disease 28 (3.3%) 33 (4.0%) 13 (3.6%) 7 (5.7%) 0.539 Heart Problems 83 (9.8%) 60 (7.3%) 53 (14.7%) 8 (6.5%) < 0.001 Stroke 17 (2.0%) 14 (1.7%) 15 (4.2%) 0 (0.0%) 0.02 Stomach or Other Digestive Disease 180 (21.2%) 210 (25.4%) 70 (19.4%) 55 (44.7%) < 0.001 Emotio0l, Nervous, or Psychiatric Problems 11 (1.3%) 9 (1.1%) 2 (0.6%) 2 (1.6%) 0.592 Memory-Related Disease 7 (0.8%) 5 (0.6%) 2 (0.6%) 4 (3.3%) 0.064 Arthritis or Rheumatism 293 (34.4%) 281 (34.0%) 168 (46.7%) 72 (58.5%) < 0.001 Laboratory Tests White blood cell(10 9 /L) 6.02 (4.90, 7.30) 5.86 (4.80, 7.30) 6.20 (5.10, 7.22) 6.10 (5.30, 7.35) 0.0952 MCV 91 (87, 95) 92 (88, 96) 90 (87, 94) 89 (74, 95) < 0.001 Platelets(10 9 /L) 203 (158, 251) 198 (157, 247) 213 (161, 264) 216 (165, 263) 0.003 BUN(mmol/L) 5.34 (4.44, 6.40) 5.47 (4.45, 6.71) 5.12 (4.37, 6.32) 6.05 (4.73, 7.36) < 0.001 Creatine(µmol/L) 66 (57, 77) 69 (59, 79) 63 (56, 71) 60 (49, 75) < 0.001 CRP(mg/L) 0.96 (0.55, 2.07) 0.79 (0.45, 1.61) 1.70 (0.85, 2.70) 0.91 (0.54, 2.20) < 0.001 Uric Acid(mmol/L) 253 (213, 309) 256 (213, 308) 258 (213, 310) 241 (212, 278) 0.321 Hematocrit(%) 41 (37, 44) 41 (37, 45) 40 (36, 43) 38 (30, 45) < 0.001 Hemoglobin(g/L) 140 (128, 153) 140 (128, 155) 137 (128, 149) 137 (124, 148) 0.023 Kidney Disease 32 (3.8%) 68 (8.2%) 27 (7.5%) 5 (4.1%) < 0.001 Median (IQR); n (%) Kruskal-Wallis rank sum test; Pearson's Chi-squared test; Fisher's exact test BMI, body mass index; MCV, mean corpuscular volume; BUN, blood urea nitrogen; CRP, C-reactive protein Specifically, regarding demographic characteristics, the median age in Group D (62 years) was significantly higher than that in the other groups (58 years, p < 0.001), indicating that age may play an important role in BRI trajectory and disease risk. Group B had the highest proportion of males (61.9%), whereas Group C had the lowest (19.2%). There was a significant imbalance in gender distribution across the groups (p < 0.001). The proportion of agricultural household registration was highest in Group D (97.6%), while non-agricultural households were lowest (2.4%) (p < 0.001), which might reflect the impact of rural-urban differences on BRI clustering patterns. Group D had the lowest educational level, with 71.5% of participants reporting no education, and only 1.6% had higher education, suggesting clear socioeconomic differences across groups (p < 0.001). In terms of physical indicators, Group C had the highest BMI (27.8) and waist circumference (97 cm), while Group B had the lowest (BMI: 20.7, waist circumference: 77 cm) (p < 0.001), suggesting that Group C exhibited more significant central obesity characteristics. Additionally, Group C had a higher burden of chronic diseases, including hypertension (38.3%), diabetes or hyperglycemia (8.3%), and dyslipidemia (14.7%), all of which were significantly higher than those in the other groups (p < 0.001). The prevalence of heart disease (14.7%) and stroke (4.2%) was also highest in Group C, indicating greater metabolic and cardiovascular risks. In contrast, Group D had the highest incidence of digestive system diseases (44.7%) and arthritis/rheumatism (58.5%). Laboratory results further reflected differences in metabolic and renal function across the groups. Group D had the highest blood urea nitrogen (BUN) level (6.05 mmol/L), the lowest creatinine (60 µmol/L), the lowest hemoglobin (137 g/L), and the lowest mean corpuscular volume (MCV) (89 fl), suggesting potential anemia, muscle wasting, malnutrition, or chronic inflammation (p < 0.001). Although Group C had a higher BMI, its creatinine levels were relatively low, which may be due to lower muscle mass (p < 0.001). 3.2 Logistic Regression Analysis of BRI Grouping and Kidney Disease Risk Based on demographic and baseline characteristics, multivariable models were further constructed. Model 1 : Adjusted for gender, age, household registration, marital status, education level, smoking, and alcohol consumption. Model 2 : In addition to the variables in Model 1, further adjusted for hypertension, dyslipidemia, diabetes or hyperglycemia, chronic lung disease, heart disease, stroke, gastrointestinal or other digestive system diseases, arthritis or rheumatism, and clinical indicators such as MCV, platelet count, creatinine, BUN, C-reactive protein (CRP), hematocrit, and hemoglobin. The relationship between BRI groups and kidney disease risk was analyzed using logistic regression, and the results are shown in Table 2 . In the univariate model, compared with Group A (the baseline group), the risk of kidney disease was significantly higher in Groups B and C. Group B had 2.3 times the risk of kidney disease compared to Group A (OR = 2.3, 95% CI: 1.49–3.54, P < 0.001). Group C had 2.08 times the risk (OR = 2.08, 95% CI: 1.22–3.52, P = 0.007). The risk in Group D was not statistically significant (OR = 1.08, 95% CI: 0.41–2.84, P = 0.869). Table 2 logistic regression analysis of kidney disease and clusters Univariable Model1 P value OR 95% CI P value — — < 0.001 2.2 1.40, 3.44 < 0.001 0.007 2.26 1.32, 3.89 0.003 0.869 1.02 0.38, 2.70 0.975 model1: adjust for gender, age, hukou, marrage, education, smoke, drink model2: adjust for gender, age, hukou, smoke, drink, education, Hypertension, dyslipidemia, disabetes or high blood sugar, chronic lung diseases, heart problems, stroke, stomach or other digestive disease, arthritis or rheumatism, MCV, platelets, creatine, BUN, CRP, hematocrit, hemoglobin After adjusting for confounders such as gender, age, household registration, marital status, education level, smoking, and alcohol consumption (Model 1), the kidney disease risk remained significantly elevated in Groups B and C. Group B’s OR was 2.2 (95% CI: 1.40–3.44, P < 0.001) while Group C’s OR was 2.26 (95% CI: 1.32–3.89, P = 0.003), and the risk in Group D remained non-significant (OR = 1.02, 95% CI: 0.38–2.70, P = 0.975). After further adjusting for underlying diseases (hypertension, diabetes, etc.) and laboratory indicators (e.g., MCV, platelet count, creatinine, BUN, CRP) (Model 2), the kidney disease risk remained significantly higher in Group B (OR = 2.51, 95% CI: 1.55–4.06, P < 0.001) and Group C (OR = 2.19, 95% CI: 1.24–3.90, P = 0.007). Group D’s risk remained statistically non-significant (OR = 1.27, 95% CI: 0.46–3.53, P = 0.648). 3.3 Subgroup Analysis To further explore the relationship between BRI clustering and kidney disease risk, stratified analyses were performed based on gender, age, household registration, education level, smoking, and drinking status, with results shown in Table 3 . Table 3 Subgroup analysis clusters A B C D OR 95% CI P value OR 95% CI P value OR 95% CI P value OR 95% CI P value Sex Male — — — 2.49 1.27, 4.90 0.008 2.1 0.73, 6.06 0.169 0.37 0.04, 3.12 0.361 Female — — — 2.49 1.21, 5.11 0.013 2.46 1.18, 5.15 0.017 2.55 0.75, 8.65 0.134 Age age < 60 — — — 2.28 1.16, 4.48 0.017 2.22 1.00, 4.91 0.05 1.73 0.35, 8.42 0.499 age ≥ 60 — — — 2.91 1.43, 5.92 0.003 2.24 0.94, 5.33 0.068 1.04 0.27, 4.04 0.958 Hukou Agriculture — — — 2.62 1.57, 4.39 < 0.001 1.93 1.02, 3.65 0.044 1.25 0.44, 3.52 0.677 non-agriculture — — — 2.34 0.37, 14.64 0.364 3.02 0.52, 17.47 0.218 0 0.00, Inf 0.993 Education Never — — — 2.68 1.32, 5.42 0.006 2.69 1.22, 5.95 0.014 2.16 0.63, 7.35 0.219 primary and above — — — 2.69 1.35, 5.33 0.005 1.85 0.76, 4.49 0.174 0.66 0.07, 5.80 0.706 Smoke never — — — 2.12 1.12, 4.01 0.021 2.18 1.12, 4.23 0.021 2 0.62, 6.41 0.246 Yes — — — 3.06 1.37, 6.81 0.006 2.3 0.63, 8.38 0.205 0.38 0.04, 3.36 0.382 Drink never — — — 2.17 1.18, 3.98 0.013 2.4 1.25, 4.60 0.009 1.44 0.39, 5.35 0.585 Yes — — — 3.19 1.38, 7.38 0.007 0.75 0.14, 3.89 0.732 1.09 0.20, 5.83 0.922 In both male and female subgroups, the kidney disease risk was significantly higher in Group B (Male: OR = 2.49, 95% CI: 1.27–4.90, P = 0.008; Female: OR = 2.49, 95% CI: 1.21–5.11, P = 0.013). However, Group C’s risk was significantly increased only in females (OR = 2.46, 95% CI: 1.18–5.15, P = 0.017), and no statistical difference was found in males (P = 0.169), suggesting that females may be more susceptible to kidney disease under conditions of central obesity and inflammation. In individuals aged < 60 years, both Group B (OR = 2.28, 95% CI: 1.16–4.48, P = 0.017) and Group C (OR = 2.22, 95% CI: 1.00–4.91, P = 0.05) had significantly elevated kidney disease risk. For individuals aged ≥ 60 years, Group B had the highest risk (OR = 2.91, 95% CI: 1.43–5.92, P = 0.003), while Group C showed an increased risk but with no statistical significance (P = 0.068). In agricultural household populations, both Group B (OR = 2.62, 95% CI: 1.57–4.39, P < 0.001) and Group C (OR = 1.93, 95% CI: 1.02–3.65, P = 0.044) showed significantly increased kidney disease risk, while, in non-agricultural household populations, there was no significant difference in risk between the groups (P > 0.05). This suggests that poorer living conditions and lack of medical resources may amplify the association between BRI and kidney disease. However, due to the smaller sample size in the non-rural population, this hypothesis needs further validation in larger sample populations. In individuals with no education, Group B (OR = 2.68, 95% CI: 1.32–5.42, P = 0.006) and Group C (OR = 2.69, 95% CI: 1.22–5.95, P = 0.014) had significantly higher kidney disease risk. Among individuals with at least primary education, only Group B showed significantly increased risk (OR = 2.69, 95% CI: 1.35–5.33, P = 0.005). In non-smokers, both Group B (OR = 2.12, 95% CI: 1.12–4.01, P = 0.021) and Group C (OR = 2.18, 95% CI: 1.12–4.23, P = 0.021) had significantly higher kidney disease risk. In smokers, only Group B showed a significant increase in risk (OR = 3.06, 95% CI: 1.37–6.81, P = 0.006). In non-drinkers, both Group B (OR = 2.17, 95% CI: 1.18–3.98, P = 0.013) and Group C (OR = 2.4, 95% CI: 1.25–4.60, P = 0.009) had significantly higher kidney disease risk. In drinkers, only Group B showed significant risk (OR = 3.19, 95% CI: 1.38–7.38, P = 0.007). 3.4 Restricted Cubic Splines Analysis To further explore the relationship between BRI and kidney disease risk, the average BRI for each participant was calculated (shown in Fig. 3 ). The restricted cubic splines (RCS) analysis revealed a nonlinear relationship between average BRI and the risk of kidney disease (P < 0.001). Both high and low levels of BRI were associated with increased kidney disease risk. Given the small sample size and high variability in Group D, a sensitivity analysis was conducted excluding Group D. The results of the RCS analysis excluding Group D showed a clearer nonlinear trend with statistical significance (P < 0.001), presenting a U-shaped curve for BRI and kidney disease risk. 4. Discussion This study, based on the CHARLS database, used a longitudinal k-means clustering method to group the dynamic changes in BRI. By integrating demographic characteristics, physical indicators, underlying diseases, and laboratory tests, the study systematically evaluated the relationship between different BRI dynamic change groups and the risk of kidney disease. The results clearly identified the association between BRI dynamic changes and kidney disease risk, and also revealed the distinct characteristics of different BRI trajectory groups, as well as their heterogeneity within specific populations. BRI, as an index of central obesity combining waist circumference and height, can more accurately reflect body fat distribution and metabolic status compared to traditional BMI and WC [ 14 ] . Currently, the body shape index (ABSI), as a new obesity measurement tool, is mainly used to assess the impact of abdominal fat on health. However, BRI is better suited to predict metabolic abnormalities [ 15 ] . Obesity is a known risk factor for the development and progression of kidney disease, and factors such as metabolic disorders and inflammatory responses contribute to this process. Therefore, this study chose to use BRI as a tool to assess the association between obesity, metabolic issues, and the occurrence and progression of kidney disease in the Chinese population. The results of this study indicate that the dynamic changes in BRI are closely associated with the risk of kidney disease, showing a U-shaped curve, which is consistent with previous study [ 16 ] . Group C showed typical characteristics of central obesity, high metabolic burden, and an inflammatory state. There is existing evidence that central obesity, through the accumulation of visceral fat, promotes the release of inflammatory factors (such as IL-6 and TNF-α), which worsens insulin resistance and glomerular hyperfiltration, ultimately leading to kidney function decline [ 17 , 18 ] . In addition, obesity is closely related to hypertension and diabetes, both of which are independent risk factors for kidney disease [ 19 ] . Group B has unique characteristics, including the lowest BMI, smallest waist circumference, but the highest proportion of males, smokers, and alcohol drinkers. Despite lacking typical obesity features, the risk of kidney disease in this group is significantly increased. Similarly, an analysis of a study on the relationship between underweight and cardiovascular-metabolic diseases showed that adults who are underweight have the highest prevalence of CKD [ 20 ] . There is evidence that unhealthy lifestyle behaviors may lead to more serious health issues in non-obese individuals (including those who are underweight) compared to obese adults [ 19 ] . Previous studies have confirmed that smoking can accelerate the onset and progression of kidney disease by triggering chronic inflammation, increasing oxidative stress, and causing endothelial dysfunction [ 21 , 22 ] . Excessive alcohol consumption can also lead to hypertension and metabolic disorders, which have a negative impact on the kidneys. However, subgroup analysis shows that Group B still exhibits a significant risk in the non-smoking and non-drinking subgroup, suggesting that smoking and drinking-related unhealthy habits cannot fully explain the increased kidney disease risk in this population. Research has pointed out that there is a J-shaped relationship between body fat and mortality risk, while lean body mass has an inverse J-shaped relationship with mortality risk [ 23 ] . An excessively low BRI may indicate a reduction in lean body mass, which could be associated with malnutrition, fatigue, decreased exercise tolerance, and muscle atrophy, thereby increasing the risk of CKD [ 16 ] . Group D individuals are characterized by advanced age, weight loss, and a higher burden of chronic diseases. The incidence of arthritis and gastrointestinal diseases is the highest, and they also exhibit signs of anemia and malnutrition. During the observation period, the BRI in this group showed a decline, but no correlation was observed between the changes in BRI and kidney disease. This may be because the decline in kidney function in older individuals is more likely due to natural aging, and the relatively short duration of BRI changes may not have been enough to produce cumulative metabolic effects. Additionally, the sample size of this group was small, with considerable individual variability and numerous confounding factors, which may have masked the true results. Subgroup analysis further revealed the moderating effects of different demographic and lifestyle factors on the dynamic changes in BRI and the risk of kidney disease. In Group B, patients showed an association with increased kidney disease risk across all subgroup analyses, suggesting that persistent low BRI caused by various factors may elevate the risk of kidney disease. The risk in Group C was more prominent in females and individuals under 60 years old. However, this seems to be inconsistent with previous research findings. Studies have pointed out that hormonal mechanisms are the main drivers of body shape and sex differences [ 24 ] , and men are more prone to obesity-related diseases than women [ 25 ] . However, the obesity in these studies did not distinguish between central obesity and non-central obesity. Due to the protective mechanisms of estrogen, young obese women often present with "gynoid" or "lower body" obesity, which is associated with a lower metabolic risk [ 26 ] . In contrast, central obesity and metabolic abnormalities reflected by BRI appear to significantly increase the risk of kidney disease in middle-aged women. Because there are fewer male patients in Group B, positive results in male patients may have been masked. 5. Limitations This study has certain limitations: The cluster analysis of the dynamic changes in BRI relies on follow-up data, which may be subject to measurement bias; a low BRI does not effectively reflect the reduction in lean body mass, and the corresponding results need further verification; the relatively small number of males in group C may lead to false-negative results for the male population; the follow-up period of the CHARLS database is relatively short, and it has not fully assessed the long-term relationship between BRI changes and kidney disease risk; the study population is limited to the Chinese population aged 45 and above, so the findings may not be generalized to other countries, regions or younger populations; and as an observational design, this study cannot establish causal relationships. Future research should further verify the association between dynamic changes in BRI and kidney disease risk through longer prospective cohorts and mechanistic studies. This study does not provide clues regarding the dynamic changes in individual BRI levels and their risk for kidney disease, and further exploration in large-scale datasets is needed. 6. Conclusion This study, using clustering analysis, is the first to explore the relationship between central obesity and kidney disease based on the dynamic changes of BRI, utilizing longitudinal data. The study further confirms the existence of a U-shaped curve relationship between BRI and kidney disease and identifies that the impact of BRI on kidney disease is time-dependent. The study finds that persistent central obesity is a risk factor for kidney disease in middle-aged women in China, while persistently low BRI is a risk factor for kidney disease in the middle-aged and elderly population in China. In these populations, early lifestyle intervention, management of metabolic abnormalities, and inflammation control can reduce the risk of kidney disease. Abbreviations BRI Body roundness index CHARLS China health and retirement longitudinal study GBD Global burden of disease AKI Acute kidney injury CKD Chronic kidney disease ESRD End-stage renal disease BMI Body mass index WC Waist circumference CAPI Computer-assisted personal interviews WBC White blood cell count CRP C-reactive protein BUN Blood urea nitrogen SD Standard deviation ANOVA Analysis of variance IQR Interquartile range OR Odds ratios CI Confidence intervals RCS Restricted Cubic Splines MCV Mean corpuscular volume ABSI A body shape index Declarations Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Statement of Ethics The study was conducted in accordance with the World Medical Association Declaration of Helsinki guidelines. The CHARLS has received approval from the Biomedical Ethics Committee of Peking University, and the ethics approval number is IRB00001052-11015. All participants provided written informed consent. Funding Sources This work was supported by National Key R & D Program of China (Grant No. 2024YFC3505700) The funder had no role in the design, data collection, data analysis, and reporting of this study. Author Contributions X.L.: Conceptualization, Writing - original draft, Writing - review & editing. J.W.: Investigation, Writing - review & editing. Z.Y.: Formal analysis, Writing - review & editing. S.H.: Investigation, Data curation, Writing - review & editing. F.Z.: Conceptualization, Methodology, Writing - review & editing. T.S.: Visualization, Supervision, Writing - review & editing. Acknowledgments We would like to express our gratitude to all members of the CHARLS team for their efforts in data collection and management. We also appreciate the invaluable contributions made by all the participants. Data availability statement The datasets used in this investigation are available in online repositories. Detailed descriptions of each survey and corresponding data have been published at http://charls.pku.edu.cn/. References GBD 2021 Urolithiasis Collaborators. The global, regional, and national burden of urolithiasis in 204 countries and territories, 2000-2021: a systematic analysis for the Global Burden of Disease Study 2021. EClinicalMedicine. 2024;78:102924. Published 2024 Nov 21. doi:10.1016/j.eclinm.2024.102924. GBD 2021 US Burden of Disease Collaborators. The burden of diseases, injuries, and risk factors by state in the USA, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;404(10469):2314-2340. doi:10.1016/S0140-6736(24)01446-6. Picken M, Long J, Williamson GA, Polichnowski AJ. Progression of Chronic Kidney Disease After Acute Kidney Injury: Role of Self-Perpetuating Versus Hemodynamic-Induced Fibrosis. Hypertension. 2016;68(4):921-928. doi:10.1161/HYPERTENSIONAHA.116.07749. Ooi YG, Sarvanandan T, Hee NKY, Lim QH, Paramasivam SS, Ratnasingam J, et al. Risk Prediction and Management of Chronic Kidney Disease in People Living with Type 2 Diabetes Mellitus. Diabetes Metab J. 2024;48(2):196-207. doi:10.4093/dmj.2023.0244. Major RW, Shepherd D, Medcalf JF, Xu G, Gray LJ, Brunskill NJ. The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: An external validation and clinical impact projection cohort study [published correction appears in PLoS Med. 2020 Jul 24;17(7):e1003313. doi: 10.1371/journal.pmed.1003313]. PLoS Med. 2019;16(11):e1002955. Published 2019 Nov 6. doi:10.1371/journal.pmed.1002955. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024;105(4S):S117-S314. doi:10.1016/j.kint.2023.10.018. de Boer IH, Sibley SD, Kestenbaum B, Sampson JN, Young B, Cleary PA, et al. Central obesity, incident microalbuminuria, and change in creatinine clearance in the epidemiology of diabetes interventions and complications study. J Am Soc Nephrol. 2007;18(1):235-243. doi:10.1681/ASN.2006040394. Lee MJ, Park JT, Park KS, Kwon YE, Han SH, Kang SW, et al. Normal body mass index with central obesity has increased risk of coronary artery calcification in Korean patients with chronic kidney disease. Kidney Int. 2016;90(6):1368-1376. doi:10.1016/j.kint.2016.09.011. Bray GA. Beyond BMI. Nutrients. 2023;15(10):2254. Published 2023 May 10. doi:10.3390/nu15102254 Tian S, Zhang X, Xu Y, Dong H. Feasibility of body roundness index for identifying a clustering of cardiometabolic abnormalities compared to BMI, waist circumference and other anthropometric indices: the China Health and Nutrition Survey, 2008 to 2009. Medicine (Baltimore). 2016;95(34):e4642. doi:10.1097/MD.0000000000004642. Fahami M, Hojati A, Farhangi MA. Body shape index (ABSI), body roundness index (BRI) and risk factors of metabolic syndrome among overweight and obese adults: a cross-sectional study. BMC Endocr Disord. 2024;24(1):230. Published 2024 Oct 28. doi:10.1186/s12902-024-01763-6. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61-68. doi:10.1093/ije/dys203. Thomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring). 2013 Nov;21(11):2264-71. doi: 10.1002/oby.20408. Thomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity. (2013) 21:2264–71. doi: 10.1002/oby.20408. Krakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS ONE. (2012) 7:e39504. doi: 10.1371/journal.pone.0039504. Zhang J, Yu X. The association between the body roundness index and the risk of chronic kidney disease in US adults. Front Med (Lausanne). 2024 Dec 18;11:1495935. doi: 10.3389/fmed.2024.1495935. PMID: 39744532; PMCID: PMC11688310. Jiang Z, Wang Y, Zhao X, Cui H, Han M, Ren X, et al. Obesity and chronic kidney disease. Am J Physiol Endocrinol Metab . 2023;324(1):E24-E41. doi:10.1152/ajpendo.00179.2022 Arabi T, Shafqat A, Sabbah BN, Fawzy NA, Shah H, Abdulkader H, et al. Obesity-related kidney disease: Beyond hypertension and insulin-resistance. Front Endocrinol (Lausanne) . 2023;13:1095211. Published 2023 Jan 16. doi:10.3389/fendo.2022.1095211 Kikuchi A, Monma T, Ozawa S, Tsuchida M, Tsuda M, Takeda F. (2021) Risk factors for multiple metabolic syndrome components in obese and non-obese Japanese individuals. Prev Med 153, 106855. Chen M, Shi S, Wang S, Huang Y, Zhou F, Zhong VW. Prevalence of cardiometabolic diseases in underweight: a nationwide cross-sectional study. Br J Nutr. 2024 Dec 28;132(12):1654-1662. doi: 10.1017/S0007114524002885IF: 3.0 Q2 . Epub 2024 Nov 11. PMID: 39523901. Choi HS, Han KD, Oh TR, Kim CS, Bae EH, Ma SK, et al. Smoking and risk of incident end-stage kidney disease in general population: A Nationwide Population-based Cohort Study from Korea. Sci Rep. 2019;9(1):19511. Published 2019 Dec 20. doi:10.1038/s41598-019-56113-7. Mercado C, Jaimes EA. Cigarette smoking as a risk factor for atherosclerosis and renal disease: novel pathogenic insights. Curr Hypertens Rep. 2007;9(1):66-72. doi:10.1007/s11906-007-0012-8. Bigaard J, Frederiksen K, Tjønneland A, Thomsen BL, Overvad K, Heitmann BL, et al. Body fat and fat-free mass and all-cause mortality. Obes Res. 2004 Jul;12(7):1042-9. doi: 10.1038/oby.2004.131. PMID: 15292467. Singh P, Covassin N, Marlatt K, Gadde KM, Heymsfield SB. Obesity, Body Composition, and Sex Hormones: Implications for Cardiovascular Risk. Compr Physiol. 2021 Dec 29;12(1):2949-2993. doi: 10.1002/cphy.c210014IF: 4.2 Q1 . PMID: 34964120; PMCID: PMC10068688. Kautzky-Willer A, Handisurya A. Metabolic diseases and associated complications: sex and gender matter! Eur J Clin Invest. 2009 Aug;39(8):631-48. doi: 10.1111/j.1365-2362.2009.02161.x. Epub 2009 Jun 3. PMID: 19496803. Karpe F, Pinnick KE. Biology of upper-body and lower-body adipose tissue--link to whole-body phenotypes. Nat Rev Endocrinol. 2015 Feb;11(2):90-100. doi: 10.1038/nrendo.2014.185. Epub 2014 Nov 4. PMID: 25365922. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7245888","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502423591,"identity":"220293fb-2904-481a-8a08-403c1ab577df","order_by":0,"name":"Xiaobin Liu","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Xiaobin","middleName":"","lastName":"Liu","suffix":""},{"id":502423592,"identity":"854bcc41-14a5-4486-8b5a-562bcf05e941","order_by":1,"name":"Jirong Wang","email":"","orcid":"","institution":"Department of Critical Care Medicine, Songtao Miao Autonomous County People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jirong","middleName":"","lastName":"Wang","suffix":""},{"id":502423593,"identity":"d1a126fb-5672-45ff-88c6-1ce5d176ff7d","order_by":2,"name":"Zaiyun Yang","email":"","orcid":"","institution":"Department of Critical Care Medicine, Songtao Miao Autonomous County People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zaiyun","middleName":"","lastName":"Yang","suffix":""},{"id":502423596,"identity":"3e881b06-cc1b-423f-8706-9030376b74bd","order_by":3,"name":"Shengyu Huang","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Shengyu","middleName":"","lastName":"Huang","suffix":""},{"id":502423601,"identity":"bd8904f9-f588-4854-917e-ccfe0a72f34b","order_by":4,"name":"Feng Zhu","email":"","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Zhu","suffix":""},{"id":502423602,"identity":"e23f5172-0ef4-4770-8134-8b0d91c3f7ab","order_by":5,"name":"Tuo Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie2RsQrCMBCGrxwEh2jWCmJfIZPi4rOkCG6Cbh06CJV0EHHVyVdwdGwJZIq4OtY30M3RqJtIWzeHfNvBfdz9dwAOx1/CiuzK4yFDzAsRxTUMQr18E+lROyUjXhhdS0HVMOjt17TXviyw2gh2q0x5kiBH6EXhnABLl6JU4bol1Ex2Wn2E8Tk8dMA3x325QoCrrZ0ySECfQ2NLf1KuBNIqTWmz2PWmocRqBTTlij7jKyRQS+F6LN5HTgj6wmhamSVIlLq+XslOt9s9irssXVUs9gn9rd3hcDgcX3kA4nFLDqTSWRYAAAAASUVORK5CYII=","orcid":"","institution":"Department of Critical Care Medicine, Shanghai East Hospital, School of Medicine, Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Tuo","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2025-07-29 17:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7245888/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7245888/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89592806,"identity":"f58e6358-1ed7-458b-8273-918c4ef13417","added_by":"auto","created_at":"2025-08-21 16:13:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":459093,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7245888/v1/674e71a992e7ad3a16adbec9.png"},{"id":89594661,"identity":"122e7633-6706-40f0-8fbb-6be505eb7917","added_by":"auto","created_at":"2025-08-21 16:37:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2400335,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eK-means Clustering and trend of 4 Groups (A: The result of K-means Clustering; B: Line chart; C: Overall trend of each group)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7245888/v1/c6eb01446d779b72c29b0d30.png"},{"id":89593459,"identity":"621431ed-1e3c-47cf-b896-02550f8826bc","added_by":"auto","created_at":"2025-08-21 16:21:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":706996,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted Cubic Spline Analysis of the Relationship Between Average BRI Value and Kidney Disease Risk (A: Overall; B: After Excluding Group D)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7245888/v1/4bbc13d44b7bd774e6efef84.png"},{"id":94242855,"identity":"864e340a-9da7-4c46-bbf8-2e59d8061c6f","added_by":"auto","created_at":"2025-10-24 04:31:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4386651,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7245888/v1/a5e9ceca-866b-40f2-99a4-0d4a061e44ac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Time Effect of the Impact of the Body Roundness Index on Kidney Disease: A Cluster Analysis Based on Longitudinal Data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eKidney disease has become an increasingly severe global public health issue, with its high morbidity and mortality rates imposing a significant burden on healthcare systems and societies worldwide. According to the Global Burden of Disease (GBD) study, kidney disease, including acute kidney injury (AKI), chronic kidney disease (CKD), and other non-specific renal disorders, has become one of the leading chronic non-communicable diseases contributing to the global health burden, following cardiovascular diseases, diabetes, and cancer \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In particular, CKD has seen a marked rise in prevalence and related mortality over the past few decades. Similarly, the incidence of AKI has increased annually and is linked to an increased risk of transitioning into CKD, exacerbating the progressive decline of kidney health \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDespite advancements in the screening, diagnosis, and treatment of kidney disease, many patients are diagnosed only when the disease has progressed to end-stage renal disease (ESRD), as early symptoms are often subtle. This late diagnosis leads to irreversible kidney damage and multi-system complications \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Research indicates that modifiable risk factors such as metabolic abnormalities, central obesity, hypertension, diabetes, and lifestyle factors play critical roles in the onset and progression of kidney disease \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Therefore, identifying high-risk populations for kidney disease, exploring modifiable risk factors related to metabolic abnormalities, and implementing effective interventions to slow disease progression are of significant clinical and public health importance in reducing the global burden of kidney disease.\u003c/p\u003e\u003cp\u003eAmong the multiple pathogenic mechanisms in the development of kidney disease, central obesity has been identified as an important, modifiable risk factor \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Central obesity can accelerate kidney function decline by exacerbating insulin resistance, triggering chronic low-grade inflammation, promoting oxidative stress, and causing endothelial dysfunction. However, traditional obesity indicators (such as body mass index [BMI] and waist circumference [WC]) have certain limitations in reflecting fat distribution patterns and related metabolic burdens, making it difficult to accurately capture fat redistribution characteristics closely associated with kidney disease risk \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn recent years, the Body Roundness Index (BRI), a novel measure that incorporates the geometric relationship between waist circumference and height, has been proposed as a more accurate way to quantify the extent of central obesity and its associated metabolic risks. Compared to traditional indicators, BRI has demonstrated superior predictive performance in forecasting cardiovascular and metabolic abnormalities \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. BRI has been significantly associated with the occurrence of metabolic syndrome and cardiovascular events. However, existing studies are predominantly cross-sectional and insufficient to explore the long-term effects of dynamic changes in obesity indicators on kidney disease risk. Given that obesity and metabolic disorders have cumulative and time-dependent effects, integrating the dynamic changes of BRI into research frameworks could help capture the potential contribution of changes in these indicators over time to kidney damage.\u003c/p\u003e\u003cp\u003eThus, this study aims to use longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) from 2011, 2013, and 2015 to identify different dynamic patterns of BRI using clustering methods like k-means and prospectively evaluate the impact of various BRI trajectories on kidney disease risk. Unlike previous studies, this research attempts to systematically explore the relationship between dynamic changes in BRI and kidney disease from a temporal perspective, thereby providing scientific evidence to optimize obesity management strategies and kidney disease prevention interventions.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Population and Design\u003c/h2\u003e\u003cp\u003eCHARLS is a nationally representative survey targeting Chinese households and individuals aged 45 years and older. The survey utilizes computer-assisted personal interviews (CAPI) to collect data through face-to-face interviews, covering demographic information and health-related issues. It is conducted by the National School of Development at Peking University and includes participants from 28 provinces across China, with the aim of collecting population and health-related data for older adults \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. CHARLS has received approval from the Biomedical Ethics Committee of Peking University, and all participants provided written informed consent. The ethics approval number is IRB00001052-11015. This study was conducted in accordance with the World Medical Association Declaration of Helsinki guidelines.\u003c/p\u003e\u003cp\u003eThis study analyzed the data from three rounds of surveys (2011, 2013, and 2015) with complete population characteristics. Individuals with pre-existing renal impairment before 2011 or those missing key information required to calculate BRI during follow-up were excluded. The final cohort included 2,160 participants. This study is a secondary analysis of the CHARLS data, conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 BRI Calculation Formula and Kidney Disease Diagnostic Criteria\u003c/h2\u003e\u003cp\u003eThe Body Roundness Index (BRI) is calculated using the following formula \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:BRI=364.2-365.5\\times\\:\\sqrt{1-{（\\frac{\\text{W}\\text{C}}{2{\\pi\\:}}）}^{2}/{\\left(0.5\\text{H}\\right)}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere WC refers to waist circumference, and H refers to height.\u003c/p\u003e\u003cp\u003eThe diagnosis of kidney disease was based on participants\u0026rsquo; self-reported doctor diagnoses from the 2015, 2018, and 2021 follow-up surveys.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Covariates Assessment\u003c/h2\u003e\u003cp\u003eDemographic information includes: age, gender, residence (urban or rural), marital status, education level, smoking, and alcohol consumption. Blood sample data includes: blood glucose, white blood cell count (WBC), C-reactive protein (CRP), blood urea nitrogen (BUN), uric acid, and other biomarkers. Medical history includes: hypertension, diabetes, hyperlipidemia, cancer, chronic lung disease, chronic heart disease, etc.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Dynamic Trend Clustering Analysis\u003c/h2\u003e\u003cp\u003eTo analyze the dynamic patterns of BRI, a longitudinal k-means clustering method was applied. First, the optimal number of clusters was determined based on the Calinski-Harabasz index. Then, based on the clustering results, a line graph was created to visually display the dynamic trend of BRI in each cluster group, highlighting the distinct change patterns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Demographic and Baseline Characteristics of Participants\u003c/h2\u003e\u003cp\u003eAfter determining the clustering results, demographic and baseline characteristics of participants in each cluster were analyzed, including gender, age, household registration type (urban or rural), marital status, education level, smoking and drinking habits, etc. Normally distributed continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and analysis of variance (ANOVA) was used to assess differences between categories. For skewed continuous variables, median and interquartile range (IQR) were reported, and the Kruskal-Wallis test was used for non-parametric comparison of differences between categories. Categorical variables were presented as frequencies and percentages, with chi-square analysis used to assess differences between categories.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Logistic Regression Models\u003c/h2\u003e\u003cp\u003eLogistic regression models were used to calculate the odds ratios (OR) and 95% confidence intervals (95% CI) for the risk of kidney disease between different BRI trajectory groups. To further explore the association between dynamic BRI trends and kidney disease risk, multivariable adjusted models were constructed after controlling for potential confounders.\u003c/p\u003e\u003cp\u003eAll models were based on logistic regression to evaluate the independent associations between BRI dynamic trend groups and kidney disease incidence. Results were presented as odds ratios (OR) and 95% confidence intervals (CI). Data analysis was performed using R language, with a significance level set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Subgroup Analysis\u003c/h2\u003e\u003cp\u003eSubgroup analyses were performed to examine the effect of BRI trajectory groups on kidney disease outcomes in different populations. Stratified analyses were conducted based on gender, age, household registration type, education level, smoking status, and alcohol consumption.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Restricted Cubic Splines (RCS) Analysis\u003c/h2\u003e\u003cp\u003eRCS analysis were employed to explore potential nonlinear relationships between average BRI and kidney disease outcomes. The average BRI was calculated as the mean value of BRI from the 2011, 2013, and 2015 surveys.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe participant selection process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Clustering Analysis Results and Basic Demographic Characteristics\u003c/h2\u003e\u003cp\u003eBased on the Calinski-Harabasz index, the clustering analysis of BRI yielded the best results when the population was divided into four groups (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A). Further, the dynamic trends for each of the four groups were plotted (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. B, C). Significant differences in baseline characteristics were observed across the groups in terms of demographic, physical indicators, underlying health conditions, and laboratory tests (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Group A had generally good health status and normal metabolic indicators. Group B consisted mostly of males with poor lifestyle habits (smoking, alcohol consumption) and consistently low BMI. Group C was characterized by persistent central obesity, high metabolic load, and inflammatory status. Group D was distinguished by older age, weight loss, high burden of chronic diseases, and early renal function decline risk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient demographics and baseline characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eclusters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA, N\u0026thinsp;=\u0026thinsp;8511\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB, N\u0026thinsp;=\u0026thinsp;8261\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC, N\u0026thinsp;=\u0026thinsp;3601\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD, N\u0026thinsp;=\u0026thinsp;1231\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (51, 64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (51, 64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (51, 65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62 (56, 68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e332 (39.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e511 (61.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69 (19.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e59 (48.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e519 (61.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e315 (38.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e291 (80.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64 (52.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHukou\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eagricultural area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e717 (84.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e742 (89.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e301 (83.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120 (97.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enon-agricultural area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e134 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarriage\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\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esingle status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105 (12.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e93 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e746 (87.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e733 (88.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e304 (84.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e108 (87.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e425 (49.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e395 (47.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e190 (52.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88 (71.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eprimary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e346 (40.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e367 (44.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e144 (40.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33 (26.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eadvanced education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 (9.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e611 (71.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e406 (49.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e312 (86.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75 (61.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e239 (28.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e416 (50.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48 (39.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e605 (71.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e478 (58.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e304 (84.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65 (52.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnce a month or less than once a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e178 (20.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e252 (30.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39 (10.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50 (40.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than once a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92 (11.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (4.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (6.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.8 (22.4, 25.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.7 (19.2, 22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.8 (26.2, 29.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.8 (19.3, 24.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist Measurement (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e87 (82, 91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (72, 81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97 (92, 102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80 (74, 86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eBasic Illness\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e196 (23.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e138 (38.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eDyslipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (6.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (4.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (14.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eDiabetes or High Blood Sugar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (3.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eCancer or Malignant Tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (0.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic Lung Diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91 (11.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (3.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.539\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart Problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (14.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (6.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (4.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStomach or Other Digestive Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180 (21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e210 (25.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70 (19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55 (44.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eEmotio0l, Nervous, or Psychiatric Problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.592\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMemory-Related Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (0.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArthritis or Rheumatism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e293 (34.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e281 (34.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e168 (46.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72 (58.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eLaboratory Tests\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite blood cell(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.02 (4.90, 7.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.86 (4.80, 7.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.20 (5.10, 7.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.10 (5.30, 7.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0952\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91 (87, 95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92 (88, 96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90 (87, 94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89 (74, 95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003ePlatelets(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e203 (158, 251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e198 (157, 247)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e213 (161, 264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e216 (165, 263)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.34 (4.44, 6.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.47 (4.45, 6.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.12 (4.37, 6.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.05 (4.73, 7.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eCreatine(\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (57, 77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69 (59, 79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63 (56, 71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60 (49, 75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eCRP(mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96 (0.55, 2.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.79 (0.45, 1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.70 (0.85, 2.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91 (0.54, 2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eUric Acid(mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e253 (213, 309)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e256 (213, 308)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e258 (213, 310)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e241 (212, 278)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematocrit(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (37, 44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (37, 45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40 (36, 43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38 (30, 45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\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\u003eHemoglobin(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140 (128, 153)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140 (128, 155)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e137 (128, 149)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e137 (124, 148)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKidney Disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eMedian (IQR); n (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eKruskal-Wallis rank sum test; Pearson's Chi-squared test; Fisher's exact test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eBMI, body mass index; MCV, mean corpuscular volume; BUN, blood urea nitrogen; CRP, C-reactive protein\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSpecifically, regarding demographic characteristics, the median age in Group D (62 years) was significantly higher than that in the other groups (58 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that age may play an important role in BRI trajectory and disease risk. Group B had the highest proportion of males (61.9%), whereas Group C had the lowest (19.2%). There was a significant imbalance in gender distribution across the groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proportion of agricultural household registration was highest in Group D (97.6%), while non-agricultural households were lowest (2.4%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which might reflect the impact of rural-urban differences on BRI clustering patterns. Group D had the lowest educational level, with 71.5% of participants reporting no education, and only 1.6% had higher education, suggesting clear socioeconomic differences across groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eIn terms of physical indicators, Group C had the highest BMI (27.8) and waist circumference (97 cm), while Group B had the lowest (BMI: 20.7, waist circumference: 77 cm) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that Group C exhibited more significant central obesity characteristics. Additionally, Group C had a higher burden of chronic diseases, including hypertension (38.3%), diabetes or hyperglycemia (8.3%), and dyslipidemia (14.7%), all of which were significantly higher than those in the other groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The prevalence of heart disease (14.7%) and stroke (4.2%) was also highest in Group C, indicating greater metabolic and cardiovascular risks. In contrast, Group D had the highest incidence of digestive system diseases (44.7%) and arthritis/rheumatism (58.5%).\u003c/p\u003e\u003cp\u003eLaboratory results further reflected differences in metabolic and renal function across the groups. Group D had the highest blood urea nitrogen (BUN) level (6.05 mmol/L), the lowest creatinine (60 \u0026micro;mol/L), the lowest hemoglobin (137 g/L), and the lowest mean corpuscular volume (MCV) (89 fl), suggesting potential anemia, muscle wasting, malnutrition, or chronic inflammation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although Group C had a higher BMI, its creatinine levels were relatively low, which may be due to lower muscle mass (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Logistic Regression Analysis of BRI Grouping and Kidney Disease Risk\u003c/h2\u003e\u003cp\u003eBased on demographic and baseline characteristics, multivariable models were further constructed. \u003cb\u003eModel 1\u003c/b\u003e: Adjusted for gender, age, household registration, marital status, education level, smoking, and alcohol consumption. \u003cb\u003eModel 2\u003c/b\u003e: In addition to the variables in Model 1, further adjusted for hypertension, dyslipidemia, diabetes or hyperglycemia, chronic lung disease, heart disease, stroke, gastrointestinal or other digestive system diseases, arthritis or rheumatism, and clinical indicators such as MCV, platelet count, creatinine, BUN, C-reactive protein (CRP), hematocrit, and hemoglobin.\u003c/p\u003e\u003cp\u003eThe relationship between BRI groups and kidney disease risk was analyzed using logistic regression, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the univariate model, compared with Group A (the baseline group), the risk of kidney disease was significantly higher in Groups B and C. Group B had 2.3 times the risk of kidney disease compared to Group A (OR\u0026thinsp;=\u0026thinsp;2.3, 95% CI: 1.49\u0026ndash;3.54, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Group C had 2.08 times the risk (OR\u0026thinsp;=\u0026thinsp;2.08, 95% CI: 1.22\u0026ndash;3.52, P\u0026thinsp;=\u0026thinsp;0.007). The risk in Group D was not statistically significant (OR\u0026thinsp;=\u0026thinsp;1.08, 95% CI: 0.41\u0026ndash;2.84, P\u0026thinsp;=\u0026thinsp;0.869).\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\u003elogistic regression analysis of kidney disease and clusters\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\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eUnivariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel1\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\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\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.40, 3.44\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\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.32, 3.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.38, 2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.975\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003emodel1: adjust for gender, age, hukou, marrage, education, smoke, drink\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003emodel2: adjust for gender, age, hukou, smoke, drink, education, Hypertension, dyslipidemia, disabetes or high blood sugar, chronic lung diseases, heart problems, stroke, stomach or other digestive disease, arthritis or rheumatism, MCV, platelets, creatine, BUN, CRP, hematocrit, hemoglobin\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter adjusting for confounders such as gender, age, household registration, marital status, education level, smoking, and alcohol consumption (Model 1), the kidney disease risk remained significantly elevated in Groups B and C. Group B\u0026rsquo;s OR was 2.2 (95% CI: 1.40\u0026ndash;3.44, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) while Group C\u0026rsquo;s OR was 2.26 (95% CI: 1.32\u0026ndash;3.89, P\u0026thinsp;=\u0026thinsp;0.003), and the risk in Group D remained non-significant (OR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 0.38\u0026ndash;2.70, P\u0026thinsp;=\u0026thinsp;0.975).\u003c/p\u003e\u003cp\u003eAfter further adjusting for underlying diseases (hypertension, diabetes, etc.) and laboratory indicators (e.g., MCV, platelet count, creatinine, BUN, CRP) (Model 2), the kidney disease risk remained significantly higher in Group B (OR\u0026thinsp;=\u0026thinsp;2.51, 95% CI: 1.55\u0026ndash;4.06, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Group C (OR\u0026thinsp;=\u0026thinsp;2.19, 95% CI: 1.24\u0026ndash;3.90, P\u0026thinsp;=\u0026thinsp;0.007). Group D\u0026rsquo;s risk remained statistically non-significant (OR\u0026thinsp;=\u0026thinsp;1.27, 95% CI: 0.46\u0026ndash;3.53, P\u0026thinsp;=\u0026thinsp;0.648).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Subgroup Analysis\u003c/h2\u003e\u003cp\u003eTo further explore the relationship between BRI clustering and kidney disease risk, stratified analyses were performed based on gender, age, household registration, education level, smoking, and drinking status, with results shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\u003eSubgroup analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eclusters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.27, 4.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.73, 6.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.04, 3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.361\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.21, 5.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.18, 5.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.75, 8.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage\u0026thinsp;\u0026lt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.16, 4.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00, 4.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.35, 8.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eage\u0026thinsp;\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.43, 5.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.94, 5.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.27, 4.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHukou\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.57, 4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.02, 3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.44, 3.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enon-agriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.37, 14.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.52, 17.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.00, Inf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.32, 5.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.22, 5.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.63, 7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eprimary and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.35, 5.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.76, 4.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.07, 5.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.706\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.12, 4.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.12, 4.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.62, 6.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.246\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.37, 6.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.63, 8.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.04, 3.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrink\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=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003enever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.18, 3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.25, 4.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.39, 5.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.585\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=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.38, 7.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.14, 3.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.20, 5.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn both male and female subgroups, the kidney disease risk was significantly higher in Group B (Male: OR\u0026thinsp;=\u0026thinsp;2.49, 95% CI: 1.27\u0026ndash;4.90, P\u0026thinsp;=\u0026thinsp;0.008; Female: OR\u0026thinsp;=\u0026thinsp;2.49, 95% CI: 1.21\u0026ndash;5.11, P\u0026thinsp;=\u0026thinsp;0.013). However, Group C\u0026rsquo;s risk was significantly increased only in females (OR\u0026thinsp;=\u0026thinsp;2.46, 95% CI: 1.18\u0026ndash;5.15, P\u0026thinsp;=\u0026thinsp;0.017), and no statistical difference was found in males (P\u0026thinsp;=\u0026thinsp;0.169), suggesting that females may be more susceptible to kidney disease under conditions of central obesity and inflammation.\u003c/p\u003e\u003cp\u003eIn individuals aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years, both Group B (OR\u0026thinsp;=\u0026thinsp;2.28, 95% CI: 1.16\u0026ndash;4.48, P\u0026thinsp;=\u0026thinsp;0.017) and Group C (OR\u0026thinsp;=\u0026thinsp;2.22, 95% CI: 1.00\u0026ndash;4.91, P\u0026thinsp;=\u0026thinsp;0.05) had significantly elevated kidney disease risk. For individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years, Group B had the highest risk (OR\u0026thinsp;=\u0026thinsp;2.91, 95% CI: 1.43\u0026ndash;5.92, P\u0026thinsp;=\u0026thinsp;0.003), while Group C showed an increased risk but with no statistical significance (P\u0026thinsp;=\u0026thinsp;0.068).\u003c/p\u003e\u003cp\u003eIn agricultural household populations, both Group B (OR\u0026thinsp;=\u0026thinsp;2.62, 95% CI: 1.57\u0026ndash;4.39, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Group C (OR\u0026thinsp;=\u0026thinsp;1.93, 95% CI: 1.02\u0026ndash;3.65, P\u0026thinsp;=\u0026thinsp;0.044) showed significantly increased kidney disease risk, while, in non-agricultural household populations, there was no significant difference in risk between the groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This suggests that poorer living conditions and lack of medical resources may amplify the association between BRI and kidney disease. However, due to the smaller sample size in the non-rural population, this hypothesis needs further validation in larger sample populations.\u003c/p\u003e\u003cp\u003eIn individuals with no education, Group B (OR\u0026thinsp;=\u0026thinsp;2.68, 95% CI: 1.32\u0026ndash;5.42, P\u0026thinsp;=\u0026thinsp;0.006) and Group C (OR\u0026thinsp;=\u0026thinsp;2.69, 95% CI: 1.22\u0026ndash;5.95, P\u0026thinsp;=\u0026thinsp;0.014) had significantly higher kidney disease risk. Among individuals with at least primary education, only Group B showed significantly increased risk (OR\u0026thinsp;=\u0026thinsp;2.69, 95% CI: 1.35\u0026ndash;5.33, P\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e\u003cp\u003eIn non-smokers, both Group B (OR\u0026thinsp;=\u0026thinsp;2.12, 95% CI: 1.12\u0026ndash;4.01, P\u0026thinsp;=\u0026thinsp;0.021) and Group C (OR\u0026thinsp;=\u0026thinsp;2.18, 95% CI: 1.12\u0026ndash;4.23, P\u0026thinsp;=\u0026thinsp;0.021) had significantly higher kidney disease risk. In smokers, only Group B showed a significant increase in risk (OR\u0026thinsp;=\u0026thinsp;3.06, 95% CI: 1.37\u0026ndash;6.81, P\u0026thinsp;=\u0026thinsp;0.006). In non-drinkers, both Group B (OR\u0026thinsp;=\u0026thinsp;2.17, 95% CI: 1.18\u0026ndash;3.98, P\u0026thinsp;=\u0026thinsp;0.013) and Group C (OR\u0026thinsp;=\u0026thinsp;2.4, 95% CI: 1.25\u0026ndash;4.60, P\u0026thinsp;=\u0026thinsp;0.009) had significantly higher kidney disease risk. In drinkers, only Group B showed significant risk (OR\u0026thinsp;=\u0026thinsp;3.19, 95% CI: 1.38\u0026ndash;7.38, P\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Restricted Cubic Splines Analysis\u003c/h2\u003e\u003cp\u003eTo further explore the relationship between BRI and kidney disease risk, the average BRI for each participant was calculated (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The restricted cubic splines (RCS) analysis revealed a nonlinear relationship between average BRI and the risk of kidney disease (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both high and low levels of BRI were associated with increased kidney disease risk. Given the small sample size and high variability in Group D, a sensitivity analysis was conducted excluding Group D. The results of the RCS analysis excluding Group D showed a clearer nonlinear trend with statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), presenting a U-shaped curve for BRI and kidney disease risk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study, based on the CHARLS database, used a longitudinal k-means clustering method to group the dynamic changes in BRI. By integrating demographic characteristics, physical indicators, underlying diseases, and laboratory tests, the study systematically evaluated the relationship between different BRI dynamic change groups and the risk of kidney disease. The results clearly identified the association between BRI dynamic changes and kidney disease risk, and also revealed the distinct characteristics of different BRI trajectory groups, as well as their heterogeneity within specific populations.\u003c/p\u003e\u003cp\u003eBRI, as an index of central obesity combining waist circumference and height, can more accurately reflect body fat distribution and metabolic status compared to traditional BMI and WC \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Currently, the body shape index (ABSI), as a new obesity measurement tool, is mainly used to assess the impact of abdominal fat on health. However, BRI is better suited to predict metabolic abnormalities \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Obesity is a known risk factor for the development and progression of kidney disease, and factors such as metabolic disorders and inflammatory responses contribute to this process. Therefore, this study chose to use BRI as a tool to assess the association between obesity, metabolic issues, and the occurrence and progression of kidney disease in the Chinese population.\u003c/p\u003e\u003cp\u003eThe results of this study indicate that the dynamic changes in BRI are closely associated with the risk of kidney disease, showing a U-shaped curve, which is consistent with previous study \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGroup C showed typical characteristics of central obesity, high metabolic burden, and an inflammatory state. There is existing evidence that central obesity, through the accumulation of visceral fat, promotes the release of inflammatory factors (such as IL-6 and TNF-α), which worsens insulin resistance and glomerular hyperfiltration, ultimately leading to kidney function decline \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In addition, obesity is closely related to hypertension and diabetes, both of which are independent risk factors for kidney disease \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGroup B has unique characteristics, including the lowest BMI, smallest waist circumference, but the highest proportion of males, smokers, and alcohol drinkers. Despite lacking typical obesity features, the risk of kidney disease in this group is significantly increased. Similarly, an analysis of a study on the relationship between underweight and cardiovascular-metabolic diseases showed that adults who are underweight have the highest prevalence of CKD \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. There is evidence that unhealthy lifestyle behaviors may lead to more serious health issues in non-obese individuals (including those who are underweight) compared to obese adults \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Previous studies have confirmed that smoking can accelerate the onset and progression of kidney disease by triggering chronic inflammation, increasing oxidative stress, and causing endothelial dysfunction \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Excessive alcohol consumption can also lead to hypertension and metabolic disorders, which have a negative impact on the kidneys. However, subgroup analysis shows that Group B still exhibits a significant risk in the non-smoking and non-drinking subgroup, suggesting that smoking and drinking-related unhealthy habits cannot fully explain the increased kidney disease risk in this population. Research has pointed out that there is a J-shaped relationship between body fat and mortality risk, while lean body mass has an inverse J-shaped relationship with mortality risk \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. An excessively low BRI may indicate a reduction in lean body mass, which could be associated with malnutrition, fatigue, decreased exercise tolerance, and muscle atrophy, thereby increasing the risk of CKD \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eGroup D individuals are characterized by advanced age, weight loss, and a higher burden of chronic diseases. The incidence of arthritis and gastrointestinal diseases is the highest, and they also exhibit signs of anemia and malnutrition. During the observation period, the BRI in this group showed a decline, but no correlation was observed between the changes in BRI and kidney disease. This may be because the decline in kidney function in older individuals is more likely due to natural aging, and the relatively short duration of BRI changes may not have been enough to produce cumulative metabolic effects. Additionally, the sample size of this group was small, with considerable individual variability and numerous confounding factors, which may have masked the true results.\u003c/p\u003e\u003cp\u003eSubgroup analysis further revealed the moderating effects of different demographic and lifestyle factors on the dynamic changes in BRI and the risk of kidney disease. In Group B, patients showed an association with increased kidney disease risk across all subgroup analyses, suggesting that persistent low BRI caused by various factors may elevate the risk of kidney disease. The risk in Group C was more prominent in females and individuals under 60 years old. However, this seems to be inconsistent with previous research findings. Studies have pointed out that hormonal mechanisms are the main drivers of body shape and sex differences \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, and men are more prone to obesity-related diseases than women \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. However, the obesity in these studies did not distinguish between central obesity and non-central obesity. Due to the protective mechanisms of estrogen, young obese women often present with \"gynoid\" or \"lower body\" obesity, which is associated with a lower metabolic risk \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In contrast, central obesity and metabolic abnormalities reflected by BRI appear to significantly increase the risk of kidney disease in middle-aged women. Because there are fewer male patients in Group B, positive results in male patients may have been masked.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eThis study has certain limitations: The cluster analysis of the dynamic changes in BRI relies on follow-up data, which may be subject to measurement bias; a low BRI does not effectively reflect the reduction in lean body mass, and the corresponding results need further verification; the relatively small number of males in group C may lead to false-negative results for the male population; the follow-up period of the CHARLS database is relatively short, and it has not fully assessed the long-term relationship between BRI changes and kidney disease risk; the study population is limited to the Chinese population aged 45 and above, so the findings may not be generalized to other countries, regions or younger populations; and as an observational design, this study cannot establish causal relationships. Future research should further verify the association between dynamic changes in BRI and kidney disease risk through longer prospective cohorts and mechanistic studies. This study does not provide clues regarding the dynamic changes in individual BRI levels and their risk for kidney disease, and further exploration in large-scale datasets is needed.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study, using clustering analysis, is the first to explore the relationship between central obesity and kidney disease based on the dynamic changes of BRI, utilizing longitudinal data. The study further confirms the existence of a U-shaped curve relationship between BRI and kidney disease and identifies that the impact of BRI on kidney disease is time-dependent. The study finds that persistent central obesity is a risk factor for kidney disease in middle-aged women in China, while persistently low BRI is a risk factor for kidney disease in the middle-aged and elderly population in China. In these populations, early lifestyle intervention, management of metabolic abnormalities, and inflammation control can reduce the risk of kidney disease.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBRI Body roundness index\u003c/p\u003e\n\u003cp\u003eCHARLS China health and retirement longitudinal study\u003c/p\u003e\n\u003cp\u003eGBD Global burden of disease \u003c/p\u003e\n\u003cp\u003eAKI Acute kidney injury \u003c/p\u003e\n\u003cp\u003eCKD Chronic kidney disease \u003c/p\u003e\n\u003cp\u003eESRD End-stage renal disease\u003c/p\u003e\n\u003cp\u003eBMI Body mass index\u003c/p\u003e\n\u003cp\u003eWC Waist circumference\u003c/p\u003e\n\u003cp\u003eCAPI Computer-assisted personal interviews \u003c/p\u003e\n\u003cp\u003eWBC White blood cell count \u003c/p\u003e\n\u003cp\u003eCRP C-reactive protein\u003c/p\u003e\n\u003cp\u003eBUN Blood urea nitrogen\u003c/p\u003e\n\u003cp\u003eSD Standard deviation\u003c/p\u003e\n\u003cp\u003eANOVA Analysis of variance\u003c/p\u003e\n\u003cp\u003eIQR Interquartile range\u003c/p\u003e\n\u003cp\u003eOR Odds ratios\u003c/p\u003e\n\u003cp\u003eCI Confidence intervals\u003c/p\u003e\n\u003cp\u003eRCS Restricted Cubic Splines\u003c/p\u003e\n\u003cp\u003eMCV Mean corpuscular volume\u003c/p\u003e\n\u003cp\u003eABSI A body shape index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the World Medical Association Declaration of Helsinki guidelines. The CHARLS has received approval from the Biomedical Ethics Committee of Peking University, and the ethics approval number is IRB00001052-11015. All participants provided written informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc472330566\"\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp id=\"_Toc472330568\"\u003eThis work was supported by National Key R \u0026amp; D Program of China (Grant No. 2024YFC3505700) The funder had no role in the design, data collection, data analysis, and reporting of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.L.: Conceptualization, Writing - original draft, Writing - review \u0026amp; editing. J.W.: Investigation, Writing - review \u0026amp; editing. Z.Y.: Formal analysis, Writing - review \u0026amp; editing. S.H.: Investigation, Data curation, Writing - review \u0026amp; editing. F.Z.: Conceptualization, Methodology, Writing - review \u0026amp; editing. T.S.: Visualization, Supervision, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to all members of the CHARLS team for their efforts in data collection and management. We also appreciate the invaluable contributions made by all the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in this investigation are available in online repositories. Detailed descriptions of each survey and corresponding data have been published at http://charls.pku.edu.cn/.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2021 Urolithiasis Collaborators. The global, regional, and national burden of urolithiasis in 204 countries and territories, 2000-2021: a systematic analysis for the Global Burden of Disease Study 2021. EClinicalMedicine. 2024;78:102924. Published 2024 Nov 21. doi:10.1016/j.eclinm.2024.102924.\u003c/li\u003e\n\u003cli\u003eGBD 2021 US Burden of Disease Collaborators. The burden of diseases, injuries, and risk factors by state in the USA, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;404(10469):2314-2340. doi:10.1016/S0140-6736(24)01446-6.\u003c/li\u003e\n\u003cli\u003ePicken M, Long J, Williamson GA, Polichnowski AJ. Progression of Chronic Kidney Disease After Acute Kidney Injury: Role of Self-Perpetuating Versus Hemodynamic-Induced Fibrosis. Hypertension. 2016;68(4):921-928. doi:10.1161/HYPERTENSIONAHA.116.07749.\u003c/li\u003e\n\u003cli\u003eOoi YG, Sarvanandan T, Hee NKY, Lim QH, Paramasivam SS, Ratnasingam J, et al. Risk Prediction and Management of Chronic Kidney Disease in People Living with Type 2 Diabetes Mellitus. Diabetes Metab J. 2024;48(2):196-207. doi:10.4093/dmj.2023.0244.\u003c/li\u003e\n\u003cli\u003eMajor RW, Shepherd D, Medcalf JF, Xu G, Gray LJ, Brunskill NJ. The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: An external validation and clinical impact projection cohort study [published correction appears in PLoS Med. 2020 Jul 24;17(7):e1003313. doi: 10.1371/journal.pmed.1003313]. PLoS Med. 2019;16(11):e1002955. Published 2019 Nov 6. doi:10.1371/journal.pmed.1002955.\u003c/li\u003e\n\u003cli\u003eKidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024;105(4S):S117-S314. doi:10.1016/j.kint.2023.10.018.\u003c/li\u003e\n\u003cli\u003ede Boer IH, Sibley SD, Kestenbaum B, Sampson JN, Young B, Cleary PA, et al. Central obesity, incident microalbuminuria, and change in creatinine clearance in the epidemiology of diabetes interventions and complications study. J Am Soc Nephrol. 2007;18(1):235-243. doi:10.1681/ASN.2006040394.\u003c/li\u003e\n\u003cli\u003eLee MJ, Park JT, Park KS, Kwon YE, Han SH, Kang SW, et al. Normal body mass index with central obesity has increased risk of coronary artery calcification in Korean patients with chronic kidney disease. Kidney Int. 2016;90(6):1368-1376. doi:10.1016/j.kint.2016.09.011.\u003c/li\u003e\n\u003cli\u003eBray GA. Beyond BMI. Nutrients. 2023;15(10):2254. Published 2023 May 10. doi:10.3390/nu15102254\u003c/li\u003e\n\u003cli\u003eTian S, Zhang X, Xu Y, Dong H. Feasibility of body roundness index for identifying a clustering of cardiometabolic abnormalities compared to BMI, waist circumference and other anthropometric indices: the China Health and Nutrition Survey, 2008 to 2009. Medicine (Baltimore). 2016;95(34):e4642. doi:10.1097/MD.0000000000004642.\u003c/li\u003e\n\u003cli\u003eFahami M, Hojati A, Farhangi MA. Body shape index (ABSI), body roundness index (BRI) and risk factors of metabolic syndrome among overweight and obese adults: a cross-sectional study. BMC Endocr Disord. 2024;24(1):230. Published 2024 Oct 28. doi:10.1186/s12902-024-01763-6.\u003c/li\u003e\n\u003cli\u003eZhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43(1):61-68. doi:10.1093/ije/dys203.\u003c/li\u003e\n\u003cli\u003eThomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity (Silver Spring). 2013 Nov;21(11):2264-71. doi: 10.1002/oby.20408.\u003c/li\u003e\n\u003cli\u003eThomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D, et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity. (2013) 21:2264\u0026ndash;71. doi: 10.1002/oby.20408.\u003c/li\u003e\n\u003cli\u003eKrakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS ONE. (2012) 7:e39504. doi: 10.1371/journal.pone.0039504.\u003c/li\u003e\n\u003cli\u003eZhang J, Yu X. The association between the body roundness index and the risk of chronic kidney disease in US adults. Front Med (Lausanne). 2024 Dec 18;11:1495935. doi: 10.3389/fmed.2024.1495935. PMID: 39744532; PMCID: PMC11688310.\u003c/li\u003e\n\u003cli\u003eJiang Z, Wang Y, Zhao X, Cui H, Han M, Ren X, et al. Obesity and chronic kidney disease. \u003cem\u003eAm J Physiol Endocrinol Metab\u003c/em\u003e. 2023;324(1):E24-E41. doi:10.1152/ajpendo.00179.2022 \u003c/li\u003e\n\u003cli\u003eArabi T, Shafqat A, Sabbah BN, Fawzy NA, Shah H, Abdulkader H, et al. Obesity-related kidney disease: Beyond hypertension and insulin-resistance. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e. 2023;13:1095211. Published 2023 Jan 16. doi:10.3389/fendo.2022.1095211\u003c/li\u003e\n\u003cli\u003eKikuchi A, Monma T, Ozawa S, Tsuchida M, Tsuda M, Takeda F. (2021) Risk factors for multiple metabolic syndrome components in obese and non-obese Japanese individuals. Prev Med 153, 106855.\u003c/li\u003e\n\u003cli\u003eChen M, Shi S, Wang S, Huang Y, Zhou F, Zhong VW. Prevalence of cardiometabolic diseases in underweight: a nationwide cross-sectional study. Br J Nutr. 2024 Dec 28;132(12):1654-1662. doi: 10.1017/S0007114524002885IF: 3.0 Q2 . Epub 2024 Nov 11. PMID: 39523901.\u003c/li\u003e\n\u003cli\u003eChoi HS, Han KD, Oh TR, Kim CS, Bae EH, Ma SK, et al. Smoking and risk of incident end-stage kidney disease in general population: A Nationwide Population-based Cohort Study from Korea. Sci Rep. 2019;9(1):19511. Published 2019 Dec 20. doi:10.1038/s41598-019-56113-7. \u003c/li\u003e\n\u003cli\u003eMercado C, Jaimes EA. Cigarette smoking as a risk factor for atherosclerosis and renal disease: novel pathogenic insights. Curr Hypertens Rep. 2007;9(1):66-72. doi:10.1007/s11906-007-0012-8.\u003c/li\u003e\n\u003cli\u003eBigaard J, Frederiksen K, Tj\u0026oslash;nneland A, Thomsen BL, Overvad K, Heitmann BL, et al. Body fat and fat-free mass and all-cause mortality. Obes Res. 2004 Jul;12(7):1042-9. doi: 10.1038/oby.2004.131. PMID: 15292467.\u003c/li\u003e\n\u003cli\u003eSingh P, Covassin N, Marlatt K, Gadde KM, Heymsfield SB. Obesity, Body Composition, and Sex Hormones: Implications for Cardiovascular Risk. Compr Physiol. 2021 Dec 29;12(1):2949-2993. doi: 10.1002/cphy.c210014IF: 4.2 Q1 . PMID: 34964120; PMCID: PMC10068688.\u003c/li\u003e\n\u003cli\u003eKautzky-Willer A, Handisurya A. Metabolic diseases and associated complications: sex and gender matter! Eur J Clin Invest. 2009 Aug;39(8):631-48. doi: 10.1111/j.1365-2362.2009.02161.x. Epub 2009 Jun 3. PMID: 19496803.\u003c/li\u003e\n\u003cli\u003eKarpe F, Pinnick KE. Biology of upper-body and lower-body adipose tissue--link to whole-body phenotypes. Nat Rev Endocrinol. 2015 Feb;11(2):90-100. doi: 10.1038/nrendo.2014.185. Epub 2014 Nov 4. PMID: 25365922.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Body Roundness Index (BRI), Central obesity, CHARLS Database, Cluster analysis, Kidney disease","lastPublishedDoi":"10.21203/rs.3.rs-7245888/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7245888/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCentral obesity is a key modifiable risk factor for kidney disease. The Body Round-ness Index (BRI) is useful for assessing central obesity and associated metabolic risks. This study aimed to investigate the correlation between body roundness index (BRI) and the risk of kidney disease and explore the possibility of using BRI monitoring to identify high-risk groups.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study used longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) including 2,160 participants. K-means clustering identified patterns in BRI changes, and logistic regression was used to assess the association between BRI and kidney disease risk. Subgroup and restricted cubic spline analyses were conducted to explore non-linear relationships and influencing factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe best clustering of BRI was achieved by dividing participants into four groups. Significant differences were observed in demographic, physical, and clinical characteristics. Logistic regression revealed that groups B (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and C (P\u0026thinsp;=\u0026thinsp;0.007) had significantly higher kidney disease risk compared to group A. No significant difference was observed for group D. Subgroup analysis showed elevated kidney disease risk in group B across all subgroups, and higher risk in women and those under 60 in group C. A non-linear, U-shaped relationship between BRI and kidney disease risk was observed, with both high and low BRI levels increasing risk.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe impact of BRI on kidney disease is time-dependent. Both persistently high and low BRI levels are risk factors, highlighting the value of BRI monitoring for early identification and prevention of high-risk populations.\u003c/p\u003e","manuscriptTitle":"The Time Effect of the Impact of the Body Roundness Index on Kidney Disease: A Cluster Analysis Based on Longitudinal Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 16:13:11","doi":"10.21203/rs.3.rs-7245888/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":"fad8e57c-d6c5-4047-a409-ad915f7cc750","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-24T04:23:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-21 16:13:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7245888","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7245888","identity":"rs-7245888","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}