Joint association of Body Roundness Index and Atherogenic Index of Plasma with new-onset stroke in middle-aged and older adults: first evidence from the China Health and Retirement Longitudinal Study

Joint association of Body Roundness Index and Atherogenic Index of Plasma with new-onset stroke in middle-aged and older adults: first evidence from the China Health and Retirement Longitudinal Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Joint association of Body Roundness Index and Atherogenic Index of Plasma with new-onset stroke in middle-aged and older adults: first evidence from the China Health and Retirement Longitudinal Study Zhuoyu Wen, Yihua Zhu, Rongrong Shao, Yixuan Zeng, Rui Li, Gelin Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7488889/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Visceral obesity and dyslipidemia are independent stroke risk factors. The Body Roundness Index (BRI) indicates visceral fat, while the Atherogenic Index of Plasma (AIP) reflects an atherogenic lipid profile. This study investigates the joint association of BRI and AIP with stroke risk in middle-aged and older adults in China. Methods A total of 7,349 participants without a stroke history at baseline (2011) were included from the China Health and Retirement Longitudinal Study (CHARLS), with follow-up until 2018. Kaplan–Meier survival curves, multivariable Cox proportional hazards regression, subgroup analysis and mediation analysis were performed. Results During the 7-year follow-up, 358 new-onset stroke cases (4.87%) were recorded. Independently, participants in the highest quartile of both BRI (HR = 1.54, 95% CI: 1.10–2.17) and AIP (HR = 1.66, 95% CI: 1.20–2.32) faced significantly higher stroke risks compared to the lowest quartile. Joint association showed that the combined High AIP & High BRI group had the highest risk (HR = 1.88, 95% CI: 1.39–2.56), with P interaction < 0.001, especially in males (HR = 2.17; 95% CI: 1.41–3.33) or those aged 45–59 years (HR = 2.02; 95% CI: 1.27–3.21). Moreover, AIP partially mediated the association between BRI and stroke (β = 0.0012, P = 0.046), accounting for 17.8% of the total effect. Conclusions A significant interaction was found between BRI and AIP levels in the association with stroke risk, our findings help formulate early screening and targeted prevention strategies for stroke. Body Roundness Index (BRI) Atherogenic Index of Plasma (AIP) Visceral Obesity Dyslipidemia Stroke Prospective Cohort Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Stroke is defined as a focal or diffuse neurological dysfunction caused by cerebrovascular insufficiency, with symptoms lasting more than 24 hours or resulting in death [ 1 ]. It is currently the fourth leading cause of disability and death worldwide [ 2 ]. With global population aging and the increasing prevalence of metabolic disorders, the incidence of stroke has shown an exponential upward trend in recent years [ 3 ]. Obesity is a key risk factor for the onset and progression of stroke. A meta-analysis of 24 studies found that higher Body Mass Index (BMI), particularly in overweight or obese individuals, was significantly associated with an increased risk of stroke [ 4 ]. Studies have shown that for every unit increase in BMI, the risk of ischemic stroke increases by approximately 5% [ 5 ]. However, BMI estimates body composition solely based on height and weight, without distinguishing between fat and muscle mass or identifying fat distribution in the body [ 6 ], which makes BMI inadequate for accurately reflecting visceral fat levels [ 7 ]. In recent years, the Body Roundness Index (BRI) has emerged as a precise indicator of visceral adiposity relative to total body fat [ 8 ]. Previous studies have shown that elevated BRI is closely linked to increased cardiovascular disease risk [ 9 ]. A cross-sectional study in a US cohort revealed a non-linear positive association between BRI and stroke [ 10 ]. A prospective study involving Chinese and UK populations confirmed the predictive value of BRI for both ischemic and hemorrhagic strokes [ 11 ]. Nevertheless, prior studies have mainly focused on the independent effect of abdominal obesity on stroke risk, and few have systematically evaluated the combined effects of abdominal obesity and other metabolic or clinical indicators. Exploring the interactions between multiple risk factors is essential for improving risk stratification and stroke prediction in specific populations. A recent Chinese cohort study demonstrated for the first time that combining BRI with the triglyceride-glucose index (TyG) significantly improves the predictive value for new-onset stroke events, compared with BRI alone [ 12 ]. Apart from BRI, the Atherogenic Index of Plasma (AIP) is another independent risk factor for stroke [ 13 ]. AIP is a sensitive marker derived from the plasma lipid profile, reflecting lipid imbalance [ 14 ]. Compared to individual lipid indices, AIP is more sensitive to the extent of atherosclerosis [ 15 ]. As a robust biomarker of dyslipidemia, AIP has been identified as a strong independent predictor of adverse cardiovascular and cerebrovascular outcomes [ 16 , 17 ], and is closely associated with cardiovascular disease (CVD) prognosis [ 18 , 19 ]. Emerging evidence indicates that AIP may operate as a metabolic intermediary linking visceral adiposity (captured by BRI) to downstream health outcomes. In a cross-sectional study of American population, AIP partially mediated the BRI–depression association, accounting for 8.64% of the total effect [ 20 ]. Another cross-sectional study in the US found that AIP has a significant indirect influence on the BRI–CVD pathway [ 21 ]. These studies support that AIP lies on the causal pathway from adiposity to cardiometabolic and neuropsychiatric outcomes. However, longitudinal studies are limited regarding the interplay between BRI, AIP and CVD. While AIP reflects lipid-related atherogenic imbalance, BRI primarily assesses visceral adiposity. Although theoretically the two may interact [ 22 ], no study has investigated the joint association of AIP and BRI with incident stroke. Therefore, to fill this gap, the present study aims to investigate the combined association of baseline AIP and BRI levels with new-onset stroke in a nationally representative cohort of middle-aged and older Chinese adults, using a longitudinal study design. We hypothesize that co-occurrence of high AIP and high BRI will significantly elevate stroke risk. We further investigated the mediating role of AIP in the association between BRI and new-onset stroke. This study may provide additional evidence for stroke risk stratification and shed light for targeted intervention for stroke in the aging population. 2. Methods 2.1 Study Design and participants This study adopted a prospective cohort design. All data were derived from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey aimed at assessing the economic, social, and health conditions of the middle-aged and elderly population in China [ 23 ]. The CHARLS cohort was established through multi-stage probability sampling, selecting participants from 450 communities across 150 counties in 28 provinces. The baseline survey was conducted between June 2011 and March 2012, including individuals aged 45 years and older. Data were collected via face-to-face interviews using standardized questionnaires, and participants were followed up biennially. Ethical approval for the CHARLS project was granted by the Ethics Committee of Peking University (IRB00001052-11015). All participants provided written informed consent prior to enrollment. The relevant datasets and accompanying documentation used in this study are publicly available on the CHARLS website ( https://charls.pku.edu.cn/en/ ). This study utilized data from the 2011 baseline and the 2018 follow-up surveys. A total of 17,707 participants were interviewed at baseline (2011–2012). 10,358 individuals were excluded for meeting one or more of the following criteria: aged < 45 years or missing data on age; lacking data on the AIP or BRI components; missing stroke data at baseline or lost to follow-up; history of stroke diagnosis at baseline. After exclusions, 7,349 participants without a history of stroke at baseline were involved in our data analysis. Participants were followed up until 2018 (Fig. 1 ). 2.2 Exposure Variables The exposure variables in this study were AIP and BRI. AIP was calculated as: AIP = log(TG/HDL-C), where triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) were both measured in mg/dL [ 24 ]. BRI was calculated using the following formula: \(\:BRI=364.2-365.5\times\:\sqrt{1-{\left(\frac{\frac{wc}{2\varvec{\pi\:}}}{0.5\times\:height}\right)}^{2}}\:\) , where WC (waist circumference) and height were both measured in centimeters [ 8 ]. We calculated the quartiles for AIP and BRI; then, a joint variable was dichotomized based on the median values of two indicators (AIP = 0.32, BRI = 3.24), and four combined exposure groups were constructed (Low AIP & Low BRI, High AIP & Low BRI, Low AIP & High BRI, High AIP & High BRI). 2.3 Outcome Assessment The primary outcome was the incidence of new-onset stroke during the follow-up period. Participants without a history of stroke at baseline who subsequently reported a physician-diagnosed stroke during follow-up were defined as incident stroke cases [ 24 ]. The following question was used to assess physician-diagnosed stroke events: “Have you ever been diagnosed with stroke by a doctor?”; the timing of the stroke event was determined based on participants’ responses to related questions: “When were you first diagnosed with or became aware of having had a stroke?” or “When was your most recent stroke?”. All participants were followed from baseline, and those who provided a positive response to the stroke diagnosis question during the 2018 follow-up were classified as having experienced a stroke. The time of stroke onset was determined by calculating the interval between the baseline assessment and the reported time of diagnosis. 2.4 Covariates Covariates were collected through structured interviews conducted by trained personnel, physical examination, or laboratory tests. Based on previous studies [ 12 , 25 , 26 ], the covariates were categorized into the following domains: sociodemographic variables including age, sex, residential location, marital status, and education level; lifestyle factors including smoking and alcohol consumption; anthropometric measures including systolic blood pressure (SBP), diastolic blood pressure (DBP); physician-reported diagnoses of diseases (diabetes, hypertension, hyperlipidemia, kidney disease, and heart disease); medications: use of antidiabetic, antihypertensive, and lipid-lowering therapies; laboratory parameters: C-reactive protein (CRP), glycated hemoglobin A1c (HbA1c), fasting plasma glucose, serum creatinine, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and uric acid (UA). Participants undergoing blood tests were required to fast overnight. Venous blood samples were collected by medically trained personnel, stored at 4°C immediately, and transported to the central laboratory in Beijing within two weeks. HbA1c was measured using high-performance liquid chromatography (HPLC) based on boronate affinity methods [ 27 ]. 2.5 Statistical Analysis Continuous variables with normal distribution were expressed as means ± standard deviations (SD), while non-normally distributed variables were expressed as medians and interquartile ranges (IQR). Categorical variables were described using frequencies and percentages. Group comparisons at baseline were conducted using independent sample t-tests or Mann–Whitney U tests for continuous variables, and Chi-square tests for categorical variables. We used Kaplan–Meier survival curves to estimate differences in cumulative stroke incidence across populations with different levels of BRI and AIP. The log-rank test was used to evaluate group differences. Based on the distribution of participants’ BRI and AIP levels, cumulative stroke risk was compared among the combined exposure groups. Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of BRI and AIP with incident stroke during the follow-up period. Three models were constructed: Model A was unadjusted; Model B was adjusted for age and sex; and Model C was further adjusted for education level, residence, marital status, smoking and alcohol consumption, history of hypertension, diabetes, heart disease, dyslipidemia, kidney disease, HbA1c, and CRP levels based on Model B. To evaluate the combined effect of BRI and AIP on cumulative stroke risk, participants with Low AIP & Low BRI were used as the reference group, and stroke incidence was compared across all different joint exposure categories. The above Cox models were used to examine the joint association between AIP & BRI and stroke risk. To explore potential moderating effect by sex and age, stratified Cox regression analyses were performed by sex (male vs. female) and age group (45–59 years vs. ≥60 years). Then, a two-step mediating effect analysis was used to evaluate the mediating role of AIP. Firstly, a fully adjusted regression model was adopted to explore the influence of BRI on AIP and the influence of AIP on stroke, aiming to determine the possibility of AIP as a mediating factor between BRI and stroke. Subsequently, the R “mediation” package was used for mediating effect analysis to evaluate the indirect, direct and total effects of BRI on stroke, with AIP as the mediator [ 28 ]. After dividing the indirect effect by the total effect, determine the percentage of the mediating effect mediated by AIP. The 95% CI of the indirect effect was estimated through a non-parametric Bootstrap method with 1000 iterations. All statistical analyses were conducted using R software (version 4.4.1), and Cox regression models were implemented using the R “survival” package. A two-sided P value < 0.05 was considered statistically significant. 3. Results 3.1 Baseline Characteristics of the Study Population A total of 7,349 participants were included in the final analysis. The mean age was 58.55 ± 8.78 years, and 44.8% were male. During the follow-up period, 358 participants (4.87%) were identified as having new-onset stroke. Compared with the non-stroke group, participants in the stroke group were older and had significantly higher prevalence of hypertension, diabetes, dyslipidemia, and heart disease (all P < 0.001). Additionally, the stroke group showed significantly higher levels of systolic blood pressure, diastolic blood pressure, fasting plasma glucose, HbA1c, triglycerides, total cholesterol, and creatinine (all P < 0.05). The stroke group also had significantly higher AIP (0.43 ± 0.33 vs. 0.35 ± 0.34, P < 0.001) and BRI (4.70 ± 1.71 vs. 4.23 ± 1.37, P < 0.001) levels than the non-stroke group. (Table 1 ) Table 1 Characteristics of study participants (N = 7349). Characteristic Total participants Stroke Non-stroke P value (N = 7349) (N = 358) (N = 6991) Age, y, mean (SD) 58.55 (8.78) 61.13 (8.51) 58.41 (8.77) < 0.001*** Age group, n(%) < 0.001*** [45,59] 4237 (60.2) 165 (47.8) 4072 (60.9) ≥ 60 2798 (39.8) 180 (52.2) 2618 (39.1) Sex, n (%) 0.817 Male 3290 (44.8) 163 (45.5) 3127 (44.8) Female 4054 (55.2) 195 (54.5) 3859 (55.2) Educational level, n (%) 0.198 Illiteracy 2084 (28.4) 110 (30.7) 1974 (28.2) Primary school and below 3046 (41.5) 155 (43.3) 2891 (41.4) Junior high school and above 2217 (30.2) 93 (26.0) 2124 (30.4) Residency, n (%) 1 Agricultural 6215 (85.0) 301 (85.0) 5914 (85.0) Non-Agricultural 1098 (15.0) 53 (15.0) 1045 (15.0) Marital status, n (%) 0.009** Unmarried 790 (10.7) 54 (15.1) 736 (10.5) Married 6559 (89.3) 304 (84.9) 6255 (89.5) Smoker, n (%) 0.123 No 4584 (62.4) 209 (58.4) 4375(62.6) Yes 2765 (37.6) 149 (41.6) 2616 (37.4) Alcohol user, n (%) 0.564 No 4937 (67.2) 235 (65.6) 4702 (67.3) Yes 2412 (32.8) 123 (34.4) 2289 (32.7) SBP, mmHg 129.03 (20.15) 138.40 (22.55) 128.56 (19.91) < 0.001*** DBP, mmHg 75.19 (11.42) 79.16 (11.45) 74.99 (11.38) < 0.001*** Diabetes mellitus, n (%) < 0.001*** No 6906 (94.8) 318 (89.8) 6588 (95.1) Yes 376 (5.2) 36 (10.2) 340 (4.9) Hypertension, n (%) < 0.001*** No 5636 (77.1) 201 (56.5) 5435 (78.2) Yes 1673 (22.9) 155 (43.5) 1518 (21.8) Dyslipidemia, n (%) < 0.001*** No 6589 (91.5) 298 (84.9) 6291 (91.8) Yes 612 (8.5) 53 (15.1) 559 (8.2) Kidney disease, n (%) 0.694 No 6847 (93.6) 331 (93.0) 6516 (93.6) Yes 467 (6.4) 25 (7.0) 442 (6.4) Heart disease, n (%) < 0.001*** No 6520 (89.1) 288 (81.1) 6232 (89.5) Yes 798 (10.9) 67 (18.9) 731 (10.5) CRP, mg/dL, mean (SD) 2.43 (6.65) 3.26 (10.08) 2.38 (6.42) 0.104 HbA1c, %, mean (SD) 5.26 (0.78) 5.42 (0.98) 5.25 (0.76) 0.001** Glucose, mg/dL, mean (SD) 109.60 (35.04) 117.36 (45.95) 109.20 (34.35) 0.001** Creatinine, mg/dL, mean (SD) 0.77 (0.18) 0.79 (0.18) 0.77 (0.18) 0.029* TC, mg/dL, mean (SD) 194.05 (38.74) 198.88 (37.89) 193.80 (38.77) 0.014* TG, mg/dL, mean (SD) 133.98 (113.52) 150.18 (121.75) 133.15 (113.03) 0.010* HDL-C, mg/dL, mean (SD) 51.35 (15.35) 48.94 (14.93) 51.48 (15.37) 0.002** LDL-C, mg/dL, mean (SD) 116.73 (34.74) 119.85 (37.48) 116.57 (34.59) 0.106 UA, mg/dL, mean (SD) 4.40 (1.22) 4.51 (1.31) 4.40 (1.21) 0.102 AIP, mean (SD) 0.35 (0.34) 0.43 (0.33) 0.35 (0.34) < 0.001*** AIP category, n (%) < 0.001*** Q1 1836 (25.0) 58 (16.2) 1778 (25.4) Q2 1836 (25.0) 86 (24.0) 1752 (25.1) Q3 1836 (25.0) 101 (28.2) 1735 (24.8) Q4 1836 (25.0) 113 (31.6) 1726 (24.7) BRI, mean (SD) 4.26 (1.40) 4.70 (1.71) 4.23 (1.37) < 0.001*** BRI category, n (%) < 0.001*** Q1 1833 (24.9) 62 (17.3) 1771 (25.3) Q2 1841 (25.1) 64 (17.9) 1777 (25.4) Q3 1836 (25.0) 106 (29.6) 1730 (24.7) Q4 1839 (25.0) 126 (35.2) 1713 (24.5) Abbreviations: SBP, Systolic blood pressure; DBP, Diastolic blood pressure; CRP, C-reactive protein; HbA1c, Glycated hemoglobin; TC, Total cholesterol; TG, Triglyceride; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; UA, uric acid; AIP, atherogenic index of plasma; BRI, body roundness index. 3.2 Kaplan–Meier Analysis Stratified by AIP, BRI, and Their Combined Categories Kaplan–Meier survival curves were constructed to compare cumulative stroke incidence across different levels of AIP, BRI, and their combined categories. As shown in Figs. 2 A and 2 B, the stroke-free survival curves were clearly separated in a stepwise pattern across quartiles of AIP and BRI ( P < 0.001), indicating that both indices can significantly stratify cumulative stroke risk. When AIP and BRI were dichotomized and combined, participants in the High AIP & High BRI group had the lowest stroke-free survival, compared to those in the Low AIP & Low BRI group, with statistically significant differences among the groups ( P < 0.001) (Fig. 2 C). 3.3. Multivariable Cox Regression of Independent and Joint Effects of AIP and BRI on Stroke Risk We evaluated the associations of BRI, AIP, and their combined categories with stroke risk using multivariable Cox proportional hazards models (Table 2 – 3 ). After adjusting for potential confounders, both BRI and AIP, as continuous variables, were independently associated with increased stroke risk, with each 1-unit increase corresponding to a hazard ratio (HR) of 1.12 (95% CI: 1.04–1.21) and 1.55 (95% CI: 1.15–2.10), respectively (both P < 0.01). Compared with the lowest quartile (Q1), participants in the highest quartile (Q4) of BRI (HR = 1.54, 95% CI: 1.10–2.17) and AIP (HR = 1.66, 95% CI: 1.20–2.32) had significantly higher stroke risk, with significant linear trends observed for both indicators (BRI: P trend = 0.002; AIP: P trend = 0.002). More importantly, in the joint exposure analysis, compared with the Low AIP & Low BRI group, participants with elevation in either indicator alone had a moderately increased risk: High AIP & Low BRI group (HR = 1.56, 95% CI: 1.10–2.20) and Low AIP & High BRI group (HR = 1.35, 95% CI: 0.95–1.93). The highest stroke risk was observed in participants with High AIP & High BRI (HR = 1.88, 95% CI: 1.39–2.56), with a statistically significant interaction between AIP and BRI ( P interaction < 0.001). Table 2 Independent association of BRI and AIP with incident stroke risk. Model A Model B Model C HR 95% CI P value HR 95% CI P value HR 95% CI P value BRI Continuous 1.22 1.15–1.29 < 0.001 1.22 1.15–1.30 < 0.001 1.12 1.04–1.21 0.002 Q1 Reference Reference Reference Q2 1.03 0.73–1.46 0.872 1.07 0.75–1.51 0.721 0.98 0.69–1.40 0.911 Q3 1.73 1.26–2.37 < 0.001 1.84 1.34–2.53 < 0.001 1.53 1.10–2.12 0.011 Q4 2.06 1.52–2.80 < 0.001 2.18 1.58-3.00 < 0.001 1.54 1.10–2.17 0.013 P trend < 0.001 < 0.001 0.002 AIP Continuous 1.88 1.42–2.48 < 0.001 1.99 1.50–2.64 < 0.001 1.55 1.15–2.10 0.004 Q1 Reference Reference Reference Q2 1.49 1.40–3.12 0.019 1.51 1.08–2.11 0.015 1.39 0.99–1.96 0.056 Q3 1.76 1.56–3.31 0.001 1.82 1.31–2.51 < 0.001 1.63 1.17–2.27 0.004 Q4 1.98 1.53–3.38 < 0.001 2.07 1.51–2.84 < 0.001 1.66 1.20–2.32 0.002 P trend < 0.001 < 0.001 0.002 Abbreviations: CI, confidence interval; HR, hazard ratio. The associations are presented as HRs (95% CI). Model A did not adjust for any covariates. Model B adjusted for age and sex. Model C further adjusted for educational level, residence, marital status, smoking history, drinking history, hypertension, diabetes, heart disease, dyslipidemia, kidney disease, glycated hemoglobin level and CRP level based on Model B. Table 3 Joint association between BRI and AIP with incident stroke risk. Model A Model B Model C HR 95% CI P value HR 95% CI P value HR 95% CI P value Low AIP & Low BRI Reference Reference Reference High AIP & Low BRI 1.36 0.96–1.93 0.087 1.42 1.00-2.02 0.051 1.35 0.95–1.93 0.099 Low AIP & High BRI 1.81 1.30–2.51 < 0.001 1.86 1.34–2.60 < 0.001 1.56 1.10–2.20 0.012 High AIP & High BRI 2.30 1.74–3.05 < 0.001 2.43 1.82–3.24 < 0.001 1.88 1.39–2.56 < 0.001 P interaction < 0.001 < 0.001 < 0.001 Abbreviations: CI, confidence interval; HR, hazard ratio. The associations are presented as HRs (95% CI). Model A did not adjust for any covariates. Model B adjusted for age and sex. Model C further adjusted for educational level, residence, marital status, smoking history, drinking history, hypertension, diabetes, heart disease, dyslipidemia, kidney disease, glycated hemoglobin level and CRP level based on Model B. 3.4 Subgroup Analysis by Sex and Age To investigate whether the associations of AIP and BRI with stroke risk differed by sex or age, we performed subgroup analyses by constructing Kaplan–Meier survival curves and multivariable Cox regression models for each subgroup (Fig. 3 and Figures S1 –S2; Table 4 and Tables S1–S4 ). Kaplan–Meier curves based on quartiles of BRI or AIP showed clear stepwise separation across all subgroups ( Figures S1 –S2 ). The separation by BRI quartiles was most evident in male participants and middle-aged individuals (aged 45–59 years), with log-rank P values < 0.0001 for both. In contrast, separation by AIP quartiles was more pronounced in female participants and older adults (aged ≥ 60 years), with log-rank P values of 0.0036 and 0.0099, respectively. Table 4 Subgroup analysis of the joint association between BRI and AIP with incident stroke risk. Model A Model B Model C HR 95% CI P value HR 95% CI P value HR 95% CI P value Male adults Low AIP & Low BRI Reference Reference Reference High AIP & Low BRI 1.22 0.76–1.95 0.406 1.30 0.81–2.09 0.269 1.27 0.78–2.04 0.336 Low AIP & High BRI 2.65 1.67–4.19 < 0.001 2.56 1.62–4.06 < 0.001 2.08 1.28–3.38 0.003 High AIP & High BRI 2.53 1.70–3.75 < 0.001 2.66 1.79–3.96 < 0.001 2.17 1.41–3.33 < 0.001 P interaction < 0.001 < 0.001 < 0.001 Female adults Low AIP & Low BRI Reference Reference Reference High AIP & Low BRI 1.56 0.92–2.67 0.101 1.57 0.92–2.68 0.097 1.44 0.84–2.47 0.188 Low AIP & High BRI 1.50 0.93–2.42 0.096 1.42 0.88–2.30 0.150 1.20 0.74–1.96 0.460 High AIP & High BRI 2.25 1.48–3.42 < 0.001 2.14 1.40–3.26 < 0.001 1.62 1.05–2.51 0.031 P interaction < 0.001 < 0.001 0.049 Middle-aged adults Low AIP & Low BRI Reference Reference Reference High AIP & Low BRI 1.26 0.73–2.15 0.407 1.24 0.72–2.12 0.435 1.11 0.63–1.93 0.723 Low AIP & High BRI 1.96 1.19–3.23 0.008 2.13 1.29–3.54 0.003 1.77 1.04–2.99 0.034 High AIP & High BRI 2.71 1.78–4.13 < 0.001 2.89 1.89–4.42 < 0.001 2.02 1.27–3.21 0.003 P interaction < 0.001 < 0.001 0.002 Older adults Low AIP & Low BRI Reference Reference Reference High AIP & Low BRI 1.62 1.00-2.62 0.052 1.63 1.00-2.64 0.050 1.60 0.98–2.60 0.060 Low AIP & High BRI 1.71 1.09–2.68 0.020 1.78 1.12–2.83 0.014 1.50 0.93–2.41 0.095 High AIP & High BRI 2.08 1.39–3.09 < 0.001 2.17 1.43–3.27 < 0.001 1.90 1.24–2.92 0.003 P interaction < 0.001 < 0.001 0.011 Abbreviations: CI, confidence interval; HR, hazard ratio. The associations are presented as HRs (95% CI). Model A did not adjust for any covariates. Model B adjusted for age and sex. Model C further adjusted for educational level, residence, marital status, smoking history, drinking history, hypertension, diabetes, heart disease, dyslipidemia, kidney disease, glycated hemoglobin level and CRP level based on Model B. Figures and Figure legends Cox regression results were consistent with the visual trends. Among male participants (HR = 1.27, 95% CI: 1.12–1.45; Table S1 ) and middle-aged participants (HR = 1.17, 95% CI: 1.04–1.32; Table S3 ), each 1-unit increase in BRI was significantly associated with elevated stroke risk. Among women and older adults, the association between AIP and stroke risk did not reach statistical significance (female participants: HR = 1.47, 95% CI: 0.97–2.25, Table S2 ; older adults: HR = 1.55, 95% CI: 1.00–2.41, Table S4 ). The joint association of AIP and BRI further demonstrated an additive effect across all subgroups. Compared with those in the Low AIP & Low BRI group, individuals in the High AIP & High BRI group exhibited higher stroke risks in all subgroups (male participants: HR = 2.17, 95% CI: 1.41–3.33; female participants: HR = 1.62, 95% CI: 1.05–2.51; middle-aged adults: HR = 2.02, 95% CI: 1.27–3.21; older adults: HR = 1.90, 95% CI: 1.24–2.92). 3.5 Mediation analysis In the mediation analysis, BRI, AIP and stroke were respectively regarded as independent variables, mediating variables and dependent variables. The mediation model and path are shown in Fig. 4 . The research results show that there is a significant association between BRI and AIP (β = 0.0711, P < 0.001), and there is also a significant correlation between AIP and stroke (β = 0.0163, P = 0.042). Further analysis indicated that BRI had a significant indirect effect on stroke through AIP (indirect effect = 0.0012, P = 0.046). This indicates that AIP plays a partial mediating role in the association between BRI and stroke, accounting for approximately 17.80% of the total effect. 4. Discussion In this nationwide prospective cohort study of middle-aged and older adults, we evaluated for the first time the independent and joint associations of AIP and BRI with the risk of incident stroke. The results indicated that both AIP and BRI were positively associated with stroke risk. Further joint analysis revealed that the risk of stroke was highest when both indices were elevated, significantly exceeding the risk associated with an elevation in either index alone. This additive effect was particularly evident in male participants and those aged 45–59 years. Of note, AIP partially mediated the association between BRI and new-onset stroke. Our study confirmed that the BRI is an independent and stable indicator of central obesity that can predict incident stroke. After adjusting for potential confounders, each 1-unit increase in BRI was associated with a 12% increase in stroke risk; similar to our results, a cross-sectional analysis based on a US cohort showed that each 1-unit increase in BRI was associated with a 5.7% increase in stroke prevalence [ 10 ]. It is noteworthy that our study adopted a prospective longitudinal design, evaluating the risk of stroke incidence rather than past stroke prevalence. This design minimizes the possibility of reverse causation, thereby making the observed effect size more reflective of the true impact of BRI on stroke development. In line with these findings, a cohort study conducted in Italy involving 468 hypertensive patients found that BRI was independently associated with carotid intima–media thickness (cIMT), and that increased cIMT could predict future stroke events [ 29 ]. These findings confirm that the predictive value of BRI for stroke risk holds true across different populations. BRI reflects visceral fat accumulation, which may contribute to stroke development through three key pathways: inflammation activation [ 30 ], hypercoagulability [ 31 ], and blood pressure variability [ 32 ]. Dysregulated lipid metabolism plays a central role throughout these processes and represents a critical link in the onset and progression of stroke. Our study further confirmed that individuals with elevated baseline AIP levels were more likely to develop stroke during follow-up. This finding is consistent with previous population-based studies. For example, a prospective cohort study in a Korean community population showed that higher cumulative AIP was significantly associated with increased risk of ischemic stroke[ 26 ]. Another prospective analysis based on a UK cohort reported that individuals with sustained high or increasing AIP levels had significantly elevated risk of CVD [ 25 ]. AIP may contribute to stroke onset and progression by regulating lipid deposition [ 33 ] and inflammatory responses [ 34 ]. Overall, AIP complements the mechanism of abdominal obesity indicated by BRI from the perspective of lipid metabolism, providing new evidence for understanding stroke risk factors. Another key finding of our study is that stroke risk was the highest when both AIP and BRI were elevated, exceeding the risk associated with either index alone, which suggests a potential additive effect. A cross-sectional analysis from a US cohort demonstrated that AIP mediated 10–15% of the effect of BRI on cardiovascular events, supporting a sequential pathway from fat distribution imbalance to dyslipidemia and vascular damage [ 21 ]. Our study provided the first evidence on this mediation from the longitudinal perspective. Notably, a previous study explored the combined predictive value of BRI and TyG for stroke [ 12 ], whereas our study focused on the joint effect of AIP and BRI from the dual dimensions of dyslipidemia and visceral adiposity, and was the first to evaluate their combined predictive value for stroke risk in a national longitudinal sample. In terms of potential mechanisms, on one hand, mobilization of visceral fat releases large amounts of free fatty acids into the liver, promoting VLDL/TG synthesis and inhibiting HDL production, thereby increasing AIP levels [ 35 ]; on the other hand, abdominal obesity is associated with increased secretion of TNF-α, IL-6, and PAI-1, contributing to chronic inflammation and a prothrombotic state [ 36 ]. Thus, when both AIP and BRI are elevated, individuals are simultaneously affected by dysregulated lipid accumulation, inflammation, and hemodynamic stress, leading to significantly increased stroke risk. In addition, AIP was more strongly associated with stroke risk in females, while BRI showed a stronger association in males. The additive effect of the two indices was observed in both sexes but was more pronounced in males and those aged 45–59 years. After menopause, the rapid decline in estrogen among females leads to reduced HDL-C and elevated TG levels, increasing sensitivity to dyslipidemia [ 37 ]. In contrast, men tend to accumulate more visceral fat, which increases secretion of free fatty acids, IL-6, and PAI-1, thereby amplifying the impact of BRI [ 31 ]. Recent research has also confirmed that ages 45–59 represent a high-risk window for accelerated accumulation of abdominal fat, increased triglycerides, and decreased HDL-C [ 38 , 39 ]. Individuals aged 60 years or above without stroke may already have undergone natural selection against high-risk phenotypes. Therefore, combined assessment of AIP and BRI may optimize stroke risk screening in middle-aged adults. Of note, our mediation analysis indicates that AIP partially mediates the association between visceral obesity (captured by BRI) and stroke events. This suggests that monitoring AIP in patients with high BRI is clinically relevant. Biologically, excessive visceral fat promotes insulin resistance and a pro-inflammatory environment in the liver [ 40 ], leading to overproduction of TG-rich lipoproteins, reduction/alteration of HDL-C, and formation of small dense LDL and residual cholesterol [ 41 ]. This atherosclerotic profile (summarized by AIP) accelerates endothelial dysfunction, oxidative stress, and pre-thrombotic state, thereby increasing cerebral vascular vulnerability [ 42 ]. Clinically, lowering TG levels and enhancing HDL functionality to reduce AIP may help mitigate stroke risk in individuals with elevated BRI. This study is a nationally representative prospective longitudinal cohort, which avoids the issues of reverse causation and selection bias inherent to cross-sectional studies, thus improving the credibility of risk estimates. Moreover, multiple confounding factors were adjusted for in the statistical analyses. However, there are still some limitations: stroke outcomes were primarily self-reported or obtained from community follow-up, lacking standardized imaging-based confirmation; although numerous covariates were adjusted, residual confounding from factors such as diets and genetics cannot be ruled out; AIP and BRI were only measured at baseline, which may underestimate the predictive value of their cumulative exposure and trajectories. Future studies should incorporate imaging validation, longitudinal biomarker trajectories, and multi-omics data to further elucidate the mechanisms and validate our findings. 5. Conclusion AIP and BRI are significant independent predictors of stroke risk longitudinally. Their combination significantly enhances risk stratification, and AIP further mediated the association between BRI and stroke. These findings provide elementary evidence for early stroke identification and targeted intervention strategies, supporting strengthened primary prevention efforts. Abbreviations AIP Atherogenic index of plasma BRI Body roundness index CHARLS China Health and Retirement Longitudinal Study CI Confidence Interval CRP C-reactive protein HDL-C High-density lipoprotein cholesterol LDL-C Low-density lipoprotein cholesterol TC Total cholesterol TG Triglyceride Declarations Supplementary Information The following additional files accompany this manuscript. Each additional file is cited at the relevant place in the text. Ethics approval and consent to participate All procedures performed in studies involving human participants were in accordance with the Declaration of Helsinki. The original CHARLS was approved by the Ethical Review Committee of Peking University, and all the participants from CHARLS provided signed informed consent. Consent for publication The publication of this manuscript has been authorized by all authors. Competing interests The authors declare no competing interests. Clinical trial number Not applicable. Funding This study was supported by the National Natural Science Foundation of China (No. 82171330 to GX), the Shenzhen High-level Hospital Construction Fund (No. 4004013 to GX), and the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515010732 to GX). Author Contribution XGL and LR designed the study. WZY, ZYH and SRR collected the data, analyzed the data, and drafted manuscript. WZY wrote specific sections of the manuscript. WZY, ZYH, SRR, CYX, LR and XGL revised the manuscript. All authors reviewed and approved the final version of manuscript. Acknowledgement We express sincere appreciation to all the members who involved in CHARLS. Data Availability The datasets analyzed in this study are from the CHARLS repository. The data are publicly available to registered users upon approval by the CHARLS team and can be requested at the CHARLS website (https:/charls.pku.edu.cn/en) . References Sacco RL, Kasner SE, Broderick JP, Caplan LR, Connors JJ, Culebras A, et al. An updated definition of stroke for the 21st century: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2013;44 7:2064–89. 10.1161/STR.0b013e318296aeca . Global regional. Lancet Neurol. 2024;23 10:973–1003. 10.1016/s1474-4422(24)00369-7 . and national burden of stroke and its risk factors, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. 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Supplementary Files 3SupplementaryMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor invited by journal 10 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 29 Aug, 2025 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-7488889","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531800213,"identity":"ca1f0b08-5340-4cac-9edc-571406525984","order_by":0,"name":"Zhuoyu Wen","email":"","orcid":"","institution":"The First Affiliated Hospital of Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Shenzhen University Medical School, Shenzhen 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19:24:14","extension":"jpg","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":345303,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/68dbd0c234cfe0d06d485553.jpg"},{"id":94139269,"identity":"b7e09795-d1ea-47a3-8d73-c45fdb05490c","added_by":"auto","created_at":"2025-10-22 19:32:14","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":213004,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/49759c3d829fa8b02a37a8f8.png"},{"id":94139265,"identity":"04a0d272-1e1b-432c-aa69-10ac8e2f2db3","added_by":"auto","created_at":"2025-10-22 19:32:14","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41209,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/7d08d6ebe0bd561d001b5448.png"},{"id":94138262,"identity":"2eca3d1c-24dd-4af2-9f35-99ad76f1e9c1","added_by":"auto","created_at":"2025-10-22 19:24:14","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":90530,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/b04385283e6db0431fffec8d.png"},{"id":94138257,"identity":"a8c11f58-9e8b-4fa7-b606-81d030863cf2","added_by":"auto","created_at":"2025-10-22 19:24:14","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82846,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/45ed146412cd885d9ec1f517.png"},{"id":94138265,"identity":"d799273b-531c-4ba2-94b6-584de0f09c7d","added_by":"auto","created_at":"2025-10-22 19:24:14","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":178688,"visible":true,"origin":"","legend":"","description":"","filename":"f4a233a147a645cab44d266f206ec8831structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/b0e50bc60b0105fe9a646115.xml"},{"id":94138264,"identity":"3e4983de-0a32-48a6-80d5-c656427f2f3f","added_by":"auto","created_at":"2025-10-22 19:24:14","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":190456,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/117fdf87649c8f3b40438997.html"},{"id":94138247,"identity":"11061eb8-b83c-45a2-9f2c-e9e8c26d18ad","added_by":"auto","created_at":"2025-10-22 19:24:14","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":203907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of participants' selection\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/d446c0247c4bf4a0ffce48c9.jpeg"},{"id":94138245,"identity":"588014d7-6218-48a3-8943-39bf53ac02ac","added_by":"auto","created_at":"2025-10-22 19:24:14","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":190241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier curves for incident stroke stratified by BRI, AIP, and their combination in the overall population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan–Meier survival curves showing the cumulative stroke-free probability according to (A) Body Roundness Index (BRI) quartiles, (B) Atherogenic Index of Plasma (AIP) quartiles, and (C) the combined classification of AIP and BRI among individuals aged 45 years and older. Participants were grouped into quartiles based on BRI and AIP levels, respectively. For the combined stratification (C), four categories were defined: Low AIP \u0026amp; Low BRI, High AIP \u0026amp; Low BRI, Low AIP \u0026amp; High BRI, and High AIP \u0026amp; High BRI.\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/1ad799c1667a8a5aae8ece43.jpeg"},{"id":94139264,"identity":"9913312f-3a9d-481b-9d69-20c9352a46f5","added_by":"auto","created_at":"2025-10-22 19:32:14","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":232838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan–Meier curves for incident stroke stratified by AIP\u0026amp;BRI in the subgroups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan–Meier survival curves showing cumulative stroke-free probability among adults in different subgroups, stratified by combined Body Roundness Index (BRI) and Atherogenic Index of Plasma (AIP) categories.\u003cbr\u003e\n(A) Male participants; (B) Female participants; (C) Participants aged 45–59 years; (D) Participants aged \u003cstrong\u003e≥ \u003c/strong\u003e60 years\u003c/p\u003e","description":"","filename":"Figure3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/f8cec969bebfbf77ef094f24.jpeg"},{"id":94138249,"identity":"10a1dd23-2227-4d1d-8408-965197d4985c","added_by":"auto","created_at":"2025-10-22 19:24:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":164560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAIP mediated the association between BRI and stroke risk\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/326936b16c11c4936b732f37.jpg"},{"id":94140817,"identity":"f98af73d-bbd9-419f-baef-fece893977cc","added_by":"auto","created_at":"2025-10-22 19:48:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2693524,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/207e408f-957e-4fc3-b8d8-4ae127807369.pdf"},{"id":94139263,"identity":"01508183-3bb6-403a-9bba-c703039e1574","added_by":"auto","created_at":"2025-10-22 19:32:14","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":832460,"visible":true,"origin":"","legend":"","description":"","filename":"3SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7488889/v1/280f2e23dee54ca7b41ec971.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Joint association of Body Roundness Index and Atherogenic Index of Plasma with new-onset stroke in middle-aged and older adults: first evidence from the China Health and Retirement Longitudinal Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eStroke is defined as a focal or diffuse neurological dysfunction caused by cerebrovascular insufficiency, with symptoms lasting more than 24 hours or resulting in death [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is currently the fourth leading cause of disability and death worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With global population aging and the increasing prevalence of metabolic disorders, the incidence of stroke has shown an exponential upward trend in recent years [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eObesity is a key risk factor for the onset and progression of stroke. A meta-analysis of 24 studies found that higher Body Mass Index (BMI), particularly in overweight or obese individuals, was significantly associated with an increased risk of stroke [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Studies have shown that for every unit increase in BMI, the risk of ischemic stroke increases by approximately 5% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, BMI estimates body composition solely based on height and weight, without distinguishing between fat and muscle mass or identifying fat distribution in the body [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which makes BMI inadequate for accurately reflecting visceral fat levels [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In recent years, the Body Roundness Index (BRI) has emerged as a precise indicator of visceral adiposity relative to total body fat [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous studies have shown that elevated BRI is closely linked to increased cardiovascular disease risk [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A cross-sectional study in a US cohort revealed a non-linear positive association between BRI and stroke [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A prospective study involving Chinese and UK populations confirmed the predictive value of BRI for both ischemic and hemorrhagic strokes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Nevertheless, prior studies have mainly focused on the independent effect of abdominal obesity on stroke risk, and few have systematically evaluated the combined effects of abdominal obesity and other metabolic or clinical indicators. Exploring the interactions between multiple risk factors is essential for improving risk stratification and stroke prediction in specific populations. A recent Chinese cohort study demonstrated for the first time that combining BRI with the triglyceride-glucose index (TyG) significantly improves the predictive value for new-onset stroke events, compared with BRI alone [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eApart from BRI, the Atherogenic Index of Plasma (AIP) is another independent risk factor for stroke [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. AIP is a sensitive marker derived from the plasma lipid profile, reflecting lipid imbalance [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Compared to individual lipid indices, AIP is more sensitive to the extent of atherosclerosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. As a robust biomarker of dyslipidemia, AIP has been identified as a strong independent predictor of adverse cardiovascular and cerebrovascular outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and is closely associated with cardiovascular disease (CVD) prognosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Emerging evidence indicates that AIP may operate as a metabolic intermediary linking visceral adiposity (captured by BRI) to downstream health outcomes. In a cross-sectional study of American population, AIP partially mediated the BRI\u0026ndash;depression association, accounting for 8.64% of the total effect [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Another cross-sectional study in the US found that AIP has a significant indirect influence on the BRI\u0026ndash;CVD pathway [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These studies support that AIP lies on the causal pathway from adiposity to cardiometabolic and neuropsychiatric outcomes. However, longitudinal studies are limited regarding the interplay between BRI, AIP and CVD.\u003c/p\u003e\u003cp\u003eWhile AIP reflects lipid-related atherogenic imbalance, BRI primarily assesses visceral adiposity. Although theoretically the two may interact [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], no study has investigated the joint association of AIP and BRI with incident stroke. Therefore, to fill this gap, the present study aims to investigate the combined association of baseline AIP and BRI levels with new-onset stroke in a nationally representative cohort of middle-aged and older Chinese adults, using a longitudinal study design. We hypothesize that co-occurrence of high AIP and high BRI will significantly elevate stroke risk. We further investigated the mediating role of AIP in the association between BRI and new-onset stroke. This study may provide additional evidence for stroke risk stratification and shed light for targeted intervention for stroke in the aging population.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and participants\u003c/h2\u003e\u003cp\u003eThis study adopted a prospective cohort design. All data were derived from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative survey aimed at assessing the economic, social, and health conditions of the middle-aged and elderly population in China [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The CHARLS cohort was established through multi-stage probability sampling, selecting participants from 450 communities across 150 counties in 28 provinces. The baseline survey was conducted between June 2011 and March 2012, including individuals aged 45 years and older. Data were collected via face-to-face interviews using standardized questionnaires, and participants were followed up biennially. Ethical approval for the CHARLS project was granted by the Ethics Committee of Peking University (IRB00001052-11015). All participants provided written informed consent prior to enrollment. The relevant datasets and accompanying documentation used in this study are publicly available on the CHARLS website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://charls.pku.edu.cn/en/\u003c/span\u003e\u003cspan address=\"https://charls.pku.edu.cn/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study utilized data from the 2011 baseline and the 2018 follow-up surveys. A total of 17,707 participants were interviewed at baseline (2011\u0026ndash;2012). 10,358 individuals were excluded for meeting one or more of the following criteria: aged\u0026thinsp;\u0026lt;\u0026thinsp;45 years or missing data on age; lacking data on the AIP or BRI components; missing stroke data at baseline or lost to follow-up; history of stroke diagnosis at baseline. After exclusions, 7,349 participants without a history of stroke at baseline were involved in our data analysis. Participants were followed up until 2018 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Exposure Variables\u003c/h2\u003e\u003cp\u003eThe exposure variables in this study were AIP and BRI. AIP was calculated as: AIP\u0026thinsp;=\u0026thinsp;log(TG/HDL-C), where triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) were both measured in mg/dL [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. BRI was calculated using the following formula:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:BRI=364.2-365.5\\times\\:\\sqrt{1-{\\left(\\frac{\\frac{wc}{2\\varvec{\\pi\\:}}}{0.5\\times\\:height}\\right)}^{2}}\\:\\)\u003c/span\u003e\u003c/span\u003e, where WC (waist circumference) and height were both measured in centimeters [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. We calculated the quartiles for AIP and BRI; then, a joint variable was dichotomized based on the median values of two indicators (AIP\u0026thinsp;=\u0026thinsp;0.32, BRI\u0026thinsp;=\u0026thinsp;3.24), and four combined exposure groups were constructed (Low AIP \u0026amp; Low BRI, High AIP \u0026amp; Low BRI, Low AIP \u0026amp; High BRI, High AIP \u0026amp; High BRI).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Outcome Assessment\u003c/h2\u003e\u003cp\u003eThe primary outcome was the incidence of new-onset stroke during the follow-up period. Participants without a history of stroke at baseline who subsequently reported a physician-diagnosed stroke during follow-up were defined as incident stroke cases [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The following question was used to assess physician-diagnosed stroke events: \u0026ldquo;Have you ever been diagnosed with stroke by a doctor?\u0026rdquo;; the timing of the stroke event was determined based on participants\u0026rsquo; responses to related questions: \u0026ldquo;When were you first diagnosed with or became aware of having had a stroke?\u0026rdquo; or \u0026ldquo;When was your most recent stroke?\u0026rdquo;. All participants were followed from baseline, and those who provided a positive response to the stroke diagnosis question during the 2018 follow-up were classified as having experienced a stroke. The time of stroke onset was determined by calculating the interval between the baseline assessment and the reported time of diagnosis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Covariates\u003c/h2\u003e\u003cp\u003eCovariates were collected through structured interviews conducted by trained personnel, physical examination, or laboratory tests. Based on previous studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the covariates were categorized into the following domains: sociodemographic variables including age, sex, residential location, marital status, and education level; lifestyle factors including smoking and alcohol consumption; anthropometric measures including systolic blood pressure (SBP), diastolic blood pressure (DBP); physician-reported diagnoses of diseases (diabetes, hypertension, hyperlipidemia, kidney disease, and heart disease); medications: use of antidiabetic, antihypertensive, and lipid-lowering therapies; laboratory parameters: C-reactive protein (CRP), glycated hemoglobin A1c (HbA1c), fasting plasma glucose, serum creatinine, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and uric acid (UA). Participants undergoing blood tests were required to fast overnight. Venous blood samples were collected by medically trained personnel, stored at 4\u0026deg;C immediately, and transported to the central laboratory in Beijing within two weeks. HbA1c was measured using high-performance liquid chromatography (HPLC) based on boronate affinity methods [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\u003cp\u003eContinuous variables with normal distribution were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD), while non-normally distributed variables were expressed as medians and interquartile ranges (IQR). Categorical variables were described using frequencies and percentages. Group comparisons at baseline were conducted using independent sample t-tests or Mann\u0026ndash;Whitney U tests for continuous variables, and Chi-square tests for categorical variables. We used Kaplan\u0026ndash;Meier survival curves to estimate differences in cumulative stroke incidence across populations with different levels of BRI and AIP. The log-rank test was used to evaluate group differences. Based on the distribution of participants\u0026rsquo; BRI and AIP levels, cumulative stroke risk was compared among the combined exposure groups. Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of BRI and AIP with incident stroke during the follow-up period. Three models were constructed: Model A was unadjusted; Model B was adjusted for age and sex; and Model C was further adjusted for education level, residence, marital status, smoking and alcohol consumption, history of hypertension, diabetes, heart disease, dyslipidemia, kidney disease, HbA1c, and CRP levels based on Model B. To evaluate the combined effect of BRI and AIP on cumulative stroke risk, participants with Low AIP \u0026amp; Low BRI were used as the reference group, and stroke incidence was compared across all different joint exposure categories. The above Cox models were used to examine the joint association between AIP \u0026amp; BRI and stroke risk. To explore potential moderating effect by sex and age, stratified Cox regression analyses were performed by sex (male vs. female) and age group (45\u0026ndash;59 years vs. \u0026ge;60 years). Then, a two-step mediating effect analysis was used to evaluate the mediating role of AIP. Firstly, a fully adjusted regression model was adopted to explore the influence of BRI on AIP and the influence of AIP on stroke, aiming to determine the possibility of AIP as a mediating factor between BRI and stroke. Subsequently, the R \u0026ldquo;mediation\u0026rdquo; package was used for mediating effect analysis to evaluate the indirect, direct and total effects of BRI on stroke, with AIP as the mediator [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. After dividing the indirect effect by the total effect, determine the percentage of the mediating effect mediated by AIP. The 95% CI of the indirect effect was estimated through a non-parametric Bootstrap method with 1000 iterations. All statistical analyses were conducted using R software (version 4.4.1), and Cox regression models were implemented using the R \u0026ldquo;survival\u0026rdquo; package. A two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Characteristics of the Study Population\u003c/h2\u003e\u003cp\u003eA total of 7,349 participants were included in the final analysis. The mean age was 58.55\u0026thinsp;\u0026plusmn;\u0026thinsp;8.78 years, and 44.8% were male. During the follow-up period, 358 participants (4.87%) were identified as having new-onset stroke. Compared with the non-stroke group, participants in the stroke group were older and had significantly higher prevalence of hypertension, diabetes, dyslipidemia, and heart disease (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the stroke group showed significantly higher levels of systolic blood pressure, diastolic blood pressure, fasting plasma glucose, HbA1c, triglycerides, total cholesterol, and creatinine (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The stroke group also had significantly higher AIP (0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 vs. 0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and BRI (4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71 vs. 4.23\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) levels than the non-stroke group. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of study participants (N\u0026thinsp;=\u0026thinsp;7349).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\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\" colname=\"c2\"\u003e\u003cp\u003eTotal participants\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eNon-stroke\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;7349)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;358)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;6991)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, y, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e58.55 (8.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e61.13 (8.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e58.41 (8.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group, n(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[45,59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4237 (60.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e165 (47.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e4072 (60.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2798 (39.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e180 (52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e2618 (39.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.817\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3290 (44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e163 (45.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3127 (44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4054 (55.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e195 (54.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e3859 (55.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational level, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIlliteracy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2084 (28.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e110 (30.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e1974 (28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school and below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3046 (41.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e155 (43.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e2891 (41.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJunior high school and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2217 (30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e93 (26.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e2124 (30.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResidency, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgricultural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6215 (85.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e301 (85.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e5914 (85.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Agricultural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1098 (15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e53 (15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e1045 (15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.009**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e790 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e54 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e736 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6559 (89.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e304 (84.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e6255 (89.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoker, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4584 (62.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e209 (58.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e4375(62.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\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\u003e2765 (37.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e149 (41.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e2616 (37.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlcohol user, n (%)\u003c/b\u003e\u003c/p\u003e\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\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4937 (67.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e235 (65.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4702 (67.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2412 (32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123 (34.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2289 (32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSBP, mmHg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e129.03 (20.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003e138.40 (22.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e128.56 (19.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDBP, mmHg\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e75.19 (11.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003e79.16 (11.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e74.99 (11.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes mellitus, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e6906 (94.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e318 (89.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6588 (95.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\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\u003e376 (5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e340 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension, n (%)\u003c/b\u003e\u003c/p\u003e\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\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e5636 (77.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e201 (56.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5435 (78.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1673 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e155 (43.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1518 (21.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDyslipidemia, n (%)\u003c/b\u003e\u003c/p\u003e\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\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e6589 (91.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e298 (84.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6291 (91.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\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\u003e612 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e559 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKidney disease, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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\u003cp\u003e0.694\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e6847 (93.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e331 (93.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6516 (93.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\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\u003e467 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e442 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeart disease, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e6520 (89.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e288 (81.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6232 (89.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\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\u003e798 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e731 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCRP, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.43 (6.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.26 (10.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.38 (6.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHbA1c, %, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.26 (0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.42 (0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.25 (0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGlucose, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e109.60 (35.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003e117.36 (45.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e109.20 (34.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.001**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCreatinine, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.77 (0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79 (0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.77 (0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.029*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTC, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e194.05 (38.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003e198.88 (37.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e193.80 (38.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.014*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTG, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e133.98 (113.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003e150.18 (121.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e133.15 (113.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.010*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHDL-C, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e51.35 (15.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003e48.94 (14.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e51.48 (15.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.002**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLDL-C, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e116.73 (34.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003e119.85 (37.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e116.57 (34.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUA, mg/dL, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4.40 (1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.51 (1.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.40 (1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAIP, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.35 (0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43 (0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.35 (0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAIP category, n (%)\u003c/b\u003e\u003c/p\u003e\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\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1836 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1778 (25.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1836 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86 (24.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1752 (25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1836 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101 (28.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1735 (24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1836 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113 (31.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1726 (24.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBRI, mean (SD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.26 (1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.70 (1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.23 (1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBRI category, n (%)\u003c/b\u003e\u003c/p\u003e\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\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1833 (24.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1771 (25.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1841 (25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64 (17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1777 (25.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1836 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106 (29.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1730 (24.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1839 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126 (35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1713 (24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: SBP, Systolic blood pressure; DBP, Diastolic blood pressure; CRP, C-reactive protein; HbA1c, Glycated hemoglobin; TC, Total cholesterol; TG, Triglyceride; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; UA, uric acid; AIP, atherogenic index of plasma; BRI, body roundness index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Kaplan\u0026ndash;Meier Analysis Stratified by AIP, BRI, and Their Combined Categories\u003c/h2\u003e\u003cp\u003eKaplan\u0026ndash;Meier survival curves were constructed to compare cumulative stroke incidence across different levels of AIP, BRI, and their combined categories. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, the stroke-free survival curves were clearly separated in a stepwise pattern across quartiles of AIP and BRI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that both indices can significantly stratify cumulative stroke risk. When AIP and BRI were dichotomized and combined, participants in the High AIP \u0026amp; High BRI group had the lowest stroke-free survival, compared to those in the Low AIP \u0026amp; Low BRI group, with statistically significant differences among the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Multivariable Cox Regression of Independent and Joint Effects of AIP and BRI on Stroke Risk\u003c/h2\u003e\u003cp\u003eWe evaluated the associations of BRI, AIP, and their combined categories with stroke risk using multivariable Cox proportional hazards models (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After adjusting for potential confounders, both BRI and AIP, as continuous variables, were independently associated with increased stroke risk, with each 1-unit increase corresponding to a hazard ratio (HR) of 1.12 (95% CI: 1.04\u0026ndash;1.21) and 1.55 (95% CI: 1.15\u0026ndash;2.10), respectively (both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Compared with the lowest quartile (Q1), participants in the highest quartile (Q4) of BRI (HR\u0026thinsp;=\u0026thinsp;1.54, 95% CI: 1.10\u0026ndash;2.17) and AIP (HR\u0026thinsp;=\u0026thinsp;1.66, 95% CI: 1.20\u0026ndash;2.32) had significantly higher stroke risk, with significant linear trends observed for both indicators (BRI: \u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;=\u0026thinsp;0.002; AIP: \u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;=\u0026thinsp;0.002). More importantly, in the joint exposure analysis, compared with the Low AIP \u0026amp; Low BRI group, participants with elevation in either indicator alone had a moderately increased risk: High AIP \u0026amp; Low BRI group (HR\u0026thinsp;=\u0026thinsp;1.56, 95% CI: 1.10\u0026ndash;2.20) and Low AIP \u0026amp; High BRI group (HR\u0026thinsp;=\u0026thinsp;1.35, 95% CI: 0.95\u0026ndash;1.93). The highest stroke risk was observed in participants with High AIP \u0026amp; High BRI (HR\u0026thinsp;=\u0026thinsp;1.88, 95% CI: 1.39\u0026ndash;2.56), with a statistically significant interaction between AIP and BRI (\u003cem\u003eP\u003c/em\u003e interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eIndependent association of BRI and AIP with incident stroke risk.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModel C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\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\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR\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\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR\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\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15\u0026ndash;1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.15\u0026ndash;1.30\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\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.04\u0026ndash;1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73\u0026ndash;1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.75\u0026ndash;1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.69\u0026ndash;1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.911\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.26\u0026ndash;2.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.34\u0026ndash;2.53\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\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.10\u0026ndash;2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.52\u0026ndash;2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.58-3.00\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\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.10\u0026ndash;2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e trend\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAIP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.42\u0026ndash;2.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.50\u0026ndash;2.64\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\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.15\u0026ndash;2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.40\u0026ndash;3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.08\u0026ndash;2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.99\u0026ndash;1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.56\u0026ndash;3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.31\u0026ndash;2.51\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\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.17\u0026ndash;2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.53\u0026ndash;3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.51\u0026ndash;2.84\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\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.20\u0026ndash;2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e trend\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: CI, confidence interval; HR, hazard ratio. The associations are presented as HRs (95% CI). Model A did not adjust for any covariates. Model B adjusted for age and sex. Model C further adjusted for educational level, residence, marital status, smoking history, drinking history, hypertension, diabetes, heart disease, dyslipidemia, kidney disease, glycated hemoglobin level and CRP level based on Model B.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\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\u003eJoint association between BRI and AIP with incident stroke risk.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModel C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\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\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR\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\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR\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\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow AIP \u0026amp; Low BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh AIP \u0026amp; Low BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96\u0026ndash;1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00-2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.95\u0026ndash;1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow AIP \u0026amp; High BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.30\u0026ndash;2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.34\u0026ndash;2.60\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\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.10\u0026ndash;2.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh AIP \u0026amp; High BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.74\u0026ndash;3.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.82\u0026ndash;3.24\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\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.39\u0026ndash;2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e interaction\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: CI, confidence interval; HR, hazard ratio. The associations are presented as HRs (95% CI). Model A did not adjust for any covariates. Model B adjusted for age and sex. Model C further adjusted for educational level, residence, marital status, smoking history, drinking history, hypertension, diabetes, heart disease, dyslipidemia, kidney disease, glycated hemoglobin level and CRP level based on Model B.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Subgroup Analysis by Sex and Age\u003c/h2\u003e\u003cp\u003eTo investigate whether the associations of AIP and BRI with stroke risk differed by sex or age, we performed subgroup analyses by constructing Kaplan\u0026ndash;Meier survival curves and multivariable Cox regression models for each subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eand Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u0026ndash;S2;\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eand Tables S1\u0026ndash;S4\u003c/b\u003e). Kaplan\u0026ndash;Meier curves based on quartiles of BRI or AIP showed clear stepwise separation across all subgroups (\u003cb\u003eFigures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u0026ndash;S2\u003c/b\u003e). The separation by BRI quartiles was most evident in male participants and middle-aged individuals (aged 45\u0026ndash;59 years), with log-rank \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for both. In contrast, separation by AIP quartiles was more pronounced in female participants and older adults (aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years), with log-rank \u003cem\u003eP\u003c/em\u003e values of 0.0036 and 0.0099, respectively.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSubgroup analysis of the joint association between BRI and AIP with incident stroke risk.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModel C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\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\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR\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\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR\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\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale adults\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow AIP \u0026amp; Low BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh AIP \u0026amp; Low BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.76\u0026ndash;1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.81\u0026ndash;2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.78\u0026ndash;2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.336\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow AIP \u0026amp; High BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.67\u0026ndash;4.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.62\u0026ndash;4.06\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\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.28\u0026ndash;3.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh AIP \u0026amp; High BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.70\u0026ndash;3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.79\u0026ndash;3.96\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\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.41\u0026ndash;3.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e interaction\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale adults\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow AIP \u0026amp; Low BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh AIP \u0026amp; Low BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.92\u0026ndash;2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.92\u0026ndash;2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.84\u0026ndash;2.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow AIP \u0026amp; High BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.93\u0026ndash;2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.88\u0026ndash;2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.74\u0026ndash;1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh AIP \u0026amp; High BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.48\u0026ndash;3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.40\u0026ndash;3.26\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\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.05\u0026ndash;2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e interaction\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMiddle-aged adults\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow AIP \u0026amp; Low BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh AIP \u0026amp; Low BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.73\u0026ndash;2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.72\u0026ndash;2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.63\u0026ndash;1.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.723\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow AIP \u0026amp; High BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.19\u0026ndash;3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.29\u0026ndash;3.54\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\u003e1.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.04\u0026ndash;2.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh AIP \u0026amp; High BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.78\u0026ndash;4.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.89\u0026ndash;4.42\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\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.27\u0026ndash;3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e interaction\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOlder adults\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow AIP \u0026amp; Low BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\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\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh AIP \u0026amp; Low BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00-2.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00-2.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.98\u0026ndash;2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow AIP \u0026amp; High BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09\u0026ndash;2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.12\u0026ndash;2.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.93\u0026ndash;2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh AIP \u0026amp; High BRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.39\u0026ndash;3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003e1.43\u0026ndash;3.27\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\u003e1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.24\u0026ndash;2.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e interaction\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\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\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviations: CI, confidence interval; HR, hazard ratio. The associations are presented as HRs (95% CI). Model A did not adjust for any covariates. Model B adjusted for age and sex. Model C further adjusted for educational level, residence, marital status, smoking history, drinking history, hypertension, diabetes, heart disease, dyslipidemia, kidney disease, glycated hemoglobin level and CRP level based on Model B.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cb\u003eFigures and Figure legends\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCox regression results were consistent with the visual trends. Among male participants (HR\u0026thinsp;=\u0026thinsp;1.27, 95% CI: 1.12\u0026ndash;1.45; \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) and middle-aged participants (HR\u0026thinsp;=\u0026thinsp;1.17, 95% CI: 1.04\u0026ndash;1.32; \u003cb\u003eTable S3\u003c/b\u003e), each 1-unit increase in BRI was significantly associated with elevated stroke risk. Among women and older adults, the association between AIP and stroke risk did not reach statistical significance (female participants: HR\u0026thinsp;=\u0026thinsp;1.47, 95% CI: 0.97\u0026ndash;2.25, \u003cb\u003eTable S2\u003c/b\u003e; older adults: HR\u0026thinsp;=\u0026thinsp;1.55, 95% CI: 1.00\u0026ndash;2.41, \u003cb\u003eTable S4\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eThe joint association of AIP and BRI further demonstrated an additive effect across all subgroups. Compared with those in the Low AIP \u0026amp; Low BRI group, individuals in the High AIP \u0026amp; High BRI group exhibited higher stroke risks in all subgroups (male participants: HR\u0026thinsp;=\u0026thinsp;2.17, 95% CI: 1.41\u0026ndash;3.33; female participants: HR\u0026thinsp;=\u0026thinsp;1.62, 95% CI: 1.05\u0026ndash;2.51; middle-aged adults: HR\u0026thinsp;=\u0026thinsp;2.02, 95% CI: 1.27\u0026ndash;3.21; older adults: HR\u0026thinsp;=\u0026thinsp;1.90, 95% CI: 1.24\u0026ndash;2.92).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Mediation analysis\u003c/h2\u003e\u003cp\u003eIn the mediation analysis, BRI, AIP and stroke were respectively regarded as independent variables, mediating variables and dependent variables. The mediation model and path are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The research results show that there is a significant association between BRI and AIP (β\u0026thinsp;=\u0026thinsp;0.0711, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and there is also a significant correlation between AIP and stroke (β\u0026thinsp;=\u0026thinsp;0.0163, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042). Further analysis indicated that BRI had a significant indirect effect on stroke through AIP (indirect effect\u0026thinsp;=\u0026thinsp;0.0012, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046). This indicates that AIP plays a partial mediating role in the association between BRI and stroke, accounting for approximately 17.80% of the total effect.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this nationwide prospective cohort study of middle-aged and older adults, we evaluated for the first time the independent and joint associations of AIP and BRI with the risk of incident stroke. The results indicated that both AIP and BRI were positively associated with stroke risk. Further joint analysis revealed that the risk of stroke was highest when both indices were elevated, significantly exceeding the risk associated with an elevation in either index alone. This additive effect was particularly evident in male participants and those aged 45\u0026ndash;59 years. Of note, AIP partially mediated the association between BRI and new-onset stroke.\u003c/p\u003e\u003cp\u003eOur study confirmed that the BRI is an independent and stable indicator of central obesity that can predict incident stroke. After adjusting for potential confounders, each 1-unit increase in BRI was associated with a 12% increase in stroke risk; similar to our results, a cross-sectional analysis based on a US cohort showed that each 1-unit increase in BRI was associated with a 5.7% increase in stroke prevalence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is noteworthy that our study adopted a prospective longitudinal design, evaluating the risk of stroke incidence rather than past stroke prevalence. This design minimizes the possibility of reverse causation, thereby making the observed effect size more reflective of the true impact of BRI on stroke development. In line with these findings, a cohort study conducted in Italy involving 468 hypertensive patients found that BRI was independently associated with carotid intima\u0026ndash;media thickness (cIMT), and that increased cIMT could predict future stroke events [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These findings confirm that the predictive value of BRI for stroke risk holds true across different populations. BRI reflects visceral fat accumulation, which may contribute to stroke development through three key pathways: inflammation activation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], hypercoagulability [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and blood pressure variability [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Dysregulated lipid metabolism plays a central role throughout these processes and represents a critical link in the onset and progression of stroke.\u003c/p\u003e\u003cp\u003eOur study further confirmed that individuals with elevated baseline AIP levels were more likely to develop stroke during follow-up. This finding is consistent with previous population-based studies. For example, a prospective cohort study in a Korean community population showed that higher cumulative AIP was significantly associated with increased risk of ischemic stroke[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Another prospective analysis based on a UK cohort reported that individuals with sustained high or increasing AIP levels had significantly elevated risk of CVD [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. AIP may contribute to stroke onset and progression by regulating lipid deposition [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and inflammatory responses [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Overall, AIP complements the mechanism of abdominal obesity indicated by BRI from the perspective of lipid metabolism, providing new evidence for understanding stroke risk factors.\u003c/p\u003e\u003cp\u003eAnother key finding of our study is that stroke risk was the highest when both AIP and BRI were elevated, exceeding the risk associated with either index alone, which suggests a potential additive effect. A cross-sectional analysis from a US cohort demonstrated that AIP mediated 10\u0026ndash;15% of the effect of BRI on cardiovascular events, supporting a sequential pathway from fat distribution imbalance to dyslipidemia and vascular damage [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our study provided the first evidence on this mediation from the longitudinal perspective. Notably, a previous study explored the combined predictive value of BRI and TyG for stroke [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], whereas our study focused on the joint effect of AIP and BRI from the dual dimensions of dyslipidemia and visceral adiposity, and was the first to evaluate their combined predictive value for stroke risk in a national longitudinal sample. In terms of potential mechanisms, on one hand, mobilization of visceral fat releases large amounts of free fatty acids into the liver, promoting VLDL/TG synthesis and inhibiting HDL production, thereby increasing AIP levels [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]; on the other hand, abdominal obesity is associated with increased secretion of TNF-α, IL-6, and PAI-1, contributing to chronic inflammation and a prothrombotic state [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Thus, when both AIP and BRI are elevated, individuals are simultaneously affected by dysregulated lipid accumulation, inflammation, and hemodynamic stress, leading to significantly increased stroke risk.\u003c/p\u003e\u003cp\u003eIn addition, AIP was more strongly associated with stroke risk in females, while BRI showed a stronger association in males. The additive effect of the two indices was observed in both sexes but was more pronounced in males and those aged 45\u0026ndash;59 years. After menopause, the rapid decline in estrogen among females leads to reduced HDL-C and elevated TG levels, increasing sensitivity to dyslipidemia [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In contrast, men tend to accumulate more visceral fat, which increases secretion of free fatty acids, IL-6, and PAI-1, thereby amplifying the impact of BRI [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Recent research has also confirmed that ages 45\u0026ndash;59 represent a high-risk window for accelerated accumulation of abdominal fat, increased triglycerides, and decreased HDL-C [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Individuals aged 60 years or above without stroke may already have undergone natural selection against high-risk phenotypes. Therefore, combined assessment of AIP and BRI may optimize stroke risk screening in middle-aged adults.\u003c/p\u003e\u003cp\u003eOf note, our mediation analysis indicates that AIP partially mediates the association between visceral obesity (captured by BRI) and stroke events. This suggests that monitoring AIP in patients with high BRI is clinically relevant. Biologically, excessive visceral fat promotes insulin resistance and a pro-inflammatory environment in the liver [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], leading to overproduction of TG-rich lipoproteins, reduction/alteration of HDL-C, and formation of small dense LDL and residual cholesterol [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This atherosclerotic profile (summarized by AIP) accelerates endothelial dysfunction, oxidative stress, and pre-thrombotic state, thereby increasing cerebral vascular vulnerability [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Clinically, lowering TG levels and enhancing HDL functionality to reduce AIP may help mitigate stroke risk in individuals with elevated BRI.\u003c/p\u003e\u003cp\u003eThis study is a nationally representative prospective longitudinal cohort, which avoids the issues of reverse causation and selection bias inherent to cross-sectional studies, thus improving the credibility of risk estimates. Moreover, multiple confounding factors were adjusted for in the statistical analyses. However, there are still some limitations: stroke outcomes were primarily self-reported or obtained from community follow-up, lacking standardized imaging-based confirmation; although numerous covariates were adjusted, residual confounding from factors such as diets and genetics cannot be ruled out; AIP and BRI were only measured at baseline, which may underestimate the predictive value of their cumulative exposure and trajectories. Future studies should incorporate imaging validation, longitudinal biomarker trajectories, and multi-omics data to further elucidate the mechanisms and validate our findings.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eAIP and BRI are significant independent predictors of stroke risk longitudinally. Their combination significantly enhances risk stratification, and AIP further mediated the association between BRI and stroke. These findings provide elementary evidence for early stroke identification and targeted intervention strategies, supporting strengthened primary prevention efforts.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIP Atherogenic index of plasma\u003c/p\u003e\u003cp\u003eBRI Body roundness index\u003c/p\u003e\u003cp\u003eCHARLS China Health and Retirement Longitudinal Study\u003c/p\u003e\u003cp\u003eCI Confidence Interval\u003c/p\u003e\u003cp\u003eCRP C-reactive protein\u003c/p\u003e\u003cp\u003eHDL-C High-density lipoprotein cholesterol\u003c/p\u003e\u003cp\u003eLDL-C Low-density lipoprotein cholesterol\u003c/p\u003e\u003cp\u003eTC Total cholesterol\u003c/p\u003e\u003cp\u003eTG Triglyceride\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe following additional files accompany this manuscript. Each additional file is cited at the relevant place in the text.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e All procedures performed in studies involving human participants were in accordance with the Declaration of Helsinki. The original CHARLS was approved by the Ethical Review Committee of Peking University, and all the participants from CHARLS provided signed informed consent.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e The publication of this manuscript has been authorized by all authors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (No. 82171330 to GX), the Shenzhen High-level Hospital Construction Fund (No. 4004013 to GX), and the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515010732 to GX).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXGL and LR designed the study. WZY, ZYH and SRR collected the data, analyzed the data, and drafted manuscript. WZY wrote specific sections of the manuscript. WZY, ZYH, SRR, CYX, LR and XGL revised the manuscript. All authors reviewed and approved the final version of manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe express sincere appreciation to all the members who involved in CHARLS.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed in this study are from the CHARLS repository. The data are publicly available to registered users upon approval by the CHARLS team and can be requested at the CHARLS website (https:/charls.pku.edu.cn/en) .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSacco RL, Kasner SE, Broderick JP, Caplan LR, Connors JJ, Culebras A, et al. 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Curr Atheroscler Rep. 2022;24 3:133\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11883-022-00994-z\u003c/span\u003e\u003cspan address=\"10.1007/s11883-022-00994-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Body Roundness Index (BRI), Atherogenic Index of Plasma (AIP), Visceral Obesity, Dyslipidemia, Stroke, Prospective Cohort","lastPublishedDoi":"10.21203/rs.3.rs-7488889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7488889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eVisceral obesity and dyslipidemia are independent stroke risk factors. The Body Roundness Index (BRI) indicates visceral fat, while the Atherogenic Index of Plasma (AIP) reflects an atherogenic lipid profile. This study investigates the joint association of BRI and AIP with stroke risk in middle-aged and older adults in China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 7,349 participants without a stroke history at baseline (2011) were included from the China Health and Retirement Longitudinal Study (CHARLS), with follow-up until 2018. Kaplan\u0026ndash;Meier survival curves, multivariable Cox proportional hazards regression, subgroup analysis and mediation analysis were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDuring the 7-year follow-up, 358 new-onset stroke cases (4.87%) were recorded. Independently, participants in the highest quartile of both BRI (HR\u0026thinsp;=\u0026thinsp;1.54, 95% CI: 1.10\u0026ndash;2.17) and AIP (HR\u0026thinsp;=\u0026thinsp;1.66, 95% CI: 1.20\u0026ndash;2.32) faced significantly higher stroke risks compared to the lowest quartile. Joint association showed that the combined High AIP \u0026amp; High BRI group had the highest risk (HR\u0026thinsp;=\u0026thinsp;1.88, 95% CI: 1.39\u0026ndash;2.56), with \u003cem\u003eP\u003c/em\u003e interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001, especially in males (HR\u0026thinsp;=\u0026thinsp;2.17; 95% CI: 1.41\u0026ndash;3.33) or those aged 45\u0026ndash;59 years (HR\u0026thinsp;=\u0026thinsp;2.02; 95% CI: 1.27\u0026ndash;3.21). Moreover, AIP partially mediated the association between BRI and stroke (β\u0026thinsp;=\u0026thinsp;0.0012, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046), accounting for 17.8% of the total effect.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eA significant interaction was found between BRI and AIP levels in the association with stroke risk, our findings help formulate early screening and targeted prevention strategies for stroke.\u003c/p\u003e","manuscriptTitle":"Joint association of Body Roundness Index and Atherogenic Index of Plasma with new-onset stroke in middle-aged and older adults: first evidence from the China Health and Retirement Longitudinal Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 19:24:09","doi":"10.21203/rs.3.rs-7488889/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-16T09:29:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323552153963361925564961995453037821933","date":"2025-10-16T08:48:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T07:46:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-10T17:16:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T08:19:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T08:19:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-08-29T12:57:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c529d19f-bf08-4824-9349-6109d2ef5e2e","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T19:24:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 19:24:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7488889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7488889","identity":"rs-7488889","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}